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--- |
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datasets: |
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- eurecom-ds/multi_dsprites |
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library_name: diffusers |
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pipeline_tag: unconditional-image-generation |
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--- |
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```python |
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# !pip install diffusers |
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from diffusers import DiffusionPipeline |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model_id = "eurecom-ds/scoresdeve-ema-multi-dsprites-64" |
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# load model and scheduler |
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pipe = DiffusionPipeline.from_pretrained(model_id, trust_remote_code=True) |
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pipe.to(device) |
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# run pipeline in inference (sample random noise and denoise) |
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generator = torch.Generator(device=device).manual_seed(46) |
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image = pipe( |
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generator=generator, |
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batch_size=1, |
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num_inference_steps=1000 |
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).images |
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# save image |
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image[0].save("sde_ve_generated_image.png") |
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``` |
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