Generate grid preview for progress image
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@ -7,7 +7,7 @@ import inspect
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import k_diffusion.sampling
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import ldm.models.diffusion.ddim
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import ldm.models.diffusion.plms
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from modules import prompt_parser, devices, processing
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from modules import prompt_parser, devices, processing, images
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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@ -89,6 +89,30 @@ def sample_to_image(samples):
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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 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(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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@ -294,6 +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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"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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@ -318,7 +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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shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
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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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shared.state.current_image = modules.sd_samplers.samples_to_image_grid(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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