Merge pull request #1 from AUTOMATIC1111/master

updating files to resolve merge conflicts
This commit is contained in:
JC-Array 2022-10-10 18:06:07 -05:00 committed by GitHub
commit aca1553bde
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
23 changed files with 1233 additions and 68 deletions

View File

@ -0,0 +1,28 @@
# Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request!
If you have a large change, pay special attention to this paragraph:
> Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature.
Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on.
**Describe what this pull request is trying to achieve.**
A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code.
**Additional notes and description of your changes**
More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of.
**Environment this was tested in**
List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box.
- OS: [e.g. Windows, Linux]
- Browser [e.g. chrome, safari]
- Graphics card [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
**Screenshots or videos of your changes**
If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made.
This is **required** for anything that touches the user interface.

View File

@ -147,10 +147,6 @@ generateOnRepeatId = appendContextMenuOption('#txt2img_generate','Generate forev
cancelGenerateForever = function(){
clearInterval(window.generateOnRepeatInterval)
let interruptbutton = gradioApp().querySelector('#txt2img_interrupt');
if(interruptbutton.offsetParent){
interruptbutton.click();
}
}
appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)

View File

@ -79,6 +79,8 @@ titles = {
"Highres. fix": "Use a two step process to partially create an image at smaller resolution, upscale, and then improve details in it without changing composition",
"Scale latent": "Uscale the image in latent space. Alternative is to produce the full image from latent representation, upscale that, and then move it back to latent space.",
"Eta noise seed delta": "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.",
"Do not add watermark to images": "If this option is enabled, watermark will not be added to created images. Warning: if you do not add watermark, you may be bevaing in an unethical manner.",
}

View File

@ -127,7 +127,7 @@ def prepare_enviroment():
if not is_installed("xformers") and xformers and platform.python_version().startswith("3.10"):
if platform.system() == "Windows":
run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/a/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
run_pip("install https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/c/xformers-0.0.14.dev0-cp310-cp310-win_amd64.whl", "xformers")
elif platform.system() == "Linux":
run_pip("install xformers", "xformers")

View File

@ -36,6 +36,7 @@ errors.run(enable_tf32, "Enabling TF32")
device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
dtype = torch.float16
dtype_vae = torch.float16
def randn(seed, shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
@ -59,9 +60,12 @@ def randn_without_seed(shape):
return torch.randn(shape, device=device)
def autocast():
def autocast(disable=False):
from modules import shared
if disable:
return contextlib.nullcontext()
if dtype == torch.float32 or shared.cmd_opts.precision == "full":
return contextlib.nullcontext()

View File

@ -207,7 +207,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
# 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:
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
@ -247,6 +247,9 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
if sampler_noises is not None:
cnt = p.sampler.number_of_needed_noises(p)
if opts.eta_noise_seed_delta > 0:
torch.manual_seed(seed + opts.eta_noise_seed_delta)
for j in range(cnt):
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
@ -259,6 +262,13 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
return x
def decode_first_stage(model, x):
with devices.autocast(disable=x.dtype == devices.dtype_vae):
x = model.decode_first_stage(x)
return x
def get_fixed_seed(seed):
if seed is None or seed == '' or seed == -1:
return int(random.randrange(4294967294))
@ -294,6 +304,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
"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,
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
}
generation_params.update(p.extra_generation_params)
@ -398,9 +409,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
# use the image collected previously in sampler loop
samples_ddim = shared.state.current_latent
samples_ddim = samples_ddim.to(devices.dtype)
x_samples_ddim = p.sd_model.decode_first_stage(samples_ddim)
samples_ddim = samples_ddim.to(devices.dtype_vae)
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim)
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim
@ -533,7 +543,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
if self.scale_latent:
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
else:
decoded_samples = self.sd_model.decode_first_stage(samples)
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")

View File

@ -12,6 +12,10 @@ import _codecs
import zipfile
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
def encode(*args):
out = _codecs.encode(*args)
return out
@ -20,7 +24,7 @@ def encode(*args):
class RestrictedUnpickler(pickle.Unpickler):
def persistent_load(self, saved_id):
assert saved_id[0] == 'storage'
return torch.storage._TypedStorage()
return TypedStorage()
def find_class(self, module, name):
if module == 'collections' and name == 'OrderedDict':

