README.md

---
license: apache-2.0
base_model:
- Lykon/DreamShaper
pipeline_tag: text-to-image
library_name: diffusers
tags:
- 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](https://arxiv.org/abs/2503.06674)", by *Yihong Luo, Tianyang Hu, Jiacheng Sun, Yujun Cai, Jing Tang*.


## User Study Time!
![user_study](user_study.jpg)
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
```python
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)
```
![sd3_compare](sd3_compare.jpg)
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
```python
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_dreamshaper](tdm_dreamshaper.jpg)

## TDM-CogVideoX-2B-LoRA
```python
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,
              use_dynamic_cfg=True).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](https://huggingface.co/Luo-Yihong/TDM_sd3_lora)
- [TDM-CogVideoX-2B-LoRA](https://huggingface.co/Luo-Yihong/TDM_CogVideoX-2B_LoRA)
- [TDM-Dreamshaper-LoRA](https://huggingface.co/Luo-Yihong/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}, 
}
```