TDM_sd3_lora

TDM: Learning Few-Step Diffusion Models by Trajectory Distribution Matching

<div style="text-align: center;"> <a href="https://tdm-t2x.github.io/"><img src="https://img.shields.io/static/v1?label=Project%20Page&message=Github-Page&color=blue&logo=github-pages" style="display: inline;"></a> &ensp; <a href="https://arxiv.org/abs/2503.06674"><img src="https://img.shields.io/static/v1?label=Paper&message=Arxiv:TDM&color=red&logo=arxiv" style="display: inline;"></a> </div>

This is the Official Repository of "Learning Few-Step Diffusion Models by Trajectory Distribution Matching", by Yihong Luo, Tianyang Hu, Jiacheng Sun, Yujun Cai, Jing Tang.

User Study Time!

Which one do you think is better? Some images are generated by Pixart-α (50 NFE). Some images are generated by TDM (4 NFE), distilling from Pixart-α in a data-free way with merely 500 training iterations and 2 A800 hours.

<details> <summary style="color: #1E88E5; cursor: pointer; font-size: 1.2em;"> Click for answer</summary> <p style="font-size: 1.2em; margin-top: 8px;">Answers of TDM's position (left to right): bottom, bottom, top, bottom, top.</p> </details>

Fast Text-to-Video Geneartion

Our proposed TDM can be easily extended to text-to-video.

<p align="center"> <img src="teacher.gif" alt="Teacher" width="100%"> <img src="student.gif" alt="Student" width="100%"> </p>

The video on the above was generated by CogVideoX-2B (100 NFE). In the same amount of time, TDM (4NFE) can generate 25 videos, as shown in the below, achieving an impressive 25 times speedup without performance degradation. (Note: The noise in the GIF is due to compression.)

Usage

TDM-SD3-LoRA

import torch
from diffusers import StableDiffusion3Pipeline, AutoencoderTiny, DPMSolverMultistepScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from diffusers.utils import make_image_grid
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16).to("cuda")
pipe.load_lora_weights('Luo-Yihong/TDM_sd3_lora', adapter_name = 'tdm') # Load TDM-LoRA
pipe.set_adapters(["tdm"], [0.125])# IMPORTANT. Please set LoRA scale to 0.125.
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd3", torch_dtype=torch.float16) # Save GPU memory.
pipe.vae.config.shift_factor = 0.0
pipe = pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained("Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers", subfolder="scheduler")
pipe.scheduler.config['flow_shift'] = 6 # the flow_shift can be changed from 1 to 6.
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
generator = torch.manual_seed(8888)
image = pipe(
    prompt="A cute panda holding a sign says TDM SOTA!",
    negative_prompt="",
    num_inference_steps=4,
    height=1024,
    width=1024,
    num_images_per_prompt = 1,
    guidance_scale=1.,
    generator = generator,
).images[0]

pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained("Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers", subfolder="scheduler")
pipe.set_adapters(["tdm"], [0.]) # Unload lora
generator = torch.manual_seed(8888)
teacher_image = pipe(
    prompt="A cute panda holding a sign says TDM SOTA!",
    negative_prompt="",
    num_inference_steps=28,
    height=1024,
    width=1024,
    num_images_per_prompt = 1,
    guidance_scale=7.,
    generator = generator,
).images[0]
make_image_grid([image,teacher_image],1,2)

The sample generated by SD3 with 56 NFE is on the right, and the sample generated by TDM with 4NFE is on the left. Which one do you feel is better?

TDM-Dreamshaper-v7-LoRA

import torch
from diffusers import DiffusionPipeline, UNet2DConditionModel, DPMSolverMultistepScheduler
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
repo_name = "Luo-Yihong/TDM_dreamshaper_LoRA"
ckpt_name = "tdm_dreamshaper.pt"
pipe = DiffusionPipeline.from_pretrained('lykon/dreamshaper-7', torch_dtype=torch.float16).to("cuda")
pipe.load_lora_weights(hf_hub_download(repo_name, ckpt_name))
pipe.scheduler = DPMSolverMultistepScheduler.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="scheduler")
generator = torch.manual_seed(317)
image = pipe(
    prompt="A close-up photo of an Asian lady with sunglasses",
    negative_prompt="",
    num_inference_steps=4,
    num_images_per_prompt = 1,
    generator = generator,
    guidance_scale=1.,
).images[0]
image

TDM-CogVideoX-2B-LoRA

import torch
from diffusers import CogVideoXPipeline
from diffusers.utils import export_to_video
pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16)
pipe.vae.enable_slicing() # Save memory
pipe.vae.enable_tiling() # Save memory
pipe.load_lora_weights("Luo-Yihong/TDM_CogVideoX-2B_LoRA")
pipe.to("cuda")
prompt = (
    "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. The "
    "panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other "
    "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, "
    "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. "
    "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical "
    "atmosphere of this unique musical performance"
)
# We train the generator on timesteps [999, 856, 665, 399].
# The official scheduler of CogVideo-X using uniform spacing, may cause inferior results.
# But TDM-LoRA still works well under 4 NFE.
# We will update the TDM-CogVideoX-LoRA soon for better performance!
generator = torch.manual_seed(8888)
frames = pipe(prompt, guidance_scale=1, 
              num_inference_steps=4, 
              num_frames=49,
              generator = generator).frames[0]
export_to_video(frames, "output-TDM.mp4", fps=8)

🔥 Pre-trained Models

We release a bucket of TDM-LoRA. Please enjoy it!

• TDM-SD3-LoRA
• TDM-CogVideoX-2B-LoRA
• TDM-Dreamshaper-LoRA

Contact

Please contact Yihong Luo ([email protected]) if you have any questions about this work.

Bibtex

@misc{luo2025tdm,
      title={Learning Few-Step Diffusion Models by Trajectory Distribution Matching}, 
      author={Yihong Luo and Tianyang Hu and Jiacheng Sun and Yujun Cai and Jing Tang},
      year={2025},
      eprint={2503.06674},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.06674}, 
}