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--- |
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tags: |
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- text-to-image |
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- stable-diffusion |
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- lora |
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- diffusers |
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- template:sd-lora |
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widget: |
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- text: steps 4 scale 1 |
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output: |
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url: images/F_iezcTbcAAvz8t.jpg |
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- text: steps 6 scale 2 |
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output: |
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url: images/F_ifIM0acAAe1ln.jpg |
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- text: steps 8 scale 2 |
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output: |
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url: images/F_ifP0yaAAA8hTQ.jpg |
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base_model: stabilityai/stable-diffusion-xl-base-1.0 |
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instance_prompt: null |
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--- |
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# LCM LoRA SDXL Rank 1 |
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LCM LoRA SDXL Rank 1 is a resized [LCM LoRA SDXL](https://huggingface.co/latent-consistency/lcm-lora-sdxl). The LoRA resized to rank 1 with [resize lora](https://github.com/kohya-ss/sd-scripts/blob/main/networks/resize_lora.py) script. This LoRA still can do inference with `LCMScheduler` and maintain the inference speed with lower steps and guidance scale while the output is improved. |
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<Gallery /> |
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## Download model |
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Weights for this model are available in Safetensors format. |
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[Download](/Linaqruf/lcm-lora-sdxl-rank1/tree/main) them in the Files & versions tab. |
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## Usage |
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LCM-LoRA 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 diffusers transformers accelerate peft |
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``` |
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### Text-to-Image |
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The adapter can be loaded with it's base model `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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import torch |
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from diffusers import LCMScheduler, AutoPipelineForText2Image |
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model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
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adapter_id = "Linaqruf/lcm-lora-sdxl-rank1" |
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pipe = AutoPipelineForText2Image.from_pretrained(model_id, 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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# load and fuse lcm lora |
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pipe.load_lora_weights(adapter_id) |
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pipe.fuse_lora() |
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prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" |
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# disable guidance_scale by passing 0 |
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image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0] |
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``` |
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## Acknowledgement |
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- https://twitter.com/2vXpSwA7/status/1726706470732091667 |
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