Forward from ://huggingface.co/tianweiy/DMD2/tree/main这个模型在 cfg=1 ,lcm karras 下工作良好，另外在在线生图器的加速模型可以使用Euler a作为替代step设置为10-12步可以获得非常好的效果。SDXL和pony均可使用。Forward from ://huggingface.co/tianweiy/DMD2/tree/mainThis model works well under cfg=1 and lcm karras, and Euler a can be used as a substitute for the acceleration model in online graph generatorsSetting step to 10-12 can achieve very good results.Both SDXL and Pony can be used.===================================================Improved Distribution Matching Distillation for Fast Image Synthesis,Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, William T. FreemanContactFeel free to contact us if you have any questions about the paper!Tianwei Yin tianweiy@mit.eduUsageWe can use the standard diffuser pipeline:4-step UNet generationimport torch from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler from huggingface_hub import hf_hub_download from safetensors.torch import load_file base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" repo_name = "tianweiy/DMD2" ckpt_name = "dmd2_sdxl_4step_unet_fp16.bin"

Load model.

unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16) unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda")) pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) prompt="a photo of a cat"

LCMScheduler's default timesteps are different from the one we used for training

image=pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, timesteps=[999, 749, 499, 249]).images[0] 4-step LoRA generationimport torch from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler from huggingface_hub import hf_hub_download from safetensors.torch import load_file base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" repo_name = "tianweiy/DMD2" ckpt_name = "dmd2_sdxl_4step_lora_fp16.safetensors"

Load model.

pipe = DiffusionPipeline.from_pretrained(base_model_id, torch_dtype=torch.float16, variant="fp16").to("cuda") pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name)) pipe.fuse_lora(lora_scale=1.0) # we might want to make the scale smaller for community models

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) prompt="a photo of a cat"

LCMScheduler's default timesteps are different from the one we used for training

image=pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0, timesteps=[999, 749, 499, 249]).images[0] 1-step UNet generationimport torch from diffusers import DiffusionPipeline, UNet2DConditionModel, LCMScheduler from huggingface_hub import hf_hub_download from safetensors.torch import load_file base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" repo_name = "tianweiy/DMD2" ckpt_name = "dmd2_sdxl_1step_unet_fp16.bin"

Load model.

unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16) unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda")) pipe = DiffusionPipeline.from_pretrained(base_model_id, unet=unet, torch_dtype=torch.float16, variant="fp16").to("cuda") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) prompt="a photo of a cat" image=pipe(prompt=prompt, num_inference_steps=1, guidance_scale=0, timesteps=[399]).images[0] 4-step T2I Adapterfrom diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, AutoencoderKL, UNet2DConditionModel, LCMScheduler from diffusers.utils import load_image, make_image_grid from controlnet_aux.canny import CannyDetector from huggingface_hub import hf_hub_download import torch

load adapter

adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")

vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)

base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" repo_name = "tianweiy/DMD2" ckpt_name = "dmd2_sdxl_4step_unet_fp16.bin"

Load model.

unet = UNet2DConditionModel.from_config(base_model_id, subfolder="unet").to("cuda", torch.float16) unet.load_state_dict(torch.load(hf_hub_download(repo_name, ckpt_name), map_location="cuda"))

pipe = StableDiffusionXLAdapterPipeline.from_pretrained( base_model_id, unet=unet, vae=vae, adapter=adapter, torch_dtype=torch.float16, variant="fp16", ).to("cuda") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.enable_xformers_memory_efficient_attention()

canny_detector = CannyDetector()

url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg" image = load_image(url)

Detect the canny map in low resolution to avoid high-frequency details

image = canny_detector(image, detect_resolution=384, image_resolution=1024)#.resize((1024, 1024))

prompt = "Mystical fairy in real, magic, 4k picture, high quality"

gen_images = pipe( prompt=prompt, image=image, num_inference_steps=4, guidance_scale=0, adapter_conditioning_scale=0.8, adapter_conditioning_factor=0.5, timesteps=[999, 749, 499, 249] ).images[0] gen_images.save('out_canny.png') For more information, please refer to the code repositoryLicenseImproved Distribution Matching Distillation is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.CitationIf you find DMD2 useful or relevant to your research, please kindly cite our papers:@article{yin2024improved, title={Improved Distribution Matching Distillation for Fast Image Synthesis}, author={Yin, Tianwei and Gharbi, Micha{"e}l and Park, Taesung and Zhang, Richard and Shechtman, Eli and Durand, Fredo and Freeman, William T}, journal={arXiv:2405.14867}, year={2024} }

@inproceedings{yin2024onestep, title={One-step Diffusion with Distribution Matching Distillation}, author={Yin, Tianwei and Gharbi, Micha{"e}l and Zhang, Richard and Shechtman, Eli and Durand, Fr{'e}do and Freeman, William T and Park, Taesung}, booktitle={CVPR}, year={2024} } AcknowledgmentsThis work was done while Tianwei Yin was a full-time student at MIT. It was developed based on our reimplementation of the original DMD paper. This work was supported by the National Science Foundation under Cooperative Agreement PHY-2019786 (The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/), by NSF Grant 2105819, by NSF CISE award 1955864, and by funding from Google, GIST, Amazon, and Quanta Computer.