From bb57f30c2de46cfca5419ad01738a41705f96cc3 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Fri, 14 Oct 2022 10:56:41 +0200 Subject: [PATCH 001/140] init --- README.md | 1 + aesthetic_embeddings/insert_embs_here.txt | 0 modules/processing.py | 17 +++- modules/sd_hijack.py | 80 ++++++++++++++++++- modules/shared.py | 5 ++ modules/textual_inversion/dataset.py | 2 +- .../textual_inversion/textual_inversion.py | 35 +++++--- modules/txt2img.py | 11 ++- modules/ui.py | 59 +++++++++----- 9 files changed, 172 insertions(+), 38 deletions(-) create mode 100644 aesthetic_embeddings/insert_embs_here.txt diff --git a/README.md b/README.md index 859a91b6..7b8d018b 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) +- Aesthetic, a way to generate images with a specific aesthetic by using clip images embds (implementation of https://github.com/vicgalle/stable-diffusion-aesthetic-gradients) ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. diff --git a/aesthetic_embeddings/insert_embs_here.txt b/aesthetic_embeddings/insert_embs_here.txt new file mode 100644 index 00000000..e69de29b diff --git a/modules/processing.py b/modules/processing.py index d5172f00..9a033759 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -316,11 +316,16 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() -def process_images(p: StableDiffusionProcessing) -> Processed: +def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, + aesthetic_imgs=None,aesthetic_slerp=False) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" + aesthetic_lr = float(aesthetic_lr) + aesthetic_weight = float(aesthetic_weight) + aesthetic_steps = int(aesthetic_steps) + if type(p.prompt) == list: - assert(len(p.prompt) > 0) + assert (len(p.prompt) > 0) else: assert p.prompt is not None @@ -394,7 +399,13 @@ def process_images(p: StableDiffusionProcessing) -> Processed: #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) #c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): - uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) + if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): + shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0) + uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], + p.steps) + if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): + shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight, + aesthetic_steps, aesthetic_imgs,aesthetic_slerp) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index c81722a0..6d5196fe 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -9,11 +9,14 @@ from torch.nn.functional import silu import modules.textual_inversion.textual_inversion from modules import prompt_parser, devices, sd_hijack_optimizations, shared -from modules.shared import opts, device, cmd_opts +from modules.shared import opts, device, cmd_opts, aesthetic_embeddings from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention import ldm.modules.diffusionmodules.model +from transformers import CLIPVisionModel, CLIPModel +import torch.optim as optim +import copy attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity @@ -109,13 +112,29 @@ class StableDiffusionModelHijack: _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) +def slerp(low, high, val): + low_norm = low/torch.norm(low, dim=1, keepdim=True) + high_norm = high/torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm*high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + return res class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped + self.clipModel = CLIPModel.from_pretrained( + self.wrapped.transformer.name_or_path + ) + del self.clipModel.vision_model self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer + # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval() + self.image_embs_name = None + self.image_embs = None + self.load_image_embs(None) + self.token_mults = {} self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] @@ -136,6 +155,23 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0: self.token_mults[ident] = mult + def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None, + aesthetic_slerp=True): + self.slerp = aesthetic_slerp + self.aesthetic_lr = aesthetic_lr + self.aesthetic_weight = aesthetic_weight + self.aesthetic_steps = aesthetic_steps + self.load_image_embs(image_embs_name) + + def load_image_embs(self, image_embs_name): + if image_embs_name is None or len(image_embs_name) == 0: + image_embs_name = None + if image_embs_name is not None and self.image_embs_name != image_embs_name: + self.image_embs_name = image_embs_name + self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device) + self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) + self.image_embs.requires_grad_(False) + def tokenize_line(self, line, used_custom_terms, hijack_comments): id_end = self.wrapped.tokenizer.eos_token_id @@ -333,7 +369,47 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) - + + if len(text[ + 0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: + if not opts.use_old_emphasis_implementation: + remade_batch_tokens = [ + [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in + remade_batch_tokens] + + tokens = torch.asarray(remade_batch_tokens).to(device) + with torch.enable_grad(): + model = copy.deepcopy(self.clipModel).to(device) + model.requires_grad_(True) + + # We optimize the model to maximize the similarity + optimizer = optim.Adam( + model.text_model.parameters(), lr=self.aesthetic_lr + ) + + for i in range(self.aesthetic_steps): + text_embs = model.get_text_features(input_ids=tokens) + text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) + sim = text_embs @ self.image_embs.T + loss = -sim + optimizer.zero_grad() + loss.mean().backward() + optimizer.step() + + zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) + if opts.CLIP_stop_at_last_layers > 1: + zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers] + zn = model.text_model.final_layer_norm(zn) + else: + zn = zn.last_hidden_state + model.cpu() + del model + + if self.slerp: + z = slerp(z, zn, self.aesthetic_weight) + else: + z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight + remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers i += 1 diff --git a/modules/shared.py b/modules/shared.py index 5901e605..cf13a10d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -30,6 +30,8 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") +parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(script_path, 'aesthetic_embeddings'), + help="aesthetic_embeddings directory(default: aesthetic_embeddings)") parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") @@ -90,6 +92,9 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None +aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in + os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} + def reload_hypernetworks(): global hypernetworks diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 67e90afe..59b2b021 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -48,7 +48,7 @@ class PersonalizedBase(Dataset): print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): try: - image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) + image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC) except Exception: continue diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fa0e33a2..b12a8e6d 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -172,7 +172,15 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn -def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt): +def batched(dataset, total, n=1): + for ndx in range(0, total, n): + yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))] + + +def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, + create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, + preview_image_prompt, batch_size=1, + gradient_accumulation=1): assert embedding_name, 'embedding not selected' shared.state.textinfo = "Initializing textual inversion training..." @@ -204,7 +212,11 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." with torch.autocast("cuda"): - ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file) + ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, + height=training_height, + repeats=shared.opts.training_image_repeats_per_epoch, + placeholder_token=embedding_name, model=shared.sd_model, + device=devices.device, template_file=template_file) hijack = sd_hijack.model_hijack @@ -223,7 +235,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate) - pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step) + pbar = tqdm.tqdm(enumerate(batched(ds, steps - ititial_step, batch_size)), total=steps - ititial_step) for i, entry in pbar: embedding.step = i + ititial_step @@ -235,17 +247,20 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini break with torch.autocast("cuda"): - c = cond_model([entry.cond_text]) + c = cond_model([e.cond_text for e in entry]) + + x = torch.stack([e.latent for e in entry]).to(devices.device) + loss = shared.sd_model(x, c)[0] - x = entry.latent.to(devices.device) - loss = shared.sd_model(x.unsqueeze(0), c)[0] del x losses[embedding.step % losses.shape[0]] = loss.item() - optimizer.zero_grad() loss.backward() - optimizer.step() + if ((i + 1) % gradient_accumulation == 0) or (i + 1 == steps - ititial_step): + optimizer.step() + optimizer.zero_grad() + epoch_num = embedding.step // len(ds) epoch_step = embedding.step - (epoch_num * len(ds)) + 1 @@ -259,7 +274,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0: last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png') - preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt + preview_text = entry[0].cond_text if preview_image_prompt == "" else preview_image_prompt p = processing.StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, @@ -305,7 +320,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini

Loss: {losses.mean():.7f}
Step: {embedding.step}
-Last prompt: {html.escape(entry.cond_text)}
+Last prompt: {html.escape(entry[-1].cond_text)}
Last saved embedding: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

