LCM_Dreamshaper_v7

Official Repository of the paper: *[Latent Consistency Models](https://arxiv.org/abs/2310.04378)*. Project Page: https://latent-consistency-models.github.io [](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model) 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). By distilling classifier-free guidance into the model's input, LCM can generate high-quality images in very short inference time. We compare the inference time at the setting of 768 x 768 resolution, CFG scale w=8, batchsize=4, using a A800 GPU. You can try out Latency Consistency Models directly on: [](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model) To run the model yourself, you can leverage the 🧨 Diffusers library: 1. Install the library: pip install --upgrade diffusers # make sure to use at least diffusers >= 0.22 pip install transformers accelerate 2. Run the model: from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7") pipe.to(torch_device="cuda", torch_dtype=torch.float32) prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" num_inference_steps = 4 images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil").images For more information, please have a look at the official docs: 👉 https://huggingface.co/docs/diffusers/api/pipelines/latent_consistency_models#latent-consistency-models 1. Install the library: pip install diffusers transformers accelerate 2. Run the model: from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img", custom_revision="main", revision="fb9c5d") pipe.to(torch_device="cuda", torch_dtype=torch.float32) prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k" num_inference_steps = 4 images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, output_type="pil").images @misc{luo2023latent, title={Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference}, author={Simian Luo and Yiqin Tan and Longbo Huang and Jian Li and Hang Zhao}, year={2023}, eprint={2310.04378}, archivePrefix={arXiv}, primaryClass={cs.CV} }