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