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import torch |
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import numpy as np |
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from huggingface_hub import HfApi |
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from diffusers import ShapEPipeline |
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from diffusers.utils import export_to_gif |
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api = HfApi() |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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batch_size = 1 |
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guidance_scale = 15.0 |
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prompt = "a red table" |
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prompt = "A chair that looks like an avocado" |
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torch.manual_seed(0) |
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repo = "openai/shap-e" |
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pipe = ShapEPipeline.from_pretrained(repo) |
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pipe = pipe.to(device) |
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generator = torch.Generator(device="cuda").manual_seed(0) |
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prompts = [ |
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"A chair that looks like an avocado", |
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"An airplane that looks like a banana", |
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"A spaceship", |
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"A birthday cupcake", |
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"A chair that looks like a tree", |
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"A green boot", |
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"A penguin", |
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"Ube ice cream cone", |
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"A bowl of vegetables", |
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] |
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for prompt in prompts: |
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images = pipe( |
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prompt, |
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num_images_per_prompt=batch_size, |
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generator=generator, |
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guidance_scale=guidance_scale, |
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num_inference_steps=64, |
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frame_size=256, |
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output_type='pil' |
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).images |
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path = f"/home/patrick/images/{'_'.join(prompt.split())}.gif" |
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export_to_gif(images[0], 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/{path.split('/')[-1]}") |
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