diff --git a/javascript/hints.js b/javascript/hints.js index 20a71dbd..65a67d50 100644 --- a/javascript/hints.js +++ b/javascript/hints.js @@ -72,6 +72,10 @@ titles = { "Checkpoint name": "Loads weights from checkpoint before making images. You can either use hash or a part of filename (as seen in settings) for checkpoint name. Recommended to use with Y axis for less switching.", "vram": "Torch active: Peak amount of VRAM used by Torch during generation, excluding cached data.\nTorch reserved: Peak amount of VRAM allocated by Torch, including all active and cached data.\nSys VRAM: Peak amount of VRAM allocation across all applications / total GPU VRAM (peak utilization%).", + + "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.", + } diff --git a/modules/processing.py b/modules/processing.py index a0f0e575..e146524d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -74,11 +74,12 @@ class StableDiffusionProcessing: self.overlay_images = overlay_images self.paste_to = None self.color_corrections = None + self.denoising_strength: float = 0 - def init(self, seed): + def init(self, all_prompts, all_seeds, all_subseeds): pass - def sample(self, x, conditioning, unconditional_conditioning): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): raise NotImplementedError() @@ -303,7 +304,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed: precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope) with torch.no_grad(), precision_scope("cuda"), ema_scope(): - p.init(seed=all_seeds[0]) + p.init(all_prompts, all_seeds, all_subseeds) if state.job_count == -1: state.job_count = p.n_iter @@ -328,13 +329,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed: for comment in model_hijack.comments: comments[comment] = 1 - # we manually generate all input noises because each one should have a specific seed - x = create_random_tensors([opt_C, p.height // opt_f, p.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p) - if p.n_iter > 1: shared.state.job = f"Batch {n+1} out of {p.n_iter}" - samples_ddim = p.sample(x=x, conditioning=c, unconditional_conditioning=uc) + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) if state.interrupted: # if we are interruped, sample returns just noise @@ -406,13 +404,64 @@ def process_images(p: StableDiffusionProcessing) -> Processed: class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None + firstphase_width = 0 + firstphase_height = 0 + firstphase_width_truncated = 0 + firstphase_height_truncated = 0 - def init(self, seed): + def __init__(self, enable_hr=False, scale_latent=True, denoising_strength=0.75, **kwargs): + super().__init__(**kwargs) + self.enable_hr = enable_hr + self.scale_latent = scale_latent + self.denoising_strength = denoising_strength + + def init(self, all_prompts, all_seeds, all_subseeds): + if self.enable_hr: + if state.job_count == -1: + state.job_count = self.n_iter * 2 + else: + state.job_count = state.job_count * 2 + + desired_pixel_count = 512 * 512 + actual_pixel_count = self.width * self.height + scale = math.sqrt(desired_pixel_count / actual_pixel_count) + + self.firstphase_width = math.ceil(scale * self.width / 64) * 64 + self.firstphase_height = math.ceil(scale * self.height / 64) * 64 + self.firstphase_width_truncated = int(scale * self.width) + self.firstphase_height_truncated = int(scale * self.height) + + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): self.sampler = samplers[self.sampler_index].constructor(self.sd_model) - def sample(self, x, conditioning, unconditional_conditioning): - samples_ddim = self.sampler.sample(self, x, conditioning, unconditional_conditioning) - return samples_ddim + 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) + 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) + + 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] + + 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 = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear") + samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) + + shared.state.nextjob() + + self.sampler = samplers[self.sampler_index].constructor(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) + samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps) + + return samples + class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None @@ -435,7 +484,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask = None self.nmask = None - def init(self, seed): + def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model) crop_region = None @@ -529,12 +578,15 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype) self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype) + # 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:], [seed + x + 1 for x in range(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, x, conditioning, unconditional_conditioning): + 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) if self.mask is not None: diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index af6811a1..1fc9d18c 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -38,9 +38,9 @@ samplers = [ samplers_for_img2img = [x for x in samplers if x.name != 'PLMS'] -def setup_img2img_steps(p): - if opts.img2img_fix_steps: - steps = int(p.steps / min(p.denoising_strength, 0.999)) +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 t_enc = p.steps - 1 else: steps = p.steps @@ -115,8 +115,8 @@ class VanillaStableDiffusionSampler: self.step += 1 return res - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning): - steps, t_enc = setup_img2img_steps(p) + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + steps, t_enc = setup_img2img_steps(p, steps) # existing code fails with cetain step counts, like 9 try: @@ -127,16 +127,16 @@ class VanillaStableDiffusionSampler: x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise) self.sampler.p_sample_ddim = self.p_sample_ddim_hook - self.mask = p.mask - self.nmask = p.nmask - self.init_latent = p.init_latent + self.mask = p.mask if hasattr(p, 'mask') else None + self.nmask = p.nmask if hasattr(p, 'nmask') else None + self.init_latent = x self.step = 0 samples = self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning) return samples - def sample(self, p, x, conditioning, unconditional_conditioning): + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): for fieldname in ['p_sample_ddim', 'p_sample_plms']: if hasattr(self.sampler, fieldname): setattr(self.sampler, fieldname, self.p_sample_ddim_hook) @@ -145,11 +145,13 @@ class VanillaStableDiffusionSampler: self.init_latent = None self.step = 0 + steps = steps or p.steps + # existing code fails with cetin step counts, like 9 try: - samples_ddim, _ = self.sampler.sample(S=p.