diff --git a/modules/txt2img.py b/modules/txt2img.py index e985242b..78342024 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -6,7 +6,14 @@ import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args): +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, + restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, + subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, + height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, + aesthetic_lr=0, + aesthetic_weight=0, aesthetic_steps=0, + aesthetic_imgs=None, + aesthetic_slerp=False, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -40,7 +47,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: - processed = process_images(p) + processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp) shared.total_tqdm.clear() diff --git a/modules/ui.py b/modules/ui.py index 220fb80b..d961d126 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -24,7 +24,8 @@ import gradio.routes from modules import sd_hijack from modules.paths import script_path -from modules.shared import opts, cmd_opts +from modules.shared import opts, cmd_opts,aesthetic_embeddings + if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags import modules.shared as shared @@ -534,6 +535,14 @@ def create_ui(wrap_gradio_gpu_call): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + with gr.Group(): + aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50) + + aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) @@ -586,25 +595,30 @@ def create_ui(wrap_gradio_gpu_call): fn=wrap_gradio_gpu_call(modules.txt2img.txt2img), _js="submit", inputs=[ - txt2img_prompt, - txt2img_negative_prompt, - txt2img_prompt_style, - txt2img_prompt_style2, - steps, - sampler_index, - restore_faces, - tiling, - batch_count, - batch_size, - cfg_scale, - seed, - subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, - height, - width, - enable_hr, - scale_latent, - denoising_strength, - ] + custom_inputs, + txt2img_prompt, + txt2img_negative_prompt, + txt2img_prompt_style, + txt2img_prompt_style2, + steps, + sampler_index, + restore_faces, + tiling, + batch_count, + batch_size, + cfg_scale, + seed, + subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, + height, + width, + enable_hr, + scale_latent, + denoising_strength, + aesthetic_lr, + aesthetic_weight, + aesthetic_steps, + aesthetic_imgs, + aesthetic_slerp + ] + custom_inputs, outputs=[ txt2img_gallery, generation_info, @@ -1097,6 +1111,9 @@ def create_ui(wrap_gradio_gpu_call): template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + batch_size = gr.Slider(minimum=1, maximum=64, step=1, label="Batch Size", value=4) + gradient_accumulation = gr.Slider(minimum=1, maximum=256, step=1, label="Gradient accumulation", + value=1) steps = gr.Number(label='Max steps', value=100000, precision=0) create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) @@ -1180,6 +1197,8 @@ def create_ui(wrap_gradio_gpu_call): template_file, save_image_with_stored_embedding, preview_image_prompt, + batch_size, + gradient_accumulation ], outputs=[ ti_output, From 37d7ffb415cd8c69b3c0bb5f61844dde0b169f78 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 15:59:37 +0200 Subject: [PATCH 002/140] fix to tokens lenght, addend embs generator, add new features to edit the embedding before the generation using text --- modules/aesthetic_clip.py | 78 ++++++++++++++++++++ modules/processing.py | 148 +++++++++++++++++++++++++------------- modules/sd_hijack.py | 111 ++++++++++++++++++---------- modules/shared.py | 4 ++ modules/txt2img.py | 10 ++- modules/ui.py | 47 ++++++++++-- 6 files changed, 302 insertions(+), 96 deletions(-) create mode 100644 modules/aesthetic_clip.py diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py new file mode 100644 index 00000000..f15cfd47 --- /dev/null +++ b/modules/aesthetic_clip.py @@ -0,0 +1,78 @@ +import itertools +import os +from pathlib import Path +import html +import gc + +import gradio as gr +import torch +from PIL import Image +from modules import shared +from modules.shared import device, aesthetic_embeddings +from transformers import CLIPModel, CLIPProcessor + +from tqdm.auto import tqdm + + +def get_all_images_in_folder(folder): + return [os.path.join(folder, f) for f in os.listdir(folder) if + os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)] + + +def check_is_valid_image_file(filename): + return filename.lower().endswith(('.png', '.jpg', '.jpeg')) + + +def batched(dataset, total, n=1): + for ndx in range(0, total, n): + yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))] + + +def iter_to_batched(iterable, n=1): + it = iter(iterable) + while True: + chunk = tuple(itertools.islice(it, n)) + if not chunk: + return + yield chunk + + +def generate_imgs_embd(name, folder, batch_size): + # clipModel = CLIPModel.from_pretrained( + # shared.sd_model.cond_stage_model.clipModel.name_or_path + # ) + model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device) + processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path) + + with torch.no_grad(): + embs = [] + for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size), + desc=f"Generating embeddings for {name}"): + if shared.state.interrupted: + break + inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device) + outputs = model.get_image_features(**inputs).cpu() + embs.append(torch.clone(outputs)) + inputs.to("cpu") + del inputs, outputs + + embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True) + + # The generated embedding will be located here + path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt") + torch.save(embs, path) + + model = model.cpu() + del model + del processor + del embs + gc.collect() + torch.cuda.empty_cache() + res = f""" + Done generating embedding for {name}! + Hypernetwork saved to {html.escape(path)} + """ + shared.update_aesthetic_embeddings() + return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", + value=sorted(aesthetic_embeddings.keys())[0] if len( + aesthetic_embeddings) > 0 else None), res, "" diff --git a/modules/processing.py b/modules/processing.py index 9a033759..ab68d63a 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -20,7 +20,6 @@ import modules.images as images import modules.styles import logging - # some of those options should not be changed at all because they would break the model, so I removed them from options. opt_C = 4 opt_f = 8 @@ -52,8 +51,13 @@ def get_correct_sampler(p): elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img): return sd_samplers.samplers_for_img2img + class StableDiffusionProcessing: - def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None): + def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, + subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, + sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, + restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, + extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None): self.sd_model = sd_model self.outpath_samples: str = outpath_samples self.outpath_grids: str = outpath_grids @@ -104,7 +108,8 @@ class StableDiffusionProcessing: class Processed: - def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None): + def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, + all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None): self.images = images_list self.prompt = p.prompt self.negative_prompt = p.negative_prompt @@ -141,7 +146,8 @@ class Processed: self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) - self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 + self.subseed = int( + self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 self.all_prompts = all_prompts or [self.prompt] self.all_seeds = all_seeds or [self.seed] @@ -181,39 +187,43 @@ class Processed: return json.dumps(obj) - def infotext(self, p: StableDiffusionProcessing, index): - return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size) + def infotext(self, p: StableDiffusionProcessing, index): + return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], + position_in_batch=index % self.batch_size, iteration=index // self.batch_size) # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3 def slerp(val, low, high): - low_norm = low/torch.norm(low, dim=1, keepdim=True) - high_norm = high/torch.norm(high, dim=1, keepdim=True) - dot = (low_norm*high_norm).sum(1) + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + dot = (low_norm * high_norm).sum(1) if dot.mean() > 0.9995: return low * val + high * (1 - val) omega = torch.acos(dot) so = torch.sin(omega) - res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high return res -def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None): +def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, + p=None): xs = [] # if we have multiple seeds, this means we are working with batch size>1; this then # enables the generation of additional tensors with noise that the sampler will use during its processing. # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to # produce the same images as with two batches [100], [101]. - if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0): + if p is not None and p.sampler is not None and ( + len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0): sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))] else: sampler_noises = None for i, seed in enumerate(seeds): - noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8) + noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else ( + shape[0], seed_resize_from_h // 8, seed_resize_from_w // 8) subnoise = None if subseeds is not None: @@ -241,7 +251,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see dx = max(-dx, 0) dy = max(-dy, 0) - x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w] + x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w] noise = x if sampler_noises is not None: @@ -293,14 +303,20 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Seed": all_seeds[index], "Face restoration": (opts.face_restoration_model if p.restore_faces else None), "Size": f"{p.width}x{p.height}", - "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), - "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')), + "Model hash": getattr(p, 'sd_model_hash', + None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), + "Model": ( + None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace( + ',', '').replace(':', '')), + "Hypernet": ( + None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace( + ':', '')), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), "Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength), - "Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), + "Seed resize from": ( + None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"), "Denoising strength": getattr(p, 'denoising_strength', None), "Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta), "Clip skip": None if clip_skip <= 1 else clip_skip, @@ -309,7 +325,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration generation_params.update(p.extra_generation_params) - generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) + generation_params_text = ", ".join( + [k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else "" @@ -317,7 +334,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, - aesthetic_imgs=None,aesthetic_slerp=False) -> Processed: + aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" aesthetic_lr = float(aesthetic_lr) @@ -385,7 +404,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh for n in range(p.n_iter): if state.skipped: state.skipped = False - + if state.interrupted: break @@ -396,16 +415,19 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh if (len(prompts) == 0): break - #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) - #c = p.sd_model.get_learned_conditioning(prompts) + # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) + # c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): - shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0) + shared.sd_model.cond_stage_model.set_aesthetic_params() uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight, - aesthetic_steps, aesthetic_imgs,aesthetic_slerp) + aesthetic_steps, aesthetic_imgs, + aesthetic_slerp, aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: @@ -413,13 +435,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh comments[comment] = 1 if p.n_iter > 1: - shared.state.job = f"Batch {n+1} out of {p.n_iter}" + shared.state.job = f"Batch {n + 1} out of {p.n_iter}" with devices.autocast(): - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, + subseed_strength=p.subseed_strength) if state.interrupted or state.skipped: - # if we are interrupted, sample returns just noise # use the image collected previously in sampler loop samples_ddim = shared.state.current_latent @@ -445,7 +467,9 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh if p.restore_faces: if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration: - images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration") + images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], + opts.samples_format, info=infotext(n, i), p=p, + suffix="-before-face-restoration") devices.torch_gc() @@ -456,7 +480,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh if p.color_corrections is not None and i < len(p.color_corrections): if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction: - images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction") + images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, + info=infotext(n, i), p=p, suffix="-before-color-correction") image = apply_color_correction(p.color_corrections[i], image) if p.overlay_images is not None and i < len(p.overlay_images): @@ -474,7 +499,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh image = image.convert('RGB') if opts.samples_save and not p.do_not_save_samples: - images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p) + images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, + info=infotext(n, i), p=p) text = infotext(n, i) infotexts.append(text) @@ -482,7 +508,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh image.info["parameters"] = text output_images.append(image) - del x_samples_ddim + del x_samples_ddim devices.torch_gc() @@ -504,10 +530,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh index_of_first_image = 1 if opts.grid_save: - images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) + images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, + info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) devices.torch_gc() - return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) + return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), + subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, + index_of_first_image=index_of_first_image, infotexts=infotexts) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): @@ -543,25 +572,34 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, + subseeds=subseeds, subseed_strength=self.subseed_strength, + seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) return samples - x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, + subseeds=subseeds, subseed_strength=self.subseed_strength, + seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f - samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2] + samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2, + truncate_x // 2:samples.shape[3] - truncate_x // 2] if self.scale_latent: - samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), + mode="bilinear") else: decoded_samples = decode_first_stage(self.sd_model, samples) if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None": - decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") + decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), + mode="bilinear") else: lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) @@ -585,13 +623,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) - noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, + subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) # GC now before running the next img2img to prevent running out of memory x = None devices.torch_gc() - samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, + steps=self.steps) return samples @@ -599,7 +640,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs): + def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, + inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, + **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -607,7 +650,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.denoising_strength: float = denoising_strength self.init_latent = None self.image_mask = mask - #self.image_unblurred_mask = None + # self.image_unblurred_mask = None self.latent_mask = None self.mask_for_overlay = None self.mask_blur = mask_blur @@ -619,7 +662,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.nmask = None def init(self, all_prompts, all_seeds, all_subseeds): - self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model) + self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, + self.sd_model) crop_region = None if self.image_mask is not None: @@ -628,7 +672,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.inpainting_mask_invert: self.image_mask = ImageOps.invert(self.image_mask) - #self.image_unblurred_mask = self.image_mask + # self.image_unblurred_mask = self.image_mask if self.mask_blur > 0: self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur)) @@ -642,7 +686,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): mask = mask.crop(crop_region) self.image_mask = images.resize_image(2, mask, self.width, self.height) - self.paste_to = (x1, y1, x2-x1, y2-y1) + self.paste_to = (x1, y1, x2 - x1, y2 - y1) else: self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height) np_mask = np.array(self.image_mask) @@ -665,7 +709,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): if self.image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) - image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L'))) + image_masked.paste(image.convert("RGBA").convert("RGBa"), + mask=ImageOps.invert(self.mask_for_overlay.convert('L'))) self.overlay_images.append(image_masked.convert('RGBA')) @@ -714,12 +759,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): # this needs to be fixed to be done in sample() using actual seeds for batches if self.inpainting_fill == 2: - self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask + self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], + all_seeds[ + 0:self.init_latent.shape[ + 0]]) * self.nmask elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, + subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, + seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 6d5196fe..192883b2 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -14,7 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention import ldm.modules.diffusionmodules.model -from transformers import CLIPVisionModel, CLIPModel +from tqdm import trange +from transformers import CLIPVisionModel, CLIPModel, CLIPTokenizer import torch.optim as optim import copy @@ -22,21 +23,25 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward + def apply_optimizations(): undo_optimizations() ldm.modules.diffusionmodules.model.nonlinearity = silu - if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)): + if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and ( + 6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward elif cmd_opts.opt_split_attention_v1: print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 - elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): + elif not cmd_opts.disable_opt_split_attention and ( + cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()): if not invokeAI_mps_available and shared.device.type == 'mps': - print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.") + print( + "The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.") print("Applying v1 cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1 else: @@ -112,14 +117,16 @@ class StableDiffusionModelHijack: _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) + def slerp(low, high, val): - low_norm = low/torch.norm(low, dim=1, keepdim=True) - high_norm = high/torch.norm(high, dim=1, keepdim=True) - omega = torch.acos((low_norm*high_norm).sum(1)) + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm * high_norm).sum(1)) so = torch.sin(omega) - res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high return res + class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() @@ -128,6 +135,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.wrapped.transformer.name_or_path ) del self.clipModel.vision_model + self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path) self.hijack: StableDiffusionModelHijack = hijack self.tokenizer = wrapped.tokenizer # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval() @@ -139,7 +147,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] - tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k] + tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if + '(' in k or ')' in k or '[' in k or ']' in k] for text, ident in tokens_with_parens: mult = 1.0 for c in text: @@ -155,8 +164,13 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0: self.token_mults[ident] = mult - def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None, - aesthetic_slerp=True): + def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, + aesthetic_slerp=True, aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False): + self.aesthetic_imgs_text = aesthetic_imgs_text + self.aesthetic_slerp_angle = aesthetic_slerp_angle + self.aesthetic_text_negative = aesthetic_text_negative self.slerp = aesthetic_slerp self.aesthetic_lr = aesthetic_lr self.aesthetic_weight = aesthetic_weight @@ -180,7 +194,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): else: parsed = [[line, 1.0]] - tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"] + tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)[ + "input_ids"] fixes = [] remade_tokens = [] @@ -196,18 +211,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if token == self.comma_token: last_comma = len(remade_tokens) - elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack: + elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), + 1) % 75 == 0 and last_comma != -1 and len( + remade_tokens) - last_comma <= opts.comma_padding_backtrack: last_comma += 1 reloc_tokens = remade_tokens[last_comma:] reloc_mults = multipliers[last_comma:] remade_tokens = remade_tokens[:last_comma] length = len(remade_tokens) - + rem = int(math.ceil(length / 75)) * 75 - length remade_tokens += [id_end] * rem + reloc_tokens multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults - + if embedding is None: remade_tokens.append(token) multipliers.append(weight) @@ -248,7 +265,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if line in cache: remade_tokens, fixes, multipliers = cache[line] else: - remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments) + remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, + hijack_comments) token_count = max(current_token_count, token_count) cache[line] = (remade_tokens, fixes, multipliers) @@ -259,7 +277,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count - def process_text_old(self, text): id_start = self.wrapped.tokenizer.bos_token_id id_end = self.wrapped.tokenizer.eos_token_id @@ -289,7 +306,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): while i < len(tokens): token = tokens[i] - embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i) + embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, + i) mult_change = self.token_mults.get(token) if opts.enable_emphasis else None if mult_change is not None: @@ -312,11 +330,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): ovf = remade_tokens[maxlen - 2:] overflowing_words = [vocab.get(int(x), "") for x in ovf] overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words)) - hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") + hijack_comments.append( + f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n") token_count = len(remade_tokens) remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens)) - remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end] + remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end] cache[tuple_tokens] = (remade_tokens, fixes, multipliers) multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers)) @@ -326,23 +345,26 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): hijack_fixes.append(fixes) batch_multipliers.append(multipliers) return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count - + def forward(self, text): use_old = opts.use_old_emphasis_implementation if use_old: - batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text) + batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old( + text) else: - batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text) + batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text( + text) self.hijack.comments += hijack_comments if len(used_custom_terms) > 0: - self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) - + self.hijack.comments.append( + "Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms])) + if use_old: self.hijack.fixes = hijack_fixes return self.process_tokens(remade_batch_tokens, batch_multipliers) - + z = None i = 0 while max(map(len, remade_batch_tokens)) != 0: @@ -356,7 +378,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if fix[0] == i: fixes.append(fix[1]) self.hijack.fixes.append(fixes) - + tokens = [] multipliers = [] for j in range(len(remade_batch_tokens)): @@ -378,19 +400,30 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_batch_tokens] tokens = torch.asarray(remade_batch_tokens).to(device) + + model = copy.deepcopy(self.clipModel).to(device) + model.requires_grad_(True) + if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: + text_embs_2 = model.get_text_features( + **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device)) + if self.aesthetic_text_negative: + text_embs_2 = self.image_embs - text_embs_2 + text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True) + img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle) + else: + img_embs = self.image_embs + with torch.enable_grad(): - model = copy.deepcopy(self.clipModel).to(device) - model.requires_grad_(True) # We optimize the model to maximize the similarity optimizer = optim.Adam( model.text_model.parameters(), lr=self.aesthetic_lr ) - for i in range(self.aesthetic_steps): + for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"): text_embs = model.get_text_features(input_ids=tokens) text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) - sim = text_embs @ self.image_embs.T + sim = text_embs @ img_embs.T loss = -sim optimizer.zero_grad() loss.mean().backward() @@ -405,6 +438,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): model.cpu() del model + zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1) if self.slerp: z = slerp(z, zn, self.aesthetic_weight) else: @@ -413,15 +447,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers i += 1 - + return z - - + def process_tokens(self, remade_batch_tokens, batch_multipliers): if not opts.use_old_emphasis_implementation: - remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens] + remade_batch_tokens = [ + [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in + remade_batch_tokens] batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers] - + tokens = torch.asarray(remade_batch_tokens).to(device) outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) @@ -461,8 +496,8 @@ class EmbeddingsWithFixes(torch.nn.Module): for fixes, tensor in zip(batch_fixes, inputs_embeds): for offset, embedding in fixes: emb = embedding.vec - emb_len = min(tensor.shape[0]-offset-1, emb.shape[0]) - tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]]) + emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0]) + tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]) vecs.append(tensor) diff --git a/modules/shared.py b/modules/shared.py index cf13a10d..7cd608ca 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -95,6 +95,10 @@ loaded_hypernetwork = None aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} +def update_aesthetic_embeddings(): + global aesthetic_embeddings + aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in + os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} def reload_hypernetworks(): global hypernetworks diff --git a/modules/txt2img.py b/modules/txt2img.py index 78342024..eedcdfe0 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -13,7 +13,11 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, - aesthetic_slerp=False, *args): + aesthetic_slerp=False, + aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False, + *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -47,7 +51,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: - processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp) + processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative) shared.total_tqdm.clear() diff --git a/modules/ui.py b/modules/ui.py index d961d126..e98e2113 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -41,6 +41,7 @@ from modules import prompt_parser from modules.images import save_image import modules.textual_inversion.ui import modules.hypernetworks.ui +import modules.aesthetic_clip # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() @@ -449,7 +450,7 @@ def create_toprow(is_img2img): with gr.Row(): negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2) with gr.Column(scale=1, elem_id="roll_col"): - sh = gr.Button(elem_id="sh", visible=True) + sh = gr.Button(elem_id="sh", visible=True) with gr.Column(scale=1, elem_id="style_neg_col"): prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1) @@ -536,9 +537,13 @@ def create_ui(wrap_gradio_gpu_call): height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Group(): - aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") - aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7) - aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50) + aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001") + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) + aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5) + with gr.Row(): + aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") + aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) + aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) @@ -617,7 +622,10 @@ def create_ui(wrap_gradio_gpu_call): aesthetic_weight, aesthetic_steps, aesthetic_imgs, - aesthetic_slerp + aesthetic_slerp, + aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative ] + custom_inputs, outputs=[ txt2img_gallery, @@ -721,7 +729,7 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False) - inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32) + inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=1024, step=4, value=32) with gr.TabItem('Batch img2img', id='batch'): hidden = '
Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else '' @@ -1071,6 +1079,17 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') + with gr.Tab(label="Create images embedding"): + new_embedding_name_ae = gr.Textbox(label="Name") + process_src_ae = gr.Textbox(label='Source directory') + batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256) + with gr.Row(): + with gr.Column(scale=3): + gr.HTML(value="") + + with gr.Column(): + create_embedding_ae = gr.Button(value="Create images embedding", variant='primary') + with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) @@ -1139,7 +1158,7 @@ def create_ui(wrap_gradio_gpu_call): fn=modules.textual_inversion.ui.create_embedding, inputs=[ new_embedding_name, - initialization_text, + process_src, nvpt, ], outputs=[ @@ -1149,6 +1168,20 @@ def create_ui(wrap_gradio_gpu_call): ] ) + create_embedding_ae.click( + fn=modules.aesthetic_clip.generate_imgs_embd, + inputs=[ + new_embedding_name_ae, + process_src_ae, + batch_ae + ], + outputs=[ + aesthetic_imgs, + ti_output, + ti_outcome, + ] + ) + create_hypernetwork.click( fn=modules.hypernetworks.ui.create_hypernetwork, inputs=[ From 6e4f5566b58e36aede83427df6c69eba8517af28 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Sat, 15 Oct 2022 23:53:49 +0800 Subject: [PATCH 003/140] sorting files --- javascript/images_history.js | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/javascript/images_history.js b/javascript/images_history.js index f7d052c3..7f0d8f42 100644 --- a/javascript/images_history.js +++ b/javascript/images_history.js @@ -96,7 +96,7 @@ function images_history_get_current_img(tabname, image_path, files){ ]; } -function images_history_delete(del_num, tabname, img_path, img_file_name, page_index, filenames, image_index){ +function images_history_delete(del_num, tabname, img_file_name, page_index, filenames, image_index){ image_index = parseInt(image_index); var tab = gradioApp().getElementById(tabname + '_images_history'); var set_btn = tab.querySelector(".images_history_set_index"); @@ -132,12 +132,12 @@ function images_history_delete(del_num, tabname, img_path, img_file_name, page_i return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index]; } -function images_history_turnpage(img_path, page_index, image_index, tabname){ +function images_history_turnpage(img_path, page_index, image_index, tabname, date_from, date_to){ var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item"); buttons.forEach(function(elem) { elem.style.display = 'block'; }) - return [img_path, page_index, image_index, tabname]; + return [img_path, page_index, image_index, tabname, date_from, date_to]; } function images_history_enable_del_buttons(){ From 4387e4fe6479c08f7bc7e42924c3a1093e3a1872 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:39:29 +0200 Subject: [PATCH 004/140] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index d0696101..5bb961b2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -599,7 +599,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.Group(): aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001") aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) - aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + with gr.Row(): aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) From f7df06a98180a2a8769b3ceebf7b6a35eca8ffc5 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:40:06 +0200 Subject: [PATCH 005/140] Update README.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b8d018b..40104833 100644 --- a/README.md +++ b/README.md @@ -70,7 +70,8 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) -- Aesthetic, a way to generate images with a specific aesthetic by using clip images embds (implementation of https://github.com/vicgalle/stable-diffusion-aesthetic-gradients) +- Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) + ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. From 9b7705e0573bddde26df4575c71f994d73a4d519 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:40:34 +0200 Subject: [PATCH 006/140] Update modules/aesthetic_clip.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/aesthetic_clip.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index f15cfd47..bcf2b073 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -70,7 +70,7 @@ def generate_imgs_embd(name, folder, batch_size): torch.cuda.empty_cache() res = f""" Done generating embedding for {name}! - Hypernetwork saved to {html.escape(path)} + Aesthetic embedding saved to {html.escape(path)} """ shared.update_aesthetic_embeddings() return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", From 0d4f5db235357aeb4c7a8738179ba33aaf5a6b75 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:40:58 +0200 Subject: [PATCH 007/140] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 5bb961b2..25eba548 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -597,7 +597,8 @@ def create_ui(wrap_gradio_gpu_call): height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Group(): - aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001") + aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) From ad9bc604a8fadcfebe72be37f66cec51e7e87fb5 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:41:18 +0200 Subject: [PATCH 008/140] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 25eba548..3b28b69c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -607,7 +607,8 @@ def create_ui(wrap_gradio_gpu_call): aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) - aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) + aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) with gr.Row(): From 3f5c3b981e46c16bb10948d012575b25170efb3b Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sat, 15 Oct 2022 18:41:46 +0200 Subject: [PATCH 009/140] Update modules/ui.py MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Víctor Gallego --- modules/ui.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 3b28b69c..1f6fcdc9 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1190,7 +1190,8 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') - with gr.Tab(label="Create images embedding"): + with gr.Tab(label="Create aesthetic images embedding"): + new_embedding_name_ae = gr.Textbox(label="Name") process_src_ae = gr.Textbox(label='Source directory') batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256) From 3d21684ee30ca5734126b8d08c05b3a0f513fe75 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 00:01:00 +0200 Subject: [PATCH 010/140] Add support to other img format, fixed dropbox update --- modules/aesthetic_clip.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index bcf2b073..68264284 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -8,7 +8,7 @@ import gradio as gr import torch from PIL import Image from modules import shared -from modules.shared import device, aesthetic_embeddings +from modules.shared import device from transformers import CLIPModel, CLIPProcessor from tqdm.auto import tqdm @@ -20,7 +20,7 @@ def get_all_images_in_folder(folder): def check_is_valid_image_file(filename): - return filename.lower().endswith(('.png', '.jpg', '.jpeg')) + return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp")) def batched(dataset, total, n=1): @@ -73,6 +73,6 @@ def generate_imgs_embd(name, folder, batch_size): Aesthetic embedding saved to {html.escape(path)} """ shared.update_aesthetic_embeddings() - return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", - value=sorted(aesthetic_embeddings.keys())[0] if len( - aesthetic_embeddings) > 0 else None), res, "" + return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding", + value=sorted(shared.aesthetic_embeddings.keys())[0] if len( + shared.aesthetic_embeddings) > 0 else None), res, "" From 9325c85f780c569d1823e422eaf51b2e497e0d3e Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 00:23:47 +0200 Subject: [PATCH 011/140] fixed dropbox update --- modules/sd_hijack.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 192883b2..491312b4 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -9,7 +9,7 @@ from torch.nn.functional import silu import modules.textual_inversion.textual_inversion from modules import prompt_parser, devices, sd_hijack_optimizations, shared -from modules.shared import opts, device, cmd_opts, aesthetic_embeddings +from modules.shared import opts, device, cmd_opts from modules.sd_hijack_optimizations import invokeAI_mps_available import ldm.modules.attention @@ -182,7 +182,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): image_embs_name = None if image_embs_name is not None and self.image_embs_name != image_embs_name: self.image_embs_name = image_embs_name - self.image_embs = torch.load(aesthetic_embeddings[self.image_embs_name], map_location=device) + self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device) self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) self.image_embs.requires_grad_(False) From 763b893f319cee280b86e63025eb55e7c16b02e7 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Sun, 16 Oct 2022 10:03:09 +0800 Subject: [PATCH 012/140] images history sorting files by date --- javascript/images_history.js | 12 +- modules/images_history.py | 261 ++++++++++++++++++++++++++--------- 2 files changed, 202 insertions(+), 71 deletions(-) diff --git a/javascript/images_history.js b/javascript/images_history.js index 7f0d8f42..ac5834c7 100644 --- a/javascript/images_history.js +++ b/javascript/images_history.js @@ -88,10 +88,10 @@ function images_history_set_image_info(button){ } -function images_history_get_current_img(tabname, image_path, files){ +function images_history_get_current_img(tabname, img_index, files){ return [ - gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), - image_path, + tabname, + gradioApp().getElementById(tabname + '_images_history_set_index').getAttribute("img_index"), files ]; } @@ -129,7 +129,7 @@ function images_history_delete(del_num, tabname, img_file_name, page_index, file setTimeout(function(btn){btn.click()}, 30, btn); } images_history_disabled_del(); - return [del_num, tabname, img_path, img_file_name, page_index, filenames, image_index]; + return [del_num, tabname, img_file_name, page_index, filenames, image_index]; } function images_history_turnpage(img_path, page_index, image_index, tabname, date_from, date_to){ @@ -170,8 +170,8 @@ function images_history_init(){ } tabs_box.classList.add(images_history_tab_list[0]); - // same as above, at page load - //load_txt2img_button.click(); + // same as above, at page load-- load very fast now + load_txt2img_button.click(); } else { setTimeout(images_history_init, 500); } diff --git a/modules/images_history.py b/modules/images_history.py index f5ef44fe..533cf51b 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -1,33 +1,74 @@ import os import shutil +import time +import hashlib +import gradio +show_max_dates_num = 3 +system_bak_path = "webui_log_and_bak" +def is_valid_date(date): + try: + time.strptime(date, "%Y%m%d") + return True + except: + return False +def reduplicative_file_move(src, dst): + def same_name_file(basename, path): + name, ext = os.path.splitext(basename) + f_list = os.listdir(path) + max_num = 0 + for f in f_list: + if len(f) <= len(basename): + continue + f_ext = f[-len(ext):] if len(ext) > 0 else "" + if f[:len(name)] == name and f_ext == ext: + if f[len(name)] == "(" and f[-len(ext)-1] == ")": + number = f[len(name)+1:-len(ext)-1] + if number.isdigit(): + if int(number) > max_num: + max_num = int(number) + return f"{name}({max_num + 1}){ext}" + name = os.path.basename(src) + save_name = os.path.join(dst, name) + if not os.path.exists(save_name): + shutil.move(src, dst) + else: + name = same_name_file(name, dst) + shutil.move(src, os.path.join(dst, name)) -def traverse_all_files(output_dir, image_list, curr_dir=None): - curr_path = output_dir if curr_dir is None else os.path.join(output_dir, curr_dir) +def traverse_all_files(curr_path, image_list, all_type=False): try: f_list = os.listdir(curr_path) except: - if curr_dir[-10:].rfind(".") > 0 and curr_dir[-4:] != ".txt": - image_list.append(curr_dir) + if all_type or curr_path[-10:].rfind(".") > 0 and curr_path[-4:] != ".txt": + image_list.append(curr_path) return image_list for file in f_list: - file = file if curr_dir is None else os.path.join(curr_dir, file) - file_path = os.path.join(curr_path, file) - if file[-4:] == ".txt": + file = os.path.join(curr_path, file) + if (not all_type) and file[-4:] == ".txt": pass - elif os.path.isfile(file_path) and file[-10:].rfind(".") > 0: + elif os.path.isfile(file) and file[-10:].rfind(".") > 0: image_list.append(file) else: - image_list = traverse_all_files(output_dir, image_list, file) + image_list = traverse_all_files(file, image_list) return image_list - -def get_recent_images(dir_name, page_index, step, image_index, tabname): - page_index = int(page_index) - f_list = os.listdir(dir_name) +def get_recent_images(dir_name, page_index, step, image_index, tabname, date_from, date_to): + #print(f"turn_page {page_index}",date_from) + if date_from is None or date_from == "": + return None, 1, None, "" image_list = [] - image_list = traverse_all_files(dir_name, image_list) - image_list = sorted(image_list, key=lambda file: -os.path.getctime(os.path.join(dir_name, file))) + date_list = auto_sorting(dir_name) + page_index = int(page_index) + today = time.strftime("%Y%m%d",time.localtime(time.time())) + for date in date_list: + if date >= date_from and date <= date_to: + path = os.path.join(dir_name, date) + if date == today and not os.path.exists(path): + continue + image_list = traverse_all_files(path, image_list) + + image_list = sorted(image_list, key=lambda file: -os.path.getctime(file)) num = 48 if tabname != "extras" else 12 max_page_index = len(image_list) // num + 1 page_index = max_page_index if page_index == -1 else page_index + step @@ -38,40 +79,101 @@ def get_recent_images(dir_name, page_index, step, image_index, tabname): image_index = int(image_index) if image_index < 0 or image_index > len(image_list) - 1: current_file = None - hidden = None else: - current_file = image_list[int(image_index)] - hidden = os.path.join(dir_name, current_file) - return [os.path.join(dir_name, file) for file in image_list], page_index, image_list, current_file, hidden, "" + current_file = image_list[image_index] + return image_list, page_index, image_list, "" + +def auto_sorting(dir_name): + #print(f"auto sorting") + bak_path = os.path.join(dir_name, system_bak_path) + if not os.path.exists(bak_path): + os.mkdir(bak_path) + log_file = None + files_list = [] + f_list = os.listdir(dir_name) + for file in f_list: + if file == system_bak_path: + continue + file_path = os.path.join(dir_name, file) + if not is_valid_date(file): + if file[-10:].rfind(".") > 0: + files_list.append(file_path) + else: + files_list = traverse_all_files(file_path, files_list, all_type=True) + + for file in files_list: + date_str = time.strftime("%Y%m%d",time.localtime(os.path.getctime(file))) + file_path = os.path.dirname(file) + hash_path = hashlib.md5(file_path.encode()).hexdigest() + path = os.path.join(dir_name, date_str, hash_path) + if not os.path.exists(path): + os.makedirs(path) + if log_file is None: + log_file = open(os.path.join(bak_path,"path_mapping.csv"),"a") + log_file.write(f"{hash_path},{file_path}\n") + reduplicative_file_move(file, path) + + date_list = [] + f_list = os.listdir(dir_name) + for f in f_list: + if is_valid_date(f): + date_list.append(f) + elif f == system_bak_path: + continue + else: + reduplicative_file_move(os.path.join(dir_name, f), bak_path) + + today = time.strftime("%Y%m%d",time.localtime(time.time())) + if today not in date_list: + date_list.append(today) + return sorted(date_list) -def first_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, 1, 0, image_index, tabname) + +def archive_images(dir_name): + date_list = auto_sorting(dir_name) + date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] + return ( + gradio.update(visible=False), + gradio.update(visible=True), + gradio.Dropdown.update(choices=date_list, value=date_list[-1]), + gradio.Dropdown.update(choices=date_list, value=date_from) + ) + +def date_to_change(dir_name, page_index, image_index, tabname, date_from, date_to): + #print("date_to", date_to) + date_list = auto_sorting(dir_name) + date_from_list = [date for date in date_list if date <= date_to] + date_from = date_from_list[0] if len(date_from_list) < show_max_dates_num else date_from_list[-show_max_dates_num] + image_list, page_index, image_list, _ =get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to) + return image_list, page_index, image_list, _, gradio.Dropdown.update(choices=date_from_list, value=date_from) + +def first_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): + return get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to) -def end_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, -1, 0, image_index, tabname) +def end_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): + return get_recent_images(dir_name, -1, 0, image_index, tabname, date_from, date_to) -def prev_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, page_index, -1, image_index, tabname) +def prev_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): + return get_recent_images(dir_name, page_index, -1, image_index, tabname, date_from, date_to) -def next_page_click(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, page_index, 1, image_index, tabname) +def next_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): + return get_recent_images(dir_name, page_index, 1, image_index, tabname, date_from, date_to) -def page_index_change(dir_name, page_index, image_index, tabname): - return get_recent_images(dir_name, page_index, 0, image_index, tabname) +def page_index_change(dir_name, page_index, image_index, tabname, date_from, date_to): + return get_recent_images(dir_name, page_index, 0, image_index, tabname, date_from, date_to) -def show_image_info(num, image_path, filenames): - # print(f"select image {num}") +def show_image_info(tabname_box, num, filenames): + # #print(f"select image {num}") file = filenames[int(num)] - return file, num, os.path.join(image_path, file) + return file, num, file - -def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, image_index): +def delete_image(delete_num, tabname, name, page_index, filenames, image_index): if name == "": return filenames, delete_num else: @@ -81,21 +183,19 @@ def delete_image(delete_num, tabname, dir_name, name, page_index, filenames, ima new_file_list = [] for name in filenames: if i >= index and i < index + delete_num: - path = os.path.join(dir_name, name) - if os.path.exists(path): - print(f"Delete file {path}") - os.remove(path) - txt_file = os.path.splitext(path)[0] + ".txt" + if os.path.exists(name): + #print(f"Delete file {name}") + os.remove(name) + txt_file = os.path.splitext(name)[0] + ".txt" if os.path.exists(txt_file): os.remove(txt_file) else: - print(f"Not exists file {path}") + #print(f"Not exists file {name}") else: new_file_list.append(name) i += 1 return new_file_list, 1 - def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): if tabname == "txt2img": dir_name = opts.outdir_txt2img_samples @@ -107,16 +207,32 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): dir_name = d[0] for p in d[1:]: dir_name = os.path.join(dir_name, p) - with gr.Row(): - renew_page = gr.Button('Renew Page', elem_id=tabname + "_images_history_renew_page") - first_page = gr.Button('First Page') - prev_page = gr.Button('Prev Page') - page_index = gr.Number(value=1, label="Page Index") - next_page = gr.Button('Next Page') - end_page = gr.Button('End Page') - with gr.Row(elem_id=tabname + "_images_history"): + + f_list = os.listdir(dir_name) + sorted_flag = os.path.exists(os.path.join(dir_name, system_bak_path)) or len(f_list) == 0 + date_list, date_from, date_to = None, None, None + if sorted_flag: + #print(sorted_flag) + date_list = auto_sorting(dir_name) + date_to = date_list[-1] + date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] + + with gr.Column(visible=sorted_flag) as page_panel: with gr.Row(): + renew_page = gr.Button('Refresh', elem_id=tabname + "_images_history_renew_page", interactive=sorted_flag) + first_page = gr.Button('First Page') + prev_page = gr.Button('Prev Page') + page_index = gr.Number(value=1, label="Page Index") + next_page = gr.Button('Next Page') + end_page = gr.Button('End Page') + + with gr.Row(elem_id=tabname + "_images_history"): with gr.Column(scale=2): + with gr.Row(): + newest = gr.Button('Newest') + date_to = gr.Dropdown(choices=date_list, value=date_to, label="Date to") + date_from = gr.Dropdown(choices=date_list, value=date_from, label="Date from") + history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) with gr.Row(): delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") @@ -128,22 +244,31 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Row(): with gr.Column(): img_file_info = gr.Textbox(label="Generate Info", interactive=False) - img_file_name = gr.Textbox(label="File Name", interactive=False) - with gr.Row(): + img_file_name = gr.Textbox(value="", label="File Name", interactive=False) # hiden items + with gr.Row(visible=False): + img_path = gr.Textbox(dir_name) + tabname_box = gr.Textbox(tabname) + image_index = gr.Textbox(value=-1) + set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index") + filenames = gr.State() + hidden = gr.Image(type="pil") + info1 = gr.Textbox() + info2 = gr.Textbox() + with gr.Column(visible=not sorted_flag) as init_warning: + with gr.Row(): + gr.Textbox("The system needs to archive the files according to the date. This requires changing the directory structure of the files", + label="Waring", + css="") + with gr.Row(): + sorted_button = gr.Button('Confirme') - img_path = gr.Textbox(dir_name.rstrip("/"), visible=False) - tabname_box = gr.Textbox(tabname, visible=False) - image_index = gr.Textbox(value=-1, visible=False) - set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index", visible=False) - filenames = gr.State() - hidden = gr.Image(type="pil", visible=False) - info1 = gr.Textbox(visible=False) - info2 = gr.Textbox(visible=False) - + + + # turn pages - gallery_inputs = [img_path, page_index, image_index, tabname_box] - gallery_outputs = [history_gallery, page_index, filenames, img_file_name, hidden, img_file_name] + gallery_inputs = [img_path, page_index, image_index, tabname_box, date_from, date_to] + gallery_outputs = [history_gallery, page_index, filenames, img_file_name] first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) @@ -154,15 +279,21 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): # page_index.change(page_index_change, inputs=[tabname_box, img_path, page_index], outputs=[history_gallery, page_index]) # other funcitons - set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, img_path, filenames], outputs=[img_file_name, image_index, hidden]) + set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, filenames], outputs=[img_file_name, image_index, hidden]) img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) - delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_path, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) + delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) - + date_to.change(date_to_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs + [date_from]) # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') + sorted_button.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from]) + newest.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from]) + + + + def create_history_tabs(gr, opts, run_pnginfo, switch_dict): with gr.Blocks(analytics_enabled=False) as images_history: From 523140d7805c644700009b8a2483ff4eb4a22304 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 10:23:30 +0200 Subject: [PATCH 013/140] ui fix --- modules/aesthetic_clip.py | 3 +-- modules/sd_hijack.py | 3 +-- modules/shared.py | 2 ++ modules/ui.py | 24 ++++++++++++++---------- 4 files changed, 18 insertions(+), 14 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index 68264284..ccb35c73 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -74,5 +74,4 @@ def generate_imgs_embd(name, folder, batch_size): """ shared.update_aesthetic_embeddings() return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding", - value=sorted(shared.aesthetic_embeddings.keys())[0] if len( - shared.aesthetic_embeddings) > 0 else None), res, "" + value="None"), res, "" diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 01fcb78f..2de2eed5 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -392,8 +392,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) - if len(text[ - 0]) != 0 and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: + if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: if not opts.use_old_emphasis_implementation: remade_batch_tokens = [ [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in diff --git a/modules/shared.py b/modules/shared.py index 3c5ffef1..e2c98b2d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -96,11 +96,13 @@ loaded_hypernetwork = None aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} +aesthetic_embeddings = aesthetic_embeddings | {"None": None} def update_aesthetic_embeddings(): global aesthetic_embeddings aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} + aesthetic_embeddings = aesthetic_embeddings | {"None": None} def reload_hypernetworks(): global hypernetworks diff --git a/modules/ui.py b/modules/ui.py index 13ba3142..4069f0d2 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -594,19 +594,23 @@ def create_ui(wrap_gradio_gpu_call): height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) with gr.Group(): - aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + with gr.Accordion("Open for Clip Aesthetic!",open=False): + with gr.Row(): + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) - aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) - aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + with gr.Row(): + aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), + label="Aesthetic imgs embedding", + value="None") - with gr.Row(): - aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") - aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) - aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) + with gr.Row(): + aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") + aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) + aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) - aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Aesthetic imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None) - - aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) From e4f8b5f00dd33b7547cc6b76fbed26bb83b37a64 Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 10:28:21 +0200 Subject: [PATCH 014/140] ui fix --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 2de2eed5..5d0590af 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -178,7 +178,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): self.load_image_embs(image_embs_name) def load_image_embs(self, image_embs_name): - if image_embs_name is None or len(image_embs_name) == 0: + if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None": image_embs_name = None if image_embs_name is not None and self.image_embs_name != image_embs_name: self.image_embs_name = image_embs_name From f62905fdf928b54aa76765e5cbde8d538d494e49 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Sun, 16 Oct 2022 21:22:38 +0800 Subject: [PATCH 015/140] images history speed up --- javascript/images_history.js | 39 +++--- modules/images_history.py | 248 ++++++++++++++++++----------------- 2 files changed, 146 insertions(+), 141 deletions(-) diff --git a/javascript/images_history.js b/javascript/images_history.js index ac5834c7..fb1356d9 100644 --- a/javascript/images_history.js +++ b/javascript/images_history.js @@ -20,7 +20,7 @@ var images_history_click_image = function(){ var images_history_click_tab = function(){ var tabs_box = gradioApp().getElementById("images_history_tab"); if (!tabs_box.classList.contains(this.getAttribute("tabname"))) { - gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_renew_page").click(); + gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click(); tabs_box.classList.add(this.getAttribute("tabname")) } } @@ -96,7 +96,7 @@ function images_history_get_current_img(tabname, img_index, files){ ]; } -function images_history_delete(del_num, tabname, img_file_name, page_index, filenames, image_index){ +function images_history_delete(del_num, tabname, image_index){ image_index = parseInt(image_index); var tab = gradioApp().getElementById(tabname + '_images_history'); var set_btn = tab.querySelector(".images_history_set_index"); @@ -107,6 +107,7 @@ function images_history_delete(del_num, tabname, img_file_name, page_index, file } }); var img_num = buttons.length / 2; + del_num = Math.min(img_num - image_index, del_num) if (img_num <= del_num){ setTimeout(function(tabname){ gradioApp().getElementById(tabname + '_images_history_renew_page').click(); @@ -114,30 +115,29 @@ function images_history_delete(del_num, tabname, img_file_name, page_index, file } else { var next_img for (var i = 0; i < del_num; i++){ - if (image_index + i < image_index + img_num){ - buttons[image_index + i].style.display = 'none'; - buttons[image_index + img_num + 1].style.display = 'none'; - next_img = image_index + i + 1 - } + buttons[image_index + i].style.display = 'none'; + buttons[image_index + i + img_num].style.display = 'none'; + next_img = image_index + i + 1 } var bnt; if (next_img >= img_num){ - btn = buttons[image_index - del_num]; + btn = buttons[image_index - 1]; } else { btn = buttons[next_img]; } setTimeout(function(btn){btn.click()}, 30, btn); } images_history_disabled_del(); - return [del_num, tabname, img_file_name, page_index, filenames, image_index]; + } -function images_history_turnpage(img_path, page_index, image_index, tabname, date_from, date_to){ +function images_history_turnpage(tabname){ + console.log("del_button") + gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled'); var buttons = gradioApp().getElementById(tabname + '_images_history').querySelectorAll(".gallery-item"); buttons.forEach(function(elem) { elem.style.display = 'block'; - }) - return [img_path, page_index, image_index, tabname, date_from, date_to]; + }) } function images_history_enable_del_buttons(){ @@ -147,7 +147,7 @@ function images_history_enable_del_buttons(){ } function images_history_init(){ - var load_txt2img_button = gradioApp().getElementById('txt2img_images_history_renew_page') + var load_txt2img_button = gradioApp().getElementById('saved_images_history_start') if (load_txt2img_button){ for (var i in images_history_tab_list ){ tab = images_history_tab_list[i]; @@ -166,7 +166,8 @@ function images_history_init(){ // this refreshes history upon tab switch // until the history is known to work well, which is not the case now, we do not do this at startup - //tab_btns[i].addEventListener('click', images_history_click_tab); + // -- load page very fast now, so better user experience by automatically activating pages + tab_btns[i].addEventListener('click', images_history_click_tab); } tabs_box.classList.add(images_history_tab_list[0]); @@ -177,7 +178,7 @@ function images_history_init(){ } } -var images_history_tab_list = ["txt2img", "img2img", "extras"]; +var images_history_tab_list = ["saved", "txt2img", "img2img", "extras"]; setTimeout(images_history_init, 500); document.addEventListener("DOMContentLoaded", function() { var mutationObserver = new MutationObserver(function(m){ @@ -188,18 +189,16 @@ document.addEventListener("DOMContentLoaded", function() { bnt.addEventListener('click', images_history_click_image, true); }); - // same as load_txt2img_button.click() above - /* var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg"); if (cls_btn){ cls_btn.addEventListener('click', function(){ - gradioApp().getElementById(tabname + '_images_history_renew_page').click(); + gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled'); }, false); - }*/ + } } }); - mutationObserver.observe( gradioApp(), { childList:true, subtree:true }); + mutationObserver.observe(gradioApp(), { childList:true, subtree:true }); }); diff --git a/modules/images_history.py b/modules/images_history.py index 7fd75005..ae0b4e40 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -3,8 +3,10 @@ import shutil import time import hashlib import gradio -show_max_dates_num = 3 + system_bak_path = "webui_log_and_bak" +loads_files_num = 216 +num_of_imgs_per_page = 36 def is_valid_date(date): try: time.strptime(date, "%Y%m%d") @@ -53,38 +55,7 @@ def traverse_all_files(curr_path, image_list, all_type=False): image_list = traverse_all_files(file, image_list) return image_list -def get_recent_images(dir_name, page_index, step, image_index, tabname, date_from, date_to): - #print(f"turn_page {page_index}",date_from) - if date_from is None or date_from == "": - return None, 1, None, "" - image_list = [] - date_list = auto_sorting(dir_name) - page_index = int(page_index) - today = time.strftime("%Y%m%d",time.localtime(time.time())) - for date in date_list: - if date >= date_from and date <= date_to: - path = os.path.join(dir_name, date) - if date == today and not os.path.exists(path): - continue - image_list = traverse_all_files(path, image_list) - - image_list = sorted(image_list, key=lambda file: -os.path.getctime(file)) - num = 48 if tabname != "extras" else 12 - max_page_index = len(image_list) // num + 1 - page_index = max_page_index if page_index == -1 else page_index + step - page_index = 1 if page_index < 1 else page_index - page_index = max_page_index if page_index > max_page_index else page_index - idx_frm = (page_index - 1) * num - image_list = image_list[idx_frm:idx_frm + num] - image_index = int(image_index) - if image_index < 0 or image_index > len(image_list) - 1: - current_file = None - else: - current_file = image_list[image_index] - return image_list, page_index, image_list, "" - -def auto_sorting(dir_name): - #print(f"auto sorting") +def auto_sorting(dir_name): bak_path = os.path.join(dir_name, system_bak_path) if not os.path.exists(bak_path): os.mkdir(bak_path) @@ -126,102 +97,131 @@ def auto_sorting(dir_name): today = time.strftime("%Y%m%d",time.localtime(time.time())) if today not in date_list: date_list.append(today) - return sorted(date_list) + return sorted(date_list, reverse=True) -def archive_images(dir_name): +def archive_images(dir_name, date_to): date_list = auto_sorting(dir_name) - date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] + today = time.strftime("%Y%m%d",time.localtime(time.time())) + date_to = today if date_to is None or date_to == "" else date_to + filenames = [] + for date in date_list: + if date <= date_to: + path = os.path.join(dir_name, date) + if date == today and not os.path.exists(path): + continue + filenames = traverse_all_files(path, filenames) + if len(filenames) > loads_files_num: + break + filenames = sorted(filenames, key=lambda file: -os.path.getctime(file)) + _, image_list, _, visible_num = get_recent_images(1, 0, filenames) return ( gradio.update(visible=False), gradio.update(visible=True), - gradio.Dropdown.update(choices=date_list, value=date_list[-1]), - gradio.Dropdown.update(choices=date_list, value=date_from) + gradio.Dropdown.update(choices=date_list, value=date_to), + date, + filenames, + 1, + image_list, + "", + visible_num ) +def system_init(dir_name): + ret = [x for x in archive_images(dir_name, None)] + ret += [gradio.update(visible=False)] + return ret -def date_to_change(dir_name, page_index, image_index, tabname, date_from, date_to): - #print("date_to", date_to) - date_list = auto_sorting(dir_name) - date_from_list = [date for date in date_list if date <= date_to] - date_from = date_from_list[0] if len(date_from_list) < show_max_dates_num else date_from_list[-show_max_dates_num] - image_list, page_index, image_list, _ =get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to) - return image_list, page_index, image_list, _, gradio.Dropdown.update(choices=date_from_list, value=date_from) +def newest_click(dir_name, date_to): + if date_to == "start": + return True, False, "start", None, None, 1, None, "" + else: + return archive_images(dir_name, time.strftime("%Y%m%d",time.localtime(time.time()))) -def first_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): - return get_recent_images(dir_name, 1, 0, image_index, tabname, date_from, date_to) - - -def end_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): - return get_recent_images(dir_name, -1, 0, image_index, tabname, date_from, date_to) - - -def prev_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): - return get_recent_images(dir_name, page_index, -1, image_index, tabname, date_from, date_to) - - -def next_page_click(dir_name, page_index, image_index, tabname, date_from, date_to): - return get_recent_images(dir_name, page_index, 1, image_index, tabname, date_from, date_to) - - -def page_index_change(dir_name, page_index, image_index, tabname, date_from, date_to): - return get_recent_images(dir_name, page_index, 0, image_index, tabname, date_from, date_to) - - -def show_image_info(tabname_box, num, filenames): - # #print(f"select image {num}") - file = filenames[int(num)] - return file, num, file - -def delete_image(delete_num, tabname, name, page_index, filenames, image_index): +def delete_image(delete_num, name, filenames, image_index, visible_num): if name == "": return filenames, delete_num else: delete_num = int(delete_num) + visible_num = int(visible_num) + image_index = int(image_index) index = list(filenames).index(name) i = 0 new_file_list = [] for name in filenames: if i >= index and i < index + delete_num: if os.path.exists(name): - #print(f"Delete file {name}") + if visible_num == image_index: + new_file_list.append(name) + continue + print(f"Delete file {name}") os.remove(name) + visible_num -= 1 txt_file = os.path.splitext(name)[0] + ".txt" if os.path.exists(txt_file): os.remove(txt_file) else: - #print(f"Not exists file {name}") + print(f"Not exists file {name}") else: new_file_list.append(name) i += 1 - return new_file_list, 1 + return new_file_list, 1, visible_num + +def get_recent_images(page_index, step, filenames): + page_index = int(page_index) + max_page_index = len(filenames) // num_of_imgs_per_page + 1 + page_index = max_page_index if page_index == -1 else page_index + step + page_index = 1 if page_index < 1 else page_index + page_index = max_page_index if page_index > max_page_index else page_index + idx_frm = (page_index - 1) * num_of_imgs_per_page + image_list = filenames[idx_frm:idx_frm + num_of_imgs_per_page] + length = len(filenames) + visible_num = num_of_imgs_per_page if idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page + visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num + return page_index, image_list, "", visible_num + +def first_page_click(page_index, filenames): + return get_recent_images(1, 0, filenames) + +def end_page_click(page_index, filenames): + return get_recent_images(-1, 0, filenames) + +def prev_page_click(page_index, filenames): + return get_recent_images(page_index, -1, filenames) + +def next_page_click(page_index, filenames): + return get_recent_images(page_index, 1, filenames) + +def page_index_change(page_index, filenames): + return get_recent_images(page_index, 0, filenames) + +def show_image_info(tabname_box, num, page_index, filenames): + file = filenames[int(num) + int((page_index - 1) * num_of_imgs_per_page)] + return file, num, file def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): - if opts.outdir_samples != "": - dir_name = opts.outdir_samples - elif tabname == "txt2img": + if tabname == "txt2img": dir_name = opts.outdir_txt2img_samples elif tabname == "img2img": dir_name = opts.outdir_img2img_samples elif tabname == "extras": dir_name = opts.outdir_extras_samples + elif tabname == "saved": + dir_name = opts.outdir_save + if not os.path.exists(dir_name): + os.makedirs(dir_name) d = dir_name.split("/") - dir_name = "/" if dir_name.startswith("/") else d[0] + dir_name = d[0] for p in d[1:]: dir_name = os.path.join(dir_name, p) f_list = os.listdir(dir_name) sorted_flag = os.path.exists(os.path.join(dir_name, system_bak_path)) or len(f_list) == 0 date_list, date_from, date_to = None, None, None - if sorted_flag: - #print(sorted_flag) - date_list = auto_sorting(dir_name) - date_to = date_list[-1] - date_from = date_list[-show_max_dates_num] if len(date_list) > show_max_dates_num else date_list[0] with gr.Column(visible=sorted_flag) as page_panel: with gr.Row(): - renew_page = gr.Button('Refresh', elem_id=tabname + "_images_history_renew_page", interactive=sorted_flag) + #renew_page = gr.Button('Refresh') first_page = gr.Button('First Page') prev_page = gr.Button('Prev Page') page_index = gr.Number(value=1, label="Page Index") @@ -231,9 +231,9 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Row(elem_id=tabname + "_images_history"): with gr.Column(scale=2): with gr.Row(): - newest = gr.Button('Newest') - date_to = gr.Dropdown(choices=date_list, value=date_to, label="Date to") - date_from = gr.Dropdown(choices=date_list, value=date_from, label="Date from") + newest = gr.Button('Refresh', elem_id=tabname + "_images_history_start") + date_from = gr.Textbox(label="Date from", interactive=False) + date_to = gr.Dropdown(value="start" if not sorted_flag else None, label="Date to") history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) with gr.Row(): @@ -247,66 +247,72 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Column(): img_file_info = gr.Textbox(label="Generate Info", interactive=False) img_file_name = gr.Textbox(value="", label="File Name", interactive=False) + # hiden items - with gr.Row(visible=False): + with gr.Row(visible=False): + visible_img_num = gr.Number() img_path = gr.Textbox(dir_name) tabname_box = gr.Textbox(tabname) image_index = gr.Textbox(value=-1) set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index") filenames = gr.State() + all_images_list = gr.State() hidden = gr.Image(type="pil") info1 = gr.Textbox() info2 = gr.Textbox() + with gr.Column(visible=not sorted_flag) as init_warning: with gr.Row(): - gr.Textbox("The system needs to archive the files according to the date. This requires changing the directory structure of the files", - label="Waring", - css="") + warning = gr.Textbox( + label="Waring", + value=f"The system needs to archive the files according to the date. This requires changing the directory structure of the files.If you have doubts about this operation, you can first back up the files in the '{dir_name}' directory" + ) + warning.style(height=100, width=50) with gr.Row(): sorted_button = gr.Button('Confirme') - - + change_date_output = [init_warning, page_panel, date_to, date_from, filenames, page_index, history_gallery, img_file_name, visible_img_num] + sorted_button.click(system_init, inputs=[img_path], outputs=change_date_output + [sorted_button]) + newest.click(newest_click, inputs=[img_path, date_to], outputs=change_date_output) + date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output) + date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + newest.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + + delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num]) + delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None) + # turn pages - gallery_inputs = [img_path, page_index, image_index, tabname_box, date_from, date_to] - gallery_outputs = [history_gallery, page_index, filenames, img_file_name] + gallery_inputs = [page_index, filenames] + gallery_outputs = [page_index, history_gallery, img_file_name, visible_img_num] - first_page.click(first_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - next_page.click(next_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - prev_page.click(prev_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - end_page.click(end_page_click, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - page_index.submit(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - renew_page.click(page_index_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs) - # page_index.change(page_index_change, inputs=[tabname_box, img_path, page_index], outputs=[history_gallery, page_index]) + first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs) + page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs) + + first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") # other funcitons - set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, filenames], outputs=[img_file_name, image_index, hidden]) - img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) - delete.click(delete_image, _js="images_history_delete", inputs=[delete_num, tabname_box, img_file_name, page_index, filenames, image_index], outputs=[filenames, delete_num]) + set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden]) + img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) - date_to.change(date_to_change, _js="images_history_turnpage", inputs=gallery_inputs, outputs=gallery_outputs + [date_from]) - # pnginfo.click(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) + switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') - sorted_button.