From 8ae0ea9deaa5a09d1e0aa8b2f8e97c38d71cdbda Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Sun, 30 Oct 2022 23:48:33 +0000 Subject: Add callback to sd_samplers --- modules/sd_samplers.py | 3 +++ 1 file changed, 3 insertions(+) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 3670b57d..30cb5c4b 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -11,6 +11,7 @@ from modules import prompt_parser, devices, processing, images from modules.shared import opts, cmd_opts, state import modules.shared as shared +from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) @@ -278,6 +279,8 @@ class CFGDenoiser(torch.nn.Module): 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]) + cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)) + if tensor.shape[1] == uncond.shape[1]: cond_in = torch.cat([tensor, uncond]) -- cgit v1.2.3 From 5b6bedf6f2ebacb7f1f5809af8e26a6a1af16e2a Mon Sep 17 00:00:00 2001 From: DepFA <35278260+dfaker@users.noreply.github.com> Date: Wed, 2 Nov 2022 00:38:17 +0000 Subject: Update class name and assign back to vars --- modules/sd_samplers.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 30cb5c4b..ebc0d896 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -11,7 +11,7 @@ from modules import prompt_parser, devices, processing, images from modules.shared import opts, cmd_opts, state import modules.shared as shared -from modules.script_callbacks import CGFDenoiserParams, cfg_denoiser_callback +from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options']) @@ -279,7 +279,11 @@ class CFGDenoiser(torch.nn.Module): 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]) - cfg_denoiser_callback(CGFDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps)) + denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps) + cfg_denoiser_callback(denoiser_params) + x_in = denoiser_params.x + image_cond_in = denoiser_params.image_cond + sigma_in = denoiser_params.sigma if tensor.shape[1] == uncond.shape[1]: cond_in = torch.cat([tensor, uncond]) -- cgit v1.2.3 From 9c67408004ed132637d10321bf44565f82055fd2 Mon Sep 17 00:00:00 2001 From: timntorres <116157310+timntorres@users.noreply.github.com> Date: Wed, 2 Nov 2022 02:18:21 -0700 Subject: Allow saving "before-highres-fix. (#4150) * Save image/s before doing highres fix. --- modules/processing.py | 17 +++++++++++++++-- modules/sd_samplers.py | 5 ++--- modules/shared.py | 1 + 3 files changed, 18 insertions(+), 5 deletions(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/processing.py b/modules/processing.py index b541ee2b..2dcf4879 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -521,7 +521,11 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: 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) + # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. + if isinstance(p, StableDiffusionProcessingTxt2Img): + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n) + else: + samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength) samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) @@ -649,7 +653,7 @@ 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): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, n=0): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: @@ -685,6 +689,15 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) + # Save a copy of the image/s before doing highres fix, if applicable. + if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix: + for i in range(self.batch_size): + # This batch's ith image. + img = sd_samplers.sample_to_image(samples, i) + # Index that accounts for both batch size and batch count. + ind = i + self.batch_size*n + images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix") + shared.state.nextjob() self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 44d4c189..d7fa89a0 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -93,9 +93,8 @@ def single_sample_to_image(sample): return Image.fromarray(x_sample) -def sample_to_image(samples): - return single_sample_to_image(samples[0]) - +def sample_to_image(samples, index=0): + return single_sample_to_image(samples[index]) def samples_to_image_grid(samples): return images.image_grid([single_sample_to_image(sample) for sample in samples]) diff --git a/modules/shared.py b/modules/shared.py index e65f6080..ce991424 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -255,6 +255,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"), "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."), "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."), + "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."), "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"), -- cgit v1.2.3 From eb5e82c7ddf5e72fa13b83bd1f12d3a07a4de1a4 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Wed, 2 Nov 2022 12:45:03 +0300 Subject: do not unnecessarily run VAE one more time when saving intermediate image with hires fix --- modules/processing.py | 39 ++++++++++++++++++++------------------- modules/sd_samplers.py | 1 + modules/shared.py | 2 +- scripts/img2imgalt.py | 3 +-- 4 files changed, 23 insertions(+), 22 deletions(-) (limited to 'modules/sd_samplers.py') diff --git a/modules/processing.py b/modules/processing.py index 2dcf4879..3a364b5f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -199,7 +199,7 @@ class StableDiffusionProcessing(): def init(self, all_prompts, all_seeds, all_subseeds): pass - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): raise NotImplementedError() def close(self): @@ -521,11 +521,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: shared.state.job = f"Batch {n+1} out of {p.n_iter}" with devices.autocast(): - # Only Txt2Img needs an extra argument, n, when saving intermediate images pre highres fix. - if isinstance(p, StableDiffusionProcessingTxt2Img): - samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, n=n) - else: - 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, prompts=prompts) samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) @@ -653,7 +649,7 @@ 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, n=0): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) if not self.enable_hr: @@ -666,9 +662,21 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] + """saves image before applying hires fix, if enabled in options; takes as an arguyment either an image or batch with latent space images""" + def save_intermediate(image, index): + if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix: + return + + if not isinstance(image, Image.Image): + image = sd_samplers.sample_to_image(image, index) + + images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix") + 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") + for i in range(samples.shape[0]): + save_intermediate(samples, i) 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) @@ -678,6 +686,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) x_sample = x_sample.astype(np.uint8) image = Image.fromarray(x_sample) + + save_intermediate(image, i) + image = images.resize_image(0, image, self.width, self.height) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) @@ -689,15 +700,6 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples)) - # Save a copy of the image/s before doing highres fix, if applicable. - if opts.save and not self.do_not_save_samples and opts.save_images_before_highres_fix: - for i in range(self.batch_size): - # This batch's ith image. - img = sd_samplers.sample_to_image(samples, i) - # Index that accounts for both batch size and batch count. - ind = i + self.batch_size*n - images.save_image(img, self.outpath_samples, "", self.all_seeds[ind], self.all_prompts[ind], opts.samples_format, suffix=f"-before-highres-fix") - shared.state.nextjob() self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model) @@ -844,8 +846,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, self.image_mask) - - def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): 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, image_conditioning=self.image_conditioning) @@ -856,4 +857,4 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): del x devices.torch_gc() - return samples \ No newline at end of file + return samples diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index d7fa89a0..c7c414ef 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -96,6 +96,7 @@ def single_sample_to_image(sample): def sample_to_image(samples, index=0): return single_sample_to_image(samples[index]) + def samples_to_image_grid(samples): return images.image_grid([single_sample_to_image(sample) for sample in samples]) diff --git a/modules/shared.py b/modules/shared.py index ce991424..01f47e38 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -256,6 +256,7 @@ options_templates.update(options_section(('saving-images', "Saving images/grids" "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."), "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."), "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."), + "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"), @@ -322,7 +323,6 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."), - "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"), "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."), "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), diff --git a/scripts/img2imgalt.py b/scripts/img2imgalt.py index 88abc093..964b75c7 100644 --- a/scripts/img2imgalt.py +++ b/scripts/img2imgalt.py @@ -166,8 +166,7 @@ class Script(scripts.Script): if override_strength: p.denoising_strength = 1.0 - - def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength): + def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): lat = (p.init_latent.cpu().numpy() * 10).astype(int) same_params = self.cache is not None and self.cache.cfg_scale == cfg and self.cache.steps == st \ -- cgit v1.2.3