View File

@ -23,7 +23,7 @@ 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 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
@ -43,10 +43,7 @@ def undo_optimizations():
def get_target_prompt_token_count(token_count):
if token_count < 75:
return 75
return math.ceil(token_count / 10) * 10
return math.ceil(max(token_count, 1) / 75) * 75
class StableDiffusionModelHijack:
@ -127,7 +124,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
self.token_mults[ident] = mult
def tokenize_line(self, line, used_custom_terms, hijack_comments):
id_start = self.wrapped.tokenizer.bos_token_id
id_end = self.wrapped.tokenizer.eos_token_id
if opts.enable_emphasis:
@ -154,7 +150,13 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
i += 1
else:
emb_len = int(embedding.vec.shape[0])
fixes.append((len(remade_tokens), embedding))
iteration = len(remade_tokens) // 75
if (len(remade_tokens) + emb_len) // 75 != iteration:
rem = (75 * (iteration + 1) - len(remade_tokens))
remade_tokens += [id_end] * rem
multipliers += [1.0] * rem
iteration += 1
fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
remade_tokens += [0] * emb_len
multipliers += [weight] * emb_len
used_custom_terms.append((embedding.name, embedding.checksum()))
@ -162,10 +164,10 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
token_count = len(remade_tokens)
prompt_target_length = get_target_prompt_token_count(token_count)
tokens_to_add = prompt_target_length - len(remade_tokens) + 1
tokens_to_add = prompt_target_length - len(remade_tokens)
remade_tokens = [id_start] + remade_tokens + [id_end] * tokens_to_add
multipliers = [1.0] + multipliers + [1.0] * tokens_to_add
remade_tokens = remade_tokens + [id_end] * tokens_to_add
multipliers = multipliers + [1.0] * tokens_to_add
return remade_tokens, fixes, multipliers, token_count
@ -262,27 +264,53 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def forward(self, text):
if opts.use_old_emphasis_implementation:
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)
else:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
self.hijack.fixes = hijack_fixes
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]))
target_token_count = get_target_prompt_token_count(token_count) + 2
if use_old:
self.hijack.fixes = hijack_fixes
return self.process_tokens(remade_batch_tokens, batch_multipliers)
position_ids_array = [min(x, 75) for x in range(target_token_count-1)] + [76]
position_ids = torch.asarray(position_ids_array, device=devices.device).expand((1, -1))
z = None
i = 0
while max(map(len, remade_batch_tokens)) != 0:
rem_tokens = [x[75:] for x in remade_batch_tokens]
rem_multipliers = [x[75:] for x in batch_multipliers]
remade_batch_tokens_of_same_length = [x + [self.wrapped.tokenizer.eos_token_id] * (target_token_count - len(x)) for x in remade_batch_tokens]
tokens = torch.asarray(remade_batch_tokens_of_same_length).to(device)
self.hijack.fixes = []
for unfiltered in hijack_fixes:
fixes = []
for fix in unfiltered:
if fix[0] == i:
fixes.append(fix[1])
self.hijack.fixes.append(fixes)
z1 = self.process_tokens([x[:75] for x in remade_batch_tokens], [x[:75] for x in batch_multipliers])
z = z1 if z is None else torch.cat((z, z1), axis=-2)
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]
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)
outputs = self.wrapped.transformer(input_ids=tokens, position_ids=position_ids, output_hidden_states=-opts.CLIP_stop_at_last_layers)
if opts.CLIP_stop_at_last_layers > 1:
z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
z = self.wrapped.transformer.text_model.final_layer_norm(z)
@ -290,7 +318,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
z = outputs.last_hidden_state
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
batch_multipliers_of_same_length = [x + [1.0] * (target_token_count - len(x)) for x in batch_multipliers]
batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(device)
original_mean = z.mean()
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)

View File

@ -13,8 +13,6 @@ from modules import shared
if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
try:
import xformers.ops
import functorch
xformers._is_functorch_available = True
shared.xformers_available = True
except Exception:
print("Cannot import xformers", file=sys.stderr)