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from]) - newest.click(archive_images, inputs=[img_path], outputs=[init_warning, page_panel, date_to, date_from]) - - - def create_history_tabs(gr, opts, run_pnginfo, switch_dict): with gr.Blocks(analytics_enabled=False) as images_history: with gr.Tabs() as tabs: - with gr.Tab("txt2img history"): - with gr.Blocks(analytics_enabled=False) as images_history_txt2img: - show_images_history(gr, opts, "txt2img", run_pnginfo, switch_dict) - with gr.Tab("img2img history"): - with gr.Blocks(analytics_enabled=False) as images_history_img2img: - show_images_history(gr, opts, "img2img", run_pnginfo, switch_dict) - with gr.Tab("extras history"): - with gr.Blocks(analytics_enabled=False) as images_history_img2img: - show_images_history(gr, opts, "extras", run_pnginfo, switch_dict) + for tab in ["saved", "txt2img", "img2img", "extras"]: + with gr.Tab(tab): + with gr.Blocks(analytics_enabled=False) as images_history_img2img: + show_images_history(gr, opts, tab, run_pnginfo, switch_dict) return images_history From a4de699e3c235d83b5a957d08779cb41cb0781bc Mon Sep 17 00:00:00 2001 From: yfszzx Date: Sun, 16 Oct 2022 22:37:12 +0800 Subject: [PATCH 016/140] Images history speed up --- javascript/images_history.js | 1 + modules/images_history.py | 7 +++++-- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/javascript/images_history.js b/javascript/images_history.js index fb1356d9..9d9d04fb 100644 --- a/javascript/images_history.js +++ b/javascript/images_history.js @@ -108,6 +108,7 @@ function images_history_delete(del_num, tabname, image_index){ }); var img_num = buttons.length / 2; del_num = Math.min(img_num - image_index, del_num) + console.log(del_num, img_num) if (img_num <= del_num){ setTimeout(function(tabname){ gradioApp().getElementById(tabname + '_images_history_renew_page').click(); diff --git a/modules/images_history.py b/modules/images_history.py index ae0b4e40..94bd16a8 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -153,6 +153,7 @@ def delete_image(delete_num, name, filenames, image_index, visible_num): if os.path.exists(name): if visible_num == image_index: new_file_list.append(name) + i += 1 continue print(f"Delete file {name}") os.remove(name) @@ -221,7 +222,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Column(visible=sorted_flag) as page_panel: with gr.Row(): - #renew_page = gr.Button('Refresh') + renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page") first_page = gr.Button('First Page') prev_page = gr.Button('Prev Page') page_index = gr.Number(value=1, label="Page Index") @@ -231,7 +232,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Row(elem_id=tabname + "_images_history"): with gr.Column(scale=2): with gr.Row(): - newest = gr.Button('Refresh', elem_id=tabname + "_images_history_start") + newest = gr.Button('Reload', elem_id=tabname + "_images_history_start") date_from = gr.Textbox(label="Date from", interactive=False) date_to = gr.Dropdown(value="start" if not sorted_flag else None, label="Date to") @@ -291,12 +292,14 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs) end_page.click(end_page_click, inputs=gallery_inputs, outputs=gallery_outputs) page_index.submit(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs) + renew_page.click(page_index_change, inputs=gallery_inputs, outputs=gallery_outputs) first_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") next_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") prev_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") end_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") page_index.submit(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") # other funcitons set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden]) From 9324cdaa3199d65c182858785dd1eca42b192b8e Mon Sep 17 00:00:00 2001 From: MalumaDev Date: Sun, 16 Oct 2022 17:53:56 +0200 Subject: [PATCH 017/140] ui fix, re organization of the code --- modules/aesthetic_clip.py | 154 +++++++++++++++++++++++++-- modules/img2img.py | 14 ++- modules/processing.py | 29 ++--- modules/sd_hijack.py | 102 +----------------- modules/sd_models.py | 5 +- modules/shared.py | 14 ++- modules/textual_inversion/dataset.py | 2 +- modules/txt2img.py | 18 ++-- modules/ui.py | 50 +++++---- 9 files changed, 232 insertions(+), 156 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index ccb35c73..34efa931 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -1,3 +1,4 @@ +import copy import itertools import os from pathlib import Path @@ -7,11 +8,12 @@ import gc import gradio as gr import torch from PIL import Image -from modules import shared -from modules.shared import device -from transformers import CLIPModel, CLIPProcessor +from torch import optim -from tqdm.auto import tqdm +from modules import shared +from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer +from tqdm.auto import tqdm, trange +from modules.shared import opts, device def get_all_images_in_folder(folder): @@ -37,12 +39,39 @@ def iter_to_batched(iterable, n=1): yield chunk +def create_ui(): + with gr.Group(): + with gr.Accordion("Open for Clip Aesthetic!", open=False): + with gr.Row(): + aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", + value=0.9) + aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + + with gr.Row(): + aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', + placeholder="Aesthetic learning rate", value="0.0001") + aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()), + label="Aesthetic imgs embedding", + value="None") + + with gr.Row(): + aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', + placeholder="This text is used to rotate the feature space of the imgs embs", + value="") + aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01, + value=0.1) + aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) + + return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative + + def generate_imgs_embd(name, folder, batch_size): # clipModel = CLIPModel.from_pretrained( # shared.sd_model.cond_stage_model.clipModel.name_or_path # ) - model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device) - processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path) + model = shared.clip_model.to(device) + processor = CLIPProcessor.from_pretrained(model.name_or_path) with torch.no_grad(): embs = [] @@ -63,7 +92,6 @@ def generate_imgs_embd(name, folder, batch_size): torch.save(embs, path) model = model.cpu() - del model del processor del embs gc.collect() @@ -74,4 +102,114 @@ def generate_imgs_embd(name, folder, batch_size): """ shared.update_aesthetic_embeddings() return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding", - value="None"), res, "" + value="None"), \ + gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), + label="Imgs embedding", + value="None"), res, "" + + +def slerp(low, high, val): + low_norm = low / torch.norm(low, dim=1, keepdim=True) + high_norm = high / torch.norm(high, dim=1, keepdim=True) + omega = torch.acos((low_norm * high_norm).sum(1)) + so = torch.sin(omega) + res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high + return res + + +class AestheticCLIP: + def __init__(self): + self.skip = False + self.aesthetic_steps = 0 + self.aesthetic_weight = 0 + self.aesthetic_lr = 0 + self.slerp = False + self.aesthetic_text_negative = "" + self.aesthetic_slerp_angle = 0 + self.aesthetic_imgs_text = "" + + self.image_embs_name = None + self.image_embs = None + self.load_image_embs(None) + + def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, + aesthetic_slerp=True, aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False): + self.aesthetic_imgs_text = aesthetic_imgs_text + self.aesthetic_slerp_angle = aesthetic_slerp_angle + self.aesthetic_text_negative = aesthetic_text_negative + self.slerp = aesthetic_slerp + self.aesthetic_lr = aesthetic_lr + self.aesthetic_weight = aesthetic_weight + self.aesthetic_steps = aesthetic_steps + self.load_image_embs(image_embs_name) + + def set_skip(self, skip): + self.skip = skip + + def load_image_embs(self, image_embs_name): + if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None": + image_embs_name = None + self.image_embs_name = None + if image_embs_name is not None and self.image_embs_name != image_embs_name: + self.image_embs_name = image_embs_name + self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device) + self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) + self.image_embs.requires_grad_(False) + + def __call__(self, z, remade_batch_tokens): + if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None: + tokenizer = shared.sd_model.cond_stage_model.tokenizer + if not opts.use_old_emphasis_implementation: + remade_batch_tokens = [ + [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in + remade_batch_tokens] + + tokens = torch.asarray(remade_batch_tokens).to(device) + + model = copy.deepcopy(shared.clip_model).to(device) + model.requires_grad_(True) + if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: + text_embs_2 = model.get_text_features( + **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device)) + if self.aesthetic_text_negative: + text_embs_2 = self.image_embs - text_embs_2 + text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True) + img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle) + else: + img_embs = self.image_embs + + with torch.enable_grad(): + + # We optimize the model to maximize the similarity + optimizer = optim.Adam( + model.text_model.parameters(), lr=self.aesthetic_lr + ) + + for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"): + text_embs = model.get_text_features(input_ids=tokens) + text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) + sim = text_embs @ img_embs.T + loss = -sim + optimizer.zero_grad() + loss.mean().backward() + optimizer.step() + + zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) + if opts.CLIP_stop_at_last_layers > 1: + zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers] + zn = model.text_model.final_layer_norm(zn) + else: + zn = zn.last_hidden_state + model.cpu() + del model + gc.collect() + torch.cuda.empty_cache() + zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1) + if self.slerp: + z = slerp(z, zn, self.aesthetic_weight) + else: + z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight + + return z diff --git a/modules/img2img.py b/modules/img2img.py index 24126774..4ed80c4b 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -56,7 +56,14 @@ def process_batch(p, input_dir, output_dir, args): processed_image.save(os.path.join(output_dir, filename)) -def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args): +def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, + aesthetic_lr=0, + aesthetic_weight=0, aesthetic_steps=0, + aesthetic_imgs=None, + aesthetic_slerp=False, + aesthetic_imgs_text="", + aesthetic_slerp_angle=0.15, + aesthetic_text_negative=False, *args): is_inpaint = mode == 1 is_batch = mode == 2 @@ -109,6 +116,11 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro inpainting_mask_invert=inpainting_mask_invert, ) + shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), + aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, + aesthetic_slerp_angle, + aesthetic_text_negative) + if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/processing.py b/modules/processing.py index 1db26c3e..685f9fcd 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -146,7 +146,8 @@ class Processed: self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0] self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0] self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1 - self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 + self.subseed = int( + self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1 self.all_prompts = all_prompts or [self.prompt] self.all_seeds = all_seeds or [self.seed] @@ -332,16 +333,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip() -def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, - aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", - aesthetic_slerp_angle=0.15, - aesthetic_text_negative=False) -> Processed: +def process_images(p: StableDiffusionProcessing) -> Processed: """this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch""" - aesthetic_lr = float(aesthetic_lr) - aesthetic_weight = float(aesthetic_weight) - aesthetic_steps = int(aesthetic_steps) - if type(p.prompt) == list: assert (len(p.prompt) > 0) else: @@ -417,16 +411,10 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh # uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) # c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): - if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): - shared.sd_model.cond_stage_model.set_aesthetic_params() + shared.aesthetic_clip.set_skip(True) uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) - if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"): - shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight, - aesthetic_steps, aesthetic_imgs, - aesthetic_slerp, aesthetic_imgs_text, - aesthetic_slerp_angle, - aesthetic_text_negative) + shared.aesthetic_clip.set_skip(False) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) if len(model_hijack.comments) > 0: @@ -582,7 +570,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -600,10 +587,12 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): seed_resize_from_w=self.seed_resize_from_w, p=self) samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) - samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] + samples = samples[:, :, self.truncate_y // 2:samples.shape[2] - self.truncate_y // 2, + self.truncate_x // 2:samples.shape[3] - self.truncate_x // 2] if opts.use_scale_latent_for_hires_fix: - samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), + mode="bilinear") else: decoded_samples = decode_first_stage(self.sd_model, samples) lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 5d0590af..227e7670 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -29,8 +29,8 @@ def apply_optimizations(): ldm.modules.diffusionmodules.model.nonlinearity = silu - - if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): + if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and ( + 6, 0) <= torch.cuda.get_device_capability(shared.device) <= (9, 0)): print("Applying xformers cross attention optimization.") ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward @@ -118,33 +118,14 @@ class StableDiffusionModelHijack: return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count) -def slerp(low, high, val): - low_norm = low / torch.norm(low, dim=1, keepdim=True) - high_norm = high / torch.norm(high, dim=1, keepdim=True) - omega = torch.acos((low_norm * high_norm).sum(1)) - so = torch.sin(omega) - res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high - return res - - class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): def __init__(self, wrapped, hijack): super().__init__() self.wrapped = wrapped - self.clipModel = CLIPModel.from_pretrained( - self.wrapped.transformer.name_or_path - ) - del self.clipModel.vision_model - self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path) - self.hijack: StableDiffusionModelHijack = hijack - self.tokenizer = wrapped.tokenizer - # self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval() - self.image_embs_name = None - self.image_embs = None - self.load_image_embs(None) self.token_mults = {} - + self.hijack: StableDiffusionModelHijack = hijack + self.tokenizer = wrapped.tokenizer self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if @@ -164,28 +145,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): if mult != 1.0: self.token_mults[ident] = mult - def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, - aesthetic_slerp=True, aesthetic_imgs_text="", - aesthetic_slerp_angle=0.15, - aesthetic_text_negative=False): - self.aesthetic_imgs_text = aesthetic_imgs_text - self.aesthetic_slerp_angle = aesthetic_slerp_angle - self.aesthetic_text_negative = aesthetic_text_negative - self.slerp = aesthetic_slerp - self.aesthetic_lr = aesthetic_lr - self.aesthetic_weight = aesthetic_weight - self.aesthetic_steps = aesthetic_steps - self.load_image_embs(image_embs_name) - - def load_image_embs(self, image_embs_name): - if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None": - image_embs_name = None - if image_embs_name is not None and self.image_embs_name != image_embs_name: - self.image_embs_name = image_embs_name - self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device) - self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) - self.image_embs.requires_grad_(False) - def tokenize_line(self, line, used_custom_terms, hijack_comments): id_end = self.wrapped.tokenizer.eos_token_id @@ -391,58 +350,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): z1 = self.process_tokens(tokens, multipliers) z = z1 if z is None else torch.cat((z, z1), axis=-2) - - if self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name != None: - if not opts.use_old_emphasis_implementation: - remade_batch_tokens = [ - [self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in - remade_batch_tokens] - - tokens = torch.asarray(remade_batch_tokens).to(device) - - model = copy.deepcopy(self.clipModel).to(device) - model.requires_grad_(True) - if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: - text_embs_2 = model.get_text_features( - **self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device)) - if self.aesthetic_text_negative: - text_embs_2 = self.image_embs - text_embs_2 - text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True) - img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle) - else: - img_embs = self.image_embs - - with torch.enable_grad(): - - # We optimize the model to maximize the similarity - optimizer = optim.Adam( - model.text_model.parameters(), lr=self.aesthetic_lr - ) - - for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"): - text_embs = model.get_text_features(input_ids=tokens) - text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) - sim = text_embs @ img_embs.T - loss = -sim - optimizer.zero_grad() - loss.mean().backward() - optimizer.step() - - zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) - if opts.CLIP_stop_at_last_layers > 1: - zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers] - zn = model.text_model.final_layer_norm(zn) - else: - zn = zn.last_hidden_state - model.cpu() - del model - - zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1) - if self.slerp: - z = slerp(z, zn, self.aesthetic_weight) - else: - z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight - + z = shared.aesthetic_clip(z, remade_batch_tokens) remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers i += 1 diff --git a/modules/sd_models.py b/modules/sd_models.py index 3aa21ec1..8e4ee435 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -20,7 +20,7 @@ checkpoints_loaded = collections.OrderedDict() try: # this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start. - from transformers import logging + from transformers import logging, CLIPModel logging.set_verbosity_error() except Exception: @@ -196,6 +196,9 @@ def load_model(): sd_hijack.model_hijack.hijack(sd_model) + if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path: + shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path) + sd_model.eval() print(f"Model loaded.") diff --git a/modules/shared.py b/modules/shared.py index e2c98b2d..e19ca779 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -3,6 +3,7 @@ import datetime import json import os import sys +from collections import OrderedDict import gradio as gr import tqdm @@ -94,15 +95,15 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None -aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in - os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} -aesthetic_embeddings = aesthetic_embeddings | {"None": None} +aesthetic_embeddings = {} def update_aesthetic_embeddings(): global aesthetic_embeddings aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} - aesthetic_embeddings = aesthetic_embeddings | {"None": None} + aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings) + +update_aesthetic_embeddings() def reload_hypernetworks(): global hypernetworks @@ -381,6 +382,11 @@ sd_upscalers = [] sd_model = None +clip_model = None + +from modules.aesthetic_clip import AestheticCLIP +aesthetic_clip = AestheticCLIP() + progress_print_out = sys.stdout diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 68ceffe3..23bb4b6a 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -49,7 +49,7 @@ class PersonalizedBase(Dataset): print("Preparing dataset...") for path in tqdm.tqdm(self.image_paths): try: - image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.Resampling.BICUBIC) + image = Image.open(path).convert('RGB').resize((self.width, self.height), PIL.Image.BICUBIC) except Exception: continue diff --git a/modules/txt2img.py b/modules/txt2img.py index 8f394d05..6cbc50fc 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -1,12 +1,17 @@ import modules.scripts -from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images +from modules.processing import StableDiffusionProcessing, Processed, StableDiffusionProcessingTxt2Img, \ + StableDiffusionProcessingImg2Img, process_images from modules.shared import opts, cmd_opts import modules.shared as shared import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int,aesthetic_lr=0, +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, + restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, + subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, + height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, + firstphase_height: int, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, @@ -41,15 +46,17 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: firstphase_height=firstphase_height if enable_hr else None, ) + shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), + aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, + aesthetic_text_negative) + if cmd_opts.enable_console_prompts: print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) processed = modules.scripts.scripts_txt2img.run(p, *args) if processed is None: - processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text, - aesthetic_slerp_angle, - aesthetic_text_negative) + processed = process_images(p) shared.total_tqdm.clear() @@ -61,4 +68,3 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: processed.images = [] return processed.images, generation_info_js, plaintext_to_html(processed.info) - diff --git a/modules/ui.py b/modules/ui.py index 4069f0d2..0e5d73f0 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -43,7 +43,7 @@ from modules.images import save_image import modules.textual_inversion.ui import modules.hypernetworks.ui -import modules.aesthetic_clip +import modules.aesthetic_clip as aesthetic_clip import modules.images_history as img_his @@ -593,23 +593,25 @@ def create_ui(wrap_gradio_gpu_call): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - with gr.Group(): - with gr.Accordion("Open for Clip Aesthetic!",open=False): - with gr.Row(): - aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) - aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + # with gr.Group(): + # with gr.Accordion("Open for Clip Aesthetic!",open=False): + # with gr.Row(): + # aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9) + # aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) + # + # with gr.Row(): + # aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") + # aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) + # aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), + # label="Aesthetic imgs embedding", + # value="None") + # + # with gr.Row(): + # aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") + # aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) + # aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) - with gr.Row(): - aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', placeholder="Aesthetic learning rate", value="0.0001") - aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) - aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), - label="Aesthetic imgs embedding", - value="None") - - with gr.Row(): - aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="") - aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1) - aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) + aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui() with gr.Row(): @@ -840,6 +842,9 @@ def create_ui(wrap_gradio_gpu_call): width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui() + + with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) tiling = gr.Checkbox(label='Tiling', value=False) @@ -944,6 +949,14 @@ def create_ui(wrap_gradio_gpu_call): inpainting_mask_invert, img2img_batch_input_dir, img2img_batch_output_dir, + aesthetic_lr_im, + aesthetic_weight_im, + aesthetic_steps_im, + aesthetic_imgs_im, + aesthetic_slerp_im, + aesthetic_imgs_text_im, + aesthetic_slerp_angle_im, + aesthetic_text_negative_im, ] + custom_inputs, outputs=[ img2img_gallery, @@ -1283,7 +1296,7 @@ def create_ui(wrap_gradio_gpu_call): ) create_embedding_ae.click( - fn=modules.aesthetic_clip.generate_imgs_embd, + fn=aesthetic_clip.generate_imgs_embd, inputs=[ new_embedding_name_ae, process_src_ae, @@ -1291,6 +1304,7 @@ def create_ui(wrap_gradio_gpu_call): ], outputs=[ aesthetic_imgs, + aesthetic_imgs_im, ti_output, ti_outcome, ] From 9d702b16f01795c3af900e0ebd70faf4b25200f6 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Mon, 17 Oct 2022 16:11:03 +0800 Subject: [PATCH 018/140] fix two little bug --- modules/images_history.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/images_history.py b/modules/images_history.py index 23045df1..1ae168ca 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -133,7 +133,7 @@ def archive_images(dir_name, date_to): date = sort_array[loads_num][2] filenames = [x[1] for x in sort_array] else: - date = sort_array[loads_num][2] + date = sort_array[-1][2] filenames = [x[1] for x in sort_array] filenames = [x[1] for x in sort_array if x[2]>= date] _, image_list, _, visible_num = get_recent_images(1, 0, filenames) @@ -334,6 +334,6 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict): with gr.Tab(tab): with gr.Blocks(analytics_enabled=False) as images_history_img2img: show_images_history(gr, opts, tab, run_pnginfo, switch_dict) - gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory") #, visible=False) + gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False) return images_history From c408a0b41cfffde184cad35b2d97346342947d83 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Mon, 17 Oct 2022 22:28:43 +0800 Subject: [PATCH 019/140] fix two bug --- launch.py | 1 - modules/images_history.py | 4 ++-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/launch.py b/launch.py index 7520cfee..088eada1 100644 --- a/launch.py +++ b/launch.py @@ -11,7 +11,6 @@ python = sys.executable git = os.environ.get('GIT', "git") index_url = os.environ.get('INDEX_URL', "") - def extract_arg(args, name): return [x for x in args if x != name], name in args diff --git a/modules/images_history.py b/modules/images_history.py index 1ae168ca..10e5b970 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -181,7 +181,8 @@ def delete_image(delete_num, name, filenames, image_index, visible_num): return new_file_list, 1, visible_num def save_image(file_name): - shutil.copy2(file_name, opts.outdir_save) + if file_name is not None and os.path.exists(file_name): + shutil.copy2(file_name, opts.outdir_save) def get_recent_images(page_index, step, filenames): page_index = int(page_index) @@ -327,7 +328,6 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict): opts = sys_opts loads_files_num = int(opts.images_history_num_per_page) num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num) - backup_flag = opts.images_history_backup with gr.Blocks(analytics_enabled=False) as images_history: with gr.Tabs() as tabs: for tab in ["txt2img", "img2img", "extras", "saved"]: From de179cf8fd8191e1a6d288e7c29a16f53da1be88 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Mon, 17 Oct 2022 22:38:46 +0800 Subject: [PATCH 020/140] fix two bug --- launch.py | 1 + 1 file changed, 1 insertion(+) diff --git a/launch.py b/launch.py index 088eada1..7520cfee 100644 --- a/launch.py +++ b/launch.py @@ -11,6 +11,7 @@ python = sys.executable git = os.environ.get('GIT', "git") index_url = os.environ.get('INDEX_URL', "") + def extract_arg(args, name): return [x for x in args if x != name], name in args From 2272cf2f35fafd5cd486bfb4ee89df5bbc625b97 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Mon, 17 Oct 2022 23:04:42 +0800 Subject: [PATCH 021/140] fix two bug --- modules/images_history.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/images_history.py b/modules/images_history.py index 10e5b970..1c1790a4 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -133,7 +133,7 @@ def archive_images(dir_name, date_to): date = sort_array[loads_num][2] filenames = [x[1] for x in sort_array] else: - date = sort_array[-1][2] + date = None if len(sort_array) == 0 else sort_array[-1][2] filenames = [x[1] for x in sort_array] filenames = [x[1] for x in sort_array if x[2]>= date] _, image_list, _, visible_num = get_recent_images(1, 0, filenames) From 2b5b62e768d892773a7ec1d5e8d8cea23aae1254 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Mon, 17 Oct 2022 23:14:03 +0800 Subject: [PATCH 022/140] fix two bug --- modules/images_history.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/images_history.py b/modules/images_history.py index 1c1790a4..20324557 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -44,7 +44,7 @@ def traverse_all_files(curr_path, image_list, all_type=False): return image_list for file in f_list: file = os.path.join(curr_path, file) - if (not all_type) and file[-4:] == ".txt": + if (not all_type) and (file[-4:] == ".txt" or file[-4:] == ".csv"): pass elif os.path.isfile(file) and file[-10:].rfind(".") > 0: image_list.append(file) @@ -182,7 +182,7 @@ def delete_image(delete_num, name, filenames, image_index, visible_num): def save_image(file_name): if file_name is not None and os.path.exists(file_name): - shutil.copy2(file_name, opts.outdir_save) + shutil.copy(file_name, opts.outdir_save) def get_recent_images(page_index, step, filenames): page_index = int(page_index) From ca023f8a459717be19d333513a0e388cf3944e74 Mon Sep 17 00:00:00 2001 From: ClashSAN <98228077+ClashSAN@users.noreply.github.com> Date: Tue, 18 Oct 2022 08:57:05 +0000 Subject: [PATCH 023/140] Update README.md --- README.md | 20 +++++++++++++------- 1 file changed, 13 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 859a91b6..a89593bf 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - One click install and run script (but you still must install python and git) - Outpainting - Inpainting +- Color Sketch - Prompt Matrix - Stable Diffusion Upscale - Attention, specify parts of text that the model should pay more attention to @@ -37,14 +38,14 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - Interrupt processing at any time - 4GB video card support (also reports of 2GB working) - Correct seeds for batches -- Prompt length validation - - get length of prompt in tokens as you type - - get a warning after generation if some text was truncated +- Live prompt token length validation - Generation parameters - parameters you used to generate images are saved with that image - in PNG chunks for PNG, in EXIF for JPEG - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI - can be disabled in settings + - drag and drop an image/text-parameters to promptbox +- Read Generation Parameters Button, loads parameters in promptbox to UI - Settings page - Running arbitrary python code from UI (must run with --allow-code to enable) - Mouseover hints for most UI elements @@ -62,7 +63,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - Img2img Alternative - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly -- Checkpoint Merger, a tab that allows you to merge two checkpoints into one +- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once - separate prompts using uppercase `AND` @@ -70,14 +71,19 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) +- History tab: view, direct and delete images conveniently within the UI +- Generate forever option +- Training Tab +- Preprocessing Image Datasets: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) + + ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. -Alternatively, use Google Colab: +Alternatively, use online services(like Google Colab): -- [Colab, maintained by Akaibu](https://colab.research.google.com/drive/1kw3egmSn-KgWsikYvOMjJkVDsPLjEMzl) -- [Colab, original by me, outdated](https://colab.research.google.com/drive/1Iy-xW9t1-OQWhb0hNxueGij8phCyluOh). +- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) ### Automatic Installation on Windows 1. Install [Python 3.10.6](https://www.python.org/downloads/windows/), checking "Add Python to PATH" From eb299527b1e5d1f83a14641647fca72e8fb305ac Mon Sep 17 00:00:00 2001 From: yfszzx Date: Tue, 18 Oct 2022 20:14:11 +0800 Subject: [PATCH 024/140] Image browser --- javascript/images_history.js | 19 ++- modules/images_history.py | 223 +++++++++++++++++++++++------------ modules/shared.py | 7 +- modules/ui.py | 2 +- uitest.bat | 2 + uitest.py | 124 +++++++++++++++++++ 6 files changed, 287 insertions(+), 90 deletions(-) create mode 100644 uitest.bat create mode 100644 uitest.py diff --git a/javascript/images_history.js b/javascript/images_history.js index 3c028bc6..182d730b 100644 --- a/javascript/images_history.js +++ b/javascript/images_history.js @@ -145,9 +145,10 @@ function images_history_enable_del_buttons(){ } function images_history_init(){ - var loaded = gradioApp().getElementById("images_history_reconstruct_directory") - if (loaded){ - var init_status = loaded.querySelector("input").checked + // var loaded = gradioApp().getElementById("images_history_reconstruct_directory") + // if (loaded){ + // var init_status = loaded.querySelector("input").checked + if (gradioApp().getElementById("images_history_finish_render")){ for (var i in images_history_tab_list ){ tab = images_history_tab_list[i]; gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor"); @@ -163,19 +164,17 @@ function images_history_init(){ for (var i in images_history_tab_list){ var tabname = images_history_tab_list[i] tab_btns[i].setAttribute("tabname", tabname); - if (init_status){ - tab_btns[i].addEventListener('click', images_history_click_tab); - } - } - if (init_status){ - tab_btns[0].click(); + // if (!init_status){ + // tab_btns[i].addEventListener('click', images_history_click_tab); + // } + tab_btns[i].addEventListener('click', images_history_click_tab); } } else { setTimeout(images_history_init, 500); } } -var images_history_tab_list = ["txt2img", "img2img", "extras", "saved"]; +var images_history_tab_list = ["custom", "txt2img", "img2img", "extras", "saved"]; setTimeout(images_history_init, 500); document.addEventListener("DOMContentLoaded", function() { var mutationObserver = new MutationObserver(function(m){ diff --git a/modules/images_history.py b/modules/images_history.py index 20324557..d56f3a25 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -4,6 +4,7 @@ import time import hashlib import gradio system_bak_path = "webui_log_and_bak" +browser_tabname = "custom" def is_valid_date(date): try: time.strptime(date, "%Y%m%d") @@ -99,13 +100,15 @@ def auto_sorting(dir_name): date_list.append(today) return sorted(date_list, reverse=True) -def archive_images(dir_name, date_to): +def archive_images(dir_name, date_to): + filenames = [] loads_num =int(opts.images_history_num_per_page * opts.images_history_pages_num) + today = time.strftime("%Y%m%d",time.localtime(time.time())) + date_to = today if date_to is None or date_to == "" else date_to + date_to_bak = date_to if opts.images_history_reconstruct_directory: - date_list = auto_sorting(dir_name) - today = time.strftime("%Y%m%d",time.localtime(time.time())) - date_to = today if date_to is None or date_to == "" else date_to + date_list = auto_sorting(dir_name) for date in date_list: if date <= date_to: path = os.path.join(dir_name, date) @@ -120,7 +123,7 @@ def archive_images(dir_name, date_to): tmparray = [(os.path.getmtime(file), file) for file in filenames ] date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400 filenames = [] - date_list = {} + date_list = {date_to:None} date = time.strftime("%Y%m%d",time.localtime(time.time())) for t, f in tmparray: date = time.strftime("%Y%m%d",time.localtime(t)) @@ -133,22 +136,29 @@ def archive_images(dir_name, date_to): date = sort_array[loads_num][2] filenames = [x[1] for x in sort_array] else: - date = None if len(sort_array) == 0 else sort_array[-1][2] + date = date_to if len(sort_array) == 0 else sort_array[-1][2] filenames = [x[1] for x in sort_array] - filenames = [x[1] for x in sort_array if x[2]>= date] - _, image_list, _, visible_num = get_recent_images(1, 0, filenames) + filenames = [x[1] for x in sort_array if x[2]>= date] + num = len(filenames) + last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000)) + date = date[:4] + "-" + date[4:6] + "-" + date[6:8] + date_to_bak = date_to_bak[:4] + "-" + date_to_bak[4:6] + "-" + date_to_bak[6:8] + load_info = f"Loaded {(num + 1) // opts.images_history_pages_num} pades, {num} images, during {date} - {date_to_bak}" + _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames) return ( gradio.Dropdown.update(choices=date_list, value=date_to), - date, + load_info, filenames, 1, image_list, "", - visible_num + "", + visible_num, + last_date_from ) -def newest_click(dir_name, date_to): - return archive_images(dir_name, time.strftime("%Y%m%d",time.localtime(time.time()))) + + def delete_image(delete_num, name, filenames, image_index, visible_num): if name == "": @@ -196,7 +206,29 @@ def get_recent_images(page_index, step, filenames): length = len(filenames) visible_num = num_of_imgs_per_page if idx_frm + num_of_imgs_per_page <= length else length % num_of_imgs_per_page visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num - return page_index, image_list, "", visible_num + return page_index, image_list, "", "", visible_num + +def newest_click(date_to): + if date_to is None: + return time.strftime("%Y%m%d",time.localtime(time.time())), [] + else: + return None, [] +def forward_click(last_date_from, date_to_recorder): + if len(date_to_recorder) == 0: + return None, [] + if last_date_from == date_to_recorder[-1]: + date_to_recorder = date_to_recorder[:-1] + if len(date_to_recorder) == 0: + return None, [] + return date_to_recorder[-1], date_to_recorder[:-1] + +def backward_click(last_date_from, date_to_recorder): + if last_date_from is None or last_date_from == "": + return time.strftime("%Y%m%d",time.localtime(time.time())), [] + if len(date_to_recorder) == 0 or last_date_from != date_to_recorder[-1]: + date_to_recorder.append(last_date_from) + return last_date_from, date_to_recorder + def first_page_click(page_index, filenames): return get_recent_images(1, 0, filenames) @@ -214,13 +246,33 @@ def page_index_change(page_index, filenames): return get_recent_images(page_index, 0, filenames) def show_image_info(tabname_box, num, page_index, filenames): - file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))] - return file, num, file + file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))] + tm = time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + return file, tm, num, file def enable_page_buttons(): return gradio.update(visible=True) +def change_dir(img_dir, date_to): + warning = None + try: + if os.path.exists(img_dir): + try: + f = os.listdir(img_dir) + except: + warning = f"'{img_dir} is not a directory" + else: + warning = "The directory is not exist" + except: + warning = "The format of the directory is incorrect" + if warning is None: + today = time.strftime("%Y%m%d",time.localtime(time.time())) + return gradio.update(visible=False), gradio.update(visible=True), None, None if date_to != today else today + else: + return gradio.update(visible=True), gradio.update(visible=False), warning, date_to + def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): + custom_dir = False if tabname == "txt2img": dir_name = opts.outdir_txt2img_samples elif tabname == "img2img": @@ -229,69 +281,85 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): dir_name = opts.outdir_extras_samples elif tabname == "saved": dir_name = opts.outdir_save + else: + custom_dir = True + dir_name = None - d = dir_name.split("/") - dir_name = d[0] - for p in d[1:]: - dir_name = os.path.join(dir_name, p) - if not os.path.exists(dir_name): - os.makedirs(dir_name) + if not custom_dir: + d = dir_name.split("/") + dir_name = d[0] + for p in d[1:]: + dir_name = os.path.join(dir_name, p) + if not os.path.exists(dir_name): + os.makedirs(dir_name) - with gr.Column() as page_panel: - with gr.Row(visible=False) as turn_page_buttons: - renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page") - first_page = gr.Button('First Page') - prev_page = gr.Button('Prev Page') - page_index = gr.Number(value=1, label="Page Index") - next_page = gr.Button('Next Page') - end_page = gr.Button('End Page') + with gr.Column() as page_panel: + with gr.Row(): + img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory") + with gr.Row(visible=False) as warning: + warning_box = gr.Textbox("Message", interactive=False) - with gr.Row(elem_id=tabname + "_images_history"): - with gr.Column(scale=2): - with gr.Row(): - newest = gr.Button('Reload', elem_id=tabname + "_images_history_start") - date_from = gr.Textbox(label="Date from", interactive=False) - date_to = gr.Dropdown(label="Date to") - - history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=6) - with gr.Row(): - delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") - delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") - - with gr.Column(): - with gr.Row(): - if tabname != "saved": - save_btn = gr.Button('Save') - pnginfo_send_to_txt2img = gr.Button('Send to txt2img') - pnginfo_send_to_img2img = gr.Button('Send to img2img') - with gr.Row(): - with gr.Column(): - img_file_info = gr.Textbox(label="Generate Info", interactive=False) - img_file_name = gr.Textbox(value="", label="File Name", interactive=False) + with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel: + with gr.Column(scale=2): + with gr.Row(): + backward = gr.Button('Backward') + date_to = gr.Dropdown(label="Date to") + forward = gr.Button('Forward') + newest = gr.Button('Reload', elem_id=tabname + "_images_history_start") + with gr.Row(): + load_info = gr.Textbox(show_label=False, interactive=False) + with gr.Row(visible=False) as turn_page_buttons: + renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page") + first_page = gr.Button('First Page') + prev_page = gr.Button('Prev Page') + page_index = gr.Number(value=1, label="Page Index") + next_page = gr.Button('Next Page') + end_page = gr.Button('End Page') + history_gallery = gr.Gallery(show_label=False, elem_id=tabname + "_images_history_gallery").style(grid=opts.images_history_grid_num) + with gr.Row(): + delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") + delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") - # hiden items - with gr.Row(visible=False): - visible_img_num = gr.Number() - img_path = gr.Textbox(dir_name) - tabname_box = gr.Textbox(tabname) - image_index = gr.Textbox(value=-1) - set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index") - filenames = gr.State() - all_images_list = gr.State() - hidden = gr.Image(type="pil") - info1 = gr.Textbox() - info2 = gr.Textbox() + with gr.Column(): + with gr.Row(): + if tabname != "saved": + save_btn = gr.Button('Save') + pnginfo_send_to_txt2img = gr.Button('Send to txt2img') + pnginfo_send_to_img2img = gr.Button('Send to img2img') + with gr.Row(): + with gr.Column(): + img_file_info = gr.Textbox(label="Generate Info", interactive=False) + img_file_name = gr.Textbox(value="", label="File Name", interactive=False) + img_file_time= gr.Textbox(value="", label="Create Time", interactive=False) - + + # hiden items + with gr.Row(): #visible=False): + visible_img_num = gr.Number() + date_to_recorder = gr.State([]) + last_date_from = gr.Textbox() + tabname_box = gr.Textbox(tabname) + image_index = gr.Textbox(value=-1) + set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index") + filenames = gr.State() + all_images_list = gr.State() + hidden = gr.Image(type="pil") + info1 = gr.Textbox() + info2 = gr.Textbox() + + img_path.submit(change_dir, inputs=[img_path, date_to], outputs=[warning, main_panel, warning_box, date_to]) #change date - change_date_output = [date_to, date_from, filenames, page_index, history_gallery, img_file_name, visible_img_num] - newest.click(newest_click, inputs=[img_path, date_to], outputs=change_date_output) - date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output) - newest.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") - date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") - date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons]) - newest.click(enable_page_buttons, inputs=None, outputs=[turn_page_buttons]) + change_date_output = [date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from] + + date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output) + date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons]) + date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + + newest.click(newest_click, inputs=[date_to], outputs=[date_to, date_to_recorder]) + forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder]) + backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder]) + #delete delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num]) @@ -301,7 +369,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): #turn page gallery_inputs = [page_index, filenames] - gallery_outputs = [page_index, history_gallery, img_file_name, visible_img_num] + gallery_outputs = [page_index, history_gallery, img_file_name, img_file_time, visible_img_num] first_page.click(first_page_click, inputs=gallery_inputs, outputs=gallery_outputs) next_page.click(next_page_click, inputs=gallery_inputs, outputs=gallery_outputs) prev_page.click(prev_page_click, inputs=gallery_inputs, outputs=gallery_outputs) @@ -317,12 +385,14 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): renew_page.click(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") # other funcitons - set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, image_index, hidden]) + set_index.click(show_image_info, _js="images_history_get_current_img", inputs=[tabname_box, image_index, page_index, filenames], outputs=[img_file_name, img_file_time, image_index, hidden]) img_file_name.change(fn=None, _js="images_history_enable_del_buttons", inputs=None, outputs=None) hidden.change(fn=run_pnginfo, inputs=[hidden], outputs=[info1, img_file_info, info2]) switch_dict["fn"](pnginfo_send_to_txt2img, switch_dict["t2i"], img_file_info, 'switch_to_txt2img') switch_dict["fn"](pnginfo_send_to_img2img, switch_dict["i2i"], img_file_info, 'switch_to_img2img_img2img') + + def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict): global opts; opts = sys_opts @@ -330,10 +400,11 @@ def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict): num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num) with gr.Blocks(analytics_enabled=False) as images_history: with gr.Tabs() as tabs: - for tab in ["txt2img", "img2img", "extras", "saved"]: + for tab in [browser_tabname, "txt2img", "img2img", "extras", "saved"]: with gr.Tab(tab): - with gr.Blocks(analytics_enabled=False) as images_history_img2img: + with gr.Blocks(analytics_enabled=False) : show_images_history(gr, opts, tab, run_pnginfo, switch_dict) - gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False) + #gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False) + gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_finish_render", visible=False) return images_history diff --git a/modules/shared.py b/modules/shared.py index c2ea4186..1811018d 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -309,10 +309,11 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}), })) -options_templates.update(options_section(('images-history', "Images history"), { - "images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"), +options_templates.update(options_section(('images-history', "Images Browser"), { + #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"), "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"), - "images_history_pages_num": OptionInfo(6, "Maximum number of pages per load "), + "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "), + "images_history_grid_num": OptionInfo(6, "Number of grids in each row"), })) diff --git a/modules/ui.py b/modules/ui.py index 43dc88fc..85abac4d 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1548,7 +1548,7 @@ Requested path was: {f} (img2img_interface, "img2img", "img2img"), (extras_interface, "Extras", "extras"), (pnginfo_interface, "PNG Info", "pnginfo"), - (images_history, "History", "images_history"), + (images_history, "Image Browser", "images_history"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "ti"), (settings_interface, "Settings", "settings"), diff --git a/uitest.bat b/uitest.bat new file mode 100644 index 00000000..ae863af6 --- /dev/null +++ b/uitest.bat @@ -0,0 +1,2 @@ +venv\Scripts\python.exe uitest.py +pause diff --git a/uitest.py b/uitest.py new file mode 100644 index 00000000..393e2d81 --- /dev/null +++ b/uitest.py @@ -0,0 +1,124 @@ +import os +import threading +import time +import importlib +import signal +import threading + +from modules.paths import script_path + +from modules import devices, sd_samplers +import modules.codeformer_model as codeformer +import modules.extras +import modules.face_restoration +import modules.gfpgan_model as gfpgan +import modules.img2img + +import modules.lowvram +import modules.paths +import modules.scripts +import modules.sd_hijack +import modules.sd_models +import modules.shared as shared +import modules.txt2img + +import modules.ui +from modules import devices +from modules import modelloader +from modules.paths import script_path +from modules.shared import cmd_opts + +modelloader.cleanup_models() +modules.sd_models.setup_model() +codeformer.setup_model(cmd_opts.codeformer_models_path) +gfpgan.setup_model(cmd_opts.gfpgan_models_path) +shared.face_restorers.append(modules.face_restoration.FaceRestoration()) +modelloader.load_upscalers() +queue_lock = threading.Lock() + + +def wrap_queued_call(func): + def f(*args, **kwargs): + with queue_lock: + res = func(*args, **kwargs) + + return res + + return f + + +def wrap_gradio_gpu_call(func, extra_outputs=None): + def f(*args, **kwargs): + devices.torch_gc() + + shared.state.sampling_step = 0 + shared.state.job_count = -1 + shared.state.job_no = 0 + shared.state.job_timestamp = shared.state.get_job_timestamp() + shared.state.current_latent = None + shared.state.current_image = None + shared.state.current_image_sampling_step = 0 + shared.state.interrupted = False + shared.state.textinfo = None + + with queue_lock: + res = func(*args, **kwargs) + + shared.state.job = "" + shared.state.job_count = 0 + + devices.torch_gc() + + return res + + return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs) + + +modules.scripts.load_scripts(os.path.join(script_path, "scripts")) + +shared.sd_model = None #modules.sd_models.load_model() +#shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model))) + + +def webui(): + # make the program just exit at ctrl+c without waiting for anything + def sigint_handler(sig, frame): + print(f'Interrupted with signal {sig} in {frame}') + os._exit(0) + + signal.signal(signal.SIGINT, sigint_handler) + + while 1: + + demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) + + demo.launch( + share=cmd_opts.share, + server_name="0.0.0.0" if cmd_opts.listen else None, + server_port=cmd_opts.port, + debug=cmd_opts.gradio_debug, + auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None, + inbrowser=cmd_opts.autolaunch, + prevent_thread_lock=True + ) + + while 1: + time.sleep(0.5) + if getattr(demo, 'do_restart', False): + time.sleep(0.5) + demo.close() + time.sleep(0.5) + break + + sd_samplers.set_samplers() + + print('Reloading Custom Scripts') + modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) + print('Reloading modules: modules.ui') + importlib.reload(modules.ui) + print('Restarting Gradio') + + + +if __name__ == "__main__": + webui() \ No newline at end of file From c6f778d9b19d7116ffb82718f6ca0b867e2f4445 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Tue, 18 Oct 2022 20:15:08 +0800 Subject: [PATCH 025/140] Image browser --- uitest.bat | 2 - uitest.py | 124 ----------------------------------------------------- 2 files changed, 126 deletions(-) delete mode 100644 uitest.bat delete mode 100644 uitest.py diff --git a/uitest.bat b/uitest.bat deleted file mode 100644 index ae863af6..00000000 --- a/uitest.bat +++ /dev/null @@ -1,2 +0,0 @@ -venv\Scripts\python.exe uitest.py -pause diff --git a/uitest.py b/uitest.py deleted file mode 100644 index 393e2d81..00000000 --- a/uitest.py +++ /dev/null @@ -1,124 +0,0 @@ -import os -import threading -import time -import importlib -import signal -import threading - -from modules.paths import script_path - -from modules import devices, sd_samplers -import modules.codeformer_model as codeformer -import modules.extras -import modules.face_restoration -import modules.gfpgan_model as gfpgan -import modules.img2img - -import modules.lowvram -import modules.paths -import modules.scripts -import modules.sd_hijack -import modules.sd_models -import modules.shared as shared -import modules.txt2img - -import modules.ui -from modules import devices -from modules import modelloader -from modules.paths import script_path -from modules.shared import cmd_opts - -modelloader.cleanup_models() -modules.sd_models.setup_model() -codeformer.setup_model(cmd_opts.codeformer_models_path) -gfpgan.setup_model(cmd_opts.gfpgan_models_path) -shared.face_restorers.append(modules.face_restoration.FaceRestoration()) -modelloader.load_upscalers() -queue_lock = threading.Lock() - - -def wrap_queued_call(func): - def f(*args, **kwargs): - with queue_lock: - res = func(*args, **kwargs) - - return res - - return f - - -def wrap_gradio_gpu_call(func, extra_outputs=None): - def f(*args, **kwargs): - devices.torch_gc() - - shared.state.sampling_step = 0 - shared.state.job_count = -1 - shared.state.job_no = 0 - shared.state.job_timestamp = shared.state.get_job_timestamp() - shared.state.current_latent = None - shared.state.current_image = None - shared.state.current_image_sampling_step = 0 - shared.state.interrupted = False - shared.state.textinfo = None - - with queue_lock: - res = func(*args, **kwargs) - - shared.state.job = "" - shared.state.job_count = 0 - - devices.torch_gc() - - return res - - return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs) - - -modules.scripts.load_scripts(os.path.join(script_path, "scripts")) - -shared.sd_model = None #modules.sd_models.load_model() -#shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model))) - - -def webui(): - # make the program just exit at ctrl+c without waiting for anything - def sigint_handler(sig, frame): - print(f'Interrupted with signal {sig} in {frame}') - os._exit(0) - - signal.signal(signal.SIGINT, sigint_handler) - - while 1: - - demo = modules.ui.create_ui(wrap_gradio_gpu_call=wrap_gradio_gpu_call) - - demo.launch( - share=cmd_opts.share, - server_name="0.0.0.0" if cmd_opts.listen else None, - server_port=cmd_opts.port, - debug=cmd_opts.gradio_debug, - auth=[tuple(cred.split(':')) for cred in cmd_opts.gradio_auth.strip('"').split(',')] if cmd_opts.gradio_auth else None, - inbrowser=cmd_opts.autolaunch, - prevent_thread_lock=True - ) - - while 1: - time.sleep(0.5) - if getattr(demo, 'do_restart', False): - time.sleep(0.5) - demo.close() - time.sleep(0.5) - break - - sd_samplers.set_samplers() - - print('Reloading Custom Scripts') - modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) - print('Reloading modules: modules.ui') - importlib.reload(modules.ui) - print('Restarting Gradio') - - - -if __name__ == "__main__": - webui() \ No newline at end of file From b7e78ef692fe912916de6e54f6e2521b000d650c Mon Sep 17 00:00:00 2001 From: yfszzx Date: Tue, 18 Oct 2022 22:21:54 +0800 Subject: [PATCH 026/140] Image browser improve --- modules/images_history.py | 43 ++++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 21 deletions(-) diff --git a/modules/images_history.py b/modules/images_history.py index d56f3a25..a40cdc0e 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -100,14 +100,15 @@ def auto_sorting(dir_name): date_list.append(today) return sorted(date_list, reverse=True) -def archive_images(dir_name, date_to): - +def archive_images(dir_name, date_to): filenames = [] - loads_num =int(opts.images_history_num_per_page * opts.images_history_pages_num) + batch_size =int(opts.images_history_num_per_page * opts.images_history_pages_num) + if batch_size <= 0: + batch_size = opts.images_history_num_per_page * 6 today = time.strftime("%Y%m%d",time.localtime(time.time())) date_to = today if date_to is None or date_to == "" else date_to date_to_bak = date_to - if opts.images_history_reconstruct_directory: + if False: #opts.images_history_reconstruct_directory: date_list = auto_sorting(dir_name) for date in date_list: if date <= date_to: @@ -115,11 +116,13 @@ def archive_images(dir_name, date_to): if date == today and not os.path.exists(path): continue filenames = traverse_all_files(path, filenames) - if len(filenames) > loads_num: + if len(filenames) > batch_size: break filenames = sorted(filenames, key=lambda file: -os.path.getmtime(file)) else: - filenames = traverse_all_files(dir_name, filenames) + filenames = traverse_all_files(dir_name, filenames) + total_num = len(filenames) + batch_count = len(filenames) + 1 // batch_size + 1 tmparray = [(os.path.getmtime(file), file) for file in filenames ] date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400 filenames = [] @@ -132,8 +135,8 @@ def archive_images(dir_name, date_to): filenames.append((t, f ,date)) date_list = sorted(list(date_list.keys()), reverse=True) sort_array = sorted(filenames, key=lambda x:-x[0]) - if len(sort_array) > loads_num: - date = sort_array[loads_num][2] + if len(sort_array) > batch_size: + date = sort_array[batch_size][2] filenames = [x[1] for x in sort_array] else: date = date_to if len(sort_array) == 0 else sort_array[-1][2] @@ -141,9 +144,9 @@ def archive_images(dir_name, date_to): filenames = [x[1] for x in sort_array if x[2]>= date] num = len(filenames) last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000)) - date = date[:4] + "-" + date[4:6] + "-" + date[6:8] - date_to_bak = date_to_bak[:4] + "-" + date_to_bak[4:6] + "-" + date_to_bak[6:8] - load_info = f"Loaded {(num + 1) // opts.images_history_pages_num} pades, {num} images, during {date} - {date_to_bak}" + date = date[:4] + "/" + date[4:6] + "/" + date[6:8] + date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8] + load_info = f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages" _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames) return ( gradio.Dropdown.update(choices=date_list, value=date_to), @@ -154,12 +157,10 @@ def archive_images(dir_name, date_to): "", "", visible_num, - last_date_from + last_date_from, + #gradio.update(visible=batch_count > 1) ) - - - def delete_image(delete_num, name, filenames, image_index, visible_num): if name == "": return filenames, delete_num @@ -295,16 +296,16 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): with gr.Column() as page_panel: with gr.Row(): - img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory") + img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir) with gr.Row(visible=False) as warning: warning_box = gr.Textbox("Message", interactive=False) with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel: with gr.Column(scale=2): - with gr.Row(): - backward = gr.Button('Backward') - date_to = gr.Dropdown(label="Date to") - forward = gr.Button('Forward') + with gr.Row() as batch_panel: + forward = gr.Button('Forward') + date_to = gr.Dropdown(label="Date to") + backward = gr.Button('Backward') newest = gr.Button('Reload', elem_id=tabname + "_images_history_start") with gr.Row(): load_info = gr.Textbox(show_label=False, interactive=False) @@ -335,7 +336,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): # hiden items - with gr.Row(): #visible=False): + with gr.Row(visible=False): visible_img_num = gr.Number() date_to_recorder = gr.State([]) last_date_from = gr.Textbox() From 538bc89c269743e56b07ef2b471d1ce0a39b6776 Mon Sep 17 00:00:00 2001 From: yfszzx Date: Wed, 19 Oct 2022 11:27:51 +0800 Subject: [PATCH 027/140] Image browser improved --- javascript/images_history.js | 81 +++++++++++---------- modules/images_history.py | 133 +++++++++++++++++++---------------- modules/shared.py | 5 ++ modules/ui.py | 2 +- 4 files changed, 119 insertions(+), 102 deletions(-) diff --git a/javascript/images_history.js b/javascript/images_history.js index 182d730b..c9aa76f8 100644 --- a/javascript/images_history.js +++ b/javascript/images_history.js @@ -17,14 +17,6 @@ var images_history_click_image = function(){ images_history_set_image_info(this); } -var images_history_click_tab = function(){ - var tabs_box = gradioApp().getElementById("images_history_tab"); - if (!tabs_box.classList.contains(this.getAttribute("tabname"))) { - gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click(); - tabs_box.classList.add(this.getAttribute("tabname")) - } -} - function images_history_disabled_del(){ gradioApp().querySelectorAll(".images_history_del_button").forEach(function(btn){ btn.setAttribute('disabled','disabled'); @@ -145,57 +137,64 @@ function images_history_enable_del_buttons(){ } function images_history_init(){ - // var loaded = gradioApp().getElementById("images_history_reconstruct_directory") - // if (loaded){ - // var init_status = loaded.querySelector("input").checked - if (gradioApp().getElementById("images_history_finish_render")){ + var tabnames = gradioApp().getElementById("images_history_tabnames_list") + if (tabnames){ + images_history_tab_list = tabnames.querySelector("textarea").value.split(",") for (var i in images_history_tab_list ){ - tab = images_history_tab_list[i]; + var tab = images_history_tab_list[i]; gradioApp().getElementById(tab + '_images_history').classList.add("images_history_cantainor"); gradioApp().getElementById(tab + '_images_history_set_index').classList.add("images_history_set_index"); gradioApp().getElementById(tab + '_images_history_del_button').classList.add("images_history_del_button"); - gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery"); - + gradioApp().getElementById(tab + '_images_history_gallery').classList.add("images_history_gallery"); + gradioApp().getElementById(tab + "_images_history_start").setAttribute("style","padding:20px;font-size:25px"); } - var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div"); - tabs_box.setAttribute("id", "images_history_tab"); - var tab_btns = tabs_box.querySelectorAll("button"); - for (var i in images_history_tab_list){ - var tabname = images_history_tab_list[i] - tab_btns[i].setAttribute("tabname", tabname); - // if (!init_status){ - // tab_btns[i].addEventListener('click', images_history_click_tab); - // } - tab_btns[i].addEventListener('click', images_history_click_tab); - } + //preload + if (gradioApp().getElementById("images_history_preload").querySelector("input").checked ){ + var tabs_box = gradioApp().getElementById("tab_images_history").querySelector("div").querySelector("div").querySelector("div"); + tabs_box.setAttribute("id", "images_history_tab"); + var tab_btns = tabs_box.querySelectorAll("button"); + for (var i in images_history_tab_list){ + var tabname = images_history_tab_list[i] + tab_btns[i].setAttribute("tabname", tabname); + tab_btns[i].addEventListener('click', function(){ + var tabs_box = gradioApp().getElementById("images_history_tab"); + if (!tabs_box.classList.contains(this.getAttribute("tabname"))) { + gradioApp().getElementById(this.getAttribute("tabname") + "_images_history_start").click(); + tabs_box.classList.add(this.getAttribute("tabname")) + } + }); + } + tab_btns[0].click() + } } else { setTimeout(images_history_init, 500); } } -var images_history_tab_list = ["custom", "txt2img", "img2img", "extras", "saved"]; +var images_history_tab_list = ""; setTimeout(images_history_init, 500); document.addEventListener("DOMContentLoaded", function() { var mutationObserver = new MutationObserver(function(m){ - for (var i in images_history_tab_list ){ - let tabname = images_history_tab_list[i] - var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item'); - buttons.forEach(function(bnt){ - bnt.addEventListener('click', images_history_click_image, true); - }); + if (images_history_tab_list != ""){ + for (var i in images_history_tab_list ){ + let tabname = images_history_tab_list[i] + var buttons = gradioApp().querySelectorAll('#' + tabname + '_images_history .gallery-item'); + buttons.forEach(function(bnt){ + bnt.addEventListener('click', images_history_click_image, true); + }); - var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg"); - if (cls_btn){ - cls_btn.addEventListener('click', function(){ - gradioApp().getElementById(tabname + '_images_history_del_button').setAttribute('disabled','disabled'); - }, false); - } + var cls_btn = gradioApp().getElementById(tabname + '_images_history_gallery').querySelector("svg"); + if (cls_btn){ + cls_btn.addEventListener('click', function(){ + gradioApp().getElementById(tabname + '_images_history_renew_page').click(); + }, false); + } - } + } + } }); mutationObserver.observe(gradioApp(), { childList:true, subtree:true }); - }); diff --git a/modules/images_history.py b/modules/images_history.py index a40cdc0e..78fd0543 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -4,7 +4,9 @@ import time import hashlib import gradio system_bak_path = "webui_log_and_bak" -browser_tabname = "custom" +custom_tab_name = "custom fold" +faverate_tab_name = "favorites" +tabs_list = ["txt2img", "img2img", "extras", faverate_tab_name] def is_valid_date(date): try: time.strptime(date, "%Y%m%d") @@ -122,7 +124,6 @@ def archive_images(dir_name, date_to): else: filenames = traverse_all_files(dir_name, filenames) total_num = len(filenames) - batch_count = len(filenames) + 1 // batch_size + 1 tmparray = [(os.path.getmtime(file), file) for file in filenames ] date_stamp = time.mktime(time.strptime(date_to, "%Y%m%d")) + 86400 filenames = [] @@ -146,10 +147,12 @@ def archive_images(dir_name, date_to): last_date_from = date_to_bak if num == 0 else time.strftime("%Y%m%d", time.localtime(time.mktime(time.strptime(date, "%Y%m%d")) - 1000)) date = date[:4] + "/" + date[4:6] + "/" + date[6:8] date_to_bak = date_to_bak[:4] + "/" + date_to_bak[4:6] + "/" + date_to_bak[6:8] - load_info = f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages" + load_info = "
" + load_info += f"{total_num} images in this directory. Loaded {num} images during {date} - {date_to_bak}, divided into {int((num + 1) // opts.images_history_num_per_page + 1)} pages" + load_info += "
" _, image_list, _, _, visible_num = get_recent_images(1, 0, filenames) return ( - gradio.Dropdown.update(choices=date_list, value=date_to), + date_to, load_info, filenames, 1, @@ -158,7 +161,7 @@ def archive_images(dir_name, date_to): "", visible_num, last_date_from, - #gradio.update(visible=batch_count > 1) + gradio.update(visible=total_num > num) ) def delete_image(delete_num, name, filenames, image_index, visible_num): @@ -209,7 +212,7 @@ def get_recent_images(page_index, step, filenames): visible_num = num_of_imgs_per_page if visible_num == 0 else visible_num return page_index, image_list, "", "", visible_num -def newest_click(date_to): +def loac_batch_click(date_to): if date_to is None: return time.strftime("%Y%m%d",time.localtime(time.time())), [] else: @@ -248,7 +251,7 @@ def page_index_change(page_index, filenames): def show_image_info(tabname_box, num, page_index, filenames): file = filenames[int(num) + int((page_index - 1) * int(opts.images_history_num_per_page))] - tm = time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + tm = "
" + time.strftime("%Y-%m-%d %H:%M:%S",time.localtime(os.path.getmtime(file))) + "
" return file, tm, num, file def enable_page_buttons(): @@ -268,9 +271,9 @@ def change_dir(img_dir, date_to): warning = "The format of the directory is incorrect" if warning is None: today = time.strftime("%Y%m%d",time.localtime(time.time())) - return gradio.update(visible=False), gradio.update(visible=True), None, None if date_to != today else today + return gradio.update(visible=False), gradio.update(visible=True), None, None if date_to != today else today, gradio.update(visible=True), gradio.update(visible=True) else: - return gradio.update(visible=True), gradio.update(visible=False), warning, date_to + return gradio.update(visible=True), gradio.update(visible=False), warning, date_to, gradio.update(visible=False), gradio.update(visible=False) def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): custom_dir = False @@ -280,7 +283,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): dir_name = opts.outdir_img2img_samples elif tabname == "extras": dir_name = opts.outdir_extras_samples - elif tabname == "saved": + elif tabname == faverate_tab_name: dir_name = opts.outdir_save else: custom_dir = True @@ -295,22 +298,26 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): os.makedirs(dir_name) with gr.Column() as page_panel: - with gr.Row(): - img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir) + with gr.Row(): + with gr.Column(scale=1, visible=not custom_dir) as load_batch_box: + load_batch = gr.Button('Load', elem_id=tabname + "_images_history_start", full_width=True) + with gr.Column(scale=4): + with gr.Row(): + img_path = gr.Textbox(dir_name, label="Images directory", placeholder="Input images directory", interactive=custom_dir) + with gr.Row(): + with gr.Column(visible=False, scale=1) as batch_panel: + with gr.Row(): + forward = gr.Button('Prev batch') + backward = gr.Button('Next batch') + with gr.Column(scale=3): + load_info = gr.HTML(visible=not custom_dir) with gr.Row(visible=False) as warning: warning_box = gr.Textbox("Message", interactive=False) with gr.Row(visible=not custom_dir, elem_id=tabname + "_images_history") as main_panel: - with gr.Column(scale=2): - with gr.Row() as batch_panel: - forward = gr.Button('Forward') - date_to = gr.Dropdown(label="Date to") - backward = gr.Button('Backward') - newest = gr.Button('Reload', elem_id=tabname + "_images_history_start") - with gr.Row(): - load_info = gr.Textbox(show_label=False, interactive=False) - with gr.Row(visible=False) as turn_page_buttons: - renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page") + with gr.Column(scale=2): + with gr.Row(visible=True) as turn_page_buttons: + #date_to = gr.Dropdown(label="Date to") first_page = gr.Button('First Page') prev_page = gr.Button('Prev Page') page_index = gr.Number(value=1, label="Page Index") @@ -322,50 +329,54 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): delete_num = gr.Number(value=1, interactive=True, label="number of images to delete consecutively next") delete = gr.Button('Delete', elem_id=tabname + "_images_history_del_button") - with gr.Column(): - with gr.Row(): - if tabname != "saved": - save_btn = gr.Button('Save') - pnginfo_send_to_txt2img = gr.Button('Send to txt2img') - pnginfo_send_to_img2img = gr.Button('Send to img2img') + with gr.Column(): with gr.Row(): with gr.Column(): - img_file_info = gr.Textbox(label="Generate Info", interactive=False) + img_file_info = gr.Textbox(label="Generate Info", interactive=False, lines=6) + gr.HTML("
") img_file_name = gr.Textbox(value="", label="File Name", interactive=False) - img_file_time= gr.Textbox(value="", label="Create Time", interactive=False) - + img_file_time= gr.HTML() + with gr.Row(): + if tabname != faverate_tab_name: + save_btn = gr.Button('Collect') + pnginfo_send_to_txt2img = gr.Button('Send to txt2img') + pnginfo_send_to_img2img = gr.Button('Send to img2img') + - # hiden items - with gr.Row(visible=False): - visible_img_num = gr.Number() - date_to_recorder = gr.State([]) - last_date_from = gr.Textbox() - tabname_box = gr.Textbox(tabname) - image_index = gr.Textbox(value=-1) - set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index") - filenames = gr.State() - all_images_list = gr.State() - hidden = gr.Image(type="pil") - info1 = gr.Textbox() - info2 = gr.Textbox() + # hiden items + with gr.Row(visible=False): + renew_page = gr.Button('Refresh page', elem_id=tabname + "_images_history_renew_page") + batch_date_to = gr.Textbox(label="Date to") + visible_img_num = gr.Number() + date_to_recorder = gr.State([]) + last_date_from = gr.Textbox() + tabname_box = gr.Textbox(tabname) + image_index = gr.Textbox(value=-1) + set_index = gr.Button('set_index', elem_id=tabname + "_images_history_set_index") + filenames = gr.State() + all_images_list = gr.State() + hidden = gr.Image(type="pil") + info1 = gr.Textbox() + info2 = gr.Textbox() - img_path.submit(change_dir, inputs=[img_path, date_to], outputs=[warning, main_panel, warning_box, date_to]) - #change date - change_date_output = [date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from] + img_path.submit(change_dir, inputs=[img_path, batch_date_to], outputs=[warning, main_panel, warning_box, batch_date_to, load_batch_box, load_info]) + + #change batch + change_date_output = [batch_date_to, load_info, filenames, page_index, history_gallery, img_file_name, img_file_time, visible_img_num, last_date_from, batch_panel] - date_to.change(archive_images, inputs=[img_path, date_to], outputs=change_date_output) - date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons]) - date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") + batch_date_to.change(archive_images, inputs=[img_path, batch_date_to], outputs=change_date_output) + batch_date_to.change(enable_page_buttons, inputs=None, outputs=[turn_page_buttons]) + batch_date_to.change(fn=None, inputs=[tabname_box], outputs=None, _js="images_history_turnpage") - newest.click(newest_click, inputs=[date_to], outputs=[date_to, date_to_recorder]) - forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder]) - backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[date_to, date_to_recorder]) + load_batch.click(loac_batch_click, inputs=[batch_date_to], outputs=[batch_date_to, date_to_recorder]) + forward.click(forward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder]) + backward.click(backward_click, inputs=[last_date_from, date_to_recorder], outputs=[batch_date_to, date_to_recorder]) #delete delete.click(delete_image, inputs=[delete_num, img_file_name, filenames, image_index, visible_img_num], outputs=[filenames, delete_num, visible_img_num]) delete.click(fn=None, _js="images_history_delete", inputs=[delete_num, tabname_box, image_index], outputs=None) - if tabname != "saved": + if tabname != faverate_tab_name: save_btn.click(save_image, inputs=[img_file_name], outputs=None) #turn page @@ -394,18 +405,20 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): -def create_history_tabs(gr, sys_opts, run_pnginfo, switch_dict): +def create_history_tabs(gr, sys_opts, cmp_ops, run_pnginfo, switch_dict): global opts; opts = sys_opts loads_files_num = int(opts.images_history_num_per_page) num_of_imgs_per_page = int(opts.images_history_num_per_page * opts.images_history_pages_num) + if cmp_ops.browse_all_images: + tabs_list.append(custom_tab_name) with gr.Blocks(analytics_enabled=False) as images_history: with gr.Tabs() as tabs: - for tab in [browser_tabname, "txt2img", "img2img", "extras", "saved"]: + for tab in tabs_list: with gr.Tab(tab): with gr.Blocks(analytics_enabled=False) : - show_images_history(gr, opts, tab, run_pnginfo, switch_dict) - #gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_reconstruct_directory", visible=False) - gradio.Checkbox(opts.images_history_reconstruct_directory, elem_id="images_history_finish_render", visible=False) - + show_images_history(gr, opts, tab, run_pnginfo, switch_dict) + gradio.Checkbox(opts.images_history_preload, elem_id="images_history_preload", visible=False) + gradio.Textbox(",".join(tabs_list), elem_id="images_history_tabnames_list", visible=False) + return images_history diff --git a/modules/shared.py b/modules/shared.py index 1811018d..4d735414 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -74,6 +74,10 @@ parser.add_argument("--disable-console-progressbars", action='store_true', help= parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) +parser.add_argument("--browse-all-images", action='store_true', help="Allow browsing all images by Image Browser", default=False) + + +cmd_opts = parser.parse_args() cmd_opts = parser.parse_args() @@ -311,6 +315,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters" options_templates.update(options_section(('images-history', "Images Browser"), { #"images_history_reconstruct_directory": OptionInfo(False, "Reconstruct output directory structure.This can greatly improve the speed of loading , but will change the original output directory structure"), + "images_history_preload": OptionInfo(False, "Preload images at startup"), "images_history_num_per_page": OptionInfo(36, "Number of pictures displayed on each page"), "images_history_pages_num": OptionInfo(6, "Minimum number of pages per load "), "images_history_grid_num": OptionInfo(6, "Number of grids in each row"), diff --git a/modules/ui.py b/modules/ui.py index 85abac4d..88f46659 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1150,7 +1150,7 @@ def create_ui(wrap_gradio_gpu_call): "i2i":img2img_paste_fields } - images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) + images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) with gr.Blocks() as modelmerger_interface: with gr.Row().style(equal_height=False): From 019a3a88f07766f2d32c32fbe8e41625f28ecb5e Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 19 Oct 2022 17:15:47 +0100 Subject: [PATCH 028/140] Update ui.py --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index d2e24880..1573ef82 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Tab(label="Train"): - gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") + gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork wiki

