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README.md
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tags:
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- generative model
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- unconditional image generation
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---
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Consistency models are a new class of generative models introduced in ["Consistency Models"](https://arxiv.org/abs/2303.01469) ([paper](https://arxiv.org/pdf/2303.01469.pdf), [code](https://github.com/openai/consistency_models)) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever.
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From the paper abstract:
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```python
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from diffusers import ConsistencyModelPipeline
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pipe = ConsistencyModelPipeline.from_pretrained("
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```
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## Usage
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device = "cuda"
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# Load the cd_cat256_l2 checkpoint.
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model_id_or_path = "
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pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
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pipe.to(device)
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tags:
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- generative model
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- unconditional image generation
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- consistency-model
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---
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**Disclaimer**: This model was added by the amazing community contributor [dg845](https://huggingface.co/dg845) ❤️
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Consistency models are a new class of generative models introduced in ["Consistency Models"](https://arxiv.org/abs/2303.01469) ([paper](https://arxiv.org/pdf/2303.01469.pdf), [code](https://github.com/openai/consistency_models)) by Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever.
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From the paper abstract:
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```python
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from diffusers import ConsistencyModelPipeline
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pipe = ConsistencyModelPipeline.from_pretrained("openai/diffusers-cd_cat256_l2")
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```
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## Usage
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device = "cuda"
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# Load the cd_cat256_l2 checkpoint.
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model_id_or_path = "openai/diffusers-cd_cat256_l2"
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pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
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pipe.to(device)
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