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--- |
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library_name: diffusers |
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base_model: stabilityai/stable-diffusion-xl-base-1.0 |
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tags: |
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- text-to-image |
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license: openrail++ |
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inference: false |
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--- |
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# Latent Consistency Model (LCM): SDXL |
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Latent Consistency Model (LCM) was proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) |
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by *Simian Luo, Yiqin Tan et al.* and [Simian Luo](https://huggingface.co/SimianLuo), [Suraj Patil](https://huggingface.co/valhalla), and [Daniel Gu](https://huggingface.co/dg845) |
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succesfully applied the same approach to create LCM for SDXL. |
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This checkpoint is a LCM distilled version of [`stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) that allows |
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to reduce the number of inference steps to only between **2 - 8 steps**. |
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## Usage |
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LCM SDXL is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first |
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install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`. |
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audio dataset from the Hugging Face Hub: |
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```bash |
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pip install --upgrade pip |
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pip install --upgrade diffusers transformers accelerate peft |
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``` |
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### Text-to-Image |
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The model can be loaded with it's base pipeline `stabilityai/stable-diffusion-xl-base-1.0`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps. |
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Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0. |
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```python |
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from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler |
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import torch |
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unet = UNet2DConditionModel.from_pretrained("latent-consistency/lcm-sdxl", torch_dtype=torch.float16, variant="fp16") |
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pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", unet=unet, torch_dtype=torch.float16, variant="fp16") |
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pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) |
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pipe.to("cuda") |
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prompt = "a close-up picture of an old man standing in the rain" |
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image = pipe(prompt, num_inference_steps=4, guidance_scale=8.0).images[0] |
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``` |
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### Image-to-Image |
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Works as well! TODO docs |
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### Inpainting |
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Works as well! TODO docs |
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### ControlNet |
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Works as well! TODO docs |
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### T2I Adapter |
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Works as well! TODO docs |
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## Speed Benchmark |
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TODO |
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## Training |
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TODO |