View File

@ -149,8 +149,13 @@ def load_model_weights(model, checkpoint_info):
model.half()
devices.dtype = torch.float32 if shared.cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if shared.cmd_opts.no_half or shared.cmd_opts.no_half_vae else torch.float16
vae_file = os.path.splitext(checkpoint_file)[0] + ".vae.pt"
if not os.path.exists(vae_file) and shared.cmd_opts.vae_path is not None:
vae_file = shared.cmd_opts.vae_path
if os.path.exists(vae_file):
print(f"Loading VAE weights from: {vae_file}")
vae_ckpt = torch.load(vae_file, map_location="cpu")
@ -158,6 +163,8 @@ def load_model_weights(model, checkpoint_info):
model.first_stage_model.load_state_dict(vae_dict)
model.first_stage_model.to(devices.dtype_vae)
model.sd_model_hash = sd_model_hash
model.sd_model_checkpoint = checkpoint_file
model.sd_checkpoint_info = checkpoint_info

View File

@ -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
from modules import prompt_parser, devices, processing
from modules.shared import opts, cmd_opts, state
import modules.shared as shared
@ -83,7 +83,7 @@ def setup_img2img_steps(p, steps=None):
def sample_to_image(samples):
x_sample = shared.sd_model.decode_first_stage(samples[0:1].type(shared.sd_model.dtype))[0]
x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[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)

View File

@ -25,6 +25,7 @@ parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to director
parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN'))
parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None)
parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats")
parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats")
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)")
@ -65,6 +66,7 @@ parser.add_argument("--autolaunch", action='store_true', help="open the webui UR
parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False)
parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False)
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)
@ -171,6 +173,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids"
"use_original_name_batch": OptionInfo(False, "Use original name for output filename during batch process in extras tab"),
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"),
}))
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
@ -259,6 +262,7 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}),
}))

View File

@ -10,6 +10,7 @@ from tqdm import tqdm
from modules import modelloader
from modules.shared import cmd_opts, opts, device
from modules.swinir_model_arch import SwinIR as net
from modules.swinir_model_arch_v2 import Swin2SR as net2
from modules.upscaler import Upscaler, UpscalerData
precision_scope = (
@ -57,6 +58,22 @@ class UpscalerSwinIR(Upscaler):
filename = path
if filename is None or not os.path.exists(filename):
return None
if filename.endswith(".v2.pth"):
model = net2(
upscale=scale,
in_chans=3,
img_size=64,
window_size=8,
img_range=1.0,
depths=[6, 6, 6, 6, 6, 6],
embed_dim=180,
num_heads=[6, 6, 6, 6, 6, 6],
mlp_ratio=2,
upsampler="nearest+conv",
resi_connection="1conv",
)
params = None
else:
model = net(
upscale=scale,
in_chans=3,
@ -70,9 +87,13 @@ class UpscalerSwinIR(Upscaler):
upsampler="nearest+conv",
resi_connection="3conv",
)
params = "params_ema"
pretrained_model = torch.load(filename)
model.load_state_dict(pretrained_model["params_ema"], strict=True)
if params is not None:
model.load_state_dict(pretrained_model[params], strict=True)
else:
model.load_state_dict(pretrained_model, strict=True)
if not cmd_opts.no_half:
model = model.half()
return model

File diff suppressed because it is too large Load Diff

View File

@ -15,11 +15,10 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
class PersonalizedBase(Dataset):
def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
self.placeholder_token = placeholder_token
self.size = size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)

View File

@ -7,8 +7,9 @@ import tqdm
from modules import shared, images
def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
size = 512
def preprocess(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption):
width = process_width
height = process_height
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
@ -55,23 +56,23 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
is_wide = ratio < 1 / 1.35
if process_split and is_tall:
img = img.resize((size, size * img.height // img.width))
img = img.resize((width, height * img.height // img.width))
top = img.crop((0, 0, size, size))
top = img.crop((0, 0, width, height))
save_pic(top, index)
bot = img.crop((0, img.height - size, size, img.height))
bot = img.crop((0, img.height - height, width, img.height))
save_pic(bot, index)
elif process_split and is_wide:
img = img.resize((size * img.width // img.height, size))
img = img.resize((width * img.width // img.height, height))
left = img.crop((0, 0, size, size))
left = img.crop((0, 0, width, height))
save_pic(left, index)
right = img.crop((img.width - size, 0, img.width, size))
right = img.crop((img.width - width, 0, img.width, height))
save_pic(right, index)
else:
img = images.resize_image(1, img, size, size)
img = images.resize_image(1, img, width, height)
save_pic(img, index)
shared.state.nextjob()