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) + samples_ddim, _ = 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) except Exception: - samples_ddim, _ = self.sampler.sample(S=p.steps+1, 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) + samples_ddim, _ = self.sampler.sample(S=steps+1, 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) return samples_ddim @@ -186,7 +188,7 @@ class CFGDenoiser(torch.nn.Module): return denoised -def extended_trange(count, *args, **kwargs): +def extended_trange(sampler, count, *args, **kwargs): state.sampling_steps = count state.sampling_step = 0 @@ -194,6 +196,9 @@ def extended_trange(count, *args, **kwargs): if state.interrupted: break + if sampler.stop_at is not None and x > sampler.stop_at: + break + yield x state.sampling_step += 1 @@ -222,6 +227,7 @@ class KDiffusionSampler: self.model_wrap_cfg = CFGDenoiser(self.model_wrap) self.sampler_noises = None self.sampler_noise_index = 0 + self.stop_at = None def callback_state(self, d): store_latent(d["denoised"]) @@ -240,8 +246,8 @@ class KDiffusionSampler: self.sampler_noise_index += 1 return res - def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning): - steps, t_enc = setup_img2img_steps(p) + def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None): + steps, t_enc = setup_img2img_steps(p, steps) sigmas = self.model_wrap.get_sigmas(steps) @@ -251,33 +257,36 @@ class KDiffusionSampler: sigma_sched = sigmas[steps - t_enc - 1:] - self.model_wrap_cfg.mask = p.mask - self.model_wrap_cfg.nmask = p.nmask - self.model_wrap_cfg.init_latent = p.init_latent + self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None + self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None + self.model_wrap_cfg.init_latent = x self.model_wrap.step = 0 self.sampler_noise_index = 0 if hasattr(k_diffusion.sampling, 'trange'): - k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs) + k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs) if self.sampler_noises is not None: k_diffusion.sampling.torch = TorchHijack(self) return self.func(self.model_wrap_cfg, xi, sigma_sched, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state) - def sample(self, p, x, conditioning, unconditional_conditioning): - sigmas = self.model_wrap.get_sigmas(p.steps) + def sample(self, p, x, conditioning, unconditional_conditioning, steps=None): + steps = steps or p.steps + + sigmas = self.model_wrap.get_sigmas(steps) x = x * sigmas[0] self.model_wrap_cfg.step = 0 self.sampler_noise_index = 0 if hasattr(k_diffusion.sampling, 'trange'): - k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(*args, **kwargs) + k_diffusion.sampling.trange = lambda *args, **kwargs: extended_trange(self, *args, **kwargs) if self.sampler_noises is not None: k_diffusion.sampling.torch = TorchHijack(self) - samples_ddim = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state) - return samples_ddim + samples = self.func(self.model_wrap_cfg, x, sigmas, extra_args={'cond': conditioning, 'uncond': unconditional_conditioning, 'cond_scale': p.cfg_scale}, disable=False, callback=self.callback_state) + + return samples diff --git a/modules/txt2img.py b/modules/txt2img.py index 30d89849..7c6de2e7 100644 --- a/modules/txt2img.py +++ b/modules/txt2img.py @@ -6,7 +6,7 @@ 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, height: int, width: int, *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, height: int, width: int, enable_hr: bool, scale_latent: bool, denoising_strength: float, *args): p = StableDiffusionProcessingTxt2Img( sd_model=shared.sd_model, outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples, @@ -28,6 +28,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: height=height, restore_faces=restore_faces, tiling=tiling, + enable_hr=enable_hr, + scale_latent=scale_latent, + denoising_strength=denoising_strength, ) print(f"\ntxt2img: {prompt}", file=shared.progress_print_out) diff --git a/modules/ui.py b/modules/ui.py index 01f07954..7422a269 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -327,6 +327,7 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: outputs=[seed, dummy_component] ) + def create_toprow(is_img2img): with gr.Row(elem_id="toprow"): with gr.Column(scale=4): @@ -392,6 +393,11 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): 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) + enable_hr = gr.Checkbox(label='Highres. fix', value=False) + + with gr.Row(visible=False) as hr_options: + scale_latent = gr.Checkbox(label='Scale latent', value=True) + 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) @@ -451,6 +457,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, height, width, + enable_hr, + scale_latent, + denoising_strength, ] + custom_inputs, outputs=[ txt2img_gallery, @@ -463,6 +472,12 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo): txt2img_prompt.submit(**txt2img_args) submit.click(**txt2img_args) + enable_hr.change( + fn=lambda x: gr_show(x), + inputs=[enable_hr], + outputs=[hr_options], + ) + interrupt.click( fn=lambda: shared.state.interrupt(), inputs=[],