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Delete woman2.webp
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Delete woman1.webp
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Delete person1.webp
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Delete diffusers.zip
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Delete cat_window.webp
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Delete bird.webp
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Upload 8 files
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Distilled (4 steps)
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zf.write(image_path, arcname=os.path.basename(image_path))
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Base (50 steps)
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from pathlib import Path
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def save_on_path(img: Image, filename: str, format_: str = None) -> Path:
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def export_images_to_zip(gallery) -> str:
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change()
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A cat
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return infer(prompt, None, mode_choice, seed, randomize_seed, width, height, num_inference_steps, guidance_scale, prompt_upsampling)
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return infer(prompt, [input_image], mode_choice, seed, randomize_seed, width, height, num_inference_steps, guidance_scale, prompt_upsampling)
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input_image = gr.Image(label="Upload the image for editing", type="pil")
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def infer_example()
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Prompt
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Adapt
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examples=examples_images,
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git+https://github.com/huggingface/diffusers.git
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Upload 10 files
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Upload 8 files
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js="window.scrollTo(0, 0)"
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js="document.getElementById('output_id').scrollIntoView()"
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js="document.getElementById('output_id')?.scrollIntoView({behavior: 'smooth', block: 'start', inline: 'nearest'})"
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Upload 9 files
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zf.write(image_path, arcname=os.path.basename(image_path))
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zf.write(image_path, arcname=os.path.basename(filepath))
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image_filename = re.sub(r"^.*[\/\\]([^\/\\]+)$", r"\1", image_path)
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import zipfile
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print(str(len(gallery)) + " images zipped")
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result_gallery = gr.Gallery(label = 'Downloadable results', show_label = True, interactive = False, elem_id = "gallery1")
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zip_path = export_compiled_transformers_to_zip(gallery)
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Zip
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Upload 8 files
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use_precompiled = False
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payload["constants_map"] = constants
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Black video
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Black video
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use_precompiled = False
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use_precompiled = True
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def drain_module_parameters(module: torch.nn.Module):
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Format
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Format
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use_precompiled = True
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class ZeroGPUCompiledModel:
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def to_serializable_dict(self) -> dict[str, Any]:
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