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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-11-27 06:40:10 +08:00
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
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4fdb53c1e9
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@ -71,6 +71,7 @@ sampler_extra_params = {
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'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
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}
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def setup_img2img_steps(p, steps=None):
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if opts.img2img_fix_steps or steps is not None:
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steps = int((steps or p.steps) / min(p.denoising_strength, 0.999)) if p.denoising_strength > 0 else 0
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@ -82,37 +83,21 @@ def setup_img2img_steps(p, steps=None):
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return steps, t_enc
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def sample_to_image(samples):
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x_sample = processing.decode_first_stage(shared.sd_model, samples[0:1])[0]
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def single_sample_to_image(sample):
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x_sample = processing.decode_first_stage(shared.sd_model, sample.unsqueeze(0))[0]
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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return Image.fromarray(x_sample)
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def sample_to_image(samples):
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return single_sample_to_image(samples[0])
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def samples_to_image_grid(samples):
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progress_images = []
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for i in range(len(samples)):
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# Decode the samples individually to reduce VRAM usage at the cost of a bit of speed.
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x_sample = processing.decode_first_stage(shared.sd_model, samples[i:i+1])[0]
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x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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progress_images.append(Image.fromarray(x_sample))
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return images.image_grid([single_sample_to_image(sample) for sample in samples])
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return images.image_grid(progress_images)
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def samples_to_image_grid_combined(samples):
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progress_images = []
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# Decode all samples at once to increase speed at the cost of VRAM usage.
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x_samples = processing.decode_first_stage(shared.sd_model, samples)
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x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
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for x_sample in x_samples:
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x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
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x_sample = x_sample.astype(np.uint8)
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progress_images.append(Image.fromarray(x_sample))
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return images.image_grid(progress_images)
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def store_latent(decoded):
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state.current_latent = decoded
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@ -294,7 +294,7 @@ options_templates.update(options_section(('interrogate', "Interrogate Options"),
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options_templates.update(options_section(('ui', "User interface"), {
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"show_progressbar": OptionInfo(True, "Show progressbar"),
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"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}),
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"progress_decode_combined": OptionInfo(False, "Decode all progress images at once. (Slighty speeds up progress generation but consumes significantly more VRAM with large batches.)"),
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"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
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"return_grid": OptionInfo(True, "Show grid in results for web"),
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"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
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"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
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@ -318,10 +318,10 @@ def check_progress_call(id_part):
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if shared.parallel_processing_allowed:
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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:
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if opts.progress_decode_combined:
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shared.state.current_image = modules.sd_samplers.samples_to_image_grid_combined(shared.state.current_latent)
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else:
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if opts.show_progress_grid:
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shared.state.current_image = modules.sd_samplers.samples_to_image_grid(shared.state.current_latent)
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else:
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shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
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shared.state.current_image_sampling_step = shared.state.sampling_step
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image = shared.state.current_image
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