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tianweiy/
DMD2

Name: DMD2
Availability: InStock

diffusers

text-to-image

stable-diffusion

diffusion distillation

2405.14867

cc-by-nc-4.0

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DMD2 Model Card

Improved Distribution Matching Distillation for Fast Image Synthesis,
Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman

Contact

Feel free to contact us if you have any questions about the paper!

Tianwei Yin tianweiy@mit.edu

Usage

We can use the standard diffuser pipeline:

4-step UNet generation

python

import 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 generation

python

import 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 generation

python

import 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 Adapter

python

from 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 repository

License

Improved Distribution Matching Distillation is released under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Citation

If you find DMD2 useful or relevant to your research, please kindly cite our papers:

bib

@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}
}

Acknowledgments

This 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.