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from diffusers import AutoPipelineForText2Image |
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import time |
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import os |
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from huggingface_hub import HfApi |
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import torch |
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from pathlib import Path |
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path = "stabilityai/stable-diffusion-xl-base-1.0" |
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api = HfApi() |
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start_time = time.time() |
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pipe = AutoPipelineForText2Image.from_pretrained(path, torch_dtype=torch.float16) |
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pipe.enable_model_cpu_offload() |
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lora_model_id = "hf-internal-testing/sdxl-0.9-kamepan-lora" |
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lora_model_id = "TheLastBen/Papercut_SDXL" |
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lora_filename = "kame_sdxl_v2-000020-16rank.safetensors" |
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lora_filename = "papercut.safetensors" |
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pipe.load_lora_weights(lora_model_id, weight_name=lora_filename) |
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prompt = "masterpiece, best quality, mountain" |
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prompt = "papercut sonic" |
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images = pipe(prompt=prompt, |
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num_inference_steps=20, |
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generator=torch.manual_seed(0) |
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).images |
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for i, image in enumerate(images): |
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file_name = f"aa_{i}" |
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path = os.path.join(Path.home(), "images", f"{file_name}.png") |
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image.save(path) |
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api.upload_file( |
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path_or_fileobj=path, |
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path_in_repo=path.split("/")[-1], |
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repo_id="patrickvonplaten/images", |
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repo_type="dataset", |
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) |
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print(f"https://huggingface.co/datasets/patrickvonplaten/images/blob/main/{file_name}.png") |
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