") with gr.Row(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") From 2ce52d32e41fb523d1494f45073fd18496e52d35 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Wed, 19 Oct 2022 16:31:12 +0000 Subject: [PATCH 029/140] fix for #3086 failing to load any previous hypernet --- modules/hypernetworks/hypernetwork.py | 60 +++++++++++++-------------- 1 file changed, 28 insertions(+), 32 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d519cd9..74300122 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -24,11 +24,10 @@ class HypernetworkModule(torch.nn.Module): def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False): super().__init__() - if layer_structure is not None: - assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" - assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" - else: - layer_structure = parse_layer_structure(dim, state_dict) + + assert layer_structure is not None, "layer_structure mut not be None" + assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" + assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" linears = [] for i in range(len(layer_structure) - 1): @@ -39,23 +38,30 @@ class HypernetworkModule(torch.nn.Module): self.linear = torch.nn.Sequential(*linears) if state_dict is not None: - try: - self.load_state_dict(state_dict) - except RuntimeError: - self.try_load_previous(state_dict) + self.fix_old_state_dict(state_dict) + self.load_state_dict(state_dict) else: for layer in self.linear: - layer.weight.data.normal_(mean = 0.0, std = 0.01) + layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() self.to(devices.device) - def try_load_previous(self, state_dict): - states = self.state_dict() - states['linear.0.bias'].copy_(state_dict['linear1.bias']) - states['linear.0.weight'].copy_(state_dict['linear1.weight']) - states['linear.1.bias'].copy_(state_dict['linear2.bias']) - states['linear.1.weight'].copy_(state_dict['linear2.weight']) + def fix_old_state_dict(self, state_dict): + changes = { + 'linear1.bias': 'linear.0.bias', + 'linear1.weight': 'linear.0.weight', + 'linear2.bias': 'linear.1.bias', + 'linear2.weight': 'linear.1.weight', + } + + for fr, to in changes.items(): + x = state_dict.get(fr, None) + if x is None: + continue + + del state_dict[fr] + state_dict[to] = x def forward(self, x): return x + self.linear(x) * self.multiplier @@ -71,18 +77,6 @@ def apply_strength(value=None): HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength -def parse_layer_structure(dim, state_dict): - i = 0 - layer_structure = [1] - - while (key := "linear.{}.weight".format(i)) in state_dict: - weight = state_dict[key] - layer_structure.append(len(weight) // dim) - i += 1 - - return layer_structure - - class Hypernetwork: filename = None name = None @@ -135,17 +129,18 @@ class Hypernetwork: state_dict = torch.load(filename, map_location='cpu') + self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) + self.add_layer_norm = state_dict.get('is_layer_norm', False) + for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], state_dict["layer_structure"], state_dict["is_layer_norm"]), - HypernetworkModule(size, sd[1], state_dict["layer_structure"], state_dict["is_layer_norm"]), + HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm), ) self.name = state_dict.get('name', self.name) self.step = state_dict.get('step', 0) - self.layer_structure = state_dict.get('layer_structure', None) - self.add_layer_norm = state_dict.get('is_layer_norm', False) self.sd_checkpoint = state_dict.get('sd_checkpoint', None) self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None) @@ -244,6 +239,7 @@ def stack_conds(conds): return torch.stack(conds) + def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): assert hypernetwork_name, 'hypernetwork not selected' From eb7ba4b713ac2fb960ecf6365b1de0c89451e583 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 19 Oct 2022 19:50:46 +0100 Subject: [PATCH 030/140] update training header text --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 1573ef82..93c0767c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1247,7 +1247,7 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Tab(label="Train"): - gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork wiki

") + gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork [wiki]

") with gr.Row(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") From 4d663055ded968831ec97f047dfa8e94036cf1c1 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 19 Oct 2022 20:33:18 +0100 Subject: [PATCH 031/140] update ui with extra training options --- modules/ui.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index 93c0767c..cdb9d335 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1206,6 +1206,7 @@ def create_ui(wrap_gradio_gpu_call): new_embedding_name = gr.Textbox(label="Name") initialization_text = gr.Textbox(label="Initialization text", value="*") nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) + overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding") with gr.Row(): with gr.Column(scale=3): @@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") + overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") with gr.Row(): with gr.Column(scale=3): @@ -1247,14 +1249,17 @@ def create_ui(wrap_gradio_gpu_call): run_preprocess = gr.Button(value="Preprocess", variant='primary') with gr.Tab(label="Train"): - gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images
Initial learning rates: 0.005 for an Embedding, 0.00001 for Hypernetwork [wiki]

") + gr.HTML(value="

Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images [wiki]

") with gr.Row(): train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())) create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name") with gr.Row(): train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") - learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005") + with gr.Row(): + embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005") + hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001") + batch_size = gr.Number(label='Batch size', value=1, precision=0) dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") @@ -1288,6 +1293,7 @@ def create_ui(wrap_gradio_gpu_call): new_embedding_name, initialization_text, nvpt, + overwrite_old_embedding, ], outputs=[ train_embedding_name, @@ -1303,6 +1309,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes, new_hypernetwork_layer_structure, new_hypernetwork_add_layer_norm, + overwrite_old_hypernetwork, ], outputs=[ train_hypernetwork_name, From 8e7097d06a6a261580d34375c9d2a9e4ffc63ffa Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 13:47:45 -0700 Subject: [PATCH 032/140] Added support for RunwayML inpainting model --- modules/processing.py | 34 +++++- modules/sd_hijack_inpainting.py | 208 ++++++++++++++++++++++++++++++++ modules/sd_models.py | 16 ++- modules/sd_samplers.py | 50 ++++++-- 4 files changed, 293 insertions(+), 15 deletions(-) create mode 100644 modules/sd_hijack_inpainting.py diff --git a/modules/processing.py b/modules/processing.py index bcb0c32c..a6c308f9 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -546,7 +546,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): if not self.enable_hr: x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) + + # The "masked-image" in this case will just be all zeros since the entire image is masked. + image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device) + image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) + + # Add the fake full 1s mask to the first dimension. + image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) + image_conditioning = image_conditioning.to(x.dtype) + + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning) return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) @@ -714,10 +723,31 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask + if self.image_mask is not None: + conditioning_mask = np.array(self.image_mask.convert("L")) + conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 + conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + + # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 + conditioning_mask = torch.round(conditioning_mask) + else: + conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + + # Create another latent image, this time with a masked version of the original input. + conditioning_mask = conditioning_mask.to(image.device) + conditioning_image = image * (1.0 - conditioning_mask) + conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) + + # Create the concatenated conditioning tensor to be fed to `c_concat` + conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) + conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) + self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) + self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning) + samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning) if self.mask is not None: samples = samples * self.nmask + self.init_latent * self.mask diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py new file mode 100644 index 00000000..7e5670d6 --- /dev/null +++ b/modules/sd_hijack_inpainting.py @@ -0,0 +1,208 @@ +import torch +import numpy as np + +from tqdm import tqdm +from einops import rearrange, repeat +from omegaconf import ListConfig + +from types import MethodType + +import ldm.models.diffusion.ddpm +import ldm.models.diffusion.ddim + +from ldm.models.diffusion.ddpm import LatentDiffusion +from ldm.models.diffusion.ddim import DDIMSampler, noise_like + +# ================================================================================================= +# Monkey patch DDIMSampler methods from RunwayML repo directly. +# Adapted from: +# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py +# ================================================================================================= +@torch.no_grad() +def sample( + self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): + ctmp = elf.inpainting_fill == 2: + self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask + elif self.inpainting_fill == 3: + self.init_latent = self.init_latent * self.mask + + if self.image_mask is not None: + conditioning_mask = np.array(self.image_mask.convert("L")) + conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 + conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + + # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 + conditioning_mask = torch.round(conditioning_mask) + else: + conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + + # Create another latent image, this time with a masked version of the original input. + conditioning_mask = conditioning_mask.to(image.device) + conditioning_image = image * (1.0 - conditioning_mask) + conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) + + # Create the concatenated conditioning tensor to be fed to `c_concat` + conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) + conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) + self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) + self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) + + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + x = create_random_tensors([opctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for DDIM sampling is {size}, eta {eta}') + + samples, intermediates = self.ddim_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + +@torch.no_grad() +def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None): + b, *_, device = *x.shape, x.device + + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [ + torch.cat([unconditional_conditioning[k][i], c[k][i]]) + for i in range(len(c[k])) + ] + else: + c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) + else: + c_in = torch.cat([unconditional_conditioning, c]) + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + +# ================================================================================================= +# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config. +# Adapted from: +# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py +# ================================================================================================= + +@torch.no_grad() +def get_unconditional_conditioning(self, batch_size, null_label=None): + if null_label is not None: + xc = null_label + if isinstance(xc, ListConfig): + xc = list(xc) + if isinstance(xc, dict) or isinstance(xc, list): + c = self.get_learned_conditioning(xc) + else: + if hasattr(xc, "to"): + xc = xc.to(self.device) + c = self.get_learned_conditioning(xc) + else: + # todo: get null label from cond_stage_model + raise NotImplementedError() + c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device) + return c + +class LatentInpaintDiffusion(LatentDiffusion): + def __init__( + self, + concat_keys=("mask", "masked_image"), + masked_image_key="masked_image", + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.masked_image_key = masked_image_key + assert self.masked_image_key in concat_keys + self.concat_keys = concat_keys + +def should_hijack_inpainting(checkpoint_info): + return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml") + +def do_inpainting_hijack(): + ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning + ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion + ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim + ldm.models.diffusion.ddim.DDIMSampler.sample = sample \ No newline at end of file diff --git a/modules/sd_models.py b/modules/sd_models.py index eae22e87..47836d25 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -9,6 +9,7 @@ from ldm.util import instantiate_from_config from modules import shared, modelloader, devices from modules.paths import models_path +from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting model_dir = "Stable-diffusion" model_path = os.path.abspath(os.path.join(models_path, model_dir)) @@ -211,6 +212,19 @@ def load_model(): print(f"Loading config from: {checkpoint_info.config}") sd_config = OmegaConf.load(checkpoint_info.config) + + if should_hijack_inpainting(checkpoint_info): + do_inpainting_hijack() + + # Hardcoded config for now... + sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion" + sd_config.model.params.use_ema = False + sd_config.model.params.conditioning_key = "hybrid" + sd_config.model.params.unet_config.params.in_channels = 9 + + # Create a "fake" config with a different name so that we know to unload it when switching models. + checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml")) + sd_model = instantiate_from_config(sd_config.model) load_model_weights(sd_model, checkpoint_info) @@ -234,7 +248,7 @@ def reload_model_weights(sd_model, info=None): if sd_model.sd_model_checkpoint == checkpoint_info.filename: return - if sd_model.sd_checkpoint_info.config != checkpoint_info.config: + if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() shared.sd_model = load_model() return shared.sd_model diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index b58e810b..9d3cf289 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -136,9 +136,15 @@ class VanillaStableDiffusionSampler: if self.stop_at is not None and self.step > self.stop_at: raise InterruptedException + # Have to unwrap the inpainting conditioning here to perform pre-preocessing + image_conditioning = None + if isinstance(cond, dict): + image_conditioning = cond["c_concat"][0] + cond = cond["c_crossattn"][0] + unconditional_conditioning = unconditional_conditioning["c_crossattn"][0] conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) - unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) + unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' cond = tensor @@ -157,6 +163,10 @@ class VanillaStableDiffusionSampler: img_orig = self.sampler.model.q_sample(self.init_latent, ts) x_dec = img_orig * self.mask + self.nmask * x_dec + if image_conditioning is not None: + cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + res = self.orig_p_sample_ddim(x_dec, cond, ts, unconditional_conditioning=unconditional_conditioning, *args, **kwargs) if self.mask is not None: @@ -182,7 +192,7 @@ class VanillaStableDiffusionSampler: self.mask = p.mask if hasattr(p, 'mask') else None self.nmask = p.nmask if hasattr(p, 'nmask') else None - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): steps, t_enc = setup_img2img_steps(p, steps) self.initialize(p) @@ -202,7 +212,7 @@ class VanillaStableDiffusionSampler: return samples - def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): self.initialize(p) self.init_latent = None @@ -210,6 +220,11 @@ class VanillaStableDiffusionSampler: steps = steps or p.steps + # Wrap the conditioning models with additional image conditioning for inpainting model + if image_conditioning is not None: + conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + # existing code fails with certain step counts, like 9 try: samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0]) @@ -228,7 +243,7 @@ class CFGDenoiser(torch.nn.Module): self.init_latent = None self.step = 0 - def forward(self, x, sigma, uncond, cond, cond_scale): + def forward(self, x, sigma, uncond, cond, cond_scale, image_cond): if state.interrupted or state.skipped: raise InterruptedException @@ -239,28 +254,29 @@ class CFGDenoiser(torch.nn.Module): repeats = [len(conds_list[i]) for i in range(batch_size)] x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) if tensor.shape[1] == uncond.shape[1]: cond_in = torch.cat([tensor, uncond]) if shared.batch_cond_uncond: - x_out = self.inner_model(x_in, sigma_in, cond=cond_in) + x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]}) else: x_out = torch.zeros_like(x_in) for batch_offset in range(0, x_out.shape[0], batch_size): a = batch_offset b = a + batch_size - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b]) + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]}) else: x_out = torch.zeros_like(x_in) batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size for batch_offset in range(0, tensor.shape[0], batch_size): a = batch_offset b = min(a + batch_size, tensor.shape[0]) - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=tensor[a:b]) + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [tensor[a:b]], "c_concat": [image_cond_in[a:b]]}) - x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=uncond) + x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]}) denoised_uncond = x_out[-uncond.shape[0]:] denoised = torch.clone(denoised_uncond) @@ -361,7 +377,7 @@ class KDiffusionSampler: return extra_params_kwargs - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None): steps, t_enc = setup_img2img_steps(p, steps) if p.sampler_noise_scheduler_override: @@ -389,11 +405,16 @@ class KDiffusionSampler: self.model_wrap_cfg.init_latent = x - samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) + samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={ + 'cond': conditioning, + 'image_cond': image_conditioning, + 'uncond': unconditional_conditioning, + 'cond_scale': p.cfg_scale + }, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples - def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None): steps = steps or p.steps if p.sampler_noise_scheduler_override: @@ -414,7 +435,12 @@ class KDiffusionSampler: else: extra_params_kwargs['sigmas'] = sigmas - samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) + samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={ + 'cond': conditioning, + 'image_cond': image_conditioning, + 'uncond': unconditional_conditioning, + 'cond_scale': p.cfg_scale + }, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples From 0719c10bf1b817364a498ee11b90d30d3d527344 Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 13:56:26 -0700 Subject: [PATCH 033/140] Fixed copying mistake --- modules/sd_hijack_inpainting.py | 79 +++++++++++---------------------- 1 file changed, 25 insertions(+), 54 deletions(-) diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 7e5670d6..d4d28d2e 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -19,63 +19,35 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py # ================================================================================================= @torch.no_grad() -def sample( - self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): +def sample(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): if conditioning is not None: if isinstance(conditioning, dict): ctmp = conditioning[list(conditioning.keys())[0]] while isinstance(ctmp, list): - ctmp = elf.inpainting_fill == 2: - self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask - elif self.inpainting_fill == 3: - self.init_latent = self.init_latent * self.mask - - if self.image_mask is not None: - conditioning_mask = np.array(self.image_mask.convert("L")) - conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 - conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) - - # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 - conditioning_mask = torch.round(conditioning_mask) - else: - conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) - - # Create another latent image, this time with a masked version of the original input. - conditioning_mask = conditioning_mask.to(image.device) - conditioning_image = image * (1.0 - conditioning_mask) - conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) - - # Create the concatenated conditioning tensor to be fed to `c_concat` - conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) - conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) - self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) - self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) - - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - x = create_random_tensors([opctmp[0] + ctmp = ctmp[0] cbs = ctmp.shape[0] if cbs != batch_size: print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") @@ -106,7 +78,6 @@ def sample( ) return samples, intermediates - @torch.no_grad() def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, From dde9f960727bfe151d418e43685a2881cf580a17 Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 14:14:24 -0700 Subject: [PATCH 034/140] added support for ddim img2img --- modules/sd_samplers.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 9d3cf289..d270e4df 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -208,6 +208,12 @@ class VanillaStableDiffusionSampler: self.init_latent = x self.step = 0 + # Wrap the conditioning models with additional image conditioning for inpainting model + if image_conditioning is not None: + conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + + samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) return samples From c418467c03db916c3e5312e6ac4a67365e196dbd Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 15:09:43 -0700 Subject: [PATCH 035/140] Don't compute latent mask if were not using it. Also added support for fixed highres_fix generation. --- modules/processing.py | 70 ++++++++++++++++++++++++++---------------- modules/sd_samplers.py | 4 +++ 2 files changed, 48 insertions(+), 26 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index a6c308f9..684e5833 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -541,12 +541,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): - self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) - - if not self.enable_hr: - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - + def create_dummy_mask(self, x): + if self.sampler.conditioning_key in {'hybrid', 'concat'}: # The "masked-image" in this case will just be all zeros since the entire image is masked. image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device) image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) @@ -555,11 +551,23 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) image_conditioning = image_conditioning.to(x.dtype) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=image_conditioning) + else: + # Dummy zero conditioning if we're not using inpainting model. + # Still takes up a bit of memory, but no encoder call. + image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device) + + return image_conditioning + + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) + + if not self.enable_hr: + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x)) return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x)) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] @@ -596,7 +604,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps, image_conditioning=self.create_dummy_mask(samples)) return samples @@ -723,26 +731,36 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask - if self.image_mask is not None: - conditioning_mask = np.array(self.image_mask.convert("L")) - conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 - conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + conditioning_key = self.sampler.conditioning_key - # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 - conditioning_mask = torch.round(conditioning_mask) + if conditioning_key in {'hybrid', 'concat'}: + if self.image_mask is not None: + conditioning_mask = np.array(self.image_mask.convert("L")) + conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 + conditioning_mask = torch.from_numpy(conditioning_mask[None, None]) + + # Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0 + conditioning_mask = torch.round(conditioning_mask) + else: + conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + + # Create another latent image, this time with a masked version of the original input. + conditioning_mask = conditioning_mask.to(image.device) + conditioning_image = image * (1.0 - conditioning_mask) + conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) + + # Create the concatenated conditioning tensor to be fed to `c_concat` + conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) + conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) + self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) + self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) else: - conditioning_mask = torch.ones(1, 1, *image.shape[-2:]) + self.image_conditioning = torch.zeros( + self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], + dtype=self.init_latent.dtype, + device=self.init_latent.device + ) - # Create another latent image, this time with a masked version of the original input. - conditioning_mask = conditioning_mask.to(image.device) - conditioning_image = image * (1.0 - conditioning_mask) - conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image)) - - # Create the concatenated conditioning tensor to be fed to `c_concat` - conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=self.init_latent.shape[-2:]) - conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1) - self.image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1) - self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d270e4df..c21be26e 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -117,6 +117,8 @@ class VanillaStableDiffusionSampler: self.config = None self.last_latent = None + self.conditioning_key = sd_model.model.conditioning_key + def number_of_needed_noises(self, p): return 0 @@ -328,6 +330,8 @@ class KDiffusionSampler: self.config = None self.last_latent = None + self.conditioning_key = sd_model.model.conditioning_key + def callback_state(self, d): step = d['i'] latent = d["denoised"] From d6ea5841374a28f3f6deb73abc251c8f0bcb240f Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:07:57 +0100 Subject: [PATCH 036/140] change html output --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d519cd9..73c1cb80 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -380,7 +380,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log Loss: {mean_loss:.7f}
Step: {hypernetwork.step}
Last prompt: {html.escape(entries[0].cond_text)}
-Last saved embedding: {html.escape(last_saved_file)}
+Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" From 166be3919b817cee5e702fd01c34afe9081b952c Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:09:40 +0100 Subject: [PATCH 037/140] allow overwrite old hn --- modules/hypernetworks/ui.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 08f75f15..f45345ea 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -10,9 +10,10 @@ from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork -def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False): +def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") - assert not os.path.exists(fn), f"file {fn} already exists" + if not overwrite_old: + assert not os.path.exists(fn), f"file {fn} already exists" if type(layer_structure) == str: layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure))) From 0087079c2d487b67b06ffc30f36ce486a74e6318 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:10:59 +0100 Subject: [PATCH 038/140] allow overwrite old embedding --- modules/textual_inversion/textual_inversion.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 3be69562..5776778b 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -153,7 +153,7 @@ class EmbeddingDatabase: return None, None -def create_embedding(name, num_vectors_per_token, init_text='*'): +def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'): cond_model = shared.sd_model.cond_stage_model embedding_layer = cond_model.wrapped.transformer.text_model.embeddings @@ -165,7 +165,8 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token] fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt") - assert not os.path.exists(fn), f"file {fn} already exists" + if not overwrite_old: + assert not os.path.exists(fn), f"file {fn} already exists" embedding = Embedding(vec, name) embedding.step = 0 From 632e8d660293081cadb145d8062e5aff0a4a8f0d Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:19:40 +0100 Subject: [PATCH 039/140] split learn rates --- modules/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index cdb9d335..d07184ee 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1342,7 +1342,7 @@ def create_ui(wrap_gradio_gpu_call): _js="start_training_textual_inversion", inputs=[ train_embedding_name, - learn_rate, + embedding_learn_rate, batch_size, dataset_directory, log_directory, @@ -1367,7 +1367,7 @@ def create_ui(wrap_gradio_gpu_call): _js="start_training_textual_inversion", inputs=[ train_hypernetwork_name, - learn_rate, + hypernetwork_learn_rate, batch_size, dataset_directory, log_directory, From c3835ec85cbb44fa3c46fa871c622b6fee235c89 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:24:24 +0100 Subject: [PATCH 040/140] pass overwrite old flag --- modules/textual_inversion/ui.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/ui.py b/modules/textual_inversion/ui.py index 36881e7a..e712284d 100644 --- a/modules/textual_inversion/ui.py +++ b/modules/textual_inversion/ui.py @@ -7,8 +7,8 @@ import modules.textual_inversion.preprocess from modules import sd_hijack, shared -def create_embedding(name, initialization_text, nvpt): - filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, init_text=initialization_text) +def create_embedding(name, initialization_text, nvpt, overwrite_old): + filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text) sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings() From 4d6b9f76a55fd0ac0f72634071032dd9c6efb409 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:27:16 +0100 Subject: [PATCH 041/140] reorder create_hypernetwork params --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index d07184ee..322c082b 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1307,9 +1307,9 @@ def create_ui(wrap_gradio_gpu_call): inputs=[ new_hypernetwork_name, new_hypernetwork_sizes, + overwrite_old_hypernetwork, new_hypernetwork_layer_structure, new_hypernetwork_add_layer_norm, - overwrite_old_hypernetwork, ], outputs=[ train_hypernetwork_name, From fbcce66601994f6ed370db36d9c238840fed6bd2 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:46:54 +0100 Subject: [PATCH 042/140] add existing caption file handling --- modules/textual_inversion/preprocess.py | 32 ++++++++++++++++++------- 1 file changed, 24 insertions(+), 8 deletions(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 886cf0c3..5c43fe13 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -48,7 +48,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) - def save_pic_with_caption(image, index): + def save_pic_with_caption(image, index, existing_caption=None): caption = "" if process_caption: @@ -66,17 +66,26 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro basename = f"{index:05}-{subindex[0]}-{filename_part}" image.save(os.path.join(dst, f"{basename}.png")) + if preprocess_txt_action == 'prepend' and existing_caption: + caption = existing_caption + ' ' + caption + elif preprocess_txt_action == 'append' and existing_caption: + caption = caption + ' ' + existing_caption + elif preprocess_txt_action == 'copy' and existing_caption: + caption = existing_caption + + caption = caption.strip() + if len(caption) > 0: with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file: file.write(caption) subindex[0] += 1 - def save_pic(image, index): + def save_pic(image, index, existing_caption=None): save_pic_with_caption(image, index) if process_flip: - save_pic_with_caption(ImageOps.mirror(image), index) + save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption) for index, imagefile in enumerate(tqdm.tqdm(files)): subindex = [0] @@ -86,6 +95,13 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro except Exception: continue + existing_caption = None + + try: + existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read() + except Exception as e: + print(e) + if shared.state.interrupted: break @@ -97,20 +113,20 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro img = img.resize((width, height * img.height // img.width)) top = img.crop((0, 0, width, height)) - save_pic(top, index) + save_pic(top, index, existing_caption=existing_caption) bot = img.crop((0, img.height - height, width, img.height)) - save_pic(bot, index) + save_pic(bot, index, existing_caption=existing_caption) elif process_split and is_wide: img = img.resize((width * img.width // img.height, height)) left = img.crop((0, 0, width, height)) - save_pic(left, index) + save_pic(left, index, existing_caption=existing_caption) right = img.crop((img.width - width, 0, img.width, height)) - save_pic(right, index) + save_pic(right, index, existing_caption=existing_caption) else: img = images.resize_image(1, img, width, height) - save_pic(img, index) + save_pic(img, index, existing_caption=existing_caption) shared.state.nextjob() From ab353b141df8eee042b0964bcb645015dabf3459 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:48:07 +0100 Subject: [PATCH 043/140] link existing txt option --- modules/ui.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/modules/ui.py b/modules/ui.py index 322c082b..7f52ac0c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1234,6 +1234,7 @@ def create_ui(wrap_gradio_gpu_call): process_dst = gr.Textbox(label='Destination directory') process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append']) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') @@ -1326,6 +1327,7 @@ def create_ui(wrap_gradio_gpu_call): process_dst, process_width, process_height, + preprocess_txt_action, process_flip, process_split, process_caption, From 9b65c4ecf4f8eb6187ee721918adebe68e9bc631 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:49:23 +0100 Subject: [PATCH 044/140] pass preprocess_txt_action param --- modules/textual_inversion/preprocess.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 5c43fe13..3713bc89 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -11,7 +11,7 @@ if cmd_opts.deepdanbooru: import modules.deepbooru as deepbooru -def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): +def preprocess(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False): try: if process_caption: shared.interrogator.load() @@ -21,7 +21,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ db_opts[deepbooru.OPT_INCLUDE_RANKS] = False deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts) - preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru) + preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru) finally: @@ -33,7 +33,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ -def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): +def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False): width = process_width height = process_height src = os.path.abspath(process_src) From 55d8c6cce6d3aef848b9f194adad2ce53064d8b7 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 00:53:29 +0100 Subject: [PATCH 045/140] default to ignore existing captions --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 7f52ac0c..bd5f1b05 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1234,7 +1234,7 @@ def create_ui(wrap_gradio_gpu_call): process_dst = gr.Textbox(label='Destination directory') process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) - preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', choices=['ignore', 'copy', 'prepend', 'append']) + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') From 6f98e89486f55b0e4657e96ce640cf1c4675d187 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Thu, 20 Oct 2022 00:10:45 +0000 Subject: [PATCH 046/140] update --- modules/hypernetworks/hypernetwork.py | 29 +++++++++++------ modules/hypernetworks/ui.py | 3 +- modules/ui.py | 45 ++++++++++++++------------- 3 files changed, 45 insertions(+), 32 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 74300122..7d617680 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -22,16 +22,20 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler class HypernetworkModule(torch.nn.Module): multiplier = 1.0 - def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False): + def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None): super().__init__() - assert layer_structure is not None, "layer_structure mut not be None" + assert layer_structure is not None, "layer_structure must not be None" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" linears = [] for i in range(len(layer_structure) - 1): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + if activation_func == "relu": + linears.append(torch.nn.ReLU()) + if activation_func == "leakyrelu": + linears.append(torch.nn.LeakyReLU()) if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) @@ -42,8 +46,9 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict) else: for layer in self.linear: - layer.weight.data.normal_(mean=0.0, std=0.01) - layer.bias.data.zero_() + if not "ReLU" in layer.__str__(): + layer.weight.data.normal_(mean=0.0, std=0.01) + layer.bias.data.zero_() self.to(devices.device) @@ -69,7 +74,8 @@ class HypernetworkModule(torch.nn.Module): def trainables(self): layer_structure = [] for layer in self.linear: - layer_structure += [layer.weight, layer.bias] + if not "ReLU" in layer.__str__(): + layer_structure += [layer.weight, layer.bias] return layer_structure @@ -81,7 +87,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None): self.filename = None self.name = name self.layers = {} @@ -90,11 +96,12 @@ class Hypernetwork: self.sd_checkpoint_name = None self.layer_structure = layer_structure self.add_layer_norm = add_layer_norm + self.activation_func = activation_func for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm), - HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func), + HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func), ) def weights(self): @@ -117,6 +124,7 @@ class Hypernetwork: state_dict['name'] = self.name state_dict['layer_structure'] = self.layer_structure state_dict['is_layer_norm'] = self.add_layer_norm + state_dict['activation_func'] = self.activation_func state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name @@ -131,12 +139,13 @@ class Hypernetwork: self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) self.add_layer_norm = state_dict.get('is_layer_norm', False) + self.activation_func = state_dict.get('activation_func', None) for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm), - HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm), + HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func), + HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func), ) self.name = state_dict.get('name', self.name) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 08f75f15..83f9547b 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -10,7 +10,7 @@ from modules import sd_hijack, shared, devices from modules.hypernetworks import hypernetwork -def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False): +def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" @@ -22,6 +22,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm enable_sizes=[int(x) for x in enable_sizes], layer_structure=layer_structure, add_layer_norm=add_layer_norm, + activation_func=activation_func, ) hypernet.save(fn) diff --git a/modules/ui.py b/modules/ui.py index d2e24880..8751fa9c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -5,43 +5,44 @@ import json import math import mimetypes import os +import platform import random +import subprocess as sp import sys import tempfile import time import traceback -import platform -import subprocess as sp from functools import partial, reduce +import gradio as gr +import gradio.routes +import gradio.utils import numpy as np +import piexif import torch from PIL import Image, PngImagePlugin -import piexif -import gradio as gr -import gradio.utils -import gradio.routes - -from modules import sd_hijack, sd_models, localization +from modules import localization, sd_hijack, sd_models from modules.paths import script_path -from modules.shared import opts, cmd_opts, restricted_opts +from modules.shared import cmd_opts, opts, restricted_opts + if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags -import modules.shared as shared -from modules.sd_samplers import samplers, samplers_for_img2img -from modules.sd_hijack import model_hijack -import modules.ldsr_model -import modules.scripts -import modules.gfpgan_model + import modules.codeformer_model -import modules.styles import modules.generation_parameters_copypaste -from modules import prompt_parser -from modules.images import save_image -import modules.textual_inversion.ui +import modules.gfpgan_model import modules.hypernetworks.ui import modules.images_history as img_his +import modules.ldsr_model +import modules.scripts +import modules.shared as shared +import modules.styles +import modules.textual_inversion.ui +from modules import prompt_parser +from modules.images import save_image +from modules.sd_hijack import model_hijack +from modules.sd_samplers import samplers, samplers_for_img2img # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() @@ -268,8 +269,8 @@ def calc_time_left(progress, threshold, label, force_display): time_since_start = time.time() - shared.state.time_start eta = (time_since_start/progress) eta_relative = eta-time_since_start - if (eta_relative > threshold and progress > 0.02) or force_display: - return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) + if (eta_relative > threshold and progress > 0.02) or force_display: + return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative)) else: return "" @@ -1219,6 +1220,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") + new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"]) with gr.Row(): with gr.Column(scale=3): @@ -1303,6 +1305,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes, new_hypernetwork_layer_structure, new_hypernetwork_add_layer_norm, + new_hypernetwork_activation_func, ], outputs=[ train_hypernetwork_name, From ba469343e6a1c6e23e82acf5feb65c6101dacbb2 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Thu, 20 Oct 2022 00:17:04 +0000 Subject: [PATCH 047/140] align ui.py imports with upstream --- modules/ui.py | 37 ++++++++++++++++++------------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/modules/ui.py b/modules/ui.py index 987b1d7d..913b23b4 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -5,44 +5,43 @@ import json import math import mimetypes import os -import platform import random -import subprocess as sp import sys import tempfile import time import traceback +import platform +import subprocess as sp from functools import partial, reduce -import gradio as gr -import gradio.routes -import gradio.utils import numpy as np -import piexif import torch from PIL import Image, PngImagePlugin +import piexif -from modules import localization, sd_hijack, sd_models +import gradio as gr +import gradio.utils +import gradio.routes + +from modules import sd_hijack, sd_models, localization from modules.paths import script_path -from modules.shared import cmd_opts, opts, restricted_opts - +from modules.shared import opts, cmd_opts, restricted_opts if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags - -import modules.codeformer_model -import modules.generation_parameters_copypaste -import modules.gfpgan_model -import modules.hypernetworks.ui -import modules.images_history as img_his +import modules.shared as shared +from modules.sd_samplers import samplers, samplers_for_img2img +from modules.sd_hijack import model_hijack import modules.ldsr_model import modules.scripts -import modules.shared as shared +import modules.gfpgan_model +import modules.codeformer_model import modules.styles -import modules.textual_inversion.ui +import modules.generation_parameters_copypaste from modules import prompt_parser from modules.images import save_image -from modules.sd_hijack import model_hijack -from modules.sd_samplers import samplers, samplers_for_img2img +import modules.textual_inversion.ui +import modules.hypernetworks.ui +import modules.images_history as img_his # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() From 858462f719c22ca9f24b94a41699653c34b5f4fb Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Thu, 20 Oct 2022 02:57:18 +0100 Subject: [PATCH 048/140] do caption copy for both flips --- modules/textual_inversion/preprocess.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 3713bc89..6bba3852 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -82,7 +82,7 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre subindex[0] += 1 def save_pic(image, index, existing_caption=None): - save_pic_with_caption(image, index) + save_pic_with_caption(image, index, existing_caption=existing_caption) if process_flip: save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption) From aa7ff2a1972f3865883e10ba28c5414cdebe8e3b Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Wed, 19 Oct 2022 21:46:13 -0700 Subject: [PATCH 049/140] Fixed non-square highres fix generation --- modules/processing.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index 684e5833..3caac25e 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -541,10 +541,13 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - def create_dummy_mask(self, x): + def create_dummy_mask(self, x, first_phase: bool = False): if self.sampler.conditioning_key in {'hybrid', 'concat'}: + height = self.firstphase_height if first_phase else self.height + width = self.firstphase_width if first_phase else self.width + # The "masked-image" in this case will just be all zeros since the entire image is masked. - image_conditioning = torch.zeros(x.shape[0], 3, self.height, self.width, device=x.device) + image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) image_conditioning = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image_conditioning)) # Add the fake full 1s mask to the first dimension. @@ -567,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x)) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True)) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] From f8733ad08be08bafb40f4299785590e11f049e96 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Thu, 20 Oct 2022 11:07:37 +0000 Subject: [PATCH 050/140] add linear as a act func (option for doin nothing) --- modules/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/ui.py b/modules/ui.py index 913b23b4..716f14b8 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1224,7 +1224,7 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") - new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["relu", "leakyrelu"]) + new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"]) with gr.Row(): with gr.Column(scale=3): From 4281f255d5e7c67515d619f53654be59a6fc1e13 Mon Sep 17 00:00:00 2001 From: wywywywy Date: Thu, 20 Oct 2022 15:31:09 +0100 Subject: [PATCH 051/140] Implemented batch count logic to Outpainting mk2 --- scripts/outpainting_mk_2.py | 36 ++++++++++++++++++++++++++---------- 1 file changed, 26 insertions(+), 10 deletions(-) diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index a6468e09..02e655e9 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -242,21 +242,37 @@ class Script(scripts.Script): out = out.crop((0, 0, res_w, res_h)) return out - img = init_image + batch_count = p.n_iter + p.n_iter = 1 + state.job_count = batch_count + all_images = [] - if left > 0: - img = expand(img, left, is_left=True) - if right > 0: - img = expand(img, right, is_right=True) - if up > 0: - img = expand(img, up, is_top=True) - if down > 0: - img = expand(img, down, is_bottom=True) + for i in range(batch_count): + img = init_image + state.job = f"Batch {i + 1} out of {state.job_count}" - res = Processed(p, [img], initial_seed_and_info[0], initial_seed_and_info[1]) + if left > 0: + img = expand(img, left, is_left=True) + if right > 0: + img = expand(img, right, is_right=True) + if up > 0: + img = expand(img, up, is_top=True) + if down > 0: + img = expand(img, down, is_bottom=True) + + all_images.append(img) + + combined_grid_image = images.image_grid(all_images) + if opts.return_grid: + all_images = [combined_grid_image] + all_images + + res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1]) if opts.samples_save: images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) + if opts.grid_save: + images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p) + return res From 9681419e422515e42444e0174355b760645a846f Mon Sep 17 00:00:00 2001 From: Milly Date: Thu, 20 Oct 2022 16:53:46 +0900 Subject: [PATCH 052/140] train: fixed preprocess image ratio --- modules/textual_inversion/preprocess.py | 54 ++++++++++++++++--------- 1 file changed, 35 insertions(+), 19 deletions(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 886cf0c3..2743bdeb 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -1,5 +1,6 @@ import os from PIL import Image, ImageOps +import math import platform import sys import tqdm @@ -38,6 +39,8 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) + split_threshold = 0.5 + overlap_ratio = 0.2 assert src != dst, 'same directory specified as source and destination' @@ -78,6 +81,29 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro if process_flip: save_pic_with_caption(ImageOps.mirror(image), index) + def split_pic(image, inverse_xy): + if inverse_xy: + from_w, from_h = image.height, image.width + to_w, to_h = height, width + else: + from_w, from_h = image.width, image.height + to_w, to_h = width, height + h = from_h * to_w // from_w + if inverse_xy: + image = image.resize((h, to_w)) + else: + image = image.resize((to_w, h)) + + split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio))) + y_step = (h - to_h) / (split_count - 1) + for i in range(split_count): + y = int(y_step * i) + if inverse_xy: + splitted = image.crop((y, 0, y + to_h, to_w)) + else: + splitted = image.crop((0, y, to_w, y + to_h)) + yield splitted + for index, imagefile in enumerate(tqdm.tqdm(files)): subindex = [0] filename = os.path.join(src, imagefile) @@ -89,26 +115,16 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pro if shared.state.interrupted: break - ratio = img.height / img.width - is_tall = ratio > 1.35 - is_wide = ratio < 1 / 1.35 + if img.height > img.width: + ratio = (img.width * height) / (img.height * width) + inverse_xy = False + else: + ratio = (img.height * width) / (img.width * height) + inverse_xy = True - if process_split and is_tall: - img = img.resize((width, height * img.height // img.width)) - - top = img.crop((0, 0, width, height)) - save_pic(top, index) - - bot = img.crop((0, img.height - height, width, img.height)) - save_pic(bot, index) - elif process_split and is_wide: - img = img.resize((width * img.width // img.height, height)) - - left = img.crop((0, 0, width, height)) - save_pic(left, index) - - right = img.crop((img.width - width, 0, img.width, height)) - save_pic(right, index) + if process_split and ratio < 1.0 and ratio <= split_threshold: + for splitted in split_pic(img, inverse_xy): + save_pic(splitted, index) else: img = images.resize_image(1, img, width, height) save_pic(img, index) From 85dd62c4c7635b8e21a75f140d093036069e97a1 Mon Sep 17 00:00:00 2001 From: Milly Date: Thu, 20 Oct 2022 22:56:45 +0900 Subject: [PATCH 053/140] train: ui: added `Split image threshold` and `Split image overlap ratio` to preprocess --- modules/textual_inversion/preprocess.py | 10 +++++----- modules/ui.py | 16 ++++++++++++++-- 2 files changed, 19 insertions(+), 7 deletions(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 2743bdeb..c8df8aa0 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -12,7 +12,7 @@ if cmd_opts.deepdanbooru: import modules.deepbooru as deepbooru -def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): +def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2): try: if process_caption: shared.interrogator.load() @@ -22,7 +22,7 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ db_opts[deepbooru.OPT_INCLUDE_RANKS] = False deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts) - preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru) + preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio) finally: @@ -34,13 +34,13 @@ def preprocess(process_src, process_dst, process_width, process_height, process_ -def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False): +def preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2): width = process_width height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) - split_threshold = 0.5 - overlap_ratio = 0.2 + split_threshold = max(0.0, min(1.0, split_threshold)) + overlap_ratio = max(0.0, min(0.9, overlap_ratio)) assert src != dst, 'same directory specified as source and destination' diff --git a/modules/ui.py b/modules/ui.py index a2dbd41e..bc7f3330 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1240,10 +1240,14 @@ def create_ui(wrap_gradio_gpu_call): with gr.Row(): process_flip = gr.Checkbox(label='Create flipped copies') - process_split = gr.Checkbox(label='Split oversized images into two') + process_split = gr.Checkbox(label='Split oversized images') process_caption = gr.Checkbox(label='Use BLIP for caption') process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False) + with gr.Row(visible=False) as process_split_extra_row: + process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05) + process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05) + with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") @@ -1251,6 +1255,12 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): run_preprocess = gr.Button(value="Preprocess", variant='primary') + process_split.change( + fn=lambda show: gr_show(show), + inputs=[process_split], + outputs=[process_split_extra_row], + ) + with gr.Tab(label="Train"): gr.HTML(value="

