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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: text-to-image |
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tags: |
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- text-to-image |
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--- |
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# Latent Consistency Models |
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Official Repository of the paper: *[Latent Consistency Models](https://arxiv.org/abs/2310.04378)*. |
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Project Page: https://latent-consistency-models.github.io |
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## Model Descriptions: |
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Copied from [SimianLuo/LCM_Dreamshaper_v7](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) to experiment with quantization. |
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Originally distilled from [Dreamshaper v7](https://huggingface.co/Lykon/dreamshaper-7) fine-tune of [Stable-Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) with only 4,000 training iterations (~32 A100 GPU Hours). |
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## Usage |
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To run the model yourself, you can leverage the 🧨 Diffusers library: |
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1. Install the library: |
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``` |
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pip install --upgrade diffusers # make sure to use at least diffusers >= 0.22 |
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pip install transformers accelerate |
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``` |
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2. Run the model: |
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```py |
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from diffusers import DiffusionPipeline |
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import torch |
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pipe = DiffusionPipeline.from_pretrained("TobDeBer/lcm_dream7") |
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# To save GPU memory, torch.float16 can be used, but it may compromise image quality. |
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pipe.to(torch_device="cuda", torch_dtype=torch.float32) |
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prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" |
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# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps. |
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num_inference_steps = 4 |
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images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images |
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``` |
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For more information, please have a look at the official docs: |
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👉 https://huggingface.co/docs/diffusers/api/pipelines/latent_consistency_models#latent-consistency-models |
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