View File

@ -156,7 +156,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
return fn
def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..."
@ -182,7 +182,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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, size=512, 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=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
hijack = sd_hijack.model_hijack
@ -200,6 +200,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
if ititial_step > steps:
return embedding, filename
tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
epoch_len = (tr_img_len * num_repeats) + tr_img_len
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, (x, text) in pbar:
embedding.step = i + ititial_step
@ -223,7 +226,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
loss.backward()
optimizer.step()
pbar.set_description(f"loss: {losses.mean():.7f}")
epoch_num = embedding.step // epoch_len
epoch_step = embedding.step - (epoch_num * epoch_len) + 1
pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@ -236,6 +242,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
sd_model=shared.sd_model,
prompt=text,
steps=20,
height=training_height,
width=training_width,
do_not_save_grid=True,
do_not_save_samples=True,
)

View File

@ -524,7 +524,7 @@ def create_ui(wrap_gradio_gpu_call):
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
@ -710,7 +710,7 @@ def create_ui(wrap_gradio_gpu_call):
tiling = gr.Checkbox(label='Tiling', value=False)
with gr.Row():
batch_count = gr.Slider(minimum=1, maximum=cmd_opts.max_batch_count, step=1, label='Batch count', value=1)
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
with gr.Group():
@ -961,7 +961,7 @@ def create_ui(wrap_gradio_gpu_call):
extras_send_to_inpaint.click(
fn=lambda x: image_from_url_text(x),
_js="extract_image_from_gallery_img2img",
_js="extract_image_from_gallery_inpaint",
inputs=[result_images],
outputs=[init_img_with_mask],
)
@ -1029,6 +1029,8 @@ def create_ui(wrap_gradio_gpu_call):
process_src = gr.Textbox(label='Source directory')
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)
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
@ -1043,13 +1045,16 @@ def create_ui(wrap_gradio_gpu_call):
run_preprocess = gr.Button(value="Preprocess", variant='primary')
with gr.Group():
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
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")
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)
steps = gr.Number(label='Max steps', value=100000, precision=0)
num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, 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)
@ -1092,6 +1097,8 @@ def create_ui(wrap_gradio_gpu_call):
inputs=[
process_src,
process_dst,
process_width,
process_height,
process_flip,
process_split,
process_caption,
@ -1110,7 +1117,10 @@ def create_ui(wrap_gradio_gpu_call):
learn_rate,
dataset_directory,
log_directory,
training_width,
training_height,
steps,
num_repeats,
create_image_every,
save_embedding_every,
template_file,

View File

@ -23,4 +23,3 @@ resize-right
torchdiffeq
kornia
lark
functorch

View File

@ -22,4 +22,3 @@ resize-right==0.0.2
torchdiffeq==0.2.3
kornia==0.6.7
lark==1.1.2
functorch==0.2.1

View File

@ -40,6 +40,22 @@ document.addEventListener("DOMContentLoaded", function() {
mutationObserver.observe( gradioApp(), { childList:true, subtree:true })
});
/**
* Add a ctrl+enter as a shortcut to start a generation
*/
document.addEventListener('keydown', function(e) {
var handled = false;
if (e.key !== undefined) {
if((e.key == "Enter" && (e.metaKey || e.ctrlKey))) handled = true;
} else if (e.keyCode !== undefined) {
if((e.keyCode == 13 && (e.metaKey || e.ctrlKey))) handled = true;
}
if (handled) {
gradioApp().querySelector("#txt2img_generate").click();
e.preventDefault();
}
})
/**
* checks that a UI element is not in another hidden element or tab content
*/

View File

@ -205,7 +205,10 @@ class Script(scripts.Script):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
if not opts.return_grid:
p.batch_size = 1
CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
def process_axis(opt, vals):

View File

@ -1,3 +1,7 @@
.container {
max-width: 100%;
}
.output-html p {margin: 0 0.5em;}
.row > *,
@ -463,3 +467,10 @@ input[type="range"]{
max-width: 32em;
padding: 0;
}
canvas[key="mask"] {
z-index: 12 !important;
filter: invert();
mix-blend-mode: multiply;
pointer-events: none;
}