Train an embedding; must specify a directory with a set of 1:1 ratio images

") with gr.Row(): @@ -1327,7 +1337,9 @@ def create_ui(wrap_gradio_gpu_call): process_flip, process_split, process_caption, - process_caption_deepbooru + process_caption_deepbooru, + process_split_threshold, + process_overlap_ratio, ], outputs=[ ti_output, From d8acd34f66ab35a91f10d66330bcc95a83bfcac6 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Thu, 20 Oct 2022 23:43:03 +0900 Subject: [PATCH 054/140] generalized some functions and option for ignoring first layer --- modules/hypernetworks/hypernetwork.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d617680..3a44b377 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -21,21 +21,27 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler class HypernetworkModule(torch.nn.Module): multiplier = 1.0 - + activation_dict = {"relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU, + "swish": torch.nn.Hardswish} + def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None): super().__init__() assert layer_structure is not None, "layer_structure must not be None" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" - + linears = [] for i in range(len(layer_structure) - 1): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) - if activation_func == "relu": - linears.append(torch.nn.ReLU()) - if activation_func == "leakyrelu": - linears.append(torch.nn.LeakyReLU()) + # if skip_first_layer because first parameters potentially contain negative values + if i < 1: continue + if activation_func in HypernetworkModule.activation_dict: + linears.append(HypernetworkModule.activation_dict[activation_func]()) + else: + print("Invalid key {} encountered as activation function!".format(activation_func)) + # if use_dropout: + linears.append(torch.nn.Dropout(p=0.3)) if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) @@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict) else: for layer in self.linear: - if not "ReLU" in layer.__str__(): + if isinstance(layer, torch.nn.Linear): layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() @@ -298,7 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + # if optimizer == "Adam": or else Adam / AdamW / etc... + optimizer = torch.optim.Adam(weights, lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: From a71e0212363979c7cbbb797c9fbd5f8cd03b29d3 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Thu, 20 Oct 2022 23:48:52 +0900 Subject: [PATCH 055/140] only linear --- modules/hypernetworks/hypernetwork.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3a44b377..905cbeef 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -35,13 +35,13 @@ class HypernetworkModule(torch.nn.Module): for i in range(len(layer_structure) - 1): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # if skip_first_layer because first parameters potentially contain negative values - if i < 1: continue + # if i < 1: continue if activation_func in HypernetworkModule.activation_dict: linears.append(HypernetworkModule.activation_dict[activation_func]()) else: print("Invalid key {} encountered as activation function!".format(activation_func)) # if use_dropout: - linears.append(torch.nn.Dropout(p=0.3)) + # linears.append(torch.nn.Dropout(p=0.3)) if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) @@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module): def trainables(self): layer_structure = [] for layer in self.linear: - if not "ReLU" in layer.__str__(): + if isinstance(layer, torch.nn.Linear): layer_structure += [layer.weight, layer.bias] return layer_structure @@ -304,8 +304,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - # if optimizer == "Adam": or else Adam / AdamW / etc... - optimizer = torch.optim.Adam(weights, lr=scheduler.learn_rate) + # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... + optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: From 91efe138b35dda65e83070c14e9eb94f481fe476 Mon Sep 17 00:00:00 2001 From: wywywywy Date: Thu, 20 Oct 2022 16:02:32 +0100 Subject: [PATCH 056/140] Implemented batch_size logic in outpainting_mk2 --- scripts/outpainting_mk_2.py | 106 +++++++++++++++++++----------------- 1 file changed, 57 insertions(+), 49 deletions(-) diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index 02e655e9..0377ab32 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -176,50 +176,53 @@ class Script(scripts.Script): state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0) - def expand(init, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False): + def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False): is_horiz = is_left or is_right is_vert = is_top or is_bottom pixels_horiz = expand_pixels if is_horiz else 0 pixels_vert = expand_pixels if is_vert else 0 - res_w = init.width + pixels_horiz - res_h = init.height + pixels_vert - process_res_w = math.ceil(res_w / 64) * 64 - process_res_h = math.ceil(res_h / 64) * 64 + images_to_process = [] + for n in range(count): + res_w = init[n].width + pixels_horiz + res_h = init[n].height + pixels_vert + process_res_w = math.ceil(res_w / 64) * 64 + process_res_h = math.ceil(res_h / 64) * 64 - img = Image.new("RGB", (process_res_w, process_res_h)) - img.paste(init, (pixels_horiz if is_left else 0, pixels_vert if is_top else 0)) - mask = Image.new("RGB", (process_res_w, process_res_h), "white") - draw = ImageDraw.Draw(mask) - draw.rectangle(( - expand_pixels + mask_blur if is_left else 0, - expand_pixels + mask_blur if is_top else 0, - mask.width - expand_pixels - mask_blur if is_right else res_w, - mask.height - expand_pixels - mask_blur if is_bottom else res_h, - ), fill="black") + img = Image.new("RGB", (process_res_w, process_res_h)) + img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0)) + mask = Image.new("RGB", (process_res_w, process_res_h), "white") + draw = ImageDraw.Draw(mask) + draw.rectangle(( + expand_pixels + mask_blur if is_left else 0, + expand_pixels + mask_blur if is_top else 0, + mask.width - expand_pixels - mask_blur if is_right else res_w, + mask.height - expand_pixels - mask_blur if is_bottom else res_h, + ), fill="black") - np_image = (np.asarray(img) / 255.0).astype(np.float64) - np_mask = (np.asarray(mask) / 255.0).astype(np.float64) - noised = get_matched_noise(np_image, np_mask, noise_q, color_variation) - out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB") + np_image = (np.asarray(img) / 255.0).astype(np.float64) + np_mask = (np.asarray(mask) / 255.0).astype(np.float64) + noised = get_matched_noise(np_image, np_mask, noise_q, color_variation) + out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB") - target_width = min(process_width, init.width + pixels_horiz) if is_horiz else img.width - target_height = min(process_height, init.height + pixels_vert) if is_vert else img.height + target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width + target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height + p.width = target_width if is_horiz else img.width + p.height = target_height if is_vert else img.height - crop_region = ( - 0 if is_left else out.width - target_width, - 0 if is_top else out.height - target_height, - target_width if is_left else out.width, - target_height if is_top else out.height, - ) + crop_region = ( + 0 if is_left else out.width - target_width, + 0 if is_top else out.height - target_height, + target_width if is_left else out.width, + target_height if is_top else out.height, + ) + mask = mask.crop(crop_region) + p.image_mask = mask - image_to_process = out.crop(crop_region) - mask = mask.crop(crop_region) + image_to_process = out.crop(crop_region) + images_to_process.append(image_to_process) - p.width = target_width if is_horiz else img.width - p.height = target_height if is_vert else img.height - p.init_images = [image_to_process] - p.image_mask = mask + p.init_images = images_to_process latent_mask = Image.new("RGB", (p.width, p.height), "white") draw = ImageDraw.Draw(latent_mask) @@ -232,44 +235,49 @@ class Script(scripts.Script): p.latent_mask = latent_mask proc = process_images(p) - proc_img = proc.images[0] if initial_seed_and_info[0] is None: initial_seed_and_info[0] = proc.seed initial_seed_and_info[1] = proc.info - out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height)) - out = out.crop((0, 0, res_w, res_h)) - return out + for proc_img in proc.images: + out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height)) + out = out.crop((0, 0, res_w, res_h)) + + return proc.images batch_count = p.n_iter + batch_size = p.batch_size p.n_iter = 1 state.job_count = batch_count - all_images = [] + all_processed_images = [] for i in range(batch_count): - img = init_image - state.job = f"Batch {i + 1} out of {state.job_count}" + imgs = [init_img] * batch_size + state.job = f"Batch {i + 1} out of {batch_count}" if left > 0: - img = expand(img, left, is_left=True) + imgs = expand(imgs, batch_size, left, is_left=True) if right > 0: - img = expand(img, right, is_right=True) + imgs = expand(imgs, batch_size, right, is_right=True) if up > 0: - img = expand(img, up, is_top=True) + imgs = expand(imgs, batch_size, up, is_top=True) if down > 0: - img = expand(img, down, is_bottom=True) + imgs = expand(imgs, batch_size, down, is_bottom=True) - all_images.append(img) + all_processed_images += imgs + + combined_grid_image = images.image_grid(all_processed_images) + all_images = all_processed_images - combined_grid_image = images.image_grid(all_images) if opts.return_grid: - all_images = [combined_grid_image] + all_images - + all_images = [combined_grid_image] + all_processed_images + res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1]) if opts.samples_save: - images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) + for img in all_processed_images: + images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) if opts.grid_save: images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p) From 18df060c3e9252f1cf79b494e7173aff4181049a Mon Sep 17 00:00:00 2001 From: wywywywy Date: Thu, 20 Oct 2022 16:16:09 +0100 Subject: [PATCH 057/140] Fixed outpainting_mk2 output cropping --- scripts/outpainting_mk_2.py | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index 0377ab32..726417e7 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -183,6 +183,7 @@ class Script(scripts.Script): pixels_vert = expand_pixels if is_vert else 0 images_to_process = [] + output_images = [] for n in range(count): res_w = init[n].width + pixels_horiz res_h = init[n].height + pixels_vert @@ -203,7 +204,7 @@ class Script(scripts.Script): np_image = (np.asarray(img) / 255.0).astype(np.float64) np_mask = (np.asarray(mask) / 255.0).astype(np.float64) noised = get_matched_noise(np_image, np_mask, noise_q, color_variation) - out = Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB") + output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode="RGB")) target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height @@ -211,15 +212,15 @@ class Script(scripts.Script): p.height = target_height if is_vert else img.height crop_region = ( - 0 if is_left else out.width - target_width, - 0 if is_top else out.height - target_height, - target_width if is_left else out.width, - target_height if is_top else out.height, + 0 if is_left else output_images[n].width - target_width, + 0 if is_top else output_images[n].height - target_height, + target_width if is_left else output_images[n].width, + target_height if is_top else output_images[n].height, ) mask = mask.crop(crop_region) p.image_mask = mask - image_to_process = out.crop(crop_region) + image_to_process = output_images[n].crop(crop_region) images_to_process.append(image_to_process) p.init_images = images_to_process @@ -240,11 +241,11 @@ class Script(scripts.Script): initial_seed_and_info[0] = proc.seed initial_seed_and_info[1] = proc.info - for proc_img in proc.images: - out.paste(proc_img, (0 if is_left else out.width - proc_img.width, 0 if is_top else out.height - proc_img.height)) - out = out.crop((0, 0, res_w, res_h)) + for n in range(count): + output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height)) + output_images[n] = output_images[n].crop((0, 0, res_w, res_h)) - return proc.images + return output_images batch_count = p.n_iter batch_size = p.batch_size From 108be15500aac590b4e00420635d7b61fccfa530 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Fri, 21 Oct 2022 01:00:41 +0900 Subject: [PATCH 058/140] fix bugs and optimizations --- modules/hypernetworks/hypernetwork.py | 93 +++++++++++++++------------ 1 file changed, 53 insertions(+), 40 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 905cbeef..893ba110 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -36,14 +36,14 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # if skip_first_layer because first parameters potentially contain negative values # if i < 1: continue + if add_layer_norm: + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) if activation_func in HypernetworkModule.activation_dict: linears.append(HypernetworkModule.activation_dict[activation_func]()) else: print("Invalid key {} encountered as activation function!".format(activation_func)) # if use_dropout: # linears.append(torch.nn.Dropout(p=0.3)) - if add_layer_norm: - linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) self.linear = torch.nn.Sequential(*linears) @@ -115,11 +115,24 @@ class Hypernetwork: for k, layers in self.layers.items(): for layer in layers: - layer.train() res += layer.trainables() return res + def eval(self): + for k, layers in self.layers.items(): + for layer in layers: + layer.eval() + for items in self.weights(): + items.requires_grad = False + + def train(self): + for k, layers in self.layers.items(): + for layer in layers: + layer.train() + for items in self.weights(): + items.requires_grad = True + def save(self, filename): state_dict = {} @@ -290,10 +303,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.sd_model.first_stage_model.to(devices.cpu) hypernetwork = shared.loaded_hypernetwork - weights = hypernetwork.weights() - for weight in weights: - weight.requires_grad = True - losses = torch.zeros((32,)) last_saved_file = "" @@ -304,10 +313,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... - optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + optimizer = torch.optim.AdamW(hypernetwork.weights(), lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) + hypernetwork.train() for i, entries in pbar: hypernetwork.step = i + ititial_step @@ -328,8 +337,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log losses[hypernetwork.step % losses.shape[0]] = loss.item() - optimizer.zero_grad() + optimizer.zero_grad(set_to_none=True) loss.backward() + del loss optimizer.step() mean_loss = losses.mean() if torch.isnan(mean_loss): @@ -346,44 +356,47 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: + torch.cuda.empty_cache() last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') + with torch.no_grad(): + hypernetwork.eval() + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) - optimizer.zero_grad() - shared.sd_model.cond_stage_model.to(devices.device) - shared.sd_model.first_stage_model.to(devices.device) + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + do_not_save_grid=True, + do_not_save_samples=True, + ) - p = processing.StableDiffusionProcessingTxt2Img( - sd_model=shared.sd_model, - do_not_save_grid=True, - do_not_save_samples=True, - ) + if preview_from_txt2img: + p.prompt = preview_prompt + p.negative_prompt = preview_negative_prompt + p.steps = preview_steps + p.sampler_index = preview_sampler_index + p.cfg_scale = preview_cfg_scale + p.seed = preview_seed + p.width = preview_width + p.height = preview_height + else: + p.prompt = entries[0].cond_text + p.steps = 20 - if preview_from_txt2img: - p.prompt = preview_prompt - p.negative_prompt = preview_negative_prompt - p.steps = preview_steps - p.sampler_index = preview_sampler_index - p.cfg_scale = preview_cfg_scale - p.seed = preview_seed - p.width = preview_width - p.height = preview_height - else: - p.prompt = entries[0].cond_text - p.steps = 20 + preview_text = p.prompt - preview_text = p.prompt + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images)>0 else None - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) - if unload: - shared.sd_model.cond_stage_model.to(devices.cpu) - shared.sd_model.first_stage_model.to(devices.cpu) + if image is not None: + shared.state.current_image = image + image.save(last_saved_image) + last_saved_image += f", prompt: {preview_text}" - if image is not None: - shared.state.current_image = image - image.save(last_saved_image) - last_saved_image += f", prompt: {preview_text}" + hypernetwork.train() shared.state.job_no = hypernetwork.step From f89829ec3a0baceb445451ad98d4fb4323e922aa Mon Sep 17 00:00:00 2001 From: aria1th <35677394+aria1th@users.noreply.github.com> Date: Fri, 21 Oct 2022 01:37:11 +0900 Subject: [PATCH 059/140] Revert "fix bugs and optimizations" This reverts commit 108be15500aac590b4e00420635d7b61fccfa530. --- modules/hypernetworks/hypernetwork.py | 93 ++++++++++++--------------- 1 file changed, 40 insertions(+), 53 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 893ba110..905cbeef 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -36,14 +36,14 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) # if skip_first_layer because first parameters potentially contain negative values # if i < 1: continue - if add_layer_norm: - linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) if activation_func in HypernetworkModule.activation_dict: linears.append(HypernetworkModule.activation_dict[activation_func]()) else: print("Invalid key {} encountered as activation function!".format(activation_func)) # if use_dropout: # linears.append(torch.nn.Dropout(p=0.3)) + if add_layer_norm: + linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) self.linear = torch.nn.Sequential(*linears) @@ -115,24 +115,11 @@ class Hypernetwork: for k, layers in self.layers.items(): for layer in layers: + layer.train() res += layer.trainables() return res - def eval(self): - for k, layers in self.layers.items(): - for layer in layers: - layer.eval() - for items in self.weights(): - items.requires_grad = False - - def train(self): - for k, layers in self.layers.items(): - for layer in layers: - layer.train() - for items in self.weights(): - items.requires_grad = True - def save(self, filename): state_dict = {} @@ -303,6 +290,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.sd_model.first_stage_model.to(devices.cpu) hypernetwork = shared.loaded_hypernetwork + weights = hypernetwork.weights() + for weight in weights: + weight.requires_grad = True + losses = torch.zeros((32,)) last_saved_file = "" @@ -313,10 +304,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log return hypernetwork, filename scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) - optimizer = torch.optim.AdamW(hypernetwork.weights(), lr=scheduler.learn_rate) + # if optimizer == "AdamW": or else Adam / AdamW / SGD, etc... + optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) - hypernetwork.train() for i, entries in pbar: hypernetwork.step = i + ititial_step @@ -337,9 +328,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log losses[hypernetwork.step % losses.shape[0]] = loss.item() - optimizer.zero_grad(set_to_none=True) + optimizer.zero_grad() loss.backward() - del loss optimizer.step() mean_loss = losses.mean() if torch.isnan(mean_loss): @@ -356,47 +346,44 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: - torch.cuda.empty_cache() last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') - with torch.no_grad(): - hypernetwork.eval() - shared.sd_model.cond_stage_model.to(devices.device) - shared.sd_model.first_stage_model.to(devices.device) - p = processing.StableDiffusionProcessingTxt2Img( - sd_model=shared.sd_model, - do_not_save_grid=True, - do_not_save_samples=True, - ) + optimizer.zero_grad() + shared.sd_model.cond_stage_model.to(devices.device) + shared.sd_model.first_stage_model.to(devices.device) - if preview_from_txt2img: - p.prompt = preview_prompt - p.negative_prompt = preview_negative_prompt - p.steps = preview_steps - p.sampler_index = preview_sampler_index - p.cfg_scale = preview_cfg_scale - p.seed = preview_seed - p.width = preview_width - p.height = preview_height - else: - p.prompt = entries[0].cond_text - p.steps = 20 + p = processing.StableDiffusionProcessingTxt2Img( + sd_model=shared.sd_model, + do_not_save_grid=True, + do_not_save_samples=True, + ) - preview_text = p.prompt + if preview_from_txt2img: + p.prompt = preview_prompt + p.negative_prompt = preview_negative_prompt + p.steps = preview_steps + p.sampler_index = preview_sampler_index + p.cfg_scale = preview_cfg_scale + p.seed = preview_seed + p.width = preview_width + p.height = preview_height + else: + p.prompt = entries[0].cond_text + p.steps = 20 - processed = processing.process_images(p) - image = processed.images[0] if len(processed.images)>0 else None + preview_text = p.prompt - if unload: - shared.sd_model.cond_stage_model.to(devices.cpu) - shared.sd_model.first_stage_model.to(devices.cpu) + processed = processing.process_images(p) + image = processed.images[0] if len(processed.images)>0 else None - if image is not None: - shared.state.current_image = image - image.save(last_saved_image) - last_saved_image += f", prompt: {preview_text}" + if unload: + shared.sd_model.cond_stage_model.to(devices.cpu) + shared.sd_model.first_stage_model.to(devices.cpu) - hypernetwork.train() + if image is not None: + shared.state.current_image = image + image.save(last_saved_image) + last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step From 92a17a7a4a13fceb3c3e25a2e854b2a7dd6eb5df Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Thu, 20 Oct 2022 09:45:03 -0700 Subject: [PATCH 060/140] Made dummy latents smaller. Minor code cleanups --- modules/processing.py | 7 ++++--- modules/sd_samplers.py | 6 ++++-- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index 3caac25e..539cde38 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: # Dummy zero conditioning if we're not using inpainting model. # Still takes up a bit of memory, but no encoder call. - image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device) + # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. + image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) return image_conditioning @@ -759,8 +760,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype) else: self.image_conditioning = torch.zeros( - self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1], - dtype=self.init_latent.dtype, + self.init_latent.shape[0], 5, 1, 1, + dtype=self.init_latent.dtype, device=self.init_latent.device ) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index c21be26e..cc682593 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler: if self.stop_at is not None and self.step > self.stop_at: raise InterruptedException - # Have to unwrap the inpainting conditioning here to perform pre-preocessing + # Have to unwrap the inpainting conditioning here to perform pre-processing image_conditioning = None if isinstance(cond, dict): image_conditioning = cond["c_concat"][0] @@ -146,7 +146,7 @@ class VanillaStableDiffusionSampler: unconditional_conditioning = unconditional_conditioning["c_crossattn"][0] conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step) - unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) + unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step) assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers' cond = tensor @@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler: img_orig = self.sampler.model.q_sample(self.init_latent, ts) x_dec = img_orig * self.mask + self.nmask * x_dec + # Wrap the image conditioning back up since the DDIM code can accept the dict directly. + # Note that they need to be lists because it just concatenates them later. if image_conditioning is not None: cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]} unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} From d1cb08bfb221cd1b0cfc6078162b4e206ea80a5c Mon Sep 17 00:00:00 2001 From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com> Date: Thu, 20 Oct 2022 22:49:06 +0300 Subject: [PATCH 061/140] fix skip and interrupt for highres. fix option --- modules/processing.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index bcb0c32c..6324ca91 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -587,9 +587,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) - - return samples + return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): From 708c3a7bd8ce68cbe1aa7c268e5a4b1980affc9f Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Thu, 20 Oct 2022 13:28:43 -0700 Subject: [PATCH 062/140] Added PLMS hijack and made sure to always replace methods --- modules/sd_hijack_inpainting.py | 163 ++++++++++++++++++++++++++++++-- modules/sd_models.py | 3 +- 2 files changed, 157 insertions(+), 9 deletions(-) diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index d4d28d2e..43938071 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -1,16 +1,14 @@ import torch -import numpy as np -from tqdm import tqdm -from einops import rearrange, repeat +from einops import repeat from omegaconf import ListConfig -from types import MethodType - import ldm.models.diffusion.ddpm import ldm.models.diffusion.ddim +import ldm.models.diffusion.plms from ldm.models.diffusion.ddpm import LatentDiffusion +from ldm.models.diffusion.plms import PLMSSampler from ldm.models.diffusion.ddim import DDIMSampler, noise_like # ================================================================================================= @@ -19,7 +17,7 @@ from ldm.models.diffusion.ddim import DDIMSampler, noise_like # https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py # ================================================================================================= @torch.no_grad() -def sample(self, +def sample_ddim(self, S, batch_size, shape, @@ -132,6 +130,153 @@ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=F return x_prev, pred_x0 +# ================================================================================================= +# Monkey patch PLMSSampler methods. +# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes. +# Adapted from: +# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py +# ================================================================================================= +@torch.no_grad() +def sample_plms(self, + S, + batch_size, + shape, + conditioning=None, + callback=None, + normals_sequence=None, + img_callback=None, + quantize_x0=False, + eta=0., + mask=None, + x0=None, + temperature=1., + noise_dropout=0., + score_corrector=None, + corrector_kwargs=None, + verbose=True, + x_T=None, + log_every_t=100, + unconditional_guidance_scale=1., + unconditional_conditioning=None, + # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... + **kwargs + ): + if conditioning is not None: + if isinstance(conditioning, dict): + ctmp = conditioning[list(conditioning.keys())[0]] + while isinstance(ctmp, list): + ctmp = ctmp[0] + cbs = ctmp.shape[0] + if cbs != batch_size: + print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") + else: + if conditioning.shape[0] != batch_size: + print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") + + self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) + # sampling + C, H, W = shape + size = (batch_size, C, H, W) + print(f'Data shape for PLMS sampling is {size}') + + samples, intermediates = self.plms_sampling(conditioning, size, + callback=callback, + img_callback=img_callback, + quantize_denoised=quantize_x0, + mask=mask, x0=x0, + ddim_use_original_steps=False, + noise_dropout=noise_dropout, + temperature=temperature, + score_corrector=score_corrector, + corrector_kwargs=corrector_kwargs, + x_T=x_T, + log_every_t=log_every_t, + unconditional_guidance_scale=unconditional_guidance_scale, + unconditional_conditioning=unconditional_conditioning, + ) + return samples, intermediates + + +@torch.no_grad() +def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, + temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, + unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None): + b, *_, device = *x.shape, x.device + + def get_model_output(x, t): + if unconditional_conditioning is None or unconditional_guidance_scale == 1.: + e_t = self.model.apply_model(x, t, c) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t] * 2) + + if isinstance(c, dict): + assert isinstance(unconditional_conditioning, dict) + c_in = dict() + for k in c: + if isinstance(c[k], list): + c_in[k] = [ + torch.cat([unconditional_conditioning[k][i], c[k][i]]) + for i in range(len(c[k])) + ] + else: + c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) + else: + c_in = torch.cat([unconditional_conditioning, c]) + + e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) + e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) + + if score_corrector is not None: + assert self.model.parameterization == "eps" + e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) + + return e_t + + alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas + alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev + sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas + sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas + + def get_x_prev_and_pred_x0(e_t, index): + # select parameters corresponding to the currently considered timestep + a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) + a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) + sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) + sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) + + # current prediction for x_0 + pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() + if quantize_denoised: + pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) + # direction pointing to x_t + dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t + noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature + if noise_dropout > 0.: + noise = torch.nn.functional.dropout(noise, p=noise_dropout) + x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise + return x_prev, pred_x0 + + e_t = get_model_output(x, t) + if len(old_eps) == 0: + # Pseudo Improved Euler (2nd order) + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index) + e_t_next = get_model_output(x_prev, t_next) + e_t_prime = (e_t + e_t_next) / 2 + elif len(old_eps) == 1: + # 2nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (3 * e_t - old_eps[-1]) / 2 + elif len(old_eps) == 2: + # 3nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12 + elif len(old_eps) >= 3: + # 4nd order Pseudo Linear Multistep (Adams-Bashforth) + e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24 + + x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index) + + return x_prev, pred_x0, e_t + # ================================================================================================= # Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config. # Adapted from: @@ -175,5 +320,9 @@ def should_hijack_inpainting(checkpoint_info): def do_inpainting_hijack(): ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion + ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim - ldm.models.diffusion.ddim.DDIMSampler.sample = sample \ No newline at end of file + ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim + + ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms + ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms \ No newline at end of file diff --git a/modules/sd_models.py b/modules/sd_models.py index 47836d25..7072db08 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -214,8 +214,6 @@ def load_model(): sd_config = OmegaConf.load(checkpoint_info.config) if should_hijack_inpainting(checkpoint_info): - do_inpainting_hijack() - # Hardcoded config for now... sd_config.model.target = "ldm.models.diffusion.ddpm.LatentInpaintDiffusion" sd_config.model.params.use_ema = False @@ -225,6 +223,7 @@ def load_model(): # Create a "fake" config with a different name so that we know to unload it when switching models. checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml")) + do_inpainting_hijack() sd_model = instantiate_from_config(sd_config.model) load_model_weights(sd_model, checkpoint_info) From d23a46ceaa76af2847f11172f32c92665c268b1b Mon Sep 17 00:00:00 2001 From: Vladimir Repin <32306715+mezotaken@users.noreply.github.com> Date: Thu, 20 Oct 2022 23:49:14 +0300 Subject: [PATCH 063/140] Different approach to skip/interrupt with highres fix --- modules/processing.py | 4 +++- modules/sd_samplers.py | 4 ++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/modules/processing.py b/modules/processing.py index 6324ca91..bcb0c32c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -587,7 +587,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x = None devices.torch_gc() - return self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) or samples + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) + + return samples class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index b58e810b..7ff77c01 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -196,6 +196,7 @@ class VanillaStableDiffusionSampler: x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) self.init_latent = x + self.last_latent = x self.step = 0 samples = self.launch_sampling(steps, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) @@ -206,6 +207,7 @@ class VanillaStableDiffusionSampler: self.initialize(p) self.init_latent = None + self.last_latent = x self.step = 0 steps = steps or p.steps @@ -388,6 +390,7 @@ class KDiffusionSampler: extra_params_kwargs['sigmas'] = sigma_sched self.model_wrap_cfg.init_latent = x + self.last_latent = x samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, xi, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) @@ -414,6 +417,7 @@ class KDiffusionSampler: else: extra_params_kwargs['sigmas'] = sigmas + self.last_latent = x samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state, **extra_params_kwargs)) return samples From 49533eed9e3aad19e9868ee140708baec4fd44be Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Thu, 20 Oct 2022 16:01:27 -0700 Subject: [PATCH 064/140] XY grid correctly re-assignes model when config changes --- modules/sd_models.py | 6 +++--- scripts/xy_grid.py | 1 + 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 7072db08..fea84630 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -204,9 +204,9 @@ def load_model_weights(model, checkpoint_info): model.sd_checkpoint_info = checkpoint_info -def load_model(): +def load_model(checkpoint_info=None): from modules import lowvram, sd_hijack - checkpoint_info = select_checkpoint() + checkpoint_info = checkpoint_info or select_checkpoint() if checkpoint_info.config != shared.cmd_opts.config: print(f"Loading config from: {checkpoint_info.config}") @@ -249,7 +249,7 @@ def reload_model_weights(sd_model, info=None): if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() - shared.sd_model = load_model() + shared.sd_model = load_model(checkpoint_info) return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 5cca168a..eff0c942 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -89,6 +89,7 @@ def apply_checkpoint(p, x, xs): if info is None: raise RuntimeError(f"Unknown checkpoint: {x}") modules.sd_models.reload_model_weights(shared.sd_model, info) + p.sd_model = shared.sd_model def confirm_checkpoints(p, xs): From 1fc278bcc642f720484a77eb169271054d3153b1 Mon Sep 17 00:00:00 2001 From: wywywywy Date: Fri, 21 Oct 2022 02:38:24 +0100 Subject: [PATCH 065/140] Fixed job count & single-output grid --- scripts/outpainting_mk_2.py | 14 +++++--------- 1 file changed, 5 insertions(+), 9 deletions(-) diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index 726417e7..633dc119 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -172,10 +172,6 @@ class Script(scripts.Script): if down > 0: down = target_h - init_img.height - up - init_image = p.init_images[0] - - state.job_count = (1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0) - def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False): is_horiz = is_left or is_right is_vert = is_top or is_bottom @@ -250,7 +246,7 @@ class Script(scripts.Script): batch_count = p.n_iter batch_size = p.batch_size p.n_iter = 1 - state.job_count = batch_count + state.job_count = batch_count * batch_size * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)) all_processed_images = [] for i in range(batch_count): @@ -268,10 +264,11 @@ class Script(scripts.Script): all_processed_images += imgs - combined_grid_image = images.image_grid(all_processed_images) all_images = all_processed_images - if opts.return_grid: + combined_grid_image = images.image_grid(all_processed_images) + unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple + if opts.return_grid and not unwanted_grid_because_of_img_count: all_images = [combined_grid_image] + all_processed_images res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1]) @@ -280,8 +277,7 @@ class Script(scripts.Script): for img in all_processed_images: images.save_image(img, p.outpath_samples, "", res.seed, p.prompt, opts.grid_format, info=res.info, p=p) - if opts.grid_save: + if opts.grid_save and not unwanted_grid_because_of_img_count: images.save_image(combined_grid_image, p.outpath_grids, "grid", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p) return res - From 0110429dc4bc004ac56573fe1a6b05cb0123678e Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 19:10:32 +0900 Subject: [PATCH 066/140] Fixed path issue while extras batch processing --- modules/extras.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index b853fa5b..f9796624 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -118,10 +118,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ while len(cached_images) > 2: del cached_images[next(iter(cached_images.keys()))] + + if opts.use_original_name_batch and image_name != None: + basename = os.path.splitext(os.path.basename(image_name))[0] + else: + basename = '' - images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, - forced_filename=image_name if opts.use_original_name_batch else None) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From aacc4c1ecbcba3cef421d8776dc5b4b239df9b42 Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:31:29 +0900 Subject: [PATCH 067/140] Added try except to extras batch from directory --- modules/extras.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index f9796624..0d817cf9 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -41,7 +41,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ return outputs, "Please select an input directory.", '' image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] for img in image_list: - image = Image.open(img) + try: + image = Image.open(img) + except Exception: + continue imageArr.append(image) imageNameArr.append(img) else: @@ -122,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = '' + basename = None - images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) + images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) if opts.enable_pnginfo: image.info = existing_pnginfo From bc16b135b527224545dca555a9d51edb0adcee2d Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:43:27 +0900 Subject: [PATCH 068/140] Fixed path issue while extras batch processing --- modules/extras.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index 0d817cf9..ac85142c 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -125,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = None + basename = '' - images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From 991a595686b8d105025d68d0e833d1cbf44cb143 Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Fri, 21 Oct 2022 09:23:13 +0900 Subject: [PATCH 069/140] sort file list in alphabetical ordering in extras --- modules/extras.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/extras.py b/modules/extras.py index ac85142c..22c5a1c1 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -39,7 +39,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if input_dir == '': return outputs, "Please select an input directory.", '' - image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] + image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)] for img in image_list: try: image = Image.open(img) From 45872181902ada06267e2de601586d512cf5df1a Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 09:00:39 +0300 Subject: [PATCH 070/140] updated readme and some small stylistic changes to code --- README.md | 1 + modules/processing.py | 14 ++++++-------- modules/sd_hijack_inpainting.py | 3 +++ 3 files changed, 10 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 859a91b6..a98bb00b 100644 --- a/README.md +++ b/README.md @@ -70,6 +70,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - No token limit for prompts (original stable diffusion lets you use up to 75 tokens) - DeepDanbooru integration, creates danbooru style tags for anime prompts (add --deepdanbooru to commandline args) - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) +- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. diff --git a/modules/processing.py b/modules/processing.py index 539cde38..21786968 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -540,11 +540,10 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f - - def create_dummy_mask(self, x, first_phase: bool = False): + def create_dummy_mask(self, x, width=None, height=None): if self.sampler.conditioning_key in {'hybrid', 'concat'}: - height = self.firstphase_height if first_phase else self.height - width = self.firstphase_width if first_phase else self.width + height = height or self.height + width = width or self.width # The "masked-image" in this case will just be all zeros since the entire image is masked. image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) @@ -571,7 +570,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): return samples x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, first_phase=True)) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.create_dummy_mask(x, self.firstphase_width, self.firstphase_height)) samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] @@ -634,6 +633,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.inpainting_mask_invert = inpainting_mask_invert self.mask = None self.nmask = None + self.image_conditioning = None def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model) @@ -735,9 +735,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): elif self.inpainting_fill == 3: self.init_latent = self.init_latent * self.mask - conditioning_key = self.sampler.conditioning_key - - if conditioning_key in {'hybrid', 'concat'}: + if self.sampler.conditioning_key in {'hybrid', 'concat'}: if self.image_mask is not None: conditioning_mask = np.array(self.image_mask.convert("L")) conditioning_mask = conditioning_mask.astype(np.float32) / 255.0 diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index 43938071..fd92a335 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -301,6 +301,7 @@ def get_unconditional_conditioning(self, batch_size, null_label=None): c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device) return c + class LatentInpaintDiffusion(LatentDiffusion): def __init__( self, @@ -314,9 +315,11 @@ class LatentInpaintDiffusion(LatentDiffusion): assert self.masked_image_key in concat_keys self.concat_keys = concat_keys + def should_hijack_inpainting(checkpoint_info): return str(checkpoint_info.filename).endswith("inpainting.ckpt") and not checkpoint_info.config.endswith("inpainting.yaml") + def do_inpainting_hijack(): ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion From 74088c2a06a975092806362aede22f82716cb011 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Thu, 20 Oct 2022 08:18:02 +0300 Subject: [PATCH 071/140] allow float sizes for hypernet's layer_structure --- modules/hypernetworks/ui.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 08f75f15..e0741d08 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -15,7 +15,7 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm assert not os.path.exists(fn), f"file {fn} already exists" if type(layer_structure) == str: - layer_structure = tuple(map(int, re.sub(r'\D', '', layer_structure))) + layer_structure = [float(x.strip()) for x in layer_structure.split(",")] hypernet = modules.hypernetworks.hypernetwork.Hypernetwork( name=name, From 60872c5b404114336f9ca0c671ba88fa4a8201c9 Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 19:10:32 +0900 Subject: [PATCH 072/140] Fixed path issue while extras batch processing --- modules/extras.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index b853fa5b..f9796624 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -118,10 +118,14 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ while len(cached_images) > 2: del cached_images[next(iter(cached_images.keys()))] + + if opts.use_original_name_batch and image_name != None: + basename = os.path.splitext(os.path.basename(image_name))[0] + else: + basename = '' - images.save_image(image, path=outpath, basename="", seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, - forced_filename=image_name if opts.use_original_name_batch else None) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From fb5a8cf0d9ed027ea3aa2e5422c946d8e6e72efe Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:31:29 +0900 Subject: [PATCH 073/140] Added try except to extras batch from directory --- modules/extras.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index f9796624..0d817cf9 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -41,7 +41,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ return outputs, "Please select an input directory.", '' image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] for img in image_list: - image = Image.open(img) + try: + image = Image.open(img) + except Exception: + continue imageArr.append(image) imageNameArr.append(img) else: @@ -122,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = '' + basename = None - images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) + images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) if opts.enable_pnginfo: image.info = existing_pnginfo From a13c3bed3cec27afe3c015d3d62db36e25b10d1f Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Thu, 20 Oct 2022 21:43:27 +0900 Subject: [PATCH 074/140] Fixed path issue while extras batch processing --- modules/extras.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/modules/extras.py b/modules/extras.py index 0d817cf9..ac85142c 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -125,10 +125,10 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if opts.use_original_name_batch and image_name != None: basename = os.path.splitext(os.path.basename(image_name))[0] else: - basename = None + basename = '' - images.save_image(image, path=outpath, basename='', seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, - no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=basename) + images.save_image(image, path=outpath, basename=basename, seed=None, prompt=None, extension=opts.samples_format, info=info, short_filename=True, + no_prompt=True, grid=False, pnginfo_section_name="extras", existing_info=existing_pnginfo, forced_filename=None) if opts.enable_pnginfo: image.info = existing_pnginfo From 9d71eef02e7395e179b8d5e61e6d91ddd8928d2e Mon Sep 17 00:00:00 2001 From: winterspringsummer Date: Fri, 21 Oct 2022 09:23:13 +0900 Subject: [PATCH 075/140] sort file list in alphabetical ordering in extras --- modules/extras.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/extras.py b/modules/extras.py index ac85142c..22c5a1c1 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -39,7 +39,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_ if input_dir == '': return outputs, "Please select an input directory.", '' - image_list = [file for file in [os.path.join(input_dir, x) for x in os.listdir(input_dir)] if os.path.isfile(file)] + image_list = [file for file in [os.path.join(input_dir, x) for x in sorted(os.listdir(input_dir))] if os.path.isfile(file)] for img in image_list: try: image = Image.open(img) From c23f666dba2b484d521d2dc4be91cf9e09312647 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 09:47:43 +0300 Subject: [PATCH 076/140] a more strict check for activation type and a more reasonable check for type of layer in hypernets --- modules/hypernetworks/hypernetwork.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 7d617680..84e7e350 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -32,10 +32,16 @@ class HypernetworkModule(torch.nn.Module): linears = [] for i in range(len(layer_structure) - 1): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) + if activation_func == "relu": linears.append(torch.nn.ReLU()) - if activation_func == "leakyrelu": + elif activation_func == "leakyrelu": linears.append(torch.nn.LeakyReLU()) + elif activation_func == 'linear' or activation_func is None: + pass + else: + raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}') + if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) @@ -46,7 +52,7 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict) else: for layer in self.linear: - if not "ReLU" in layer.__str__(): + if type(layer) == torch.nn.Linear: layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() @@ -74,7 +80,7 @@ class HypernetworkModule(torch.nn.Module): def trainables(self): layer_structure = [] for layer in self.linear: - if not "ReLU" in layer.__str__(): + if type(layer) == torch.nn.Linear: layer_structure += [layer.weight, layer.bias] return layer_structure From 5f4fec307c14dd7f817244ffa92e8a4a64abed0b Mon Sep 17 00:00:00 2001 From: Stephen Date: Thu, 20 Oct 2022 11:32:17 -0400 Subject: [PATCH 077/140] [Bugfix][API] - Fix API arg in launch script --- webui.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/webui.py b/webui.py index 177bef74..87589064 100644 --- a/webui.py +++ b/webui.py @@ -118,7 +118,8 @@ def api_only(): api.launch(server_name="0.0.0.0" if cmd_opts.listen else "127.0.0.1", port=cmd_opts.port if cmd_opts.port else 7861) -def webui(launch_api=False): +def webui(): + launch_api = cmd_opts.api initialize() while 1: @@ -158,4 +159,4 @@ if __name__ == "__main__": if cmd_opts.nowebui: api_only() else: - webui(cmd_opts.api) + webui() From 7157e5d064741fa57ca81a2c6432a651f21ee82f Mon Sep 17 00:00:00 2001 From: Patryk Wychowaniec Date: Thu, 20 Oct 2022 19:22:59 +0200 Subject: [PATCH 078/140] interrogate: Fix CLIP-interrogation on CPU Currently, trying to perform CLIP interrogation on a CPU fails, saying: ``` RuntimeError: "slow_conv2d_cpu" not implemented for 'Half' ``` This merge request fixes this issue by detecting whether the target device is CPU and, if so, force-enabling `--no-half` and passing `device="cpu"` to `clip.load()` (which then does some extra tricks to ensure it works correctly on CPU). --- modules/interrogate.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/modules/interrogate.py b/modules/interrogate.py index 64b91eb4..65b05d34 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -28,9 +28,11 @@ class InterrogateModels: clip_preprocess = None categories = None dtype = None + running_on_cpu = None def __init__(self, content_dir): self.categories = [] + self.running_on_cpu = devices.device_interrogate == torch.device("cpu") if os.path.exists(content_dir): for filename in os.listdir(content_dir): @@ -53,7 +55,11 @@ class InterrogateModels: def load_clip_model(self): import clip - model, preprocess = clip.load(clip_model_name) + if self.running_on_cpu: + model, preprocess = clip.load(clip_model_name, device="cpu") + else: + model, preprocess = clip.load(clip_model_name) + model.eval() model = model.to(devices.device_interrogate) @@ -62,14 +68,14 @@ class InterrogateModels: def load(self): if self.blip_model is None: self.blip_model = self.load_blip_model() - if not shared.cmd_opts.no_half: + if not shared.cmd_opts.no_half and not self.running_on_cpu: self.blip_model = self.blip_model.half() self.blip_model = self.blip_model.to(devices.device_interrogate) if self.clip_model is None: self.clip_model, self.clip_preprocess = self.load_clip_model() - if not shared.cmd_opts.no_half: + if not shared.cmd_opts.no_half and not self.running_on_cpu: self.clip_model = self.clip_model.half() self.clip_model = self.clip_model.to(devices.device_interrogate) From b69c37d25e4ffc56e8f8c247fa2c38b4648cefb7 Mon Sep 17 00:00:00 2001 From: guaneec Date: Thu, 20 Oct 2022 22:21:12 +0800 Subject: [PATCH 079/140] Allow datasets with only 1 image in TI --- modules/textual_inversion/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 23bb4b6a..5b1c5002 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -83,7 +83,7 @@ class PersonalizedBase(Dataset): self.dataset.append(entry) - assert len(self.dataset) > 1, "No images have been found in the dataset." + assert len(self.dataset) > 0, "No images have been found in the dataset." self.length = len(self.dataset) * repeats // batch_size self.initial_indexes = np.arange(len(self.dataset)) @@ -91,7 +91,7 @@ class PersonalizedBase(Dataset): self.shuffle() def shuffle(self): - self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] + self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0]).numpy()] def create_text(self, filename_text): text = random.choice(self.lines) From 5245c7a4935f67b677da0f5a1fc2b74c074aa0e2 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 12:21:32 -0700 Subject: [PATCH 080/140] Issue #2921-Give PNG info to Hypernet previews. --- modules/hypernetworks/hypernetwork.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 84e7e350..68c8f26d 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -256,6 +256,9 @@ def stack_conds(conds): def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): + # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency. + from modules import images + assert hypernetwork_name, 'hypernetwork not selected' path = shared.hypernetworks.get(hypernetwork_name, None) @@ -298,6 +301,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log last_saved_file = "" last_saved_image = "" + forced_filename = "" ititial_step = hypernetwork.step or 0 if ititial_step > steps: @@ -345,7 +349,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log }) if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0: - last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png') + forced_filename = f'{hypernetwork_name}-{hypernetwork.step}' + last_saved_image = os.path.join(images_dir, forced_filename) optimizer.zero_grad() shared.sd_model.cond_stage_model.to(devices.device) @@ -381,7 +386,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if image is not None: shared.state.current_image = image - image.save(last_saved_image) + last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename) last_saved_image += f", prompt: {preview_text}" shared.state.job_no = hypernetwork.step From 6014fb8afbe05c8d02fffe7a36a2e48128713bd2 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 12:22:23 -0700 Subject: [PATCH 081/140] Do nothing if image file already exists. --- modules/images.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/modules/images.py b/modules/images.py index b9589563..550e53ae 100644 --- a/modules/images.py +++ b/modules/images.py @@ -416,7 +416,11 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /') path = os.path.join(path, dirname) - os.makedirs(path, exist_ok=True) + try: + os.makedirs(path, exist_ok=True) + except FileExistsError: + # If the file already exists, continue and allow said file to be overwritten. + pass if forced_filename is None: basecount = get_next_sequence_number(path, basename) From 4ff274e1e35bb642687253ce744d2cfa738ab293 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 12:32:22 -0700 Subject: [PATCH 082/140] Revise comments. --- modules/hypernetworks/hypernetwork.py | 2 +- modules/images.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 68c8f26d..3f96361c 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -256,7 +256,7 @@ def stack_conds(conds): def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): - # images is required here to give training previews their infotext. Importing this at the very top causes a circular dependency. + # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images assert hypernetwork_name, 'hypernetwork not selected' diff --git a/modules/images.py b/modules/images.py index 550e53ae..b8834e3c 100644 --- a/modules/images.py +++ b/modules/images.py @@ -419,7 +419,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i try: os.makedirs(path, exist_ok=True) except FileExistsError: - # If the file already exists, continue and allow said file to be overwritten. + # If the file already exists, allow said file to be overwritten. pass if forced_filename is None: From 2273e752fb3e578f1047f6d38b96330b07bf61a9 Mon Sep 17 00:00:00 2001 From: timntorres Date: Wed, 19 Oct 2022 14:23:48 -0700 Subject: [PATCH 083/140] Remove redundant try/except. --- modules/images.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/modules/images.py b/modules/images.py index b8834e3c..b9589563 100644 --- a/modules/images.py +++ b/modules/images.py @@ -416,11 +416,7 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i dirname = apply_filename_pattern(opts.directories_filename_pattern or "[prompt_words]", p, seed, prompt).strip('\\ /') path = os.path.join(path, dirname) - try: - os.makedirs(path, exist_ok=True) - except FileExistsError: - # If the file already exists, allow said file to be overwritten. - pass + os.makedirs(path, exist_ok=True) if forced_filename is None: basecount = get_next_sequence_number(path, basename) From 03a1e288c4973dd2dff57a97469b40f146b6fccf Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 10:13:24 +0300 Subject: [PATCH 084/140] turns out LayerNorm also has weight and bias and needs to be pre-multiplied and trained for hypernets --- modules/hypernetworks/hypernetwork.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3274a802..b1a5d0c7 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -52,7 +52,7 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict) else: for layer in self.linear: - if type(layer) == torch.nn.Linear: + if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: layer.weight.data.normal_(mean=0.0, std=0.01) layer.bias.data.zero_() @@ -80,7 +80,7 @@ class HypernetworkModule(torch.nn.Module): def trainables(self): layer_structure = [] for layer in self.linear: - if type(layer) == torch.nn.Linear: + if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm: layer_structure += [layer.weight, layer.bias] return layer_structure From bf30673f5132c8f28357b31224c54331e788d3e7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 10:19:25 +0300 Subject: [PATCH 085/140] Fix Hypernet infotext string split bug for PR #3283 --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/processing.py b/modules/processing.py index 21786968..d1deffa9 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.filename.split('\\')[-1].split('.')[0]), + "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), From 1ed227b3b57f06f4152be3bfc9f83b0a839a2604 Mon Sep 17 00:00:00 2001 From: Leo Mozoloa Date: Fri, 21 Oct 2022 10:57:40 +0200 Subject: [PATCH 086/140] wtf is happening --- .github/ISSUE_TEMPLATE/config.yml | 5 +++++ 1 file changed, 5 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/config.yml diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000..f58c94a9 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: WebUI Community Support + url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions + about: Please ask and answer questions here. From 003d2c7fe427edde299274c9e0d5fa59734e7f7e Mon Sep 17 00:00:00 2001 From: ClashSAN <98228077+ClashSAN@users.noreply.github.com> Date: Fri, 21 Oct 2022 11:40:37 +0000 Subject: [PATCH 087/140] Update README.md --- README.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index a89593bf..348aaf87 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - have as many embeddings as you want and use any names you like for them - use multiple embeddings with different numbers of vectors per token - works with half precision floating point numbers + - train embeddings on 8GB (also reports of 6GB working) - Extras tab with: - GFPGAN, neural network that fixes faces - CodeFormer, face restoration tool as an alternative to GFPGAN @@ -60,7 +61,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - CLIP interrogator, a button that tries to guess prompt from an image - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway - Batch Processing, process a group of files using img2img -- Img2img Alternative +- Img2img Alternative, reverse Euler method of cross attention control - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions - Reloading checkpoints on the fly - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one @@ -73,15 +74,22 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add --xformers to commandline args) - History tab: view, direct and delete images conveniently within the UI - Generate forever option -- Training Tab -- Preprocessing Image Datasets: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) +- Training tab + - hypernetworks and embeddings options + - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime) +- Clip skip +- Use Hypernetworks +- Use VAEs +- Estimated completion time in progress bar +- API +- Support for dedicated inpainting model by RunwayML. ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. -Alternatively, use online services(like Google Colab): +Alternatively, use online services (like Google Colab): - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services) From df5706409386cc2e88718bd9101045587c39f8bb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 16:10:51 +0300 Subject: [PATCH 088/140] do not load aesthetic clip model until it's needed add refresh button for aesthetic embeddings add aesthetic params to images' infotext --- modules/aesthetic_clip.py | 40 ++++++++++++++--- modules/generation_parameters_copypaste.py | 18 +++++++- modules/img2img.py | 5 +-- modules/processing.py | 4 +- modules/sd_models.py | 3 -- modules/txt2img.py | 4 +- modules/ui.py | 52 +++++++++++++++------- style.css | 2 +- 8 files changed, 89 insertions(+), 39 deletions(-) diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py index 34efa931..8c828541 100644 --- a/modules/aesthetic_clip.py +++ b/modules/aesthetic_clip.py @@ -40,6 +40,8 @@ def iter_to_batched(iterable, n=1): def create_ui(): + import modules.ui + with gr.Group(): with gr.Accordion("Open for Clip Aesthetic!", open=False): with gr.Row(): @@ -55,6 +57,8 @@ def create_ui(): label="Aesthetic imgs embedding", value="None") + modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings") + with gr.Row(): aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", @@ -66,11 +70,21 @@ def create_ui(): return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative +aesthetic_clip_model = None + + +def aesthetic_clip(): + global aesthetic_clip_model + + if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path: + aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path) + aesthetic_clip_model.cpu() + + return aesthetic_clip_model + + def generate_imgs_embd(name, folder, batch_size): - # clipModel = CLIPModel.from_pretrained( - # shared.sd_model.cond_stage_model.clipModel.name_or_path - # ) - model = shared.clip_model.to(device) + model = aesthetic_clip().to(device) processor = CLIPProcessor.from_pretrained(model.name_or_path) with torch.no_grad(): @@ -91,7 +105,7 @@ def generate_imgs_embd(name, folder, batch_size): path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt") torch.save(embs, path) - model = model.cpu() + model.cpu() del processor del embs gc.collect() @@ -132,7 +146,7 @@ class AestheticCLIP: self.image_embs = None self.load_image_embs(None) - def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, + def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, aesthetic_slerp=True, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False): @@ -145,6 +159,18 @@ class AestheticCLIP: self.aesthetic_steps = aesthetic_steps self.load_image_embs(image_embs_name) + if self.image_embs_name is not None: + p.extra_generation_params.update({ + "Aesthetic LR": aesthetic_lr, + "Aesthetic weight": aesthetic_weight, + "Aesthetic steps": aesthetic_steps, + "Aesthetic embedding": self.image_embs_name, + "Aesthetic slerp": aesthetic_slerp, + "Aesthetic text": aesthetic_imgs_text, + "Aesthetic text negative": aesthetic_text_negative, + "Aesthetic slerp angle": aesthetic_slerp_angle, + }) + def set_skip(self, skip): self.skip = skip @@ -168,7 +194,7 @@ class AestheticCLIP: tokens = torch.asarray(remade_batch_tokens).to(device) - model = copy.deepcopy(shared.clip_model).to(device) + model = copy.deepcopy(aesthetic_clip()).to(device) model.requires_grad_(True) if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: text_embs_2 = model.get_text_features( diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 0f041449..f73647da 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -4,13 +4,22 @@ import gradio as gr from modules.shared import script_path from modules import shared -re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)" +re_param_code = r'\s*([\w ]+):\s*("(?:\\|\"|[^\"])+"|[^,]*)(?:,|$)' re_param = re.compile(re_param_code) re_params = re.compile(r"^(?:" + re_param_code + "){3,}$") re_imagesize = re.compile(r"^(\d+)x(\d+)$") type_of_gr_update = type(gr.update()) +def quote(text): + if ',' not in str(text): + return text + + text = str(text) + text = text.replace('\\', '\\\\') + text = text.replace('"', '\\"') + return f'"{text}"' + def parse_generation_parameters(x: str): """parses generation parameters string, the one you see in text field under the picture in UI: ``` @@ -83,7 +92,12 @@ def connect_paste(button, paste_fields, input_comp, js=None): else: try: valtype = type(output.value) - val = valtype(v) + + if valtype == bool and v == "False": + val = False + else: + val = valtype(v) + res.append(gr.update(value=val)) except Exception: res.append(gr.update()) diff --git a/modules/img2img.py b/modules/img2img.py index bc7c66bc..eea5199b 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -109,10 +109,7 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro inpainting_mask_invert=inpainting_mask_invert, ) - shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), - aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, - aesthetic_slerp_angle, - aesthetic_text_negative) + shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative) if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/processing.py b/modules/processing.py index d1deffa9..f0852cd5 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -12,7 +12,7 @@ from skimage import exposure from typing import Any, Dict, List, Optional import modules.sd_hijack -from modules import devices, prompt_parser, masking, sd_samplers, lowvram +from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste from modules.sd_hijack import model_hijack from modules.shared import opts, cmd_opts, state import modules.shared as shared @@ -318,7 +318,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration generation_params.update(p.extra_generation_params) - generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None]) + generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None]) negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else "" diff --git a/modules/sd_models.py b/modules/sd_models.py index 05a1df28..b1c91b0d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -234,9 +234,6 @@ def load_model(checkpoint_info=None): sd_hijack.model_hijack.hijack(sd_model) - if shared.clip_model is None or shared.clip_model.transformer.name_or_path != sd_model.cond_stage_model.wrapped.transformer.name_or_path: - shared.clip_model = CLIPModel.from_pretrained(sd_model.cond_stage_model.wrapped.transformer.name_or_path) - sd_model.eval() print(f"Model loaded.") diff --git a/modules/txt2img.py b/modules/txt2img.py index 32ed1d8d..1761cfa2 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -36,9 +36,7 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: firstphase_height=firstphase_height if enable_hr else None, ) - shared.aesthetic_clip.set_aesthetic_params(float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), - aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, - aesthetic_text_negative) + shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative) if cmd_opts.enable_console_prompts: print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/ui.py b/modules/ui.py index 381ca925..0d020de6 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -597,27 +597,29 @@ def apply_setting(key, value): return value +def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): + def refresh(): + refresh_method() + args = refreshed_args() if callable(refreshed_args) else refreshed_args + + for k, v in args.items(): + setattr(refresh_component, k, v) + + return gr.update(**(args or {})) + + refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) + refresh_button.click( + fn=refresh, + inputs=[], + outputs=[refresh_component] + ) + return refresh_button + + def create_ui(wrap_gradio_gpu_call): import modules.img2img import modules.txt2img - def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): - def refresh(): - refresh_method() - args = refreshed_args() if callable(refreshed_args) else refreshed_args - - for k, v in args.items(): - setattr(refresh_component, k, v) - - return gr.update(**(args or {})) - - refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id) - refresh_button.click( - fn = refresh, - inputs = [], - outputs = [refresh_component] - ) - return refresh_button with gr.Blocks(analytics_enabled=False) as txt2img_interface: txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False) @@ -802,6 +804,14 @@ def create_ui(wrap_gradio_gpu_call): (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), (firstphase_width, "First pass size-1"), (firstphase_height, "First pass size-2"), + (aesthetic_lr, "Aesthetic LR"), + (aesthetic_weight, "Aesthetic weight"), + (aesthetic_steps, "Aesthetic steps"), + (aesthetic_imgs, "Aesthetic embedding"), + (aesthetic_slerp, "Aesthetic slerp"), + (aesthetic_imgs_text, "Aesthetic text"), + (aesthetic_text_negative, "Aesthetic text negative"), + (aesthetic_slerp_angle, "Aesthetic slerp angle"), ] txt2img_preview_params = [ @@ -1077,6 +1087,14 @@ def create_ui(wrap_gradio_gpu_call): (seed_resize_from_w, "Seed resize from-1"), (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), + (aesthetic_lr_im, "Aesthetic LR"), + (aesthetic_weight_im, "Aesthetic weight"), + (aesthetic_steps_im, "Aesthetic steps"), + (aesthetic_imgs_im, "Aesthetic embedding"), + (aesthetic_slerp_im, "Aesthetic slerp"), + (aesthetic_imgs_text_im, "Aesthetic text"), + (aesthetic_text_negative_im, "Aesthetic text negative"), + (aesthetic_slerp_angle_im, "Aesthetic slerp angle"), ] token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) diff --git a/style.css b/style.css index 26ae36a5..5d2bacc9 100644 --- a/style.css +++ b/style.css @@ -477,7 +477,7 @@ input[type="range"]{ padding: 0; } -#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{ +#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_aesthetic_embeddings{ max-width: 2.5em; min-width: 2.5em; height: 2.4em; From 9286fe53de2eef91f13cc3ad5938ddf67ecc8413 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 16:38:06 +0300 Subject: [PATCH 089/140] make aestetic embedding ciompatible with prompts longer than 75 tokens --- modules/sd_hijack.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 36198a3c..1f8587d1 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -332,8 +332,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): multipliers.append([1.0] * 75) z1 = self.process_tokens(tokens, multipliers) + z1 = shared.aesthetic_clip(z1, remade_batch_tokens) z = z1 if z is None else torch.cat((z, z1), axis=-2) - z = shared.aesthetic_clip(z, remade_batch_tokens) remade_batch_tokens = rem_tokens batch_multipliers = rem_multipliers From d0ea471b0cdaede163c6e7f6fae8535f5c3cd226 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 21 Oct 2022 14:04:41 +0100 Subject: [PATCH 090/140] Use opts in textual_inversion image_embedding.py for dynamic fonts --- modules/textual_inversion/image_embedding.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index 898ce3b3..c50b1e7b 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -5,6 +5,7 @@ import zlib from PIL import Image, PngImagePlugin, ImageDraw, ImageFont from fonts.ttf import Roboto import torch +from modules.shared import opts class EmbeddingEncoder(json.JSONEncoder): From 306e2ff6ab8f4c7e94ab55f4f08ab8f94d73d287 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Fri, 21 Oct 2022 14:47:21 +0100 Subject: [PATCH 091/140] Update image_embedding.py --- modules/textual_inversion/image_embedding.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py index c50b1e7b..ea653806 100644 --- a/modules/textual_inversion/image_embedding.py +++ b/modules/textual_inversion/image_embedding.py @@ -134,7 +134,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t from math import cos image = srcimage.copy() - + fontsize = 32 if textfont is None: try: textfont = ImageFont.truetype(opts.font or Roboto, fontsize) @@ -151,7 +151,7 @@ def caption_image_overlay(srcimage, title, footerLeft, footerMid, footerRight, t image = Image.alpha_composite(image.convert('RGBA'), gradient.resize(image.size)) draw = ImageDraw.Draw(image) - fontsize = 32 + font = ImageFont.truetype(textfont, fontsize) padding = 10 From 85cb5918ee7c97cafafe4149880ed82f1933d919 Mon Sep 17 00:00:00 2001 From: parsec501 <105080989+parsec501@users.noreply.github.com> Date: Fri, 21 Oct 2022 14:43:23 +0200 Subject: [PATCH 092/140] Make commit hash mandatory field --- .github/ISSUE_TEMPLATE/bug_report.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml index 35802a53..9c2ff313 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.yml +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -45,6 +45,8 @@ body: attributes: label: Commit where the problem happens description: Which commit are you running ? (copy the **Commit hash** shown in the cmd/terminal when you launch the UI) + validations: + required: true - type: dropdown id: platforms attributes: From 51e3dc9ccad157d7161b697a246e26c868d46a7c Mon Sep 17 00:00:00 2001 From: timntorres Date: Fri, 21 Oct 2022 02:11:12 -0700 Subject: [PATCH 093/140] Sanitize hypernet name input. --- modules/hypernetworks/ui.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 266f04f6..e6f50a1f 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -11,6 +11,9 @@ from modules.hypernetworks import hypernetwork def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, add_layer_norm=False, activation_func=None): + # Remove illegal characters from name. + name = "".join( x for x in name if (x.isalnum() or x in "._- ")) + fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") if not overwrite_old: assert not os.path.exists(fn), f"file {fn} already exists" From 19818f023cfafc472c6c241cab0b72896a168481 Mon Sep 17 00:00:00 2001 From: timntorres Date: Fri, 21 Oct 2022 02:14:02 -0700 Subject: [PATCH 094/140] Match hypernet name with filename in all cases. --- modules/hypernetworks/hypernetwork.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b1a5d0c7..6d392be4 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -340,7 +340,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log pbar.set_description(f"loss: {mean_loss:.7f}") if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: - last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt') + temp = hypernetwork.name + # Before saving, change name to match current checkpoint. + hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}' + last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt') hypernetwork.save(last_saved_file) textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { @@ -405,6 +408,9 @@ Last saved image: {html.escape(last_saved_image)}
