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from diffusers import DiffusionPipeline |
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import torch |
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import time |
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import os |
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from pathlib import Path |
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from huggingface_hub import HfApi |
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api = HfApi() |
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start_time = time.time() |
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pipe = DiffusionPipeline.from_pretrained("/home/patrick/if", torch_dtype=torch.float16, variant="fp16", use_safetensors=True) |
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pipe.enable_model_cpu_offload() |
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generator = torch.Generator("cuda").manual_seed(0) |
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prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' |
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image = pipe(prompt, generator=generator).images[0] |
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path = os.path.join(Path.home(), "images", "if.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/if.png") |
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