hypernetwork.sd_checkpoint = checkpoint.hash hypernetwork.sd_checkpoint_name = checkpoint.model_name + # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention). + hypernetwork.name = hypernetwork_name + filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt') hypernetwork.save(filename) return hypernetwork, filename From fccad18a59e3c2c33fefbbb1763c6a87a3a68eba Mon Sep 17 00:00:00 2001 From: timntorres Date: Fri, 21 Oct 2022 02:17:26 -0700 Subject: [PATCH 095/140] Refer to Hypernet's name, sensibly, by its name variable. --- modules/processing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/processing.py b/modules/processing.py index f0852cd5..ff1ec4c9 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -304,7 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration "Size": f"{p.width}x{p.height}", "Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash), "Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')), - "Hypernet": (None if shared.loaded_hypernetwork is None else os.path.splitext(os.path.basename(shared.loaded_hypernetwork.filename))[0]), + "Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name), "Batch size": (None if p.batch_size < 2 else p.batch_size), "Batch pos": (None if p.batch_size < 2 else position_in_batch), "Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]), From 272fa527bbe93143668ffc16838107b7dca35b40 Mon Sep 17 00:00:00 2001 From: timntorres Date: Fri, 21 Oct 2022 02:41:55 -0700 Subject: [PATCH 096/140] Remove unused variable. --- modules/hypernetworks/hypernetwork.py | 1 - 1 file changed, 1 deletion(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 6d392be4..47d91ea5 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -340,7 +340,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log pbar.set_description(f"loss: {mean_loss:.7f}") if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: - temp = hypernetwork.name # Before saving, change name to match current checkpoint. hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}' last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt') From 02e4d4694dd9254a6ca9f05c2eb7b01ea508abc7 Mon Sep 17 00:00:00 2001 From: Rcmcpe Date: Fri, 21 Oct 2022 15:53:35 +0800 Subject: [PATCH 097/140] Change option description of unload_models_when_training --- modules/shared.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/shared.py b/modules/shared.py index 5c675b80..41d7f08e 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -266,7 +266,7 @@ options_templates.update(options_section(('system', "System"), { })) options_templates.update(options_section(('training', "Training"), { - "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP from VRAM when training"), + "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."), "dataset_filename_word_regex": OptionInfo("", "Filename word regex"), "dataset_filename_join_string": OptionInfo(" ", "Filename join string"), "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}), From 704036ff07b71bf86cadcbbff2bcfeebdd1ed3a6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 17:11:42 +0300 Subject: [PATCH 098/140] make aspect ratio overlay work regardless of selected localization --- javascript/aspectRatioOverlay.js | 36 +++++++++++++++----------------- javascript/dragdrop.js | 2 +- modules/ui.py | 4 ++-- 3 files changed, 20 insertions(+), 22 deletions(-) diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js index 96f1c00d..d3ca2781 100644 --- a/javascript/aspectRatioOverlay.js +++ b/javascript/aspectRatioOverlay.js @@ -3,12 +3,12 @@ let currentWidth = null; let currentHeight = null; let arFrameTimeout = setTimeout(function(){},0); -function dimensionChange(e,dimname){ +function dimensionChange(e, is_width, is_height){ - if(dimname == 'Width'){ + if(is_width){ currentWidth = e.target.value*1.0 } - if(dimname == 'Height'){ + if(is_height){ currentHeight = e.target.value*1.0 } @@ -98,22 +98,20 @@ onUiUpdate(function(){ var inImg2img = Boolean(gradioApp().querySelector("button.rounded-t-lg.border-gray-200")) if(inImg2img){ let inputs = gradioApp().querySelectorAll('input'); - inputs.forEach(function(e){ - let parentLabel = e.parentElement.querySelector('label') - if(parentLabel && parentLabel.innerText){ - if(!e.classList.contains('scrollwatch')){ - if(parentLabel.innerText == 'Width' || parentLabel.innerText == 'Height'){ - e.addEventListener('input', function(e){dimensionChange(e,parentLabel.innerText)} ) - e.classList.add('scrollwatch') - } - if(parentLabel.innerText == 'Width'){ - currentWidth = e.value*1.0 - } - if(parentLabel.innerText == 'Height'){ - currentHeight = e.value*1.0 - } - } - } + inputs.forEach(function(e){ + var is_width = e.parentElement.id == "img2img_width" + var is_height = e.parentElement.id == "img2img_height" + + if((is_width || is_height) && !e.classList.contains('scrollwatch')){ + e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} ) + e.classList.add('scrollwatch') + } + if(is_width){ + currentWidth = e.value*1.0 + } + if(is_height){ + currentHeight = e.value*1.0 + } }) } }); diff --git a/javascript/dragdrop.js b/javascript/dragdrop.js index 070cf255..3ed1cb3c 100644 --- a/javascript/dragdrop.js +++ b/javascript/dragdrop.js @@ -43,7 +43,7 @@ function dropReplaceImage( imgWrap, files ) { window.document.addEventListener('dragover', e => { const target = e.composedPath()[0]; const imgWrap = target.closest('[data-testid="image"]'); - if ( !imgWrap && target.placeholder.indexOf("Prompt") == -1) { + if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) { return; } e.stopPropagation(); diff --git a/modules/ui.py b/modules/ui.py index 0d020de6..85f95792 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -879,8 +879,8 @@ def create_ui(wrap_gradio_gpu_call): sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index") with gr.Group(): - width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512) - height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512) + width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512, elem_id="img2img_width") + height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512, elem_id="img2img_height") with gr.Row(): restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1) From 3d898044e5e55dca1698e9b5b7d3558b5b78675a Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 17:26:30 +0300 Subject: [PATCH 099/140] batch_size does not affect job count --- scripts/outpainting_mk_2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/outpainting_mk_2.py b/scripts/outpainting_mk_2.py index 633dc119..2afd4aa5 100644 --- a/scripts/outpainting_mk_2.py +++ b/scripts/outpainting_mk_2.py @@ -246,7 +246,7 @@ class Script(scripts.Script): batch_count = p.n_iter batch_size = p.batch_size p.n_iter = 1 - state.job_count = batch_count * batch_size * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)) + state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0)) all_processed_images = [] for i in range(batch_count): From ac0aa2b18efeeb9220a5994c8dd54c7cdda7cc40 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 17:35:51 +0300 Subject: [PATCH 100/140] loading SD VAE, see PR #3303 --- modules/sd_models.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index b1c91b0d..d99dbce8 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -155,6 +155,9 @@ def get_state_dict_from_checkpoint(pl_sd): return pl_sd +vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"} + + def load_model_weights(model, checkpoint_info): checkpoint_file = checkpoint_info.filename sd_model_hash = checkpoint_info.hash @@ -186,7 +189,7 @@ def load_model_weights(model, checkpoint_info): if os.path.exists(vae_file): print(f"Loading VAE weights from: {vae_file}") vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) - vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"} + vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} model.first_stage_model.load_state_dict(vae_dict) model.first_stage_model.to(devices.dtype_vae) From 24ce67a13bd74202d298cd8e2a306d90214980d8 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 17:41:47 +0300 Subject: [PATCH 101/140] make aspect ratio overlay work regardless of selected localization, pt2 --- javascript/aspectRatioOverlay.js | 19 +++++-------------- 1 file changed, 5 insertions(+), 14 deletions(-) diff --git a/javascript/aspectRatioOverlay.js b/javascript/aspectRatioOverlay.js index d3ca2781..66f26a22 100644 --- a/javascript/aspectRatioOverlay.js +++ b/javascript/aspectRatioOverlay.js @@ -18,22 +18,13 @@ function dimensionChange(e, is_width, is_height){ return; } - var img2imgMode = gradioApp().querySelector('#mode_img2img.tabs > div > button.rounded-t-lg.border-gray-200') - if(img2imgMode){ - img2imgMode=img2imgMode.innerText - }else{ - return; - } - - var redrawImage = gradioApp().querySelector('div[data-testid=image] img'); - var inpaintImage = gradioApp().querySelector('#img2maskimg div[data-testid=image] img') - var targetElement = null; - if(img2imgMode=='img2img' && redrawImage){ - targetElement = redrawImage; - }else if(img2imgMode=='Inpaint' && inpaintImage){ - targetElement = inpaintImage; + var tabIndex = get_tab_index('mode_img2img') + if(tabIndex == 0){ + targetElement = gradioApp().querySelector('div[data-testid=image] img'); + } else if(tabIndex == 1){ + targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img'); } if(targetElement){ From f49c08ea566385db339c6628f65c3a121033f67c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Fri, 21 Oct 2022 18:46:02 +0300 Subject: [PATCH 102/140] prevent error spam when processing images without txt files for captions --- modules/textual_inversion/preprocess.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py index 17e4ddc1..33eaddb6 100644 --- a/modules/textual_inversion/preprocess.py +++ b/modules/textual_inversion/preprocess.py @@ -122,11 +122,10 @@ def preprocess_work(process_src, process_dst, process_width, process_height, pre continue existing_caption = None - - try: - existing_caption = open(os.path.splitext(filename)[0] + '.txt', 'r').read() - except Exception as e: - print(e) + existing_caption_filename = os.path.splitext(filename)[0] + '.txt' + if os.path.exists(existing_caption_filename): + with open(existing_caption_filename, 'r', encoding="utf8") as file: + existing_caption = file.read() if shared.state.interrupted: break From 57eb54b838faa383c10079e1bb5471b7bee6a695 Mon Sep 17 00:00:00 2001 From: Extraltodeus Date: Sat, 22 Oct 2022 00:11:07 +0200 Subject: [PATCH 103/140] implement CUDA device selection by ID --- modules/devices.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/modules/devices.py b/modules/devices.py index eb422583..8a159282 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -1,7 +1,6 @@ +import sys, os, shlex import contextlib - import torch - from modules import errors # has_mps is only available in nightly pytorch (for now), `getattr` for compatibility @@ -9,10 +8,26 @@ has_mps = getattr(torch, 'has_mps', False) cpu = torch.device("cpu") +def extract_device_id(args, name): + for x in range(len(args)): + if name in args[x]: return args[x+1] + return None def get_optimal_device(): if torch.cuda.is_available(): - return torch.device("cuda") + # CUDA device selection support: + if "shared" not in sys.modules: + commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop. + sys.argv += shlex.split(commandline_args) + device_id = extract_device_id(sys.argv, '--device-id') + else: + device_id = shared.cmd_opts.device_id + + if device_id is not None: + cuda_device = f"cuda:{device_id}" + return torch.device(cuda_device) + else: + return torch.device("cuda") if has_mps: return torch.device("mps") From 29bfacd63cb5c73b9643d94f255cca818fd49d9c Mon Sep 17 00:00:00 2001 From: Extraltodeus Date: Sat, 22 Oct 2022 00:12:46 +0200 Subject: [PATCH 104/140] implement CUDA device selection, --device-id arg --- modules/shared.py | 1 + 1 file changed, 1 insertion(+) diff --git a/modules/shared.py b/modules/shared.py index 41d7f08e..03032a47 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -80,6 +80,7 @@ parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencode parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) parser.add_argument("--api", action='store_true', help="use api=True to launch the api with the webui") parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the api instead of the webui") +parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None) cmd_opts = parser.parse_args() restricted_opts = [ From 2b91251637078e04472c91a06a8d9c4db9c1dcf0 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 12:23:45 +0300 Subject: [PATCH 105/140] removed aesthetic gradients as built-in added support for extensions --- .gitignore | 2 +- extensions/put extension here.txt | 0 modules/aesthetic_clip.py | 241 ------------------------------ modules/images_history.py | 2 +- modules/img2img.py | 5 +- modules/processing.py | 35 +++-- modules/script_callbacks.py | 42 ++++++ modules/scripts.py | 210 +++++++++++++++++++------- modules/sd_hijack.py | 1 - modules/sd_models.py | 7 +- modules/shared.py | 19 --- modules/txt2img.py | 5 +- modules/ui.py | 83 ++-------- webui.py | 7 +- 14 files changed, 249 insertions(+), 410 deletions(-) create mode 100644 extensions/put extension here.txt delete mode 100644 modules/aesthetic_clip.py create mode 100644 modules/script_callbacks.py diff --git a/.gitignore b/.gitignore index f9c3357c..2f1e08ed 100644 --- a/.gitignore +++ b/.gitignore @@ -27,4 +27,4 @@ __pycache__ notification.mp3 /SwinIR /textual_inversion -.vscode \ No newline at end of file +.vscode diff --git a/extensions/put extension here.txt b/extensions/put extension here.txt new file mode 100644 index 00000000..e69de29b diff --git a/modules/aesthetic_clip.py b/modules/aesthetic_clip.py deleted file mode 100644 index 8c828541..00000000 --- a/modules/aesthetic_clip.py +++ /dev/null @@ -1,241 +0,0 @@ -import copy -import itertools -import os -from pathlib import Path -import html -import gc - -import gradio as gr -import torch -from PIL import Image -from torch import optim - -from modules import shared -from transformers import CLIPModel, CLIPProcessor, CLIPTokenizer -from tqdm.auto import tqdm, trange -from modules.shared import opts, device - - -def get_all_images_in_folder(folder): - return [os.path.join(folder, f) for f in os.listdir(folder) if - os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)] - - -def check_is_valid_image_file(filename): - return filename.lower().endswith(('.png', '.jpg', '.jpeg', ".gif", ".tiff", ".webp")) - - -def batched(dataset, total, n=1): - for ndx in range(0, total, n): - yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))] - - -def iter_to_batched(iterable, n=1): - it = iter(iterable) - while True: - chunk = tuple(itertools.islice(it, n)) - if not chunk: - return - yield chunk - - -def create_ui(): - import modules.ui - - with gr.Group(): - with gr.Accordion("Open for Clip Aesthetic!", open=False): - with gr.Row(): - aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", - value=0.9) - aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=5) - - with gr.Row(): - aesthetic_lr = gr.Textbox(label='Aesthetic learning rate', - placeholder="Aesthetic learning rate", value="0.0001") - aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False) - aesthetic_imgs = gr.Dropdown(sorted(shared.aesthetic_embeddings.keys()), - label="Aesthetic imgs embedding", - value="None") - - modules.ui.create_refresh_button(aesthetic_imgs, shared.update_aesthetic_embeddings, lambda: {"choices": sorted(shared.aesthetic_embeddings.keys())}, "refresh_aesthetic_embeddings") - - with gr.Row(): - aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', - placeholder="This text is used to rotate the feature space of the imgs embs", - value="") - aesthetic_slerp_angle = gr.Slider(label='Slerp angle', minimum=0, maximum=1, step=0.01, - value=0.1) - aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False) - - return aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative - - -aesthetic_clip_model = None - - -def aesthetic_clip(): - global aesthetic_clip_model - - if aesthetic_clip_model is None or aesthetic_clip_model.name_or_path != shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path: - aesthetic_clip_model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.wrapped.transformer.name_or_path) - aesthetic_clip_model.cpu() - - return aesthetic_clip_model - - -def generate_imgs_embd(name, folder, batch_size): - model = aesthetic_clip().to(device) - processor = CLIPProcessor.from_pretrained(model.name_or_path) - - with torch.no_grad(): - embs = [] - for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size), - desc=f"Generating embeddings for {name}"): - if shared.state.interrupted: - break - inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device) - outputs = model.get_image_features(**inputs).cpu() - embs.append(torch.clone(outputs)) - inputs.to("cpu") - del inputs, outputs - - embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True) - - # The generated embedding will be located here - path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt") - torch.save(embs, path) - - model.cpu() - del processor - del embs - gc.collect() - torch.cuda.empty_cache() - res = f""" - Done generating embedding for {name}! - Aesthetic embedding saved to {html.escape(path)} - """ - shared.update_aesthetic_embeddings() - return gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), label="Imgs embedding", - value="None"), \ - gr.Dropdown.update(choices=sorted(shared.aesthetic_embeddings.keys()), - label="Imgs embedding", - value="None"), res, "" - - -def slerp(low, high, val): - low_norm = low / torch.norm(low, dim=1, keepdim=True) - high_norm = high / torch.norm(high, dim=1, keepdim=True) - omega = torch.acos((low_norm * high_norm).sum(1)) - so = torch.sin(omega) - res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high - return res - - -class AestheticCLIP: - def __init__(self): - self.skip = False - self.aesthetic_steps = 0 - self.aesthetic_weight = 0 - self.aesthetic_lr = 0 - self.slerp = False - self.aesthetic_text_negative = "" - self.aesthetic_slerp_angle = 0 - self.aesthetic_imgs_text = "" - - self.image_embs_name = None - self.image_embs = None - self.load_image_embs(None) - - def set_aesthetic_params(self, p, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None, - aesthetic_slerp=True, aesthetic_imgs_text="", - aesthetic_slerp_angle=0.15, - aesthetic_text_negative=False): - self.aesthetic_imgs_text = aesthetic_imgs_text - self.aesthetic_slerp_angle = aesthetic_slerp_angle - self.aesthetic_text_negative = aesthetic_text_negative - self.slerp = aesthetic_slerp - self.aesthetic_lr = aesthetic_lr - self.aesthetic_weight = aesthetic_weight - self.aesthetic_steps = aesthetic_steps - self.load_image_embs(image_embs_name) - - if self.image_embs_name is not None: - p.extra_generation_params.update({ - "Aesthetic LR": aesthetic_lr, - "Aesthetic weight": aesthetic_weight, - "Aesthetic steps": aesthetic_steps, - "Aesthetic embedding": self.image_embs_name, - "Aesthetic slerp": aesthetic_slerp, - "Aesthetic text": aesthetic_imgs_text, - "Aesthetic text negative": aesthetic_text_negative, - "Aesthetic slerp angle": aesthetic_slerp_angle, - }) - - def set_skip(self, skip): - self.skip = skip - - def load_image_embs(self, image_embs_name): - if image_embs_name is None or len(image_embs_name) == 0 or image_embs_name == "None": - image_embs_name = None - self.image_embs_name = None - if image_embs_name is not None and self.image_embs_name != image_embs_name: - self.image_embs_name = image_embs_name - self.image_embs = torch.load(shared.aesthetic_embeddings[self.image_embs_name], map_location=device) - self.image_embs /= self.image_embs.norm(dim=-1, keepdim=True) - self.image_embs.requires_grad_(False) - - def __call__(self, z, remade_batch_tokens): - if not self.skip and self.aesthetic_steps != 0 and self.aesthetic_lr != 0 and self.aesthetic_weight != 0 and self.image_embs_name is not None: - tokenizer = shared.sd_model.cond_stage_model.tokenizer - if not opts.use_old_emphasis_implementation: - remade_batch_tokens = [ - [tokenizer.bos_token_id] + x[:75] + [tokenizer.eos_token_id] for x in - remade_batch_tokens] - - tokens = torch.asarray(remade_batch_tokens).to(device) - - model = copy.deepcopy(aesthetic_clip()).to(device) - model.requires_grad_(True) - if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0: - text_embs_2 = model.get_text_features( - **tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device)) - if self.aesthetic_text_negative: - text_embs_2 = self.image_embs - text_embs_2 - text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True) - img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle) - else: - img_embs = self.image_embs - - with torch.enable_grad(): - - # We optimize the model to maximize the similarity - optimizer = optim.Adam( - model.text_model.parameters(), lr=self.aesthetic_lr - ) - - for _ in trange(self.aesthetic_steps, desc="Aesthetic optimization"): - text_embs = model.get_text_features(input_ids=tokens) - text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True) - sim = text_embs @ img_embs.T - loss = -sim - optimizer.zero_grad() - loss.mean().backward() - optimizer.step() - - zn = model.text_model(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers) - if opts.CLIP_stop_at_last_layers > 1: - zn = zn.hidden_states[-opts.CLIP_stop_at_last_layers] - zn = model.text_model.final_layer_norm(zn) - else: - zn = zn.last_hidden_state - model.cpu() - del model - gc.collect() - torch.cuda.empty_cache() - zn = torch.concat([zn[77 * i:77 * (i + 1)] for i in range(max(z.shape[1] // 77, 1))], 1) - if self.slerp: - z = slerp(z, zn, self.aesthetic_weight) - else: - z = z * (1 - self.aesthetic_weight) + zn * self.aesthetic_weight - - return z diff --git a/modules/images_history.py b/modules/images_history.py index 78fd0543..bc5cf11f 100644 --- a/modules/images_history.py +++ b/modules/images_history.py @@ -310,7 +310,7 @@ def show_images_history(gr, opts, tabname, run_pnginfo, switch_dict): forward = gr.Button('Prev batch') backward = gr.Button('Next batch') with gr.Column(scale=3): - load_info = gr.HTML(visible=not custom_dir) + load_info = gr.HTML(visible=not custom_dir) with gr.Row(visible=False) as warning: warning_box = gr.Textbox("Message", interactive=False) diff --git a/modules/img2img.py b/modules/img2img.py index eea5199b..8d9f7cf9 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -56,7 +56,7 @@ def process_batch(p, input_dir, output_dir, args): processed_image.save(os.path.join(output_dir, filename)) -def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args): +def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, init_img, init_img_with_mask, init_img_inpaint, init_mask_inpaint, mask_mode, steps: int, sampler_index: int, mask_blur: int, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, *args): is_inpaint = mode == 1 is_batch = mode == 2 @@ -109,7 +109,8 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro inpainting_mask_invert=inpainting_mask_invert, ) - shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative) + p.scripts = modules.scripts.scripts_txt2img + p.script_args = args if shared.cmd_opts.enable_console_prompts: print(f"\nimg2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/processing.py b/modules/processing.py index ff1ec4c9..372489f7 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -104,6 +104,12 @@ class StableDiffusionProcessing(): self.seed_resize_from_h = 0 self.seed_resize_from_w = 0 + self.scripts = None + self.script_args = None + self.all_prompts = None + self.all_seeds = None + self.all_subseeds = None + def init(self, all_prompts, all_seeds, all_subseeds): pass @@ -350,32 +356,35 @@ def process_images(p: StableDiffusionProcessing) -> Processed: shared.prompt_styles.apply_styles(p) if type(p.prompt) == list: - all_prompts = p.prompt + p.all_prompts = p.prompt else: - all_prompts = p.batch_size * p.n_iter * [p.prompt] + p.all_prompts = p.batch_size * p.n_iter * [p.prompt] if type(seed) == list: - all_seeds = seed + p.all_seeds = seed else: - all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(all_prompts))] + p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))] if type(subseed) == list: - all_subseeds = subseed + p.all_subseeds = subseed else: - all_subseeds = [int(subseed) + x for x in range(len(all_prompts))] + p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))] def infotext(iteration=0, position_in_batch=0): - return create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration, position_in_batch) + return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch) if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings: model_hijack.embedding_db.load_textual_inversion_embeddings() + if p.scripts is not None: + p.scripts.run_alwayson_scripts(p) + infotexts = [] output_images = [] with torch.no_grad(), p.sd_model.ema_scope(): with devices.autocast(): - p.init(all_prompts, all_seeds, all_subseeds) + p.init(p.all_prompts, p.all_seeds, p.all_subseeds) if state.job_count == -1: state.job_count = p.n_iter @@ -387,9 +396,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if state.interrupted: break - prompts = all_prompts[n * p.batch_size:(n + 1) * p.batch_size] - seeds = all_seeds[n * p.batch_size:(n + 1) * p.batch_size] - subseeds = all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] + prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size] + seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size] + subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size] if (len(prompts) == 0): break @@ -490,10 +499,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed: index_of_first_image = 1 if opts.grid_save: - images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) + images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True) devices.torch_gc() - return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) + return Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], all_prompts=p.all_prompts, all_seeds=p.all_seeds, all_subseeds=p.all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts) class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py new file mode 100644 index 00000000..866b7acd --- /dev/null +++ b/modules/script_callbacks.py @@ -0,0 +1,42 @@ + +callbacks_model_loaded = [] +callbacks_ui_tabs = [] + + +def clear_callbacks(): + callbacks_model_loaded.clear() + callbacks_ui_tabs.clear() + + +def model_loaded_callback(sd_model): + for callback in callbacks_model_loaded: + callback(sd_model) + + +def ui_tabs_callback(): + res = [] + + for callback in callbacks_ui_tabs: + res += callback() or [] + + return res + + +def on_model_loaded(callback): + """register a function to be called when the stable diffusion model is created; the model is + passed as an argument""" + callbacks_model_loaded.append(callback) + + +def on_ui_tabs(callback): + """register a function to be called when the UI is creating new tabs. + The function must either return a None, which means no new tabs to be added, or a list, where + each element is a tuple: + (gradio_component, title, elem_id) + + gradio_component is a gradio component to be used for contents of the tab (usually gr.Blocks) + title is tab text displayed to user in the UI + elem_id is HTML id for the tab + """ + callbacks_ui_tabs.append(callback) + diff --git a/modules/scripts.py b/modules/scripts.py index 1039fa9c..65f25f49 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -1,86 +1,153 @@ import os import sys import traceback +from collections import namedtuple import modules.ui as ui import gradio as gr from modules.processing import StableDiffusionProcessing -from modules import shared +from modules import shared, paths, script_callbacks + +AlwaysVisible = object() + class Script: filename = None args_from = None args_to = None + alwayson = False + + infotext_fields = None + """if set in ui(), this is a list of pairs of gradio component + text; the text will be used when + parsing infotext to set the value for the component; see ui.py's txt2img_paste_fields for an example + """ - # The title of the script. This is what will be displayed in the dropdown menu. def title(self): + """this function should return the title of the script. This is what will be displayed in the dropdown menu.""" + raise NotImplementedError() - # How the script is displayed in the UI. See https://gradio.app/docs/#components - # for the different UI components you can use and how to create them. - # Most UI components can return a value, such as a boolean for a checkbox. - # The returned values are passed to the run method as parameters. def ui(self, is_img2img): + """this function should create gradio UI elements. See https://gradio.app/docs/#components + The return value should be an array of all components that are used in processing. + Values of those returned componenbts will be passed to run() and process() functions. + """ + pass - # Determines when the script should be shown in the dropdown menu via the - # returned value. As an example: - # is_img2img is True if the current tab is img2img, and False if it is txt2img. - # Thus, return is_img2img to only show the script on the img2img tab. def show(self, is_img2img): + """ + is_img2img is True if this function is called for the img2img interface, and Fasle otherwise + + This function should return: + - False if the script should not be shown in UI at all + - True if the script should be shown in UI if it's scelected in the scripts drowpdown + - script.AlwaysVisible if the script should be shown in UI at all times + """ + return True - # This is where the additional processing is implemented. The parameters include - # self, the model object "p" (a StableDiffusionProcessing class, see - # processing.py), and the parameters returned by the ui method. - # Custom functions can be defined here, and additional libraries can be imported - # to be used in processing. The return value should be a Processed object, which is - # what is returned by the process_images method. - def run(self, *args): + def run(self, p, *args): + """ + This function is called if the script has been selected in the script dropdown. + It must do all processing and return the Processed object with results, same as + one returned by processing.process_images. + + Usually the processing is done by calling the processing.process_images function. + + args contains all values returned by components from ui() + """ + raise NotImplementedError() - # The description method is currently unused. - # To add a description that appears when hovering over the title, amend the "titles" - # dict in script.js to include the script title (returned by title) as a key, and - # your description as the value. + def process(self, p, *args): + """ + This function is called before processing begins for AlwaysVisible scripts. + scripts. You can modify the processing object (p) here, inject hooks, etc. + """ + + pass + def describe(self): + """unused""" return "" +current_basedir = paths.script_path + + +def basedir(): + """returns the base directory for the current script. For scripts in the main scripts directory, + this is the main directory (where webui.py resides), and for scripts in extensions directory + (ie extensions/aesthetic/script/aesthetic.py), this is extension's directory (extensions/aesthetic) + """ + return current_basedir + + scripts_data = [] +ScriptFile = namedtuple("ScriptFile", ["basedir", "filename", "path"]) +ScriptClassData = namedtuple("ScriptClassData", ["script_class", "path", "basedir"]) -def load_scripts(basedir): - if not os.path.exists(basedir): - return +def list_scripts(scriptdirname, extension): + scripts_list = [] - for filename in sorted(os.listdir(basedir)): - path = os.path.join(basedir, filename) + basedir = os.path.join(paths.script_path, scriptdirname) + if os.path.exists(basedir): + for filename in sorted(os.listdir(basedir)): + scripts_list.append(ScriptFile(paths.script_path, filename, os.path.join(basedir, filename))) - if os.path.splitext(path)[1].lower() != '.py': - continue + extdir = os.path.join(paths.script_path, "extensions") + if os.path.exists(extdir): + for dirname in sorted(os.listdir(extdir)): + dirpath = os.path.join(extdir, dirname) + if not os.path.isdir(dirpath): + continue - if not os.path.isfile(path): - continue + for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))): + scripts_list.append(ScriptFile(dirpath, filename, os.path.join(dirpath, scriptdirname, filename))) + scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)] + + return scripts_list + + +def load_scripts(): + global current_basedir + scripts_data.clear() + script_callbacks.clear_callbacks() + + scripts_list = list_scripts("scripts", ".py") + + syspath = sys.path + + for scriptfile in sorted(scripts_list): try: - with open(path, "r", encoding="utf8") as file: + if scriptfile.basedir != paths.script_path: + sys.path = [scriptfile.basedir] + sys.path + current_basedir = scriptfile.basedir + + with open(scriptfile.path, "r", encoding="utf8") as file: text = file.read() from types import ModuleType - compiled = compile(text, path, 'exec') - module = ModuleType(filename) + compiled = compile(text, scriptfile.path, 'exec') + module = ModuleType(scriptfile.filename) exec(compiled, module.__dict__) for key, script_class in module.__dict__.items(): if type(script_class) == type and issubclass(script_class, Script): - scripts_data.append((script_class, path)) + scripts_data.append(ScriptClassData(script_class, scriptfile.path, scriptfile.basedir)) except Exception: - print(f"Error loading script: {filename}", file=sys.stderr) + print(f"Error loading script: {scriptfile.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) + finally: + sys.path = syspath + current_basedir = paths.script_path + def wrap_call(func, filename, funcname, *args, default=None, **kwargs): try: @@ -96,56 +163,80 @@ def wrap_call(func, filename, funcname, *args, default=None, **kwargs): class ScriptRunner: def __init__(self): self.scripts = [] + self.selectable_scripts = [] + self.alwayson_scripts = [] self.titles = [] + self.infotext_fields = [] def setup_ui(self, is_img2img): - for script_class, path in scripts_data: + for script_class, path, basedir in scripts_data: script = script_class() script.filename = path - if not script.show(is_img2img): - continue + visibility = script.show(is_img2img) - self.scripts.append(script) + if visibility == AlwaysVisible: + self.scripts.append(script) + self.alwayson_scripts.append(script) + script.alwayson = True - self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.scripts] + elif visibility: + self.scripts.append(script) + self.selectable_scripts.append(script) - dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index") - dropdown.save_to_config = True - inputs = [dropdown] + self.titles = [wrap_call(script.title, script.filename, "title") or f"{script.filename} [error]" for script in self.selectable_scripts] - for script in self.scripts: + inputs = [None] + inputs_alwayson = [True] + + def create_script_ui(script, inputs, inputs_alwayson): script.args_from = len(inputs) script.args_to = len(inputs) controls = wrap_call(script.ui, script.filename, "ui", is_img2img) if controls is None: - continue + return for control in controls: control.custom_script_source = os.path.basename(script.filename) - control.visible = False + if not script.alwayson: + control.visible = False + + if script.infotext_fields is not None: + self.infotext_fields += script.infotext_fields inputs += controls + inputs_alwayson += [script.alwayson for _ in controls] script.args_to = len(inputs) + for script in self.alwayson_scripts: + with gr.Group(): + create_script_ui(script, inputs, inputs_alwayson) + + dropdown = gr.Dropdown(label="Script", choices=["None"] + self.titles, value="None", type="index") + dropdown.save_to_config = True + inputs[0] = dropdown + + for script in self.selectable_scripts: + create_script_ui(script, inputs, inputs_alwayson) + def select_script(script_index): - if 0 < script_index <= len(self.scripts): - script = self.scripts[script_index-1] + if 0 < script_index <= len(self.selectable_scripts): + script = self.selectable_scripts[script_index-1] args_from = script.args_from args_to = script.args_to else: args_from = 0 args_to = 0 - return [ui.gr_show(True if i == 0 else args_from <= i < args_to) for i in range(len(inputs))] + return [ui.gr_show(True if i == 0 else args_from <= i < args_to or is_alwayson) for i, is_alwayson in enumerate(inputs_alwayson)] def init_field(title): if title == 'None': return script_index = self.titles.index(title) - script = self.scripts[script_index] + script = self.selectable_scripts[script_index] for i in range(script.args_from, script.args_to): inputs[i].visible = True @@ -164,7 +255,7 @@ class ScriptRunner: if script_index == 0: return None - script = self.scripts[script_index-1] + script = self.selectable_scripts[script_index-1] if script is None: return None @@ -176,6 +267,15 @@ class ScriptRunner: return processed + def run_alwayson_scripts(self, p): + for script in self.alwayson_scripts: + try: + script_args = p.script_args[script.args_from:script.args_to] + script.process(p, *script_args) + except Exception: + print(f"Error running alwayson script: {script.filename}", file=sys.stderr) + print(traceback.format_exc(), file=sys.stderr) + def reload_sources(self): for si, script in list(enumerate(self.scripts)): with open(script.filename, "r", encoding="utf8") as file: @@ -197,19 +297,21 @@ class ScriptRunner: self.scripts[si].args_from = args_from self.scripts[si].args_to = args_to + scripts_txt2img = ScriptRunner() scripts_img2img = ScriptRunner() + def reload_script_body_only(): scripts_txt2img.reload_sources() scripts_img2img.reload_sources() -def reload_scripts(basedir): +def reload_scripts(): global scripts_txt2img, scripts_img2img - scripts_data.clear() - load_scripts(basedir) + load_scripts() scripts_txt2img = ScriptRunner() scripts_img2img = ScriptRunner() + diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 1f8587d1..0f10828e 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -332,7 +332,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module): multipliers.append([1.0] * 75) z1 = self.process_tokens(tokens, multipliers) - z1 = shared.aesthetic_clip(z1, remade_batch_tokens) z = z1 if z is None else torch.cat((z, z1), axis=-2) remade_batch_tokens = rem_tokens diff --git a/modules/sd_models.py b/modules/sd_models.py index d99dbce8..f9b3063d 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -7,7 +7,7 @@ from omegaconf import OmegaConf from ldm.util import instantiate_from_config -from modules import shared, modelloader, devices +from modules import shared, modelloader, devices, script_callbacks from modules.paths import models_path from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting @@ -238,6 +238,9 @@ def load_model(checkpoint_info=None): sd_hijack.model_hijack.hijack(sd_model) sd_model.eval() + shared.sd_model = sd_model + + script_callbacks.model_loaded_callback(sd_model) print(f"Model loaded.") return sd_model @@ -252,7 +255,7 @@ def reload_model_weights(sd_model, info=None): if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info): checkpoints_loaded.clear() - shared.sd_model = load_model(checkpoint_info) + load_model(checkpoint_info) return shared.sd_model if shared.cmd_opts.lowvram or shared.cmd_opts.medvram: diff --git a/modules/shared.py b/modules/shared.py index 0dbe360d..7d786f07 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -31,7 +31,6 @@ parser.add_argument("--no-half-vae", action='store_true', help="do not switch th parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") -parser.add_argument("--aesthetic_embeddings-dir", type=str, default=os.path.join(models_path, 'aesthetic_embeddings'), help="aesthetic_embeddings directory(default: aesthetic_embeddings)") parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory") parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") @@ -109,21 +108,6 @@ os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True) hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) loaded_hypernetwork = None - -os.makedirs(cmd_opts.aesthetic_embeddings_dir, exist_ok=True) -aesthetic_embeddings = {} - - -def update_aesthetic_embeddings(): - global aesthetic_embeddings - aesthetic_embeddings = {f.replace(".pt", ""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in - os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")} - aesthetic_embeddings = OrderedDict(**{"None": None}, **aesthetic_embeddings) - - -update_aesthetic_embeddings() - - def reload_hypernetworks(): global hypernetworks @@ -415,9 +399,6 @@ sd_model = None clip_model = None -from modules.aesthetic_clip import AestheticCLIP -aesthetic_clip = AestheticCLIP() - progress_print_out = sys.stdout diff --git a/modules/txt2img.py b/modules/txt2img.py index 1761cfa2..c9d5a090 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -7,7 +7,7 @@ import modules.processing as processing from modules.ui import plaintext_to_html -def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="", aesthetic_slerp_angle=0.15, aesthetic_text_negative=False, *args): +def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -36,7 +36,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: firstphase_height=firstphase_height if enable_hr else None, ) - shared.aesthetic_clip.set_aesthetic_params(p, float(aesthetic_lr), float(aesthetic_weight), int(aesthetic_steps), aesthetic_imgs, aesthetic_slerp, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative) + p.scripts = modules.scripts.scripts_txt2img + p.script_args = args if cmd_opts.enable_console_prompts: print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/ui.py b/modules/ui.py index 70a9cf10..c977482c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -23,10 +23,10 @@ import gradio as gr import gradio.utils import gradio.routes -from modules import sd_hijack, sd_models, localization +from modules import sd_hijack, sd_models, localization, script_callbacks from modules.paths import script_path -from modules.shared import opts, cmd_opts, restricted_opts, aesthetic_embeddings +from modules.shared import opts, cmd_opts, restricted_opts if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags @@ -44,7 +44,6 @@ from modules.images import save_image import modules.textual_inversion.ui import modules.hypernetworks.ui -import modules.aesthetic_clip as aesthetic_clip import modules.images_history as img_his @@ -662,8 +661,6 @@ def create_ui(wrap_gradio_gpu_call): seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() - aesthetic_weight, aesthetic_steps, aesthetic_lr, aesthetic_slerp, aesthetic_imgs, aesthetic_imgs_text, aesthetic_slerp_angle, aesthetic_text_negative = aesthetic_clip.create_ui() - with gr.Group(): custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False) @@ -718,14 +715,6 @@ def create_ui(wrap_gradio_gpu_call): denoising_strength, firstphase_width, firstphase_height, - aesthetic_lr, - aesthetic_weight, - aesthetic_steps, - aesthetic_imgs, - aesthetic_slerp, - aesthetic_imgs_text, - aesthetic_slerp_angle, - aesthetic_text_negative ] + custom_inputs, outputs=[ @@ -804,14 +793,7 @@ def create_ui(wrap_gradio_gpu_call): (hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)), (firstphase_width, "First pass size-1"), (firstphase_height, "First pass size-2"), - (aesthetic_lr, "Aesthetic LR"), - (aesthetic_weight, "Aesthetic weight"), - (aesthetic_steps, "Aesthetic steps"), - (aesthetic_imgs, "Aesthetic embedding"), - (aesthetic_slerp, "Aesthetic slerp"), - (aesthetic_imgs_text, "Aesthetic text"), - (aesthetic_text_negative, "Aesthetic text negative"), - (aesthetic_slerp_angle, "Aesthetic slerp angle"), + *modules.scripts.scripts_txt2img.infotext_fields ] txt2img_preview_params = [ @@ -896,8 +878,6 @@ def create_ui(wrap_gradio_gpu_call): seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs() - aesthetic_weight_im, aesthetic_steps_im, aesthetic_lr_im, aesthetic_slerp_im, aesthetic_imgs_im, aesthetic_imgs_text_im, aesthetic_slerp_angle_im, aesthetic_text_negative_im = aesthetic_clip.create_ui() - with gr.Group(): custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True) @@ -988,14 +968,6 @@ def create_ui(wrap_gradio_gpu_call): inpainting_mask_invert, img2img_batch_input_dir, img2img_batch_output_dir, - aesthetic_lr_im, - aesthetic_weight_im, - aesthetic_steps_im, - aesthetic_imgs_im, - aesthetic_slerp_im, - aesthetic_imgs_text_im, - aesthetic_slerp_angle_im, - aesthetic_text_negative_im, ] + custom_inputs, outputs=[ img2img_gallery, @@ -1087,14 +1059,7 @@ def create_ui(wrap_gradio_gpu_call): (seed_resize_from_w, "Seed resize from-1"), (seed_resize_from_h, "Seed resize from-2"), (denoising_strength, "Denoising strength"), - (aesthetic_lr_im, "Aesthetic LR"), - (aesthetic_weight_im, "Aesthetic weight"), - (aesthetic_steps_im, "Aesthetic steps"), - (aesthetic_imgs_im, "Aesthetic embedding"), - (aesthetic_slerp_im, "Aesthetic slerp"), - (aesthetic_imgs_text_im, "Aesthetic text"), - (aesthetic_text_negative_im, "Aesthetic text negative"), - (aesthetic_slerp_angle_im, "Aesthetic slerp angle"), + *modules.scripts.scripts_img2img.infotext_fields ] token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter]) @@ -1217,9 +1182,9 @@ def create_ui(wrap_gradio_gpu_call): ) #images history images_history_switch_dict = { - "fn":modules.generation_parameters_copypaste.connect_paste, - "t2i":txt2img_paste_fields, - "i2i":img2img_paste_fields + "fn": modules.generation_parameters_copypaste.connect_paste, + "t2i": txt2img_paste_fields, + "i2i": img2img_paste_fields } images_history = img_his.create_history_tabs(gr, opts, cmd_opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict) @@ -1264,18 +1229,6 @@ def create_ui(wrap_gradio_gpu_call): with gr.Column(): create_embedding = gr.Button(value="Create embedding", variant='primary') - with gr.Tab(label="Create aesthetic images embedding"): - - new_embedding_name_ae = gr.Textbox(label="Name") - process_src_ae = gr.Textbox(label='Source directory') - batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256) - with gr.Row(): - with gr.Column(scale=3): - gr.HTML(value="") - - with gr.Column(): - create_embedding_ae = gr.Button(value="Create images embedding", variant='primary') - with gr.Tab(label="Create hypernetwork"): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) @@ -1375,21 +1328,6 @@ def create_ui(wrap_gradio_gpu_call): ] ) - create_embedding_ae.click( - fn=aesthetic_clip.generate_imgs_embd, - inputs=[ - new_embedding_name_ae, - process_src_ae, - batch_ae - ], - outputs=[ - aesthetic_imgs, - aesthetic_imgs_im, - ti_output, - ti_outcome, - ] - ) - create_hypernetwork.click( fn=modules.hypernetworks.ui.create_hypernetwork, inputs=[ @@ -1580,10 +1518,10 @@ Requested path was: {f} if not opts.same_type(value, opts.data_labels[key].default): return gr.update(visible=True), opts.dumpjson() + oldval = opts.data.get(key, None) if cmd_opts.hide_ui_dir_config and key in restricted_opts: return gr.update(value=oldval), opts.dumpjson() - oldval = opts.data.get(key, None) opts.data[key] = value if oldval != value: @@ -1692,9 +1630,12 @@ Requested path was: {f} (images_history, "Image Browser", "images_history"), (modelmerger_interface, "Checkpoint Merger", "modelmerger"), (train_interface, "Train", "ti"), - (settings_interface, "Settings", "settings"), ] + interfaces += script_callbacks.ui_tabs_callback() + + interfaces += [(settings_interface, "Settings", "settings")] + with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file: css = file.read() diff --git a/webui.py b/webui.py index 87589064..b1deca1b 100644 --- a/webui.py +++ b/webui.py @@ -71,6 +71,7 @@ def wrap_gradio_gpu_call(func, extra_outputs=None): return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs) + def initialize(): modelloader.cleanup_models() modules.sd_models.setup_model() @@ -79,9 +80,9 @@ def initialize(): shared.face_restorers.append(modules.face_restoration.FaceRestoration()) modelloader.load_upscalers() - modules.scripts.load_scripts(os.path.join(script_path, "scripts")) + modules.scripts.load_scripts() - shared.sd_model = modules.sd_models.load_model() + modules.sd_models.load_model() shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model))) shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork))) shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength) @@ -145,7 +146,7 @@ def webui(): sd_samplers.set_samplers() print('Reloading Custom Scripts') - modules.scripts.reload_scripts(os.path.join(script_path, "scripts")) + modules.scripts.reload_scripts() print('Reloading modules: modules.ui') importlib.reload(modules.ui) print('Refreshing Model List') From 6398dc9b1049f242576ca309f95a3fb1e654951c Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 13:34:49 +0300 Subject: [PATCH 106/140] further support for extensions --- .gitignore | 1 + README.md | 3 +-- modules/scripts.py | 44 +++++++++++++++++++++++++++++++++++--------- modules/ui.py | 19 ++++++++++--------- style.css | 2 +- 5 files changed, 48 insertions(+), 21 deletions(-) diff --git a/.gitignore b/.gitignore index 2f1e08ed..8fa05852 100644 --- a/.gitignore +++ b/.gitignore @@ -28,3 +28,4 @@ notification.mp3 /SwinIR /textual_inversion .vscode +/extensions diff --git a/README.md b/README.md index 5b5dc8ba..6853aea0 100644 --- a/README.md +++ b/README.md @@ -83,8 +83,7 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - Estimated completion time in progress bar - API - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. -- Aesthetic Gradients, a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) - +- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. diff --git a/modules/scripts.py b/modules/scripts.py index 65f25f49..9323af3e 100644 --- a/modules/scripts.py +++ b/modules/scripts.py @@ -102,17 +102,39 @@ def list_scripts(scriptdirname, extension): if os.path.exists(extdir): for dirname in sorted(os.listdir(extdir)): dirpath = os.path.join(extdir, dirname) - if not os.path.isdir(dirpath): + scriptdirpath = os.path.join(dirpath, scriptdirname) + + if not os.path.isdir(scriptdirpath): continue - for filename in sorted(os.listdir(os.path.join(dirpath, scriptdirname))): - scripts_list.append(ScriptFile(dirpath, filename, os.path.join(dirpath, scriptdirname, filename))) + for filename in sorted(os.listdir(scriptdirpath)): + scripts_list.append(ScriptFile(dirpath, filename, os.path.join(scriptdirpath, filename))) scripts_list = [x for x in scripts_list if os.path.splitext(x.path)[1].lower() == extension and os.path.isfile(x.path)] return scripts_list +def list_files_with_name(filename): + res = [] + + dirs = [paths.script_path] + + extdir = os.path.join(paths.script_path, "extensions") + if os.path.exists(extdir): + dirs += [os.path.join(extdir, d) for d in sorted(os.listdir(extdir))] + + for dirpath in dirs: + if not os.path.isdir(dirpath): + continue + + path = os.path.join(dirpath, filename) + if os.path.isfile(filename): + res.append(path) + + return res + + def load_scripts(): global current_basedir scripts_data.clear() @@ -276,7 +298,7 @@ class ScriptRunner: print(f"Error running alwayson script: {script.filename}", file=sys.stderr) print(traceback.format_exc(), file=sys.stderr) - def reload_sources(self): + def reload_sources(self, cache): for si, script in list(enumerate(self.scripts)): with open(script.filename, "r", encoding="utf8") as file: args_from = script.args_from @@ -286,9 +308,12 @@ class ScriptRunner: from types import ModuleType - compiled = compile(text, filename, 'exec') - module = ModuleType(script.filename) - exec(compiled, module.__dict__) + module = cache.get(filename, None) + if module is None: + compiled = compile(text, filename, 'exec') + module = ModuleType(script.filename) + exec(compiled, module.__dict__) + cache[filename] = module for key, script_class in module.__dict__.items(): if type(script_class) == type and issubclass(script_class, Script): @@ -303,8 +328,9 @@ scripts_img2img = ScriptRunner() def reload_script_body_only(): - scripts_txt2img.reload_sources() - scripts_img2img.reload_sources() + cache = {} + scripts_txt2img.reload_sources(cache) + scripts_img2img.reload_sources(cache) def reload_scripts(): diff --git a/modules/ui.py b/modules/ui.py index c977482c..29986124 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1636,13 +1636,15 @@ Requested path was: {f} interfaces += [(settings_interface, "Settings", "settings")] - with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file: - css = file.read() + css = "" + + for cssfile in modules.scripts.list_files_with_name("style.css"): + with open(cssfile, "r", encoding="utf8") as file: + css += file.read() + "\n" if os.path.exists(os.path.join(script_path, "user.css")): with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file: - usercss = file.read() - css += usercss + css += file.read() + "\n" if not cmd_opts.no_progressbar_hiding: css += css_hide_progressbar @@ -1865,9 +1867,9 @@ def load_javascript(raw_response): with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile: javascript = f'' - jsdir = os.path.join(script_path, "javascript") - for filename in sorted(os.listdir(jsdir)): - with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile: + scripts_list = modules.scripts.list_scripts("javascript", ".js") + for basedir, filename, path in scripts_list: + with open(path, "r", encoding="utf8") as jsfile: javascript += f"\n" if cmd_opts.theme is not None: @@ -1885,6 +1887,5 @@ def load_javascript(raw_response): gradio.routes.templates.TemplateResponse = template_response -reload_javascript = partial(load_javascript, - gradio.routes.templates.TemplateResponse) +reload_javascript = partial(load_javascript, gradio.routes.templates.TemplateResponse) reload_javascript() diff --git a/style.css b/style.css index 5d2bacc9..26ae36a5 100644 --- a/style.css +++ b/style.css @@ -477,7 +477,7 @@ input[type="range"]{ padding: 0; } -#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization, #refresh_aesthetic_embeddings{ +#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{ max-width: 2.5em; min-width: 2.5em; height: 2.4em; From 5aa9525046b7520d39fe8fc8c5c6cc10ab4d5fdb Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 13:40:07 +0300 Subject: [PATCH 107/140] updated readme with info about Aesthetic Gradients --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index 6853aea0..1a0e4f6a 100644 --- a/README.md +++ b/README.md @@ -85,6 +85,16 @@ Check the [custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-web - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML. - via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients)) +## Where are Aesthetic Gradients?!?! +Aesthetic Gradients are now an extension. You can install it using git: + +```commandline +git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients extensions/aesthetic-gradients +``` + +After running this command, make sure that you have `aesthetic-gradients` dir in webui's `extensions` directory and restart +the UI. The interface for Aesthetic Gradients should appear exactly the same as it was. + ## Installation and Running Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs. From 50b5504401e50b6c94eba41b37fe212b2f27b792 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 14:04:14 +0300 Subject: [PATCH 108/140] remove parsing command line from devices.py --- modules/devices.py | 14 +++++--------- modules/lowvram.py | 9 ++++----- 2 files changed, 9 insertions(+), 14 deletions(-) diff --git a/modules/devices.py b/modules/devices.py index 8a159282..dc1f3cdd 100644 --- a/modules/devices.py +++ b/modules/devices.py @@ -15,14 +15,10 @@ def extract_device_id(args, name): def get_optimal_device(): if torch.cuda.is_available(): - # CUDA device selection support: - if "shared" not in sys.modules: - commandline_args = os.environ.get('COMMANDLINE_ARGS', "") #re-parse the commandline arguments because using the shared.py module creates an import loop. - sys.argv += shlex.split(commandline_args) - device_id = extract_device_id(sys.argv, '--device-id') - else: - device_id = shared.cmd_opts.device_id - + from modules import shared + + device_id = shared.cmd_opts.device_id + if device_id is not None: cuda_device = f"cuda:{device_id}" return torch.device(cuda_device) @@ -49,7 +45,7 @@ def enable_tf32(): errors.run(enable_tf32, "Enabling TF32") -device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device() +device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = None dtype = torch.float16 dtype_vae = torch.float16 diff --git a/modules/lowvram.py b/modules/lowvram.py index 7eba1349..f327c3df 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -1,9 +1,8 @@ import torch -from modules.devices import get_optimal_device +from modules import devices module_in_gpu = None cpu = torch.device("cpu") -device = gpu = get_optimal_device() def send_everything_to_cpu(): @@ -33,7 +32,7 @@ def setup_for_low_vram(sd_model, use_medvram): if module_in_gpu is not None: module_in_gpu.to(cpu) - module.to(gpu) + module.to(devices.device) module_in_gpu = module # see below for register_forward_pre_hook; @@ -51,7 +50,7 @@ def setup_for_low_vram(sd_model, use_medvram): # send the model to GPU. Then put modules back. the modules will be in CPU. stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = None, None, None - sd_model.to(device) + sd_model.to(devices.device) sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.model = stored # register hooks for those the first two models @@ -70,7 +69,7 @@ def setup_for_low_vram(sd_model, use_medvram): # so that only one of them is in GPU at a time stored = diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = None, None, None, None - sd_model.model.to(device) + sd_model.model.to(devices.device) diff_model.input_blocks, diff_model.middle_block, diff_model.output_blocks, diff_model.time_embed = stored # install hooks for bits of third model From 0e8ca8e7af05be22d7d2c07a47c3c7febe0f0ab6 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Sat, 22 Oct 2022 11:07:00 +0000 Subject: [PATCH 109/140] add dropout --- modules/hypernetworks/hypernetwork.py | 70 ++++++++++++++++----------- modules/hypernetworks/ui.py | 10 ++-- modules/ui.py | 45 +++++++++-------- 3 files changed, 72 insertions(+), 53 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 905cbeef..e493f366 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -1,47 +1,60 @@ +import csv import datetime import glob import html import os import sys import traceback -import tqdm -import csv -import torch - -from ldm.util import default -from modules import devices, shared, processing, sd_models -import torch -from torch import einsum -from einops import rearrange, repeat import modules.textual_inversion.dataset +import torch +import tqdm +from einops import rearrange, repeat +from ldm.util import default +from modules import devices, processing, sd_models, shared from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler +from torch import einsum class HypernetworkModule(torch.nn.Module): multiplier = 1.0 - activation_dict = {"relu": torch.nn.ReLU, "leakyrelu": torch.nn.LeakyReLU, "elu": torch.nn.ELU, - "swish": torch.nn.Hardswish} - - def __init__(self, dim, state_dict=None, layer_structure=None, add_layer_norm=False, activation_func=None): + activation_dict = { + "relu": torch.nn.ReLU, + "leakyrelu": torch.nn.LeakyReLU, + "elu": torch.nn.ELU, + "swish": torch.nn.Hardswish, + } + + def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False): super().__init__() assert layer_structure is not None, "layer_structure must not be None" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" - + assert activation_func not in self.activation_dict.keys() + "linear", f"Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'" + linears = [] for i in range(len(layer_structure) - 1): + + # Add a fully-connected layer linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1]))) - # if skip_first_layer because first parameters potentially contain negative values - # if i < 1: continue - if activation_func in HypernetworkModule.activation_dict: - linears.append(HypernetworkModule.activation_dict[activation_func]()) + + # Add an activation func + if activation_func == "linear": + pass + elif activation_func in self.activation_dict: + linears.append(self.activation_dict[activation_func]()) else: - print("Invalid key {} encountered as activation function!".format(activation_func)) - # if use_dropout: - # linears.append(torch.nn.Dropout(p=0.3)) + raise NotImplementedError( + "Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'" + ) + + # Add dropout + if use_dropout: + linears.append(torch.nn.Dropout(p=0.3)) + + # Add layer normalization if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) @@ -93,7 +106,7 @@ class Hypernetwork: filename = None name = None - def __init__(self, name=None, enable_sizes=None, layer_structure=None, add_layer_norm=False, activation_func=None): + def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False): self.filename = None self.name = name self.layers = {} @@ -101,13 +114,14 @@ class Hypernetwork: self.sd_checkpoint = None self.sd_checkpoint_name = None self.layer_structure = layer_structure - self.add_layer_norm = add_layer_norm self.activation_func = activation_func + self.add_layer_norm = add_layer_norm + self.use_dropout = use_dropout for size in enable_sizes or []: self.layers[size] = ( - HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func), - HypernetworkModule(size, None, self.layer_structure, self.add_layer_norm, self.activation_func), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), ) def weights(self): @@ -129,8 +143,9 @@ class Hypernetwork: state_dict['step'] = self.step state_dict['name'] = self.name state_dict['layer_structure'] = self.layer_structure - state_dict['is_layer_norm'] = self.add_layer_norm state_dict['activation_func'] = self.activation_func + state_dict['is_layer_norm'] = self.add_layer_norm + state_dict['use_dropout'] = self.use_dropout state_dict['sd_checkpoint'] = self.sd_checkpoint state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name @@ -144,8 +159,9 @@ class Hypernetwork: state_dict = torch.load(filename, map_location='cpu') self.layer_structure = state_dict.get('layer_structure', [1, 2, 1]) - self.add_layer_norm = state_dict.get('is_layer_norm', False) self.activation_func = state_dict.get('activation_func', None) + self.add_layer_norm = state_dict.get('is_layer_norm', False) + self.use_dropout = state_dict.get('use_dropout', False) for size, sd in state_dict.items(): if type(size) == int: diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 1a5a27d8..5f6f17b6 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -3,14 +3,13 @@ import os import re import gradio as gr - -import modules.textual_inversion.textual_inversion import modules.textual_inversion.preprocess -from modules import sd_hijack, shared, devices +import modules.textual_inversion.textual_inversion +from modules import devices, sd_hijack, shared from modules.hypernetworks import hypernetwork -def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm=False, activation_func=None): +def create_hypernetwork(name, enable_sizes, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False): fn = os.path.join(shared.cmd_opts.hypernetwork_dir, f"{name}.pt") assert not os.path.exists(fn), f"file {fn} already exists" @@ -21,8 +20,9 @@ def create_hypernetwork(name, enable_sizes, layer_structure=None, add_layer_norm name=name, enable_sizes=[int(x) for x in enable_sizes], layer_structure=layer_structure, - add_layer_norm=add_layer_norm, activation_func=activation_func, + add_layer_norm=add_layer_norm, + use_dropout=use_dropout, ) hypernet.save(fn) diff --git a/modules/ui.py b/modules/ui.py index 716f14b8..d4b32c05 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -5,43 +5,44 @@ import json import math import mimetypes import os +import platform import random +import subprocess as sp import sys import tempfile import time import traceback -import platform -import subprocess as sp from functools import partial, reduce +import gradio as gr +import gradio.routes +import gradio.utils import numpy as np +import piexif import torch from PIL import Image, PngImagePlugin -import piexif -import gradio as gr -import gradio.utils -import gradio.routes - -from modules import sd_hijack, sd_models, localization +from modules import localization, sd_hijack, sd_models from modules.paths import script_path -from modules.shared import opts, cmd_opts, restricted_opts +from modules.shared import cmd_opts, opts, restricted_opts + if cmd_opts.deepdanbooru: from modules.deepbooru import get_deepbooru_tags -import modules.shared as shared -from modules.sd_samplers import samplers, samplers_for_img2img -from modules.sd_hijack import model_hijack -import modules.ldsr_model -import modules.scripts -import modules.gfpgan_model + import modules.codeformer_model -import modules.styles import modules.generation_parameters_copypaste -from modules import prompt_parser -from modules.images import save_image -import modules.textual_inversion.ui +import modules.gfpgan_model import modules.hypernetworks.ui import modules.images_history as img_his +import modules.ldsr_model +import modules.scripts +import modules.shared as shared +import modules.styles +import modules.textual_inversion.ui +from modules import prompt_parser +from modules.images import save_image +from modules.sd_hijack import model_hijack +from modules.sd_samplers import samplers, samplers_for_img2img # this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI mimetypes.init() @@ -1223,8 +1224,9 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_name = gr.Textbox(label="Name") new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"]) new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") + new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu", "elu", "swish"]) new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") - new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"]) + new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") with gr.Row(): with gr.Column(scale=3): @@ -1308,8 +1310,9 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_name, new_hypernetwork_sizes, new_hypernetwork_layer_structure, - new_hypernetwork_add_layer_norm, new_hypernetwork_activation_func, + new_hypernetwork_add_layer_norm, + new_hypernetwork_use_dropout ], outputs=[ train_hypernetwork_name, From 1cd3ed7def40198f46d30f74dd37d2906ebdbaa6 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 14:28:56 +0300 Subject: [PATCH 110/140] fix for extensions without style.css --- modules/ui.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/modules/ui.py b/modules/ui.py index 29986124..d8d52db1 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1639,6 +1639,9 @@ Requested path was: {f} css = "" for cssfile in modules.scripts.list_files_with_name("style.css"): + if not os.path.isfile(cssfile): + continue + with open(cssfile, "r", encoding="utf8") as file: css += file.read() + "\n" From 7fd90128eb6d1820045bfe2c2c1269661023a712 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 14:48:43 +0300 Subject: [PATCH 111/140] added a guard for hypernet training that will stop early if weights are getting no gradients --- modules/hypernetworks/hypernetwork.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 47d91ea5..46039a49 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -310,6 +310,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log scheduler = LearnRateScheduler(learn_rate, steps, ititial_step) optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate) + steps_without_grad = 0 + pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: hypernetwork.step = i + ititial_step @@ -332,8 +334,17 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log losses[hypernetwork.step % losses.shape[0]] = loss.item() optimizer.zero_grad() + weights[0].grad = None loss.backward() + + if weights[0].grad is None: + steps_without_grad += 1 + else: + steps_without_grad = 0 + assert steps_without_grad < 10, 'no gradient found for the trained weight after backward() for 10 steps in a row; this is a bug; training cannot continue' + optimizer.step() + mean_loss = losses.mean() if torch.isnan(mean_loss): raise RuntimeError("Loss diverged.") From fccba4729db341a299db3343e3264fecd9459a07 Mon Sep 17 00:00:00 2001 From: discus0434 Date: Sat, 22 Oct 2022 12:02:41 +0000 Subject: [PATCH 112/140] add an option to avoid dying relu --- modules/hypernetworks/hypernetwork.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index b7a04038..3132a56c 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -32,7 +32,6 @@ class HypernetworkModule(torch.nn.Module): assert layer_structure is not None, "layer_structure must not be None" assert layer_structure[0] == 1, "Multiplier Sequence should start with size 1!" assert layer_structure[-1] == 1, "Multiplier Sequence should end with size 1!" - assert activation_func not in self.activation_dict.keys() + "linear", f"Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'" linears = [] for i in range(len(layer_structure) - 1): @@ -43,12 +42,13 @@ class HypernetworkModule(torch.nn.Module): # Add an activation func if activation_func == "linear" or activation_func is None: pass + # If ReLU, Skip adding it to the first layer to avoid dying ReLU + elif activation_func == "relu" and i < 1: + pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) else: - raise RuntimeError( - "Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'" - ) + raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}') # Add dropout if use_dropout: @@ -166,8 +166,8 @@ class Hypernetwork: for size, sd in state_dict.items(): if type(size) == int: self.layers[size] = ( - HypernetworkModule(size, sd[0], self.layer_structure, self.add_layer_norm, self.activation_func), - HypernetworkModule(size, sd[1], self.layer_structure, self.add_layer_norm, self.activation_func), + HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), + HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.add_layer_norm, self.use_dropout), ) self.name = state_dict.get('name', self.name) From 7912acef725832debef58c4c7bf8ec22fb446c0b Mon Sep 17 00:00:00 2001 From: discus0434 Date: Sat, 22 Oct 2022 13:00:44 +0000 Subject: [PATCH 113/140] small fix --- modules/hypernetworks/hypernetwork.py | 12 +++++------- modules/ui.py | 1 - 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3132a56c..7d12e0ff 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -42,22 +42,20 @@ class HypernetworkModule(torch.nn.Module): # Add an activation func if activation_func == "linear" or activation_func is None: pass - # If ReLU, Skip adding it to the first layer to avoid dying ReLU - elif activation_func == "relu" and i < 1: - pass elif activation_func in self.activation_dict: linears.append(self.activation_dict[activation_func]()) else: raise RuntimeError(f'hypernetwork uses an unsupported activation function: {activation_func}') - # Add dropout - if use_dropout: - linears.append(torch.nn.Dropout(p=0.3)) - # Add layer normalization if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) + # Add dropout + if use_dropout: + p = 0.5 if 0 <= i <= len(layer_structure) - 3 else 0.2 + linears.append(torch.nn.Dropout(p=p)) + self.linear = torch.nn.Sequential(*linears) if state_dict is not None: diff --git a/modules/ui.py b/modules/ui.py index cd118552..eca887ca 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1244,7 +1244,6 @@ def create_ui(wrap_gradio_gpu_call): new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") - new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=["linear", "relu", "leakyrelu"]) with gr.Row(): with gr.Column(scale=3): From 6a4fa73a38935a18779ce1809892730fd1572bee Mon Sep 17 00:00:00 2001 From: discus0434 Date: Sat, 22 Oct 2022 13:44:39 +0000 Subject: [PATCH 114/140] small fix --- modules/hypernetworks/hypernetwork.py | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3372aae2..3bc71ee5 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -51,10 +51,9 @@ class HypernetworkModule(torch.nn.Module): if add_layer_norm: linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1]))) - # Add dropout - if use_dropout: - p = 0.5 if 0 <= i <= len(layer_structure) - 3 else 0.2 - linears.append(torch.nn.Dropout(p=p)) + # Add dropout expect last layer + if use_dropout and i < len(layer_structure) - 3: + linears.append(torch.nn.Dropout(p=0.3)) self.linear = torch.nn.Sequential(*linears) From d37cfffd537cd29309afbcb192c4f979995c6a34 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 19:18:56 +0300 Subject: [PATCH 115/140] added callback for creating new settings in extensions --- modules/script_callbacks.py | 11 +++++++++++ modules/shared.py | 19 +++++++++++++++++-- modules/ui.py | 6 +++++- 3 files changed, 33 insertions(+), 3 deletions(-) diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 866b7acd..1270e50f 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -1,6 +1,7 @@ callbacks_model_loaded = [] callbacks_ui_tabs = [] +callbacks_ui_settings = [] def clear_callbacks(): @@ -22,6 +23,11 @@ def ui_tabs_callback(): return res +def ui_settings_callback(): + for callback in callbacks_ui_settings: + callback() + + def on_model_loaded(callback): """register a function to be called when the stable diffusion model is created; the model is passed as an argument""" @@ -40,3 +46,8 @@ def on_ui_tabs(callback): """ callbacks_ui_tabs.append(callback) + +def on_ui_settings(callback): + """register a function to be called before UI settingsare populated; add your settings + by using shared.opts.add_option(shared.OptionInfo(...)) """ + callbacks_ui_settings.append(callback) diff --git a/modules/shared.py b/modules/shared.py index 5d83971e..d9cb65ef 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -165,13 +165,13 @@ def realesrgan_models_names(): class OptionInfo: - def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None): + def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None): self.default = default self.label = label self.component = component self.component_args = component_args self.onchange = onchange - self.section = None + self.section = section self.refresh = refresh @@ -327,6 +327,7 @@ options_templates.update(options_section(('images-history', "Images Browser"), { })) + class Options: data = None data_labels = options_templates @@ -389,6 +390,20 @@ class Options: d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()} return json.dumps(d) + def add_option(self, key, info): + self.data_labels[key] = info + + def reorder(self): + """reorder settings so that all items related to section always go together""" + + section_ids = {} + settings_items = self.data_labels.items() + for k, item in settings_items: + if item.section not in section_ids: + section_ids[item.section] = len(section_ids) + + self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])} + opts = Options() if os.path.exists(config_filename): diff --git a/modules/ui.py b/modules/ui.py index d8d52db1..2849b111 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1461,6 +1461,9 @@ def create_ui(wrap_gradio_gpu_call): components = [] component_dict = {} + script_callbacks.ui_settings_callback() + opts.reorder() + def open_folder(f): if not os.path.exists(f): print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.') @@ -1564,7 +1567,8 @@ Requested path was: {f} previous_section = item.section - gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='

{}

'.format(item.section[1])) + elem_id, text = item.section + gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='

{}

'.format(text)) if k in quicksettings_names: quicksettings_list.append((i, k, item)) From dbc8ab65f6d496459a76547776b656c96ad1350d Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 19:19:17 +0300 Subject: [PATCH 116/140] typo --- modules/script_callbacks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 1270e50f..5bcccd67 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -48,6 +48,6 @@ def on_ui_tabs(callback): def on_ui_settings(callback): - """register a function to be called before UI settingsare populated; add your settings + """register a function to be called before UI settings are populated; add your settings by using shared.opts.add_option(shared.OptionInfo(...)) """ callbacks_ui_settings.append(callback) From 72383abacdc6a101704a6f73758ce4d0bb68c9d1 Mon Sep 17 00:00:00 2001 From: Greendayle Date: Sat, 22 Oct 2022 16:50:07 +0200 Subject: [PATCH 117/140] Deepdanbooru linux fix --- modules/deepbooru.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 8914662d..3c34ab7c 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -50,7 +50,8 @@ def create_deepbooru_process(threshold, deepbooru_opts): the tags. """ from modules import shared # prevents circular reference - shared.deepbooru_process_manager = multiprocessing.Manager() + context = multiprocessing.get_context("spawn") + shared.deepbooru_process_manager = context.Manager() shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() shared.deepbooru_process_return["value"] = -1 From e38625011cd4955da4bc67fe95d1d0f4c0c53899 Mon Sep 17 00:00:00 2001 From: Greendayle Date: Sat, 22 Oct 2022 16:56:52 +0200 Subject: [PATCH 118/140] fix part2 --- modules/deepbooru.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/deepbooru.py b/modules/deepbooru.py index 3c34ab7c..8bbc90a4 100644 --- a/modules/deepbooru.py +++ b/modules/deepbooru.py @@ -55,7 +55,7 @@ def create_deepbooru_process(threshold, deepbooru_opts): shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue() shared.deepbooru_process_return = shared.deepbooru_process_manager.dict() shared.deepbooru_process_return["value"] = -1 - shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts)) + shared.deepbooru_process = context.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold, deepbooru_opts)) shared.deepbooru_process.start() From 7613ea12f267143ceb70a9aeb45eb20aca086e3e Mon Sep 17 00:00:00 2001 From: random_thoughtss Date: Fri, 21 Oct 2022 11:32:56 -0700 Subject: [PATCH 119/140] Fixed img2imgalt after inpainting update --- scripts/img2imgalt.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index d438175c..88abc093 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -34,6 +34,9 @@ def find_noise_for_image(p, cond, uncond, cfg_scale, steps): sigma_in = torch.cat([sigmas[i] * s_in] * 2) cond_in = torch.cat([uncond, cond]) + image_conditioning = torch.cat([p.image_conditioning] * 2) + cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} + c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] t = dnw.sigma_to_t(sigma_in) @@ -78,6 +81,9 @@ def find_noise_for_image_sigma_adjustment(p, cond, uncond, cfg_scale, steps): sigma_in = torch.cat([sigmas[i - 1] * s_in] * 2) cond_in = torch.cat([uncond, cond]) + image_conditioning = torch.cat([p.image_conditioning] * 2) + cond_in = {"c_concat": [image_conditioning], "c_crossattn": [cond_in]} + c_out, c_in = [K.utils.append_dims(k, x_in.ndim) for k in dnw.get_scalings(sigma_in)] if i == 1: @@ -194,7 +200,7 @@ class Script(scripts.Script): p.seed = p.seed + 1 - return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning) + return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning) p.sample = sample_extra From 96ee7d77077eb3c1eacff802e9ccf194adc04592 Mon Sep 17 00:00:00 2001 From: Kris57 <49682577+yuuki76@users.noreply.github.com> Date: Tue, 18 Oct 2022 14:16:26 +0900 Subject: [PATCH 120/140] add ja localization --- localizations/ja_JP.json | 413 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 413 insertions(+) create mode 100644 localizations/ja_JP.json diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json new file mode 100644 index 00000000..5da28cb6 --- /dev/null +++ b/localizations/ja_JP.json @@ -0,0 +1,413 @@ +{ + "⤡": "⤡", + "⊞": "⊞", + "×": "×", + "❮": "❮", + "❯": "❯", + "Loading...": "読み込み中...", + "view": "view", + "api": "api", + "•": "•", + "gradioで作ろう": "gradioで作ろう", + "Stable Diffusion checkpoint": "Stable Diffusion checkpoint", + "txt2img": "txt2img", + "img2img": "img2img", + "Extras": "その他", + "PNG Info": "PNG Info", + "History": "履歴", + "Checkpoint Merger": "Checkpoint Merger", + "Train": "学習", + "Settings": "設定", + "Prompt": "プロンプト", + "Negative prompt": "ネガティブ プロンプト", + "Run": "実行", + "Skip": "スキップ", + "Interrupt": "中断", + "Generate": "生成！", + "Style 1": "スタイル 1", + "Style 2": "スタイル 2", + "Label": "Label", + "File": "ファイル", + "ここにファイルをドロップ": "ここにファイルをドロップ", + "-": "-", + "または": "または", + "クリックしてアップロード": "クリックしてアップロード", + "Image": "Image", + "Check progress": "Check progress", + "Check progress (first)": "Check progress (first)", + "Sampling Steps": "Sampling Steps", + "Sampling method": "Sampling method", + "Euler a": "Euler a", + "Euler": "Euler", + "LMS": "LMS", + "Heun": "Heun", + "DPM2": "DPM2", + "DPM2 a": "DPM2 a", + "DPM fast": "DPM fast", + "DPM adaptive": "DPM adaptive", + "LMS Karras": "LMS Karras", + "DPM2 Karras": "DPM2 Karras", + "DPM2 a Karras": "DPM2 a Karras", + "DDIM": "DDIM", + "PLMS": "PLMS", + "Width": "幅", + "Height": "高さ", + "Restore faces": "顔修復", + "Tiling": "テクスチャ生成モード", + "Highres. fix": "*高解像度 fix", + "Firstpass width": "Firstpass width", + "Firstpass height": "Firstpass height", + "Denoising strength": "Denoising 強度", + "Batch count": "連続生成回数", + "Batch size": "同時生成枚数", + "CFG Scale": "CFG Scale", + "Seed": "Seed", + "Extra": "Extra", + "Variation seed": "Variation seed", + "Variation strength": "Variation 強度", + "Resize seed from width": "Resize seed from width", + "Resize seed from height": "Resize seed from height", + "Script": "Script", + "None": "None", + "Prompt matrix": "Prompt matrix", + "Prompts from file or textbox": "Prompts from file or textbox", + "X/Y plot": "X/Y plot", + "Put variable parts at start of prompt": "Put variable parts at start of prompt", + "Show Textbox": "Show Textbox", + "File with inputs": "File with inputs", + "Prompts": "Prompts", + "X type": "X type", + "Nothing": "Nothing", + "Var. seed": "Var. seed", + "Var. strength": "Var. 強度", + "Steps": "Steps", + "Prompt S/R": "Prompt S/R", + "Prompt order": "Prompt order", + "Sampler": "Sampler", + "Checkpoint name": "Checkpoint name", + "Hypernetwork": "Hypernetwork", + "Hypernet str.": "Hypernet 強度", + "Sigma Churn": "Sigma Churn", + "Sigma min": "Sigma min", + "Sigma max": "Sigma max", + "Sigma noise": "Sigma noise", + "Eta": "Eta", + "Clip skip": "Clip skip", + "Denoising": "Denoising", + "X values": "X values", + "Y type": "Y type", + "Y values": "Y values", + "Draw legend": "Draw legend", + "Include Separate Images": "Include Separate Images", + "Keep -1 for seeds": "Keep -1 for seeds", + "ここに画像をドロップ": "ここに画像をドロップ", + "Save": "保存", + "Send to img2img": "img2imgに送る", + "Send to inpaint": "inpaintに送る", + "Send to extras": "その他タブに送る", + "Make Zip when Save?": "保存するときZipも同時に作る", + "Textbox": "Textbox", + "Interrogate\nCLIP": "Interrogate\nCLIP", + "Interrogate\nDeepBooru": "Interrogate\nDeepBooru", + "Inpaint": "Inpaint", + "Batch img2img": "Batch img2img", + "Image for img2img": "Image for img2img", + "Image for inpainting with mask": "Image for inpainting with mask", + "Mask": "Mask", + "Mask blur": "Mask blur", + "Mask mode": "Mask mode", + "Draw mask": "Draw mask", + "Upload mask": "Upload mask", + "Masking mode": "Masking mode", + "Inpaint masked": "Inpaint masked", + "Inpaint not masked": "Inpaint not masked", + "Masked content": "Masked content", + "fill": "fill", + "original": "original", + "latent noise": "latent noise", + "latent nothing": "latent nothing", + "Inpaint at full resolution": "Inpaint at full resolution", + "Inpaint at full resolution padding, pixels": "Inpaint at full resolution padding, pixels", + "Process images in a directory on the same machine where the server is running.": "Process images in a directory on the same machine where the server is running.", + "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Use an empty output directory to save pictures normally instead of writing to the output directory.", + "Input directory": "Input directory", + "Output directory": "Output directory", + "Resize mode": "Resize mode", + "Just resize": "Just resize", + "Crop and resize": "Crop and resize", + "Resize and fill": "Resize and fill", + "img2img alternative test": "img2img alternative test", + "Loopback": "Loopback", + "Outpainting mk2": "Outpainting mk2", + "Poor man's outpainting": "Poor man's outpainting", + "SD upscale": "SD upscale", + "should be 2 or lower.": "should be 2 or lower.", + "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)", + "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", + "Original prompt": "Original prompt", + "Original negative prompt": "Original negative prompt", + "Override `Sampling Steps` to the same value as `Decode steps`?": "Override `Sampling Steps` to the same value as `Decode steps`?", + "Decode steps": "Decode steps", + "Override `Denoising strength` to 1?": "Override `Denoising strength` to 1?", + "Decode CFG scale": "Decode CFG scale", + "Randomness": "Randomness", + "Sigma adjustment for finding noise for image": "Sigma adjustment for finding noise for image", + "Loops": "Loops", + "Denoising strength change factor": "Denoising strength change factor", + "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8", + "Pixels to expand": "Pixels to expand", + "Outpainting direction": "Outpainting direction", + "left": "left", + "right": "right", + "up": "up", + "down": "down", + "Fall-off exponent (lower=higher detail)": "Fall-off exponent (lower=higher detail)", + "Color variation": "Color variation", + "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Will upscale the image to twice the dimensions; use width and height sliders to set tile size", + "Tile overlap": "Tile overlap", + "Upscaler": "Upscaler", + "Lanczos": "Lanczos", + "LDSR": "LDSR", + "BSRGAN 4x": "BSRGAN 4x", + "ESRGAN_4x": "ESRGAN_4x", + "ScuNET GAN": "ScuNET GAN", + "ScuNET PSNR": "ScuNET PSNR", + "SwinIR 4x": "SwinIR 4x", + "Single Image": "Single Image", + "Batch Process": "Batch Process", + "Batch from Directory": "Batch from Directory", + "Source": "Source", + "Show result images": "Show result images", + "Scale by": "Scale by", + "Scale to": "Scale to", + "Resize": "Resize", + "Crop to fit": "Crop to fit", + "Upscaler 2": "Upscaler 2", + "Upscaler 2 visibility": "Upscaler 2 visibility", + "GFPGAN visibility": "GFPGAN visibility", + "CodeFormer visibility": "CodeFormer visibility", + "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)", + "Open output directory": "出力フォルダを開く", + "Send to txt2img": "Send to txt2img", + "txt2img history": "txt2img history", + "img2img history": "img2img history", + "extras history": "extras history", + "Renew Page": "Renew Page", + "First Page": "First Page", + "Prev Page": "Prev Page", + "Page Index": "Page Index", + "Next Page": "Next Page", + "End Page": "End Page", + "number of images to delete consecutively next": "number of images to delete consecutively next", + "Delete": "Delete", + "Generate Info": "Generate Info", + "File Name": "File Name", + "set_index": "set_index", + "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your", + "checkpoint": "checkpoint", + "directory.": "directory.", + "Primary model (A)": "Primary model (A)", + "Secondary model (B)": "Secondary model (B)", + "Tertiary model (C)": "Tertiary model (C)", + "Custom Name (Optional)": "Custom Name (Optional)", + "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A", + "Interpolation Method": "Interpolation Method", + "Weighted sum": "Weighted sum", + "Add difference": "Add difference", + "Save as float16": "Save as float16", + "See": "See", + "wiki": "wiki", + "for detailed explanation.": "for detailed explanation.", + "Create embedding": "Create embedding", + "Create hypernetwork": "Create hypernetwork", + "Preprocess images": "Preprocess images", + "Name": "Name", + "Initialization text": "Initialization text", + "Number of vectors per token": "Number of vectors per token", + "Modules": "Modules", + "Source directory": "Source directory", + "Destination directory": "Destination directory", + "Create flipped copies": "Create flipped copies", + "Split oversized images into two": "Split oversized images into two", + "Use BLIP for caption": "Use BLIP for caption", + "Use deepbooru for caption": "Use deepbooru for caption", + "Preprocess": "Preprocess", + "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images", + "Embedding": "Embedding", + "Learning rate": "Learning rate", + "Dataset directory": "Dataset directory", + "Log directory": "Log directory", + "Prompt template file": "Prompt template file", + "Max steps": "Max steps", + "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable", + "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable", + "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks", + "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews", + "Train Hypernetwork": "Train Hypernetwork", + "Train Embedding": "Train Embedding", + "Apply settings": "Apply settings", + "Saving images/grids": "Saving images/grids", + "Always save all generated images": "Always save all generated images", + "File format for images": "File format for images", + "Images filename pattern": "Images filename pattern", + "Always save all generated image grids": "Always save all generated image grids", + "File format for grids": "File format for grids", + "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid", + "Do not save grids consisting of one picture": "Do not save grids consisting of one picture", + "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)", + "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", + "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files", + "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.", + "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.", + "Quality for saved jpeg images": "Quality for saved jpeg images", + "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG", + "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab", + "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image", + "Do not add watermark to images": "Do not add watermark to images", + "Paths for saving": "Paths for saving", + "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below", + "Output directory for txt2img images": "Output directory for txt2img images", + "Output directory for img2img images": "Output directory for img2img images", + "Output directory for images from extras tab": "Output directory for images from extras tab", + "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below", + "Output directory for txt2img grids": "Output directory for txt2img grids", + "Output directory for img2img grids": "Output directory for img2img grids", + "Directory for saving images using the Save button": "Directory for saving images using the Save button", + "Saving to a directory": "Saving to a directory", + "Save images to a subdirectory": "Save images to a subdirectory", + "Save grids to a subdirectory": "Save grids to a subdirectory", + "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory", + "Directory name pattern": "Directory name pattern", + "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern", + "Upscaling": "Upscaling", + "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.", + "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", + "Tile size for all SwinIR.": "Tile size for all SwinIR.", + "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.", + "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster", + "Upscaler for img2img": "Upscaler for img2img", + "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix", + "Face restoration": "Face restoration", + "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", + "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing", + "System": "System", + "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.", + "Always print all generation info to standard output": "Always print all generation info to standard output", + "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.", + "Training": "Training", + "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training", + "Filename word regex": "Filename word regex", + "Filename join string": "Filename join string", + "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number", + "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable", + "Stable Diffusion": "Stable Diffusion", + "Checkpoints to cache in RAM": "Checkpoints to cache in RAM", + "Hypernetwork strength": "Hypernetwork strength", + "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.", + "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results", + "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).", + "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.", + "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention", + "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.", + "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image", + "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", + "Filter NSFW content": "Filter NSFW content", + "Stop At last layers of CLIP model": "Stop At last layers of CLIP model", + "Interrogate Options": "Interrogate Options", + "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM", + "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv", + "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).", + "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP", + "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)", + "Interrogate: maximum description length": "Interrogate: maximum description length", + "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)", + "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold", + "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically", + "use spaces for tags in deepbooru": "use spaces for tags in deepbooru", + "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)", + "User interface": "User interface", + "Show progressbar": "Show progressbar", + "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.", + "Show grid in results for web": "Show grid in results for web", + "Do not show any images in results for web": "Do not show any images in results for web", + "Add model hash to generation information": "Add model hash to generation information", + "Add model name to generation information": "Add model name to generation information", + "Font for image grids that have text": "Font for image grids that have text", + "Enable full page image viewer": "Enable full page image viewer", + "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer", + "Show generation progress in window title.": "Show generation progress in window title.", + "Quicksettings list": "Quicksettings list", + "Localization (requires restart)": "Localization (requires restart)", + "ja_JP": "ja_JP", + "Sampler parameters": "Sampler parameters", + "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)", + "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM", + "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers", + "img2img DDIM discretize": "img2img DDIM discretize", + "uniform": "uniform", + "quad": "quad", + "sigma churn": "sigma churn", + "sigma tmin": "sigma tmin", + "sigma noise": "sigma noise", + "Eta noise seed delta": "Eta noise seed delta", + "Request browser notifications": "Request browser notifications", + "Download localization template": "Download localization template", + "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)", + "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)", + "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)", + "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)", + "Add a random artist to the prompt.": "Add a random artist to the prompt.", + "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", + "Save style": "Save style", + "Apply selected styles to current prompt": "Apply selected styles to current prompt", + "Stop processing current image and continue processing.": "Stop processing current image and continue processing.", + "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.", + "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both", + "Do not do anything special": "Do not do anything special", + "Which algorithm to use to produce the image": "Which algorithm to use to produce the image", + "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help", + "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting", + "Produce an image that can be tiled.": "Produce an image that can be tiled.", + "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition", + "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", + "How many batches of images to create": "How many batches of images to create", + "How many image to create in a single batch": "How many image to create in a single batch", + "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results", + "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result", + "Set seed to -1, which will cause a new random number to be used every time": "Set seed to -1, which will cause a new random number to be used every time", + "Reuse seed from last generation, mostly useful if it was randomed": "Reuse seed from last generation, mostly useful if it was randomed", + "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.", + "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).", + "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution", + "Separate values for X axis using commas.": "Separate values for X axis using commas.", + "Separate values for Y axis using commas.": "Separate values for Y axis using commas.", + "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.", + "Open images output directory": "Open images output directory", + "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.", + "What to put inside the masked area before processing it with Stable Diffusion.": "What to put inside the masked area before processing it with Stable Diffusion.", + "fill it with colors of the image": "fill it with colors of the image", + "keep whatever was there originally": "keep whatever was there originally", + "fill it with latent space noise": "fill it with latent space noise", + "fill it with latent space zeroes": "fill it with latent space zeroes", + "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image", + "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.", + "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.", + "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.", + "How many times to repeat processing an image and using it as input for the next iteration": "How many times to repeat processing an image and using it as input for the next iteration", + "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.", + "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.", + "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.", + "Leave blank to save images to the default path.": "Leave blank to save images to the default path.", + "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M", + "Result = A + (B - C) * M": "Result = A + (B - C) * M", + "Path to directory with input images": "Path to directory with input images", + "Path to directory where to write outputs": "Path to directory where to write outputs", + "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", + "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.", + "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", + "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network", + "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", + "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.", + "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.", + "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing." +} \ No newline at end of file From eb2dae196e5d901162d477f24c1ebb2597b13dfb Mon Sep 17 00:00:00 2001 From: Kris57 <49682577+yuuki76@users.noreply.github.com> Date: Tue, 18 Oct 2022 14:40:07 +0900 Subject: [PATCH 121/140] add ja translation --- localizations/ja_JP.json | 48 ++++++++++++++++++++-------------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json index 5da28cb6..27bd342a 100644 --- a/localizations/ja_JP.json +++ b/localizations/ja_JP.json @@ -13,9 +13,9 @@ "txt2img": "txt2img", "img2img": "img2img", "Extras": "その他", - "PNG Info": "PNG Info", + "PNG Info": "PNG内の情報を表示", "History": "履歴", - "Checkpoint Merger": "Checkpoint Merger", + "Checkpoint Merger": "Checkpointの統合", "Train": "学習", "Settings": "設定", "Prompt": "プロンプト", @@ -35,8 +35,8 @@ "Image": "Image", "Check progress": "Check progress", "Check progress (first)": "Check progress (first)", - "Sampling Steps": "Sampling Steps", - "Sampling method": "Sampling method", + "Sampling Steps": "サンプリング回数", + "Sampling method": "サンプリングアルゴリズム", "Euler a": "Euler a", "Euler": "Euler", "LMS": "LMS", @@ -57,18 +57,18 @@ "Highres. fix": "*高解像度 fix", "Firstpass width": "Firstpass width", "Firstpass height": "Firstpass height", - "Denoising strength": "Denoising 強度", + "Denoising strength": "ノイズ除去 強度", "Batch count": "連続生成回数", "Batch size": "同時生成枚数", "CFG Scale": "CFG Scale", - "Seed": "Seed", - "Extra": "Extra", - "Variation seed": "Variation seed", + "Seed": "シード値", + "Extra": "その他", + "Variation seed": "Variation シード値", "Variation strength": "Variation 強度", "Resize seed from width": "Resize seed from width", "Resize seed from height": "Resize seed from height", - "Script": "Script", - "None": "None", + "Script": "スクリプト", + "None": "なし", "Prompt matrix": "Prompt matrix", "Prompts from file or textbox": "Prompts from file or textbox", "X/Y plot": "X/Y plot", @@ -84,7 +84,7 @@ "Prompt S/R": "Prompt S/R", "Prompt order": "Prompt order", "Sampler": "Sampler", - "Checkpoint name": "Checkpoint name", + "Checkpoint name": "Checkpoint名", "Hypernetwork": "Hypernetwork", "Hypernet str.": "Hypernet 強度", "Sigma Churn": "Sigma Churn", @@ -103,13 +103,13 @@ "ここに画像をドロップ": "ここに画像をドロップ", "Save": "保存", "Send to img2img": "img2imgに送る", - "Send to inpaint": "inpaintに送る", + "Send to inpaint": "描き直しに送る", "Send to extras": "その他タブに送る", "Make Zip when Save?": "保存するときZipも同時に作る", "Textbox": "Textbox", "Interrogate\nCLIP": "Interrogate\nCLIP", "Interrogate\nDeepBooru": "Interrogate\nDeepBooru", - "Inpaint": "Inpaint", + "Inpaint": "描き直し(Inpaint)", "Batch img2img": "Batch img2img", "Image for img2img": "Image for img2img", "Image for inpainting with mask": "Image for inpainting with mask", @@ -132,16 +132,16 @@ "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Use an empty output directory to save pictures normally instead of writing to the output directory.", "Input directory": "Input directory", "Output directory": "Output directory", - "Resize mode": "Resize mode", - "Just resize": "Just resize", - "Crop and resize": "Crop and resize", - "Resize and fill": "Resize and fill", + "Resize mode": "リサイズモード", + "Just resize": "リサイズのみ", + "Crop and resize": "切り取ってからリサイズ", + "Resize and fill": "リサイズして埋める", "img2img alternative test": "img2img alternative test", "Loopback": "Loopback", "Outpainting mk2": "Outpainting mk2", "Poor man's outpainting": "Poor man's outpainting", - "SD upscale": "SD upscale", - "should be 2 or lower.": "should be 2 or lower.", + "SD upscale": "SD アップスケール", + "should be 2 or lower.": "2以下にすること", "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)", "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", "Original prompt": "Original prompt", @@ -370,19 +370,19 @@ "Produce an image that can be tiled.": "Produce an image that can be tiled.", "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition", "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", - "How many batches of images to create": "How many batches of images to create", - "How many image to create in a single batch": "How many image to create in a single batch", + "How many batches of images to create": "バッチ処理を何回行うか", + "How many image to create in a single batch": "1回のバッチ処理で何枚の画像を生成するか", "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results", "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result", - "Set seed to -1, which will cause a new random number to be used every time": "Set seed to -1, which will cause a new random number to be used every time", - "Reuse seed from last generation, mostly useful if it was randomed": "Reuse seed from last generation, mostly useful if it was randomed", + "Set seed to -1, which will cause a new random number to be used every time": "シード値を -1 に設定するとランダムに生成します。", + "Reuse seed from last generation, mostly useful if it was randomed": "前回生成時のシード値を読み出す。(ランダム生成時に便利)", "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.", "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).", "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution", "Separate values for X axis using commas.": "Separate values for X axis using commas.", "Separate values for Y axis using commas.": "Separate values for Y axis using commas.", "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.", - "Open images output directory": "Open images output directory", + "Open images output directory": "画像の出力フォルダを開く", "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.", "What to put inside the masked area before processing it with Stable Diffusion.": "What to put inside the masked area before processing it with Stable Diffusion.", "fill it with colors of the image": "fill it with colors of the image", From 070fda592bf80fb348ffe8e17b7c71cc288db729 Mon Sep 17 00:00:00 2001 From: Kris57 <49682577+yuuki76@users.noreply.github.com> Date: Tue, 18 Oct 2022 15:20:00 +0900 Subject: [PATCH 122/140] add ja translation --- localizations/ja_JP.json | 128 +++++++++++++++++++-------------------- 1 file changed, 64 insertions(+), 64 deletions(-) diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json index 27bd342a..11f747b4 100644 --- a/localizations/ja_JP.json +++ b/localizations/ja_JP.json @@ -26,13 +26,13 @@ "Generate": "生成！", "Style 1": "スタイル 1", "Style 2": "スタイル 2", - "Label": "Label", + "Label": "ラベル", "File": "ファイル", "ここにファイルをドロップ": "ここにファイルをドロップ", "-": "-", "または": "または", "クリックしてアップロード": "クリックしてアップロード", - "Image": "Image", + "Image": "画像", "Check progress": "Check progress", "Check progress (first)": "Check progress (first)", "Sampling Steps": "サンプリング回数", @@ -54,12 +54,12 @@ "Height": "高さ", "Restore faces": "顔修復", "Tiling": "テクスチャ生成モード", - "Highres. fix": "*高解像度 fix", + "Highres. fix": "(※)高解像度 fix", "Firstpass width": "Firstpass width", "Firstpass height": "Firstpass height", "Denoising strength": "ノイズ除去 強度", - "Batch count": "連続生成回数", - "Batch size": "同時生成枚数", + "Batch count": "バッチ生成回数", + "Batch size": "バッチあたり生成枚数", "CFG Scale": "CFG Scale", "Seed": "シード値", "Extra": "その他", @@ -77,16 +77,16 @@ "File with inputs": "File with inputs", "Prompts": "Prompts", "X type": "X type", - "Nothing": "Nothing", + "Nothing": "なし", "Var. seed": "Var. seed", "Var. strength": "Var. 強度", - "Steps": "Steps", + "Steps": "ステップ数", "Prompt S/R": "Prompt S/R", "Prompt order": "Prompt order", - "Sampler": "Sampler", + "Sampler": "サンプラー", "Checkpoint name": "Checkpoint名", "Hypernetwork": "Hypernetwork", - "Hypernet str.": "Hypernet 強度", + "Hypernet str.": "Hypernet強度", "Sigma Churn": "Sigma Churn", "Sigma min": "Sigma min", "Sigma max": "Sigma max", @@ -192,78 +192,78 @@ "txt2img history": "txt2img history", "img2img history": "img2img history", "extras history": "extras history", - "Renew Page": "Renew Page", - "First Page": "First Page", - "Prev Page": "Prev Page", - "Page Index": "Page Index", - "Next Page": "Next Page", - "End Page": "End Page", + "Renew Page": "更新", + "First Page": "最初のぺージへ", + "Prev Page": "前ページへ", + "Page Index": "ページ番号", + "Next Page": "次ページへ", + "End Page": "最後のページへ", "number of images to delete consecutively next": "number of images to delete consecutively next", - "Delete": "Delete", + "Delete": "削除", "Generate Info": "Generate Info", "File Name": "File Name", "set_index": "set_index", - "A merger of the two checkpoints will be generated in your": "A merger of the two checkpoints will be generated in your", + "A merger of the two checkpoints will be generated in your": "統合されたチェックポイントはあなたの", "checkpoint": "checkpoint", - "directory.": "directory.", - "Primary model (A)": "Primary model (A)", - "Secondary model (B)": "Secondary model (B)", - "Tertiary model (C)": "Tertiary model (C)", - "Custom Name (Optional)": "Custom Name (Optional)", - "Multiplier (M) - set to 0 to get model A": "Multiplier (M) - set to 0 to get model A", + "directory.": "フォルダに保存されます.", + "Primary model (A)": "1つめのmodel (A)", + "Secondary model (B)": "2つめのmodel (B)", + "Tertiary model (C)": "3つめのmodel (C)", + "Custom Name (Optional)": "Custom Name (任意)", + "Multiplier (M) - set to 0 to get model A": "Multiplier (M) 0にすると完全にmodel Aとなります", "Interpolation Method": "Interpolation Method", "Weighted sum": "Weighted sum", "Add difference": "Add difference", - "Save as float16": "Save as float16", - "See": "See", + "Save as float16": "float16で保存", + "See": "詳細な説明については", "wiki": "wiki", - "for detailed explanation.": "for detailed explanation.", - "Create embedding": "Create embedding", - "Create hypernetwork": "Create hypernetwork", - "Preprocess images": "Preprocess images", - "Name": "Name", + "for detailed explanation.": "を見てください。", + "Create embedding": "Embeddingを作る", + "Create hypernetwork": "Hypernetworkを作る", + "Preprocess images": "画像の前処理", + "Name": "ファイル名", "Initialization text": "Initialization text", "Number of vectors per token": "Number of vectors per token", "Modules": "Modules", - "Source directory": "Source directory", - "Destination directory": "Destination directory", - "Create flipped copies": "Create flipped copies", - "Split oversized images into two": "Split oversized images into two", - "Use BLIP for caption": "Use BLIP for caption", - "Use deepbooru for caption": "Use deepbooru for caption", - "Preprocess": "Preprocess", - "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Train an embedding; must specify a directory with a set of 1:1 ratio images", + "Source directory": "入力フォルダ", + "Destination directory": "出力フォルダ", + "Create flipped copies": "反転画像を生成する", + "Split oversized images into two": "大きすぎる画像を2分割する", + "Use BLIP for caption": "BLIPで説明をつける", + "Use deepbooru for caption": "deepbooruで説明をつける", + "Preprocess": "前処理開始", + "Train an embedding; must specify a directory with a set of 1:1 ratio images": "embeddingの学習をします;データセット内の画像は正方形でなければなりません。", "Embedding": "Embedding", - "Learning rate": "Learning rate", - "Dataset directory": "Dataset directory", - "Log directory": "Log directory", + "Learning rate": "学習率", + "Dataset directory": "データセットフォルダ", + "Log directory": "ログフォルダ", "Prompt template file": "Prompt template file", - "Max steps": "Max steps", - "Save an image to log directory every N steps, 0 to disable": "Save an image to log directory every N steps, 0 to disable", - "Save a copy of embedding to log directory every N steps, 0 to disable": "Save a copy of embedding to log directory every N steps, 0 to disable", - "Save images with embedding in PNG chunks": "Save images with embedding in PNG chunks", + "Max steps": "最大ステップ数", + "Save an image to log directory every N steps, 0 to disable": "指定したステップ数ごとに画像を生成し、ログに保存する。0で無効化。", + "Save a copy of embedding to log directory every N steps, 0 to disable": "指定したステップ数ごとにEmbeddingのコピーをログに保存する。0で無効化。", + "Save images with embedding in PNG chunks": "保存する画像にembeddingを埋め込む", "Read parameters (prompt, etc...) from txt2img tab when making previews": "Read parameters (prompt, etc...) from txt2img tab when making previews", - "Train Hypernetwork": "Train Hypernetwork", - "Train Embedding": "Train Embedding", + "Train Hypernetwork": "Hypernetworkの学習を開始", + "Train Embedding": "Embeddingの学習を開始", "Apply settings": "Apply settings", - "Saving images/grids": "Saving images/grids", - "Always save all generated images": "Always save all generated images", - "File format for images": "File format for images", - "Images filename pattern": "Images filename pattern", - "Always save all generated image grids": "Always save all generated image grids", - "File format for grids": "File format for grids", - "Add extended info (seed, prompt) to filename when saving grid": "Add extended info (seed, prompt) to filename when saving grid", - "Do not save grids consisting of one picture": "Do not save grids consisting of one picture", - "Prevent empty spots in grid (when set to autodetect)": "Prevent empty spots in grid (when set to autodetect)", - "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", - "Save text information about generation parameters as chunks to png files": "Save text information about generation parameters as chunks to png files", - "Create a text file next to every image with generation parameters.": "Create a text file next to every image with generation parameters.", - "Save a copy of image before doing face restoration.": "Save a copy of image before doing face restoration.", - "Quality for saved jpeg images": "Quality for saved jpeg images", - "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG", + "Saving images/grids": "画像/グリッドの保存", + "Always save all generated images": "生成された画像をすべて保存する", + "File format for images": "画像ファイルの保存形式", + "Images filename pattern": "ファイル名のパターン", + "Always save all generated image grids": "グリッド画像を常に保存する", + "File format for grids": "グリッド画像の保存形式", + "Add extended info (seed, prompt) to filename when saving grid": "保存するグリッド画像のファイル名に追加情報(シード値、プロンプト)を加える", + "Do not save grids consisting of one picture": "1画像からなるグリッド画像は保存しない", + "Prevent empty spots in grid (when set to autodetect)": "(自動設定のとき)グリッドに空隙が生じるのを防ぐ", + "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "グリッドの列数; -1で自動設定、0でバッチ生成回数と同じにする", + "Save text information about generation parameters as chunks to png files": "生成に関するパラメーターをpng画像に含める", + "Create a text file next to every image with generation parameters.": "保存する画像とともに生成パラメータをテキストファイルで保存する", + "Save a copy of image before doing face restoration.": "顔修復を行う前にコピーを保存しておく。", + "Quality for saved jpeg images": "JPG保存時の画質", + "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "PNG画像が4MBを超えるか、どちらか1辺の長さが4000を超えたなら、ダウンスケールしてコピーを別にJPGで保存する", "Use original name for output filename during batch process in extras tab": "Use original name for output filename during batch process in extras tab", "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image", - "Do not add watermark to images": "Do not add watermark to images", + "Do not add watermark to images": "電子透かしを画像に追加しない", "Paths for saving": "Paths for saving", "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below", "Output directory for txt2img images": "Output directory for txt2img images", @@ -403,7 +403,7 @@ "Path to directory with input images": "Path to directory with input images", "Path to directory where to write outputs": "Path to directory where to write outputs", "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", - "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.", + "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "このオプションを有効にすると、作成された画像にウォーターマークが追加されなくなります。警告:ウォーターマークを追加しない場合、非倫理的な行動とみなされる場合があります。", "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network", "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", From 7043f4eff3913ac1ed0ae1621f622c90437c6843 Mon Sep 17 00:00:00 2001 From: Kris57 <49682577+yuuki76@users.noreply.github.com> Date: Tue, 18 Oct 2022 15:57:39 +0900 Subject: [PATCH 123/140] improve ja translation --- localizations/ja_JP.json | 92 ++++++++++++++++++++-------------------- 1 file changed, 46 insertions(+), 46 deletions(-) diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json index 11f747b4..87809d72 100644 --- a/localizations/ja_JP.json +++ b/localizations/ja_JP.json @@ -54,7 +54,7 @@ "Height": "高さ", "Restore faces": "顔修復", "Tiling": "テクスチャ生成モード", - "Highres. fix": "(※)高解像度 fix", + "Highres. fix": "高解像度 fix(マウスオーバーで詳細)", "Firstpass width": "Firstpass width", "Firstpass height": "Firstpass height", "Denoising strength": "ノイズ除去 強度", @@ -75,7 +75,7 @@ "Put variable parts at start of prompt": "Put variable parts at start of prompt", "Show Textbox": "Show Textbox", "File with inputs": "File with inputs", - "Prompts": "Prompts", + "Prompts": "プロンプト", "X type": "X type", "Nothing": "なし", "Var. seed": "Var. seed", @@ -123,26 +123,26 @@ "Inpaint not masked": "Inpaint not masked", "Masked content": "Masked content", "fill": "fill", - "original": "original", + "original": "オリジナル", "latent noise": "latent noise", "latent nothing": "latent nothing", "Inpaint at full resolution": "Inpaint at full resolution", "Inpaint at full resolution padding, pixels": "Inpaint at full resolution padding, pixels", "Process images in a directory on the same machine where the server is running.": "Process images in a directory on the same machine where the server is running.", "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Use an empty output directory to save pictures normally instead of writing to the output directory.", - "Input directory": "Input directory", - "Output directory": "Output directory", + "Input directory": "入力フォルダ", + "Output directory": "出力フォルダ", "Resize mode": "リサイズモード", "Just resize": "リサイズのみ", "Crop and resize": "切り取ってからリサイズ", "Resize and fill": "リサイズして埋める", "img2img alternative test": "img2img alternative test", - "Loopback": "Loopback", + "Loopback": "ループバック", "Outpainting mk2": "Outpainting mk2", "Poor man's outpainting": "Poor man's outpainting", "SD upscale": "SD アップスケール", "should be 2 or lower.": "2以下にすること", - "Override `Sampling method` to Euler?(this method is built for it)": "Override `Sampling method` to Euler?(this method is built for it)", + "Override `Sampling method` to Euler?(this method is built for it)": "サンプリングアルゴリズムをEulerに上書きする(そうすることを前提に設計されています)", "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", "Original prompt": "Original prompt", "Original negative prompt": "Original negative prompt", @@ -157,15 +157,15 @@ "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8", "Pixels to expand": "Pixels to expand", "Outpainting direction": "Outpainting direction", - "left": "left", - "right": "right", - "up": "up", - "down": "down", + "left": "左", + "right": "右", + "up": "上", + "down": "下", "Fall-off exponent (lower=higher detail)": "Fall-off exponent (lower=higher detail)", "Color variation": "Color variation", "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Will upscale the image to twice the dimensions; use width and height sliders to set tile size", "Tile overlap": "Tile overlap", - "Upscaler": "Upscaler", + "Upscaler": "アップスケーラー", "Lanczos": "Lanczos", "LDSR": "LDSR", "BSRGAN 4x": "BSRGAN 4x", @@ -173,25 +173,25 @@ "ScuNET GAN": "ScuNET GAN", "ScuNET PSNR": "ScuNET PSNR", "SwinIR 4x": "SwinIR 4x", - "Single Image": "Single Image", - "Batch Process": "Batch Process", - "Batch from Directory": "Batch from Directory", - "Source": "Source", - "Show result images": "Show result images", - "Scale by": "Scale by", - "Scale to": "Scale to", - "Resize": "Resize", - "Crop to fit": "Crop to fit", - "Upscaler 2": "Upscaler 2", + "Single Image": "単一画像", + "Batch Process": "バッチ処理", + "Batch from Directory": "フォルダからバッチ処理", + "Source": "入力", + "Show result images": "出力画像を表示", + "Scale by": "倍率指定", + "Scale to": "解像度指定", + "Resize": "倍率", + "Crop to fit": "合うように切り抜き", + "Upscaler 2": "アップスケーラー 2", "Upscaler 2 visibility": "Upscaler 2 visibility", "GFPGAN visibility": "GFPGAN visibility", "CodeFormer visibility": "CodeFormer visibility", "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "CodeFormer weight (0 = maximum effect, 1 = minimum effect)", "Open output directory": "出力フォルダを開く", - "Send to txt2img": "Send to txt2img", - "txt2img history": "txt2img history", - "img2img history": "img2img history", - "extras history": "extras history", + "Send to txt2img": "txt2imgに送る", + "txt2img history": "txt2imgの履歴", + "img2img history": "img2imgの履歴", + "extras history": "その他タブの履歴", "Renew Page": "更新", "First Page": "最初のぺージへ", "Prev Page": "前ページへ", @@ -200,8 +200,8 @@ "End Page": "最後のページへ", "number of images to delete consecutively next": "number of images to delete consecutively next", "Delete": "削除", - "Generate Info": "Generate Info", - "File Name": "File Name", + "Generate Info": "生成情報", + "File Name": "ファイル名", "set_index": "set_index", "A merger of the two checkpoints will be generated in your": "統合されたチェックポイントはあなたの", "checkpoint": "checkpoint", @@ -265,27 +265,27 @@ "When using 'Save' button, only save a single selected image": "When using 'Save' button, only save a single selected image", "Do not add watermark to images": "電子透かしを画像に追加しない", "Paths for saving": "Paths for saving", - "Output directory for images; if empty, defaults to three directories below": "Output directory for images; if empty, defaults to three directories below", - "Output directory for txt2img images": "Output directory for txt2img images", - "Output directory for img2img images": "Output directory for img2img images", - "Output directory for images from extras tab": "Output directory for images from extras tab", - "Output directory for grids; if empty, defaults to two directories below": "Output directory for grids; if empty, defaults to two directories below", - "Output directory for txt2img grids": "Output directory for txt2img grids", - "Output directory for img2img grids": "Output directory for img2img grids", - "Directory for saving images using the Save button": "Directory for saving images using the Save button", - "Saving to a directory": "Saving to a directory", - "Save images to a subdirectory": "Save images to a subdirectory", - "Save grids to a subdirectory": "Save grids to a subdirectory", - "When using \"Save\" button, save images to a subdirectory": "When using \"Save\" button, save images to a subdirectory", - "Directory name pattern": "Directory name pattern", + "Output directory for images; if empty, defaults to three directories below": "画像の保存先フォルダ(下項目のデフォルト値になります)", + "Output directory for txt2img images": "txt2imgで作った画像の保存先フォルダ", + "Output directory for img2img images": "img2imgで作った画像の保存先フォルダ", + "Output directory for images from extras tab": "その他タブで作った画像の保存先フォルダ", + "Output directory for grids; if empty, defaults to two directories below": "画像の保存先フォルダ(下項目のデフォルト値になります)", + "Output directory for txt2img grids": "txt2imgで作ったグリッドの保存先フォルダ", + "Output directory for img2img grids": "img2imgで作ったグリッドの保存先フォルダ", + "Directory for saving images using the Save button": "保存ボタンを押したときの画像の保存先フォルダ", + "Saving to a directory": "フォルダについて", + "Save images to a subdirectory": "画像をサブフォルダに保存する", + "Save grids to a subdirectory": "グリッドをサブフォルダに保存する", + "When using \"Save\" button, save images to a subdirectory": "保存ボタンを押した時、画像をサブフォルダに保存する", + "Directory name pattern": "フォルダ名のパターン", "Max prompt words for [prompt_words] pattern": "Max prompt words for [prompt_words] pattern", - "Upscaling": "Upscaling", - "Tile size for ESRGAN upscalers. 0 = no tiling.": "Tile size for ESRGAN upscalers. 0 = no tiling.", - "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", + "Upscaling": "アップスケール", + "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGANのタイルサイズ。0とするとタイルしない。", + "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGANのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。", "Tile size for all SwinIR.": "Tile size for all SwinIR.", "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.", "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster", - "Upscaler for img2img": "Upscaler for img2img", + "Upscaler for img2img": "img2imgで使うアップスケーラー", "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix", "Face restoration": "Face restoration", "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", @@ -368,7 +368,7 @@ "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help", "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting", "Produce an image that can be tiled.": "Produce an image that can be tiled.", - "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition", + "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "2ステップで、まず部分的に小さい解像度で画像を作成し、その後アップスケールすることで、構図を変えずにディテールが改善されます。", "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", "How many batches of images to create": "バッチ処理を何回行うか", "How many image to create in a single batch": "1回のバッチ処理で何枚の画像を生成するか", From 0262bf64ddbf85e05ddd120929138d3c5dac3bac Mon Sep 17 00:00:00 2001 From: Kris57 <49682577+yuuki76@users.noreply.github.com> Date: Tue, 18 Oct 2022 16:38:31 +0900 Subject: [PATCH 124/140] improve ja translation --- localizations/ja_JP.json | 48 ++++++++++++++++++++-------------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json index 87809d72..fc958656 100644 --- a/localizations/ja_JP.json +++ b/localizations/ja_JP.json @@ -282,19 +282,19 @@ "Upscaling": "アップスケール", "Tile size for ESRGAN upscalers. 0 = no tiling.": "ESRGANのタイルサイズ。0とするとタイルしない。", "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "ESRGANのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。", - "Tile size for all SwinIR.": "Tile size for all SwinIR.", - "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Tile overlap, in pixels for SwinIR. Low values = visible seam.", + "Tile size for all SwinIR.": "SwinIRのタイルサイズ", + "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "SwinIRのタイルの重複部分のピクセル数。少なくするとつなぎ目が見えやすくなる。", "LDSR processing steps. Lower = faster": "LDSR processing steps. Lower = faster", "Upscaler for img2img": "img2imgで使うアップスケーラー", - "Upscale latent space image when doing hires. fix": "Upscale latent space image when doing hires. fix", - "Face restoration": "Face restoration", - "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", + "Upscale latent space image when doing hires. fix": "高解像度 fix時に潜在空間(latent space)の画像をアップスケールする", + "Face restoration": "顔修復", + "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormerの重みパラメーター;0が最大で1が最小", "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing", - "System": "System", + "System": "システム設定", "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.", - "Always print all generation info to standard output": "Always print all generation info to standard output", + "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する", "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.", - "Training": "Training", + "Training": "学習", "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training", "Filename word regex": "Filename word regex", "Filename join string": "Filename join string", @@ -307,15 +307,15 @@ "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results", "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).", "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.", - "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention", - "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Use old emphasis implementation. Can be useful to reproduce old seeds.", + "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調: (text)とするとモデルはtextをより強く扱い、[text]とするとモデルはtextをより弱く扱います。", + "Use old emphasis implementation. Can be useful to reproduce old seeds.": "古い強調の実装を使う。古い生成物を再現するのに使えます。", "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image", "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", "Filter NSFW content": "Filter NSFW content", - "Stop At last layers of CLIP model": "Stop At last layers of CLIP model", - "Interrogate Options": "Interrogate Options", - "Interrogate: keep models in VRAM": "Interrogate: keep models in VRAM", - "Interrogate: use artists from artists.csv": "Interrogate: use artists from artists.csv", + "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか(stop…layers of CLIP model)", + "Interrogate Options": "Interrogate 設定", + "Interrogate: keep models in VRAM": "Interrogate: モデルをVRAMに保持する", + "Interrogate: use artists from artists.csv": "Interrogate: artists.csvにある芸術家などの名称を利用する", "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).", "Interrogate: num_beams for BLIP": "Interrogate: num_beams for BLIP", "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)", @@ -337,9 +337,9 @@ "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer", "Show generation progress in window title.": "Show generation progress in window title.", "Quicksettings list": "Quicksettings list", - "Localization (requires restart)": "Localization (requires restart)", + "Localization (requires restart)": "言語 (プログラムの再起動が必要)", "ja_JP": "ja_JP", - "Sampler parameters": "Sampler parameters", + "Sampler parameters": "サンプラー parameters", "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)", "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM", "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers", @@ -350,16 +350,16 @@ "sigma tmin": "sigma tmin", "sigma noise": "sigma noise", "Eta noise seed delta": "Eta noise seed delta", - "Request browser notifications": "Request browser notifications", - "Download localization template": "Download localization template", - "Reload custom script bodies (No ui updates, No restart)": "Reload custom script bodies (No ui updates, No restart)", - "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)", + "Request browser notifications": "ブラウザ通知の許可を要求する", + "Download localization template": "ローカライゼーション用のテンプレートをダウンロードする", + "Reload custom script bodies (No ui updates, No restart)": "カスタムスクリプトを再読み込み (UIは変更されず、再起動もしません。)", + "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradioを再起動してコンポーネントをリフレッシュする (Custom Scripts, ui.py, js, cssのみ影響を受ける)", "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)", "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)", "Add a random artist to the prompt.": "Add a random artist to the prompt.", "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", - "Save style": "Save style", - "Apply selected styles to current prompt": "Apply selected styles to current prompt", + "Save style": "スタイルを保存する", + "Apply selected styles to current prompt": "現在のプロンプトに選択したスタイルを適用する", "Stop processing current image and continue processing.": "Stop processing current image and continue processing.", "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.", "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both", @@ -379,8 +379,8 @@ "Seed of a different picture to be mixed into the generation.": "Seed of a different picture to be mixed into the generation.", "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).", "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution", - "Separate values for X axis using commas.": "Separate values for X axis using commas.", - "Separate values for Y axis using commas.": "Separate values for Y axis using commas.", + "Separate values for X axis using commas.": "X軸に用いる値をカンマ(,)で区切って入力してください。", + "Separate values for Y axis using commas.": "Y軸に用いる値をカンマ(,)で区切って入力してください。", "Write image to a directory (default - log/images) and generation parameters into csv file.": "Write image to a directory (default - log/images) and generation parameters into csv file.", "Open images output directory": "画像の出力フォルダを開く", "How much to blur the mask before processing, in pixels.": "How much to blur the mask before processing, in pixels.", From f613c6b8c532dbcfb3570cdbb3ce56a0d2821d0b Mon Sep 17 00:00:00 2001 From: Kris57 <49682577+yuuki76@users.noreply.github.com> Date: Tue, 18 Oct 2022 23:01:41 +0900 Subject: [PATCH 125/140] improve ja translation --- localizations/ja_JP.json | 66 ++++++++++++++++++++-------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json index fc958656..a9f6cb20 100644 --- a/localizations/ja_JP.json +++ b/localizations/ja_JP.json @@ -163,7 +163,7 @@ "down": "下", "Fall-off exponent (lower=higher detail)": "Fall-off exponent (lower=higher detail)", "Color variation": "Color variation", - "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Will upscale the image to twice the dimensions; use width and height sliders to set tile size", + "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "画像を2倍の大きさにアップスケールします。幅と高さのスライダーでタイルの大きさを設定します。", "Tile overlap": "Tile overlap", "Upscaler": "アップスケーラー", "Lanczos": "Lanczos", @@ -210,10 +210,10 @@ "Secondary model (B)": "2つめのmodel (B)", "Tertiary model (C)": "3つめのmodel (C)", "Custom Name (Optional)": "Custom Name (任意)", - "Multiplier (M) - set to 0 to get model A": "Multiplier (M) 0にすると完全にmodel Aとなります", - "Interpolation Method": "Interpolation Method", - "Weighted sum": "Weighted sum", - "Add difference": "Add difference", + "Multiplier (M) - set to 0 to get model A": "Multiplier (M) 0にすると完全にmodel Aとなります (ツールチップ参照)", + "Interpolation Method": "混合(Interpolation)方式", + "Weighted sum": "加重平均", + "Add difference": "差を加える", "Save as float16": "float16で保存", "See": "詳細な説明については", "wiki": "wiki", @@ -289,7 +289,7 @@ "Upscale latent space image when doing hires. fix": "高解像度 fix時に潜在空間(latent space)の画像をアップスケールする", "Face restoration": "顔修復", "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "CodeFormerの重みパラメーター;0が最大で1が最小", - "Move face restoration model from VRAM into RAM after processing": "Move face restoration model from VRAM into RAM after processing", + "Move face restoration model from VRAM into RAM after processing": "処理終了後、顔修復モデルをVRAMからRAMへと移動する", "System": "システム設定", "VRAM usage polls per second during generation. Set to 0 to disable.": "VRAM usage polls per second during generation. Set to 0 to disable.", "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する", @@ -301,9 +301,9 @@ "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number", "Save an csv containing the loss to log directory every N steps, 0 to disable": "Save an csv containing the loss to log directory every N steps, 0 to disable", "Stable Diffusion": "Stable Diffusion", - "Checkpoints to cache in RAM": "Checkpoints to cache in RAM", + "Checkpoints to cache in RAM": "RAMにキャッシュするCheckpoint数", "Hypernetwork strength": "Hypernetwork strength", - "Apply color correction to img2img results to match original colors.": "Apply color correction to img2img results to match original colors.", + "Apply color correction to img2img results to match original colors.": "元画像に合わせてimg2imgの結果を色補正する", "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results", "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).", "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.", @@ -325,24 +325,24 @@ "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically", "use spaces for tags in deepbooru": "use spaces for tags in deepbooru", "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)", - "User interface": "User interface", - "Show progressbar": "Show progressbar", - "Show image creation progress every N sampling steps. Set 0 to disable.": "Show image creation progress every N sampling steps. Set 0 to disable.", - "Show grid in results for web": "Show grid in results for web", - "Do not show any images in results for web": "Do not show any images in results for web", - "Add model hash to generation information": "Add model hash to generation information", - "Add model name to generation information": "Add model name to generation information", - "Font for image grids that have text": "Font for image grids that have text", - "Enable full page image viewer": "Enable full page image viewer", - "Show images zoomed in by default in full page image viewer": "Show images zoomed in by default in full page image viewer", - "Show generation progress in window title.": "Show generation progress in window title.", + "User interface": "UI設定", + "Show progressbar": "プログレスバーを表示", + "Show image creation progress every N sampling steps. Set 0 to disable.": "指定したステップ数ごとに画像の生成過程を表示する。0で無効化。", + "Show grid in results for web": "WebUI上でグリッド表示", + "Do not show any images in results for web": "WebUI上で一切画像を表示しない", + "Add model hash to generation information": "モデルのハッシュ値を生成情報に追加", + "Add model name to generation information": "モデルの名称を生成情報に追加", + "Font for image grids that have text": "画像グリッド内のテキストフォント", + "Enable full page image viewer": "フルページの画像ビューワーを有効化", + "Show images zoomed in by default in full page image viewer": "フルページ画像ビューアでデフォルトで画像を拡大して表示する", + "Show generation progress in window title.": "ウィンドウのタイトルで生成の進捗を表示", "Quicksettings list": "Quicksettings list", "Localization (requires restart)": "言語 (プログラムの再起動が必要)", "ja_JP": "ja_JP", "Sampler parameters": "サンプラー parameters", - "Hide samplers in user interface (requires restart)": "Hide samplers in user interface (requires restart)", - "eta (noise multiplier) for DDIM": "eta (noise multiplier) for DDIM", - "eta (noise multiplier) for ancestral samplers": "eta (noise multiplier) for ancestral samplers", + "Hide samplers in user interface (requires restart)": "使わないサンプリングアルゴリズムを隠す (再起動が必要)", + "eta (noise multiplier) for DDIM": "DDIMで用いるeta (noise multiplier)", + "eta (noise multiplier) for ancestral samplers": "ancestral サンプラーで用いるeta (noise multiplier)", "img2img DDIM discretize": "img2img DDIM discretize", "uniform": "uniform", "quad": "quad", @@ -356,17 +356,17 @@ "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Gradioを再起動してコンポーネントをリフレッシュする (Custom Scripts, ui.py, js, cssのみ影響を受ける)", "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "プロンプト (Ctrl+Enter か Alt+Enter を押して生成)", "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "ネガティブ プロンプト (Ctrl+Enter か Alt+Enter を押して生成)", - "Add a random artist to the prompt.": "Add a random artist to the prompt.", + "Add a random artist to the prompt.": "芸術家などの名称をプロンプトに追加", "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Read generation parameters from prompt or last generation if prompt is empty into user interface.", "Save style": "スタイルを保存する", "Apply selected styles to current prompt": "現在のプロンプトに選択したスタイルを適用する", - "Stop processing current image and continue processing.": "Stop processing current image and continue processing.", - "Stop processing images and return any results accumulated so far.": "Stop processing images and return any results accumulated so far.", + "Stop processing current image and continue processing.": "現在の処理を中断し、その後の処理は続ける", + "Stop processing images and return any results accumulated so far.": "処理を中断し、それまでに出来た結果を表示する", "Style to apply; styles have components for both positive and negative prompts and apply to both": "Style to apply; styles have components for both positive and negative prompts and apply to both", - "Do not do anything special": "Do not do anything special", - "Which algorithm to use to produce the image": "Which algorithm to use to produce the image", - "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help", - "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - best at inpainting", + "Do not do anything special": "特別なことをなにもしない", + "Which algorithm to use to produce the image": "どのアルゴリズムを使って生成するか", + "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - 非常に独創的で、ステップ数によって全く異なる画像が得られる、ステップ数を30～40より高く設定しても効果がない。", + "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit Models - 描き直しには最適", "Produce an image that can be tiled.": "Produce an image that can be tiled.", "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "2ステップで、まず部分的に小さい解像度で画像を作成し、その後アップスケールすることで、構図を変えずにディテールが改善されます。", "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.", @@ -397,15 +397,15 @@ "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.", "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.", "A directory on the same machine where the server is running.": "A directory on the same machine where the server is running.", - "Leave blank to save images to the default path.": "Leave blank to save images to the default path.", - "Result = A * (1 - M) + B * M": "Result = A * (1 - M) + B * M", - "Result = A + (B - C) * M": "Result = A + (B - C) * M", + "Leave blank to save images to the default path.": "空欄でデフォルトの場所へ画像を保存", + "Result = A * (1 - M) + B * M": "結果モデル = A * (1 - M) + B * M", + "Result = A + (B - C) * M": "結果モデル = A + (B - C) * M", "Path to directory with input images": "Path to directory with input images", "Path to directory where to write outputs": "Path to directory where to write outputs", "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "このオプションを有効にすると、作成された画像にウォーターマークが追加されなくなります。警告:ウォーターマークを追加しない場合、非倫理的な行動とみなされる場合があります。", "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.", - "Restore low quality faces using GFPGAN neural network": "Restore low quality faces using GFPGAN neural network", + "Restore low quality faces using GFPGAN neural network": "GFPGANを用いて低クオリティーの画像を修復", "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.", "This string will be used to join split words into a single line if the option above is enabled.": "This string will be used to join split words into a single line if the option above is enabled.", "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.", From 774be6d2f271415a82d9a83147e8ee8bbad018d0 Mon Sep 17 00:00:00 2001 From: Kris57 <49682577+yuuki76@users.noreply.github.com> Date: Tue, 18 Oct 2022 23:29:34 +0900 Subject: [PATCH 126/140] improve ja translation --- localizations/ja_JP.json | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/localizations/ja_JP.json b/localizations/ja_JP.json index a9f6cb20..514b579e 100644 --- a/localizations/ja_JP.json +++ b/localizations/ja_JP.json @@ -76,7 +76,7 @@ "Show Textbox": "Show Textbox", "File with inputs": "File with inputs", "Prompts": "プロンプト", - "X type": "X type", + "X type": "X軸の種類", "Nothing": "なし", "Var. seed": "Var. seed", "Var. strength": "Var. 強度", @@ -94,12 +94,12 @@ "Eta": "Eta", "Clip skip": "Clip skip", "Denoising": "Denoising", - "X values": "X values", - "Y type": "Y type", - "Y values": "Y values", - "Draw legend": "Draw legend", + "X values": "Xの値", + "Y type": "Y軸の種類", + "Y values": "Yの値", + "Draw legend": "凡例を描画", "Include Separate Images": "Include Separate Images", - "Keep -1 for seeds": "Keep -1 for seeds", + "Keep -1 for seeds": "シード値を-1で固定", "ここに画像をドロップ": "ここに画像をドロップ", "Save": "保存", "Send to img2img": "img2imgに送る", @@ -295,7 +295,7 @@ "Always print all generation info to standard output": "常にすべての生成に関する情報を標準出力(stdout)に出力する", "Add a second progress bar to the console that shows progress for an entire job.": "Add a second progress bar to the console that shows progress for an entire job.", "Training": "学習", - "Unload VAE and CLIP from VRAM when training": "Unload VAE and CLIP from VRAM when training", + "Unload VAE and CLIP from VRAM when training": "学習を行う際、VAEとCLIPをVRAMから削除する", "Filename word regex": "Filename word regex", "Filename join string": "Filename join string", "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Number of repeats for a single input image per epoch; used only for displaying epoch number", @@ -304,14 +304,14 @@ "Checkpoints to cache in RAM": "RAMにキャッシュするCheckpoint数", "Hypernetwork strength": "Hypernetwork strength", "Apply color correction to img2img results to match original colors.": "元画像に合わせてimg2imgの結果を色補正する", - "Save a copy of image before applying color correction to img2img results": "Save a copy of image before applying color correction to img2img results", - "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).", + "Save a copy of image before applying color correction to img2img results": "色補正をする前の画像も保存する", + "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "img2imgでスライダーで指定されたステップ数を正確に実行する（通常は、ノイズ除去を少なくするためにより少ないステップ数で実行します）。", "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.", "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "強調: (text)とするとモデルはtextをより強く扱い、[text]とするとモデルはtextをより弱く扱います。", "Use old emphasis implementation. Can be useful to reproduce old seeds.": "古い強調の実装を使う。古い生成物を再現するのに使えます。", "Make K-diffusion samplers produce same images in a batch as when making a single image": "Make K-diffusion samplers produce same images in a batch as when making a single image", "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", - "Filter NSFW content": "Filter NSFW content", + "Filter NSFW content": "NSFW(≒R-18)なコンテンツを検閲する", "Stop At last layers of CLIP model": "最後から何層目でCLIPを止めるか(stop…layers of CLIP model)", "Interrogate Options": "Interrogate 設定", "Interrogate: keep models in VRAM": "Interrogate: モデルをVRAMに保持する", @@ -321,10 +321,10 @@ "Interrogate: minimum description length (excluding artists, etc..)": "Interrogate: minimum description length (excluding artists, etc..)", "Interrogate: maximum description length": "Interrogate: maximum description length", "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: maximum number of lines in text file (0 = No limit)", - "Interrogate: deepbooru score threshold": "Interrogate: deepbooru score threshold", - "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooru sort alphabetically", - "use spaces for tags in deepbooru": "use spaces for tags in deepbooru", - "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)", + "Interrogate: deepbooru score threshold": "Interrogate: deepbooruで拾う単語のスコア閾値", + "Interrogate: deepbooru sort alphabetically": "Interrogate: deepbooruで単語をアルファベット順に並べる", + "use spaces for tags in deepbooru": "deepbooruのタグでスペースを使う", + "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "deepbooruで括弧をエスケープする(\\) (強調を示す()ではなく、文字通りの()であることをモデルに示すため)", "User interface": "UI設定", "Show progressbar": "プログレスバーを表示", "Show image creation progress every N sampling steps. Set 0 to disable.": "指定したステップ数ごとに画像の生成過程を表示する。0で無効化。", From 324c7c732dd9afc3d4c397c354797ae5d655b514 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 20:09:37 +0300 Subject: [PATCH 127/140] record First pass size as 0x0 for #3328 --- modules/processing.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/processing.py b/modules/processing.py index 372489f7..27c669b0 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -524,6 +524,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): else: state.job_count = state.job_count * 2 + self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}" + if self.firstphase_width == 0 or self.firstphase_height == 0: desired_pixel_count = 512 * 512 actual_pixel_count = self.width * self.height @@ -545,7 +547,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): firstphase_width_truncated = self.firstphase_height * self.width / self.height firstphase_height_truncated = self.firstphase_height - self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}" self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f From 0df94d3fcf9d1fc47c4d39039352a3d5b3380c1f Mon Sep 17 00:00:00 2001 From: MrCheeze Date: Sat, 22 Oct 2022 12:59:21 -0400 Subject: [PATCH 128/140] fix aesthetic gradients doing nothing after loading a different model --- modules/sd_models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index f9b3063d..49dc3238 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -236,12 +236,11 @@ def load_model(checkpoint_info=None): sd_model.to(shared.device) sd_hijack.model_hijack.hijack(sd_model) + script_callbacks.model_loaded_callback(sd_model) sd_model.eval() shared.sd_model = sd_model - script_callbacks.model_loaded_callback(sd_model) - print(f"Model loaded.") return sd_model @@ -268,6 +267,7 @@ def reload_model_weights(sd_model, info=None): load_model_weights(sd_model, checkpoint_info) sd_hijack.model_hijack.hijack(sd_model) + script_callbacks.model_loaded_callback(sd_model) if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram: sd_model.to(devices.device) From 321bacc6a9eaf4a25f31279f288fa752be507a20 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 20:15:12 +0300 Subject: [PATCH 129/140] call model_loaded_callback after setting shared.sd_model in case scripts refer to it using that --- modules/sd_models.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/modules/sd_models.py b/modules/sd_models.py index 49dc3238..e697bb72 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -236,11 +236,12 @@ def load_model(checkpoint_info=None): sd_model.to(shared.device) sd_hijack.model_hijack.hijack(sd_model) - script_callbacks.model_loaded_callback(sd_model) sd_model.eval() shared.sd_model = sd_model + script_callbacks.model_loaded_callback(sd_model) + print(f"Model loaded.") return sd_model From 24694e5983d0944b901892cb101878e6dec89a20 Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Sun, 23 Oct 2022 01:57:58 +0900 Subject: [PATCH 130/140] Update hypernetwork.py --- modules/hypernetworks/hypernetwork.py | 55 +++++++++++++++++++++------ 1 file changed, 44 insertions(+), 11 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 3bc71ee5..81132be4 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -16,6 +16,7 @@ from modules.textual_inversion import textual_inversion from modules.textual_inversion.learn_schedule import LearnRateScheduler from torch import einsum +from statistics import stdev, mean class HypernetworkModule(torch.nn.Module): multiplier = 1.0 @@ -268,6 +269,32 @@ def stack_conds(conds): return torch.stack(conds) +def log_statistics(loss_info:dict, key, value): + if key not in loss_info: + loss_info[key] = [value] + else: + loss_info[key].append(value) + if len(loss_info) > 1024: + loss_info.pop(0) + + +def statistics(data): + total_information = f"loss:{mean(data):.3f}"+u"\u00B1"+f"({stdev(data)/ (len(data)**0.5):.3f})" + recent_data = data[-32:] + recent_information = f"recent 32 loss:{mean(recent_data):.3f}"+u"\u00B1"+f"({stdev(recent_data)/ (len(recent_data)**0.5):.3f})" + return total_information, recent_information + + +def report_statistics(loss_info:dict): + keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x])) + for key in keys: + info, recent = statistics(loss_info[key]) + print("Loss statistics for file " + key) + print(info) + print(recent) + + + def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height): # images allows training previews to have infotext. Importing it at the top causes a circular import problem. from modules import images @@ -310,7 +337,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log for weight in weights: weight.requires_grad = True - losses = torch.zeros((32,)) + size = len(ds.indexes) + loss_dict = {} + losses = torch.zeros((size,)) + previous_mean_loss = 0 + print("Mean loss of {} elements".format(size)) last_saved_file = "" last_saved_image = "" @@ -329,7 +360,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: hypernetwork.step = i + ititial_step - + if loss_dict and i % size == 0: + previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict) + scheduler.apply(optimizer, hypernetwork.step) if scheduler.finished: break @@ -346,7 +379,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log del c losses[hypernetwork.step % losses.shape[0]] = loss.item() - + for entry in entries: + log_statistics(loss_dict, entry.filename, loss.item()) + optimizer.zero_grad() weights[0].grad = None loss.backward() @@ -359,10 +394,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log optimizer.step() - mean_loss = losses.mean() - if torch.isnan(mean_loss): + if torch.isnan(losses[hypernetwork.step % losses.shape[0]]): raise RuntimeError("Loss diverged.") - pbar.set_description(f"loss: {mean_loss:.7f}") + pbar.set_description(f"dataset loss: {previous_mean_loss:.7f}") if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0: # Before saving, change name to match current checkpoint. @@ -371,7 +405,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log hypernetwork.save(last_saved_file) textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), { - "loss": f"{mean_loss:.7f}", + "loss": f"{previous_mean_loss:.7f}", "learn_rate": scheduler.learn_rate }) @@ -420,14 +454,15 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log shared.state.textinfo = f"""

-Loss: {mean_loss:.7f}
+Loss: {previous_mean_loss:.7f}
Step: {hypernetwork.step}
Last prompt: {html.escape(entries[0].cond_text)}
Last saved hypernetwork: {html.escape(last_saved_file)}
Last saved image: {html.escape(last_saved_image)}

""" - + + report_statistics(loss_dict) checkpoint = sd_models.select_checkpoint() hypernetwork.sd_checkpoint = checkpoint.hash @@ -438,5 +473,3 @@ Last saved image: {html.escape(last_saved_image)}
hypernetwork.save(filename) return hypernetwork, filename - - From 4fdb53c1e9962507fc8336dad9a0fabfe6c418c0 Mon Sep 17 00:00:00 2001 From: Unnoen Date: Wed, 19 Oct 2022 21:38:10 +1100 Subject: [PATCH 131/140] Generate grid preview for progress image --- modules/sd_samplers.py | 26 +++++++++++++++++++++++++- modules/shared.py | 1 + modules/ui.py | 5 ++++- 3 files changed, 30 insertions(+), 2 deletions(-) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index f58a29b9..74a480e5 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -7,7 +7,7 @@ import inspect import k_diffusion.sampling import ldm.models.diffusion.ddim import ldm.models.diffusion.plms -from modules import prompt_parser, devices, processing +from modules import prompt_parser, devices, processing, images from modules.shared import opts, cmd_opts, state import modules.shared as shared @@ -89,6 +89,30 @@ def sample_to_image(samples): x_sample = x_sample.astype(np.uint8) return Image.fromarray(x_sample) +def samples_to_image_grid(samples): + progress_images = [] + for i in range(len(samples)): + # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed. + x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0] + x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) + x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) + x_sample = x_sample.astype(np.uint8) + progress_images.append(Image.fromarray(x_sample)) + + return images.image_grid(progress_images) + +def samples_to_image_grid_combined(samples): + progress_images = [] + # Decode all samples at once to increase speed at the cost of VRAM usage. + x_samples = processing.decode_first_stage(shared.sd_model, samples) + x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) + + for x_sample in x_samples: + x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) + x_sample = x_sample.astype(np.uint8) + progress_images.append(Image.fromarray(x_sample)) + + return images.image_grid(progress_images) def store_latent(decoded): state.current_latent = decoded diff --git a/modules/shared.py b/modules/shared.py index d9cb65ef..95d6e225 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -294,6 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}), + "progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"), "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), diff --git a/modules/ui.py b/modules/ui.py index 56c233ab..de0abc7e 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -318,7 +318,10 @@ def check_progress_call(id_part): if shared.parallel_processing_allowed: if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None: - shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent) + if opts.progress_decode_combined: + shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent) + else: + shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent) shared.state.current_image_sampling_step = shared.state.sampling_step image = shared.state.current_image From d213d6ca6f90094cb45c11e2f3cb37d25a8d1f94 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 20:48:13 +0300 Subject: [PATCH 132/140] removed the option to use 2x more memory when generating previews added an option to always only show one image in previews removed duplicate code --- modules/sd_samplers.py | 33 +++++++++------------------------ modules/shared.py | 2 +- modules/ui.py | 6 +++--- 3 files changed, 13 insertions(+), 28 deletions(-) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 74a480e5..0b408a70 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -71,6 +71,7 @@ sampler_extra_params = { 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'], } + def setup_img2img_steps(p, steps=None): if opts.img2img_fix_steps or steps is not None: steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0 @@ -82,37 +83,21 @@ def setup_img2img_steps(p, steps=None): return steps, t_enc -def sample_to_image(samples): - x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0] +def single_sample_to_image(sample): + x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0] x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) return Image.fromarray(x_sample) + +def sample_to_image(samples): + return single_sample_to_image(samples[0]) + + def samples_to_image_grid(samples): - progress_images = [] - for i in range(len(samples)): - # Decode the samples individually to reduce VRAM usage at the cost of a bit of speed. - x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0] - x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) - x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) - x_sample = x_sample.astype(np.uint8) - progress_images.append(Image.fromarray(x_sample)) + return images.image_grid([single_sample_to_image(sample) for sample in samples]) - return images.image_grid(progress_images) - -def samples_to_image_grid_combined(samples): - progress_images = [] - # Decode all samples at once to increase speed at the cost of VRAM usage. - x_samples = processing.decode_first_stage(shared.sd_model, samples) - x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0) - - for x_sample in x_samples: - x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) - x_sample = x_sample.astype(np.uint8) - progress_images.append(Image.fromarray(x_sample)) - - return images.image_grid(progress_images) def store_latent(decoded): state.current_latent = decoded diff --git a/modules/shared.py b/modules/shared.py index 95d6e225..25bfc895 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -294,7 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"), options_templates.update(options_section(('ui', "User interface"), { "show_progressbar": OptionInfo(True, "Show progressbar"), "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 32, "step": 1}), - "progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"), + "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"), "return_grid": OptionInfo(True, "Show grid in results for web"), "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"), "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), diff --git a/modules/ui.py b/modules/ui.py index de0abc7e..ffa14cac 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -318,10 +318,10 @@ def check_progress_call(id_part): if shared.parallel_processing_allowed: if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None: - if opts.progress_decode_combined: - shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent) - else: + if opts.show_progress_grid: shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent) + else: + shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent) shared.state.current_image_sampling_step = shared.state.sampling_step image = shared.state.current_image From be748e8b086bd9834d08bdd9160649a5e7700af7 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 22:05:22 +0300 Subject: [PATCH 133/140] add --freeze-settings commandline argument to disable changing settings --- modules/shared.py | 1 + modules/ui.py | 11 +++++++++-- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/modules/shared.py b/modules/shared.py index 25bfc895..b55371d3 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -64,6 +64,7 @@ parser.add_argument("--port", type=int, help="launch gradio with given server po parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json')) parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False) +parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False) parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json')) parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option") parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) diff --git a/modules/ui.py b/modules/ui.py index ffa14cac..2311572c 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -580,6 +580,9 @@ def apply_setting(key, value): if value is None: return gr.update() + if shared.cmd_opts.freeze_settings: + return gr.update() + # dont allow model to be swapped when model hash exists in prompt if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap: return gr.update() @@ -1501,6 +1504,8 @@ Requested path was: {f} def run_settings(*args): changed = 0 + assert not shared.cmd_opts.freeze_settings, "changing settings is disabled" + for key, value, comp in zip(opts.data_labels.keys(), args, components): if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default): return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson() @@ -1530,6 +1535,8 @@ Requested path was: {f} return f'{changed} settings changed.', opts.dumpjson() def run_settings_single(value, key): + assert not shared.cmd_opts.freeze_settings, "changing settings is disabled" + if not opts.same_type(value, opts.data_labels[key].default): return gr.update(visible=True), opts.dumpjson() @@ -1582,7 +1589,7 @@ Requested path was: {f} elem_id, text = item.section gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='

{}

'.format(text)) - if k in quicksettings_names: + if k in quicksettings_names and not shared.cmd_opts.freeze_settings: quicksettings_list.append((i, k, item)) components.append(dummy_component) else: @@ -1615,7 +1622,7 @@ Requested path was: {f} def reload_scripts(): modules.scripts.reload_script_body_only() - reload_javascript() # need to refresh the html page + reload_javascript() # need to refresh the html page reload_script_bodies.click( fn=reload_scripts, From ca5a9e79dc28eeaa3a161427a82e34703bf15765 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Sat, 22 Oct 2022 22:06:54 +0300 Subject: [PATCH 134/140] fix for img2img color correction in a batch #3218 --- modules/processing.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/modules/processing.py b/modules/processing.py index 27c669b0..b1877b80 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -403,8 +403,6 @@ def process_images(p: StableDiffusionProcessing) -> Processed: if (len(prompts) == 0): break - #uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt]) - #c = p.sd_model.get_learned_conditioning(prompts) with devices.autocast(): uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps) c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps) @@ -716,6 +714,10 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0) if self.overlay_images is not None: self.overlay_images = self.overlay_images * self.batch_size + + if self.color_corrections is not None and len(self.color_corrections) == 1: + self.color_corrections = self.color_corrections * self.batch_size + elif len(imgs) <= self.batch_size: self.batch_size = len(imgs) batch_images = np.array(imgs) From 48dbf99e84045ee7af55bc5b1b86492a240e631e Mon Sep 17 00:00:00 2001 From: AngelBottomless <35677394+aria1th@users.noreply.github.com> Date: Sun, 23 Oct 2022 04:17:16 +0900 Subject: [PATCH 135/140] Allow tracking real-time loss Someone had 6000 images in their dataset, and it was shown as 0, which was confusing. This will allow tracking real time dataset-average loss for registered objects. --- modules/hypernetworks/hypernetwork.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 81132be4..99fd0f8f 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -360,7 +360,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step) for i, entries in pbar: hypernetwork.step = i + ititial_step - if loss_dict and i % size == 0: + if len(loss_dict) > 0: previous_mean_loss = sum(i[-1] for i in loss_dict.values()) / len(loss_dict) scheduler.apply(optimizer, hypernetwork.step) From 1fbfc052eb529d8cf8ce5baf578bcf93d0280c29 Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 23 Oct 2022 05:43:34 +0100 Subject: [PATCH 136/140] Update hypernetwork.py --- modules/hypernetworks/hypernetwork.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index 99fd0f8f..98a7b62e 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -288,10 +288,13 @@ def statistics(data): def report_statistics(loss_info:dict): keys = sorted(loss_info.keys(), key=lambda x: sum(loss_info[x]) / len(loss_info[x])) for key in keys: - info, recent = statistics(loss_info[key]) - print("Loss statistics for file " + key) - print(info) - print(recent) + try: + print("Loss statistics for file " + key) + info, recent = statistics(loss_info[key]) + print(info) + print(recent) + except Exception as e: + print(e) From a7c213d0f5ebb10722629b8490a5863f9ce6c4fa Mon Sep 17 00:00:00 2001 From: Stephen Date: Fri, 21 Oct 2022 19:27:40 -0400 Subject: [PATCH 137/140] [API][Feature] - Add img2img API endpoint --- modules/api/api.py | 58 +++++++++++++++++++++++++++++++++++---- modules/api/processing.py | 11 ++++++-- modules/processing.py | 2 +- 3 files changed, 63 insertions(+), 8 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index 5b0c934e..a04f2428 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -1,5 +1,5 @@ -from modules.api.processing import StableDiffusionProcessingAPI -from modules.processing import StableDiffusionProcessingTxt2Img, process_images +from modules.api.processing import StableDiffusionTxt2ImgProcessingAPI, StableDiffusionImg2ImgProcessingAPI +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images from modules.sd_samplers import all_samplers from modules.extras import run_pnginfo import modules.shared as shared @@ -10,6 +10,7 @@ from pydantic import BaseModel, Field, Json import json import io import base64 +from PIL import Image sampler_to_index = lambda name: next(filter(lambda row: name.lower() == row[1].name.lower(), enumerate(all_samplers)), None) @@ -18,6 +19,11 @@ class TextToImageResponse(BaseModel): parameters: Json info: Json +class ImageToImageResponse(BaseModel): + images: list[str] = Field(default=None, title="Image", description="The generated image in base64 format.") + parameters: Json + info: Json + class Api: def __init__(self, app, queue_lock): @@ -25,8 +31,9 @@ class Api: self.app = app self.queue_lock = queue_lock self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) + self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"]) - def text2imgapi(self, txt2imgreq: StableDiffusionProcessingAPI ): + def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) if sampler_index is None: @@ -54,8 +61,49 @@ class Api: - def img2imgapi(self): - raise NotImplementedError + def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): + sampler_index = sampler_to_index(img2imgreq.sampler_index) + + if sampler_index is None: + raise HTTPException(status_code=404, detail="Sampler not found") + + + init_images = img2imgreq.init_images + if init_images is None: + raise HTTPException(status_code=404, detail="Init image not found") + + + populate = img2imgreq.copy(update={ # Override __init__ params + "sd_model": shared.sd_model, + "sampler_index": sampler_index[0], + "do_not_save_samples": True, + "do_not_save_grid": True + } + ) + p = StableDiffusionProcessingImg2Img(**vars(populate)) + + imgs = [] + for img in init_images: + # if has a comma, deal with prefix + if "," in img: + img = img.split(",")[1] + # convert base64 to PIL image + img = base64.b64decode(img) + img = Image.open(io.BytesIO(img)) + imgs = [img] * p.batch_size + + p.init_images = imgs + # Override object param + with self.queue_lock: + processed = process_images(p) + + b64images = [] + for i in processed.images: + buffer = io.BytesIO() + i.save(buffer, format="png") + b64images.append(base64.b64encode(buffer.getvalue())) + + return ImageToImageResponse(images=b64images, parameters=json.dumps(vars(img2imgreq)), info=json.dumps(processed.info)) def extrasapi(self): raise NotImplementedError diff --git a/modules/api/processing.py b/modules/api/processing.py index 4c541241..9f1d65c0 100644 --- a/modules/api/processing.py +++ b/modules/api/processing.py @@ -1,7 +1,8 @@ +from array import array from inflection import underscore from typing import Any, Dict, Optional from pydantic import BaseModel, Field, create_model -from modules.processing import StableDiffusionProcessingTxt2Img +from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img import inspect @@ -92,8 +93,14 @@ class PydanticModelGenerator: DynamicModel.__config__.allow_mutation = True return DynamicModel -StableDiffusionProcessingAPI = PydanticModelGenerator( +StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingTxt2Img", StableDiffusionProcessingTxt2Img, [{"key": "sampler_index", "type": str, "default": "Euler"}] +).generate_model() + +StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( + "StableDiffusionProcessingImg2Img", + StableDiffusionProcessingImg2Img, + [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}] ).generate_model() \ No newline at end of file diff --git a/modules/processing.py b/modules/processing.py index b1877b80..1557ed8c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs): + def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs): super().__init__(**kwargs) self.init_images = init_images From 9e1a8b7734a2881451a2efbf80def011ea41ba49 Mon Sep 17 00:00:00 2001 From: Stephen Date: Sat, 22 Oct 2022 15:42:00 -0400 Subject: [PATCH 138/140] non-implemented mask with any type --- modules/api/api.py | 4 ++++ modules/api/processing.py | 2 +- modules/processing.py | 2 +- 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index a04f2428..3df6ff96 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -72,6 +72,10 @@ class Api: if init_images is None: raise HTTPException(status_code=404, detail="Init image not found") + mask = img2imgreq.mask + if mask: + raise HTTPException(status_code=400, detail="Mask not supported yet") + populate = img2imgreq.copy(update={ # Override __init__ params "sd_model": shared.sd_model, diff --git a/modules/api/processing.py b/modules/api/processing.py index 9f1d65c0..f551fa35 100644 --- a/modules/api/processing.py +++ b/modules/api/processing.py @@ -102,5 +102,5 @@ StableDiffusionTxt2ImgProcessingAPI = PydanticModelGenerator( StableDiffusionImg2ImgProcessingAPI = PydanticModelGenerator( "StableDiffusionProcessingImg2Img", StableDiffusionProcessingImg2Img, - [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}] + [{"key": "sampler_index", "type": str, "default": "Euler"}, {"key": "init_images", "type": list, "default": None}, {"key": "denoising_strength", "type": float, "default": 0.75}, {"key": "mask", "type": str, "default": None}] ).generate_model() \ No newline at end of file diff --git a/modules/processing.py b/modules/processing.py index 1557ed8c..ff83023c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -623,7 +623,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: str=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs): + def __init__(self, init_images: list=None, resize_mode: int=0, denoising_strength: float=0.75, mask: Any=None, mask_blur: int=4, inpainting_fill: int=0, inpaint_full_res: bool=True, inpaint_full_res_padding: int=0, inpainting_mask_invert: int=0, **kwargs): super().__init__(**kwargs) self.init_images = init_images From 5dc0739ecdc1ade8fcf4eb77f2a503ef12489f32 Mon Sep 17 00:00:00 2001 From: Stephen Date: Sat, 22 Oct 2022 17:10:28 -0400 Subject: [PATCH 139/140] working mask --- modules/api/api.py | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/modules/api/api.py b/modules/api/api.py index 3df6ff96..3caa83a4 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -33,6 +33,14 @@ class Api: self.app.add_api_route("/sdapi/v1/txt2img", self.text2imgapi, methods=["POST"]) self.app.add_api_route("/sdapi/v1/img2img", self.img2imgapi, methods=["POST"]) + def __base64_to_image(self, base64_string): + # if has a comma, deal with prefix + if "," in base64_string: + base64_string = base64_string.split(",")[1] + imgdata = base64.b64decode(base64_string) + # convert base64 to PIL image + return Image.open(io.BytesIO(imgdata)) + def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): sampler_index = sampler_to_index(txt2imgreq.sampler_index) @@ -74,26 +82,22 @@ class Api: mask = img2imgreq.mask if mask: - raise HTTPException(status_code=400, detail="Mask not supported yet") + mask = self.__base64_to_image(mask) populate = img2imgreq.copy(update={ # Override __init__ params "sd_model": shared.sd_model, "sampler_index": sampler_index[0], "do_not_save_samples": True, - "do_not_save_grid": True + "do_not_save_grid": True, + "mask": mask } ) p = StableDiffusionProcessingImg2Img(**vars(populate)) imgs = [] for img in init_images: - # if has a comma, deal with prefix - if "," in img: - img = img.split(",")[1] - # convert base64 to PIL image - img = base64.b64decode(img) - img = Image.open(io.BytesIO(img)) + img = self.__base64_to_image(img) imgs = [img] * p.batch_size p.init_images = imgs From 1ef32c8b8fa3e16a1e7b287eb19d4fc943d1f2a5 Mon Sep 17 00:00:00 2001 From: kabachuha Date: Sun, 23 Oct 2022 00:01:13 +0300 Subject: [PATCH 140/140] Add ru_RU localization --- localizations/ru_RU.json | 475 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 475 insertions(+) create mode 100644 localizations/ru_RU.json diff --git a/localizations/ru_RU.json b/localizations/ru_RU.json new file mode 100644 index 00000000..664d36ea --- /dev/null +++ b/localizations/ru_RU.json @@ -0,0 +1,475 @@ +{ + "⤡": "⤡", + "⊞": "⊞", + "×": "×", + "❮": "❮", + "❯": "❯", + "Loading...": "Загрузка...", + "view": "просмотр", + "api": "api", + "•": "•", + "built with gradio": "На основе Gradio", + "Stable Diffusion checkpoint": "Веса Stable Diffusion", + "txt2img": "текст-в-рисунок", + "img2img": "рисунок-в-рисунок", + "Extras": "Дополнения", + "PNG Info": "Информация о PNG", + "Image Browser": "Просмотр изображений", + "History": "Журнал", + "Checkpoint Merger": "Слияние весов", + "Train": "Обучение", + "Create aesthetic embedding": "Создать эмбеддинг эстетики", + "Settings": "Настройки", + "Prompt": "Запрос", + "Negative prompt": "Исключающий запрос", + "Run": "Запустить", + "Skip": "Пропустить", + "Interrupt": "Прервать", + "Generate": "Создать", + "Style 1": "Стиль 1", + "Style 2": "Стиль 2", + "Label": "Метка", + "File": "Файл", + "Drop File Here": "Перетащите файл сюда", + "-": "-", + "or": "или", + "Click to Upload": "Нажмите, чтобы загрузить", + "Image": "Рисунок", + "Check progress": "Узнать состояние", + "Check progress (first)": "Узнать состояние первого", + "Sampling Steps": "Шагов семплера", + "Sampling method": "Метод семплирования", + "Euler a": "Euler a", + "Euler": "Euler", + "LMS": "LMS", + "Heun": "Heun", + "DPM2": "DPM2", + "DPM2 a": "DPM2 a", + "DPM fast": "DPM fast", + "DPM adaptive": "DPM adaptive", + "LMS Karras": "LMS Karras", + "DPM2 Karras": "DPM2 Karras", + "DPM2 a Karras": "DPM2 a Karras", + "DDIM": "DDIM", + "PLMS": "PLMS", + "Width": "Ширина", + "Height": "Высота", + "Restore faces": "Восстановить лица", + "Tiling": "Замощение", + "Highres. fix": "HD-режим", + "Firstpass width": "Ширина первого прохода", + "Firstpass height": "Высота первого прохода", + "Denoising strength": "Сила шумоподавления", + "Batch count": "Рисунков подряд", + "Batch size": "Рисунков параллельно", + "CFG Scale": "Близость к запросу", + "Seed": "Семя", + "Extra": "Дополнения", + "Variation seed": "Вариация семени", + "Variation strength": "Вариация шумоподавления", + "Resize seed from width": "Поправка в семя от ширины", + "Resize seed from height": "Поправка в семя от высоты", + "Open for Clip Aesthetic!": "Clip-эстетика!", + "▼": "▼", + "Aesthetic weight": "Вес эстетики", + "Aesthetic steps": "Шагов эстетики", + "Aesthetic learning rate": "Скорость обучения эстетики", + "Slerp interpolation": "Slerp-интерполяция", + "Aesthetic imgs embedding": "Рисунки - эмбеддинги эстетики", + "None": "Ничего", + "Aesthetic text for imgs": "Имя эстетики рисунков", + "Slerp angle": "Угол slerp", + "Is negative text": "Это текст для исключения", + "Script": "Скрипт", + "Prompt matrix": "Матрица запросов", + "Prompts from file or textbox": "Запросы из файла или текста", + "X/Y plot": "X/Y-график", + "Put variable parts at start of prompt": "Переменное начало запроса", + "Show Textbox": "Показать текстовый ввод", + "File with inputs": "Файл входа", + "Prompts": "Запросы", + "X type": "Ось X", + "Nothing": "Ничего", + "Var. seed": "Вариация семени", + "Var. strength": "Вариация силы", + "Steps": "Число шагов", + "Prompt S/R": "Вариация запроса", + "Prompt order": "Порядок запросов", + "Sampler": "Семплер", + "Checkpoint name": "Имя файла весов", + "Hypernetwork": "Гиперсеть", + "Hypernet str.": "Строка гиперсети", + "Sigma Churn": "Возмущение сигмы", + "Sigma min": "Мин. сигма", + "Sigma max": "Макс. сигма", + "Sigma noise": "Сигма-шум", + "Eta": "Расчётное время", + "Clip skip": "Пропустить Clip", + "Denoising": "Шумоподавление", + "X values": "Значения X", + "Y type": "Тип Y", + "Y values": "Значения Y", + "Draw legend": "Легенда графика", + "Include Separate Images": "Включить отдельные рисунки", + "Keep -1 for seeds": "-1 для семени", + "Drop Image Here": "Перетащите рисунок сюда", + "Save": "Сохранить", + "Send to img2img": "В рисунок-в-рисунок", + "Send to inpaint": "В режим врисовывания", + "Send to extras": "В дополнения", + "Make Zip when Save?": "Создать zip при сохранении?", + "Textbox": "Текст", + "Interrogate\nCLIP": "Распознавание\nCLIP", + "Interrogate\nDeepBooru": "Распознавание\nDeepBooru", + "Inpaint": "врисовать", + "Batch img2img": "рисунок-в-рисунок (набор)", + "Image for img2img": "рисунок-в-рисунок (вход)", + "Image for inpainting with mask": "врисовать (вход с трафаретом)", + "Mask": "Трафарет", + "Mask blur": "Размытие трафарета", + "Mask mode": "Режим трафарета", + "Draw mask": "Нарисовать трафарет", + "Upload mask": "Загрузить трафарет", + "Masking mode": "Режим трафарета", + "Inpaint masked": "Внутри трафарета", + "Inpaint not masked": "Вне трафарета", + "Masked content": "Под трафаретом", + "fill": "залить", + "original": "сохранить", + "latent noise": "латентный шум", + "latent nothing": "латентная пустота", + "Inpaint at full resolution": "Врисовать при полном разрешении", + "Inpaint at full resolution padding, pixels": "Врисовать с достройкой до полного разрешения, в пикселях", + "Process images in a directory on the same machine where the server is running.": "Обрабатывать рисунки на том же компьютере, где сервер", + "Use an empty output directory to save pictures normally instead of writing to the output directory.": "Использовать пустую папку вместо того, чтобы выводить в output", + "Disabled when launched with --hide-ui-dir-config.": "Выключено при запуске с --hide-ui-dir-config", + "Input directory": "Папка входа", + "Output directory": "Папка выхода", + "Resize mode": "Масштабирование", + "Just resize": "Только сжать", + "Crop and resize": "Сжать и обрезать", + "Resize and fill": "Сжать и залить", + "img2img alternative test": "рисунок-в-рисунок (альтернатива)", + "Loopback": "Прокручивание", + "Outpainting mk2": "Обрисовыватель mk2", + "Poor man's outpainting": "Хоть какой-то обрисовыватель", + "SD upscale": "SD-апскейл", + "should be 2 or lower.": "должно быть меньше равно 2", + "Override `Sampling method` to Euler?(this method is built for it)": "Сменить метод семплирования на метод Эйлера?(скрипт строился с его учётом)", + "Override `prompt` to the same value as `original prompt`?(and `negative prompt`)": "Сменить `запрос` на `изначальный запрос`?(и `запрос-исключение`)", + "Original prompt": "Изначальный запрос", + "Original negative prompt": "Изначальный запрос-исключение", + "Override `Sampling Steps` to the same value as `Decode steps`?": "Сменить число шагов на число шагов декодирования?", + "Decode steps": "Шагов декодирования", + "Override `Denoising strength` to 1?": "Сменить силу шумоподавления на 1?", + "Decode CFG scale": "Близость к запросу декодирования", + "Randomness": "Случайность", + "Sigma adjustment for finding noise for image": "Поправка к сигме подбора шума для рисунка", + "Loops": "Циклов", + "Denoising strength change factor": "Множитель силы шумоподавления", + "Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8": "Рекоммендуемые настройки： Число шагов：80-100，Метод：Euler a，Шумоподавление：0.8", + "Pixels to expand": "Пикселов расширить", + "Outpainting direction": "Направление обрисовывания", + "left": "влево", + "right": "вправо", + "up": "вверх", + "down": "вниз", + "Fall-off exponent (lower=higher detail)": "Степень затухания （меньше=больше деталей）", + "Color variation": "Вариация цвета", + "Will upscale the image to twice the dimensions; use width and height sliders to set tile size": "Расширит рисунок дважды； ползунки ширины и высоты устанавливают размеры плиток", + "Tile overlap": "Перекрытие плиток", + "Upscaler": "Апскейлер", + "Lanczos": "Lanczos", + "LDSR": "LDSR", + "BSRGAN 4x": "BSRGAN 4x", + "ESRGAN_4x": "ESRGAN_4x", + "R-ESRGAN 4x+ Anime6B": "R-ESRGAN 4x+ Anime6B", + "ScuNET GAN": "ScuNET GAN", + "ScuNET PSNR": "ScuNET PSNR", + "SwinIR_4x": "SwinIR 4x", + "Single Image": "Один рисунок", + "Batch Process": "Набор рисунков", + "Batch from Directory": "Рисунки из папки", + "Source": "Вход", + "Show result images": "Показать результаты", + "Scale by": "Увеличить в", + "Scale to": "Увеличить до", + "Resize": "Масштабировать", + "Crop to fit": "Обрезать до рамки", + "Upscaler 2": "Апскейлер 2", + "Upscaler 2 visibility": "Видимость Апскейлера 2", + "GFPGAN visibility": "Видимость GFPGAN", + "CodeFormer visibility": "Видимость CodeFormer", + "CodeFormer weight (0 = maximum effect, 1 = minimum effect)": "Вес CodeFormer (0 = максимальное действие, 1 = минимальное)", + "Open output directory": "Открыть папку выхода", + "Send to txt2img": "В текст-в-рисунок", + "txt2img history": "журнал текста-в-рисунок", + "img2img history": "журнал рисунка-в-рисунок", + "extras history": "журнал дополнений", + "Renew Page": "Обновить страницу", + "extras": "дополнения", + "favorites": "избранное", + "Load": "Загрузить", + "Images directory": "Папка с рисунками", + "Prev batch": "Пред. набор", + "Next batch": "След. набор", + "First Page": "Первая страница", + "Prev Page": "Пред. страница", + "Page Index": "Список страниц", + "Next Page": "След. страница", + "End Page": "Конец страницы", + "number of images to delete consecutively next": "сколько рисунков удалить подряд", + "Delete": "Удалить", + "Generate Info": "Сведения о генерации", + "File Name": "Имя файла", + "Collect": "Накопить", + "Refresh page": "Обновить страницу", + "Date to": "Дата", + "Number": "Число", + "set_index": "индекс", + "Checkbox": "Галочка", + "A merger of the two checkpoints will be generated in your": "Слияние весов будет создано, где хранятся", + "checkpoint": "ckpt", + "directory.": "веса", + "Primary model (A)": "Первичная модель (A)", + "Secondary model (B)": "Вторичная модель (B)", + "Tertiary model (C)": "Третичная модель (C)", + "Custom Name (Optional)": "Произвольное имя (необязательно)", + "Multiplier (M) - set to 0 to get model A": "Множитель (M) - 0 даст модель A", + "Interpolation Method": "Метод интерполяции", + "Weighted sum": "Взвешенная сумма", + "Add difference": "Сумма разностей", + "Save as float16": "Сохранить как float16", + "See": "См.", + "wiki": "вики", + "for detailed explanation.": "для подробных объяснений.", + "Create embedding": "Создать эмбеддинг", + "Create aesthetic images embedding": "Создать эмбеддинг эстетики по рисункам", + "Create hypernetwork": "Создать гиперсеть", + "Preprocess images": "Предобработать рисунки", + "Name": "Имя", + "Initialization text": "Соответствующий текст", + "Number of vectors per token": "Векторов на токен", + "Overwrite Old Embedding": "Перезаписать эмбеддинг", + "Source directory": "Исходная папка", + "Modules": "Модули", + "Enter hypernetwork layer structure": "Структура слоёв гиперсети", + "Add layer normalization": "Добавить нормализацию слоёв", + "Overwrite Old Hypernetwork": "Перезаписать гиперсеть", + "Select activation function of hypernetwork": "Функция активации гиперсети", + "linear": "линейная", + "relu": "relu", + "leakyrelu": "leakyrelu", + "Destination directory": "Папка назначения", + "Existing Caption txt Action": "Что делать с предыдущим текстом", + "ignore": "игнорировать", + "copy": "копировать", + "prepend": "в начало", + "append": "в конец", + "Create flipped copies": "Создать отражённые копии", + "Split oversized images into two": "Поделить слишком большие рисунки пополам", + "Split oversized images": "Поделить слишком большие рисунки", + "Use BLIP for caption": "Использовать BLIP для названий", + "Use deepbooru for caption": "Использовать deepbooru для тегов", + "Split image threshold": "Порог разделения рисунков", + "Split image overlap ratio": "Пропорции разделения рисунков", + "Preprocess": "Предобработка", + "Train an embedding; must specify a directory with a set of 1:1 ratio images": "Обучить эмбеддинг； укажите папку рисунков с пропорциями 1:1", + "Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images": "Обучить эмбеддинг или гиперсеть； укажите папку рисунков с пропорциями 1:1", + "[wiki]": "[вики]", + "Embedding": "Эмбеддинг", + "Embedding Learning rate": "Скорость обучения эмбеддинга", + "Hypernetwork Learning rate": "Скорость обучения гиперсети", + "Learning rate": "Скорость обучения", + "Dataset directory": "Папка датасета", + "Log directory": "Папка журнала", + "Prompt template file": "Файл шаблона запроса", + "Max steps": "Макс. шагов", + "Save an image to log directory every N steps, 0 to disable": "Сохранять рисунок каждые N шагов, 0 чтобы отключить", + "Save a copy of embedding to log directory every N steps, 0 to disable": "Сохранять эмбеддинг каждые N шагов, 0 чтобы отключить", + "Save images with embedding in PNG chunks": "Сохранить рисунок с эмбеддингом в виде PNG-фрагментов", + "Read parameters (prompt, etc...) from txt2img tab when making previews": "Считать параметры (запрос и т.д.) из вкладки текст-в-рисунок для предпросмотра", + "Train Hypernetwork": "Обучить гиперсеть", + "Train Embedding": "Обучить эмбеддинг", + "Create an aesthetic embedding out of any number of images": "Создать эмбеддинг эстетики по любому числу рисунков", + "Create images embedding": "Создать эмбеддинг рисунков", + "Apply settings": "Применить настройки", + "Saving images/grids": "Сохранение рисунков/таблиц", + "Always save all generated images": "Всегда сохранять созданные рисунки", + "File format for images": "Формат файла рисунков", + "Images filename pattern": "Формат имени файлов рисунков", + "Always save all generated image grids": "Всегда сохранять созданные таблицы", + "File format for grids": "Формат файла таблиц", + "Add extended info (seed, prompt) to filename when saving grid": "Вставлять доп. сведения (семя, запрос) в имя файла таблиц", + "Do not save grids consisting of one picture": "Не сохранять таблицы из одного рисунка", + "Prevent empty spots in grid (when set to autodetect)": "Не допускать пустоты в таблицах （автообнаружение）", + "Grid row count; use -1 for autodetect and 0 for it to be same as batch size": "Число строк таблицы； -1, чтобы автоматически, 0 — размер набора", + "Save text information about generation parameters as chunks to png files": "Встроить сведения о генерации в файлы png", + "Create a text file next to every image with generation parameters.": "Создать текстовый файл для каждого рисунка с параметрами генерации", + "Save a copy of image before doing face restoration.": "Сохранить копию перед восстановлением лиц", + "Quality for saved jpeg images": "Качество jpeg-рисунков", + "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG": "Если размер PNG больше 4МБ или рисунок шире 4000 пикселей, пересжать в JPEG", + "Use original name for output filename during batch process in extras tab": "Использовать исходное имя выходного файла для обработки набора во вкладке дополнений", + "When using 'Save' button, only save a single selected image": "Сохранять только один рисунок при нажатии кнопки Сохранить", + "Do not add watermark to images": "Не добавлять водяной знак", + "Paths for saving": "Папки сохранений", + "Output directory for images; if empty, defaults to three directories below": "Папка выхода рисунков; если пусто, использует те, что ниже", + "Output directory for txt2img images": "Папка выхода текста-в-рисунок", + "Output directory for img2img images": "Папка выхода рисунка-в-рисунок", + "Output directory for images from extras tab": "Папка выхода для дополнений", + "Output directory for grids; if empty, defaults to two directories below": "Папка выхода таблиц; если пусто, использует папки выше", + "Output directory for txt2img grids": "Папка выхода текста-в-рисунок", + "Output directory for img2img grids": "Папка выхода рисунка-в-рисунок", + "Directory for saving images using the Save button": "Папка выхода для кнопки Сохранить", + "Saving to a directory": "Сохранить в папку", + "Save images to a subdirectory": "Сохранить рисунки в подпапку", + "Save grids to a subdirectory": "Сохранить таблицы в подпапку", + "When using \"Save\" button, save images to a subdirectory": "При нажатии кнопки Сохранить, сложить рисунки в подпапку", + "Directory name pattern": "Шаблон имени папки", + "Max prompt words for [prompt_words] pattern": "Макс. число слов для шаблона [prompt_words]", + "Upscaling": "Апскейл", + "Tile size for ESRGAN upscalers. 0 = no tiling.": "Размер плитки для ESRGAN. 0 = нет замощения", + "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.": "Наложение плиток ESRGAN, в пикселях. Меньше = выделеннее шов", + "Tile size for all SwinIR.": "Размер плиток SwinIR", + "Tile overlap, in pixels for SwinIR. Low values = visible seam.": "Наложение плиток SwinIR, в пикселях. Меньше = выделеннее шов", + "LDSR processing steps. Lower = faster": "Число шагов LDSR. Меньше = быстрее", + "Upscaler for img2img": "Апскейлер рисунка-в-рисунок", + "Upscale latent space image when doing hires. fix": "Апскейлить образ латентного пространства для HD-режима", + "Face restoration": "Восстановление лиц", + "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect": "Вес CodeFormer； 0 = максимальное действие； 1 = минимальное", + "Move face restoration model from VRAM into RAM after processing": "Переместить модель восстановления лиц из ВОЗУ в ОЗУ после обработки", + "System": "Система", + "VRAM usage polls per second during generation. Set to 0 to disable.": "Сколько раз в секунду следить за потреблением ВОЗУ. 0, чтобы отключить", + "Always print all generation info to standard output": "Выводить все сведения о генерации в стандартный вывод", + "Add a second progress bar to the console that shows progress for an entire job.": "Вторая шкала прогресса для всей задачи", + "Training": "Обучение", + "Unload VAE and CLIP from VRAM when training": "Убрать VAE и CLIP из ВОЗУ на время обучения", + "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM.": "Переместить VAE и CLIP в ОЗУ на время обучения гиперсети. Сохраняет ВОЗУ", + "Filename word regex": "Regex имени файла", + "Filename join string": "Дополнить к имени файла", + "Number of repeats for a single input image per epoch; used only for displaying epoch number": "Число повторов для каждого рисунка за эпоху; используется только, чтобы отобразить число эпохи", + "Save an csv containing the loss to log directory every N steps, 0 to disable": "Сохранять csv с параметром loss в папку журнала каждые N шагов, 0 - отключить", + "Stable Diffusion": "Stable Diffusion", + "Checkpoints to cache in RAM": "Удерживать веса в ОЗУ", + "Hypernetwork strength": "Сила гиперсети", + "Apply color correction to img2img results to match original colors.": "Цветокоррекция вывода рисунка-в-рисунок, сохраняющая исходные цвета", + "Save a copy of image before applying color correction to img2img results": "Сохранить копию рисунка перед цветокоррекцией", + "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising).": "В режиме рисунок-в-рисунок сделать ровно указанное ползунком число шагов (обычно шумоподавление их уменьшает)", + "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply.": "Включить квантование К-семплерах для более резких и чистых результатов. Может потребовать поменять семя. Требует перезапуска.", + "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention": "Скобки: (понятие) - больше внимания к тексту, [понятие] - меньше внимания к тексту", + "Use old emphasis implementation. Can be useful to reproduce old seeds.": "Включить старую обработку скобок. Может потребоваться, чтобы воспроизвести старые семена.", + "Make K-diffusion samplers produce same images in a batch as when making a single image": "Заставить семплеры K-diffusion производить тот же самый рисунок в наборе, как и в единичной генерации", + "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens": "Увеличить связность, достраивая запрос от последней запятой до n токенов, когда используется свыше 75 токенов", + "Filter NSFW content": "Фильтровать небезопасный контент", + "Stop At last layers of CLIP model": "Остановиться на последних слоях модели CLIP", + "Interrogate Options": "Опции распознавания", + "Interrogate: keep models in VRAM": "Распознавание: хранить модели в ВОЗУ", + "Interrogate: use artists from artists.csv": "Распознавание: использовать художников из artists.csv", + "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators).": "Распознавание: включить ранжирование совпавших тегов в результате (не работает для распознавателей-создателей заголовков)", + "Interrogate: num_beams for BLIP": "Распознавание: num_beams для BLIP", + "Interrogate: minimum description length (excluding artists, etc..)": "Распознавание: минимальная длина описания (исключая художников и т.п.)", + "Interrogate: maximum description length": "Распознавание: максимальная длина описания", + "CLIP: maximum number of lines in text file (0 = No limit)": "CLIP: максимальное число строк в текстовом файле (0 = без ограничений)", + "Interrogate: deepbooru score threshold": "Распознавание: ограничение счёта deepbooru", + "Interrogate: deepbooru sort alphabetically": "Распознавание: сортировать deepbooru по алфавиту", + "use spaces for tags in deepbooru": "Пробелы для тегов deepbooru", + "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)": "Использовать скобки в deepbooru как обычные скобки, а не для усиления", + "User interface": "Пользовательский интерфейс", + "Show progressbar": "Шкала прогресса", + "Show image creation progress every N sampling steps. Set 0 to disable.": "Показывать процесс созданния рисунка каждые N шагов. 0 - отключить", + "Show grid in results for web": "Показать таблицу в выводе браузера", + "Do not show any images in results for web": "Не показывать выходные рисунки в браузере", + "Add model hash to generation information": "Добавить хеш весов к параметрам генерации", + "Add model name to generation information": "Добавить имя весов к параметрам генерации", + "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint.": "При считывании параметров генерации из текста в интерфейс, не менять выбранную модель/веса.", + "Font for image grids that have text": "Шрифт для таблиц, содержащих текст", + "Enable full page image viewer": "Включить полноэкранный просмотр картинок", + "Show images zoomed in by default in full page image viewer": "По умолчанию увеличивать картинки в полноэкранном просмотре", + "Show generation progress in window title.": "Отображать прогресс в имени вкладки", + "Quicksettings list": "Список быстрых настроек", + "Localization (requires restart)": "Перевод （требует перезапуск）", + "Sampler parameters": "Параметры семплера", + "Hide samplers in user interface (requires restart)": "Убрать семплеры из интерфейса (требует перезапуск)", + "eta (noise multiplier) for DDIM": "eta (множитель шума) DDIM", + "eta (noise multiplier) for ancestral samplers": "eta (множитель шума) для ancestral-семплеров", + "img2img DDIM discretize": "дискретизация DDIM для рисунка-в-рисунок", + "uniform": "однородная", + "quad": "квадратичная", + "sigma churn": "сигма-вариация", + "sigma tmin": "сигма-tmin", + "sigma noise": "сигма-шум", + "Eta noise seed delta": "Eta (дельта шума семени)", + "Images Browser": "Просмотр изображений", + "Preload images at startup": "Предзагружать рисунки во время запуска", + "Number of pictures displayed on each page": "Число рисунков на каждой странице", + "Minimum number of pages per load": "Мин. число загружаемых страниц", + "Number of grids in each row": "Число таблиц в каждой строке", + "Request browser notifications": "Запросить уведомления браузера", + "Download localization template": "Загрузить щаблон перевода", + "Reload custom script bodies (No ui updates, No restart)": "Перезагрузить пользовательские скрипты (не требует обновления интерфейса и перезапуска)", + "Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)": "Перезагрузить Gradio и обновить компоненты (только пользовательские скрипты, ui.py, js и css)", + "Prompt (press Ctrl+Enter or Alt+Enter to generate)": "Запрос （нажмите Ctrl+Enter или Alt+Enter для генерации）", + "Negative prompt (press Ctrl+Enter or Alt+Enter to generate)": "Запрос-исключение （нажмите Ctrl+Enter или Alt+Enter для генерации）", + "Add a random artist to the prompt.": "Добавить случайного художника к запросу", + "Read generation parameters from prompt or last generation if prompt is empty into user interface.": "Считать параметры генерации из запроса или из предыдущей генерации в пользовательский интерфейс, если пусто", + "Save style": "Сохранить стиль", + "Apply selected styles to current prompt": "Применить выбранные стили к текущему промпту", + "Stop processing current image and continue processing.": "Прекратить обрабатывать текущий рисунок, но продолжить работу", + "Stop processing images and return any results accumulated so far.": "Прекратить обрабатку рисунков и вернуть всё, что успели сделать.", + "Style to apply; styles have components for both positive and negative prompts and apply to both": "Стиль к применению; стили содержат как запрос, так и исключение, и применяют их оба", + "Do not do anything special": "Не делать ничего особенного", + "Which algorithm to use to produce the image": "Какой алгоритм использовать для того, чтобы произвести рисунок", + "Euler Ancestral - very creative, each can get a completely different picture depending on step count, setting steps to higher than 30-40 does not help": "Euler Ancestral - очень творческий, в зависимости от числа шагов может привести совершенно к различным результатам, выше 30-40 лучше не ставить", + "Denoising Diffusion Implicit Models - best at inpainting": "Denoising Diffusion Implicit модели - лучше всего для обрисовки", + "Produce an image that can be tiled.": "Сделать из рисунка непрерывную обёртку", + "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition": "Применить двушаговый процесс, чтобы создать рисунок на меньшем разрешении, апскейлнуть, а затем улучшить детали без смены композиции", + "Determines how little respect the algorithm should have for image's content. At 0, nothing will change, and at 1 you'll get an unrelated image. With values below 1.0, processing will take less steps than the Sampling Steps slider specifies.": "Определяет, насколько сильно алгоритм будет опираться на содержание изображения. 0 - не меняет ничего, 1 - совсем не связанный выход. Меньше 1.0 процесс использует меньше шагов, чем указано их ползунком.", + "How many batches of images to create": "Сколько создать наборов из картинок", + "How many image to create in a single batch": "Сколько картинок создать в каждом наборе", + "Classifier Free Guidance Scale - how strongly the image should conform to prompt - lower values produce more creative results": "Classifier Free Guidance Scale: насколько сильно изображение должно соответсвтовать запросу — меньшие значения приведут к более свободным итогам", + "A value that determines the output of random number generator - if you create an image with same parameters and seed as another image, you'll get the same result": "Значение, которое определяет выход генератора случайных чисел — если вы создадите рисунок с теми же параметрами и семенем, как у другого изображения, вы получите тот же результат", + "Set seed to -1, which will cause a new random number to be used every time": "Установить семя в -1, что вызовет каждый раз случайное число", + "Reuse seed from last generation, mostly useful if it was randomed": "Использовать семя предыдущей генерации, обычно полезно, если оно было случайным", + "Seed of a different picture to be mixed into the generation.": "Семя с другого рисунка, подмешенного в генерацию.", + "How strong of a variation to produce. At 0, there will be no effect. At 1, you will get the complete picture with variation seed (except for ancestral samplers, where you will just get something).": "Насколько сильную вариацию произвести. При 0м значении действия не будет. Для 1 вы получите полноценный рисунок с семенем вариации (кроме ancestral-семплеров, где вы просто что-то получите).", + "Make an attempt to produce a picture similar to what would have been produced with same seed at specified resolution": "Попытаться воспроизвести изображение, похожее на то, чтобы получилось с тем же семенем на выбранном разрешении", + "This text is used to rotate the feature space of the imgs embs": "Этот текст используется, чтобы произвести вращение пространства признаков из эмбеддинга рисунков", + "Separate values for X axis using commas.": "Отдельные значения оси X через запятую.", + "Separate values for Y axis using commas.": "Отдельные значения оси Y через запятую.", + "Write image to a directory (default - log/images) and generation parameters into csv file.": "Записать изображение в папку (по-умолчанию - log/images), а параметры генерации - в csv файл", + "Open images output directory": "Открыть папку сохранения изображений", + "How much to blur the mask before processing, in pixels.": "Насколько пикселей размыть трафарет перед обработкой", + "What to put inside the masked area before processing it with Stable Diffusion.": "Что поместить в область под трафаретом перед обработкой Stable Diffusion", + "fill it with colors of the image": "залить цветами изображения", + "keep whatever was there originally": "сохранить то, что было до этого", + "fill it with latent space noise": "залить латентным шумом", + "fill it with latent space zeroes": "залить латентными нулями", + "Upscale masked region to target resolution, do inpainting, downscale back and paste into original image": "апскейл до нужного разрешения, врисовка, сжатие до начального размера и вставка в исходный рисунок", + "Resize image to target resolution. Unless height and width match, you will get incorrect aspect ratio.": "Масшабировать изображение до нужного разрешения. Если только высота и ширина не совпадают, вы получите неверное соотношение сторон.", + "Resize the image so that entirety of target resolution is filled with the image. Crop parts that stick out.": "Масштабировать изображение так, чтобы им заполнялось всё выбранное выходное разрешение. Обрезать выступающие части", + "Resize the image so that entirety of image is inside target resolution. Fill empty space with image's colors.": "Масштабировать изображение так, всё изображение помещалось в выбранное выходное разрешение. Заполнить пустое место цветами изображения.", + "How many times to repeat processing an image and using it as input for the next iteration": "Сколько раз повторить обработку изображения и использовать её как вход для следующией итерации", + "In loopback mode, on each loop the denoising strength is multiplied by this value. <1 means decreasing variety so your sequence will converge on a fixed picture. >1 means increasing variety so your sequence will become more and more chaotic.": "В режиме прокрутки, для каждого цикла сила шумоподавления умножается на это значение. <1 уменьшает вариации так, чтобы последовательность сошлась на какой-то одной картинке. >1 увеличивает вариации, так что ваша последовательность станет всё более и более сумбурной.", + "For SD upscale, how much overlap in pixels should there be between tiles. Tiles overlap so that when they are merged back into one picture, there is no clearly visible seam.": "Для SD-апскейла, как много перекрытия в пикселях должно быть между плитками. Плитки перекрываются таким образом, чтобы они могли сойтись обратно в единое изображение, без видимого шва.", + "A directory on the same machine where the server is running.": "Папка на той же машине, где запущен сервер", + "Leave blank to save images to the default path.": "Оставьте пустым, чтобы сохранить рисунки в папку по-умолчанию", + "Result = A * (1 - M) + B * M": "Выход = A * (1 - M) + B * M", + "Result = A + (B - C) * M": "Выход = A + (B - C) * M", + "1st and last digit must be 1. ex:'1, 2, 1'": "1я и последняя цифры должны быть 1. напр.'1, 2, 1'", + "how fast should the training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.": "как быстро будет происходить обучение. Меньшие значения увеличат время обучения, но высокие могут нарушить сходимость модели (не будет создавать должные результаты) и/или сломать эмбеддинг. (Это случилось, если вы видете Loss: nan в текстовом окне вывода обучения. В этом случае вам придётся восстанавливать эмбеддинг вручную из старой, не повреждённой резервной копии).\n\nВы также можете указать единичное значение или последовательность из нескольких, используя следующий синтаксис:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nБудет обучаться со скоростью 0.005 первые 100 шагов, затем 1e-3 до 1000 шагов, после 1e-5 для всех оставшихся шагов.", + "Path to directory with input images": "Путь к папке со входными изображениями", + "Path to directory where to write outputs": "Путь к папке, в которую записывать результаты", + "Use following tags to define how filenames for images are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Используйте следующие теги, чтобы определить, как подбираются названия файлов для изображений: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; если пусто, используется значение по-умолчанию", + "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be behaving in an unethical manner.": "Когда эта опция включена, на созданные изображения не будет добавляться водяной знак. Предупреждение: не добавляя водяной знак, вы, вероятно, ведёте себя аморально.", + "Use following tags to define how subdirectories for images and grids are chosen: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; leave empty for default.": "Используйте следующие теги, чтобы определить, как подбираются названия подпапок для рисунков и табоиц: [steps], [cfg], [prompt], [prompt_no_styles], [prompt_spaces], [width], [height], [styles], [sampler], [seed], [model_hash], [prompt_words], [date], [datetime], [job_timestamp]; если пусто, используется значение по-умолчанию", + "Restore low quality faces using GFPGAN neural network": "Восстановить низкокачественные лица, используя нейросеть GFPGAN", + "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.": "Это регулярное выражение будет использовано, чтобы извлечь слова из имени файла, и они будут соединены с текстом в метке ниже как вход во время обучения. Оставьте пустым, чтобы сохранить имя файла как есть", + "This string will be used to join split words into a single line if the option above is enabled.": "Эта строка будет использована, чтобы объединить разделённые слова в одну строку, если включена опция выше.", + "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply.": "Список имён настроек, разделённый запятыми, предназначенных для быстрого доступа через панель наверху, а не через привычную вкладку настроек. Для применения требует перезапуска.", + "If this values is non-zero, it will be added to seed and used to initialize RNG for noises when using samplers with Eta. You can use this to produce even more variation of images, or you can use this to match images of other software if you know what you are doing.": "Если это значение не нулевое, оно будет добавлено к семени и использовано для инициалицации ГСЧ шума семплеров с параметром Eta. Вы можете использовать это, чтобы произвести ещё больше вариаций рисунков, либо же для того, чтобы подойти близко к результатам других программ, если знаете, что делаете.", + "Enable Autocomplete": "Включить автодополнение", + "Allowed categories for random artists selection when using the Roll button": "Разрешённые категории художников для случайного выбора при использовании кнопки + три", + "Roll three": "+ три", + "Generate forever": "Непрерывная генерация", + "Cancel generate forever": "Отключить непрерывную генерацию" +}