| --- |
| license: cc-by-nc-4.0 |
| library_name: pytorch |
| pipeline_tag: unconditional-image-generation |
| datasets: |
| - ILSVRC/imagenet-1k |
| tags: |
| - image-generation |
| - class-conditional-image-generation |
| - flow-matching |
| - pixel-space |
| - imagenet |
| arxiv: "2608.05811" |
| extra_gated_heading: "EG-FM Model Weights Access Application" |
| extra_gated_description: "Please complete this form to request access to the EG-FM model weights. Requests are reviewed manually." |
| extra_gated_button_content: "Submit access request" |
| extra_gated_prompt: "Thank you for your interest in EG-FM. To support responsible use of the model weights, please provide accurate information below. Our team will normally review your request within 1 business days. Using an institutional, academic, or corporate email address may help us verify your application." |
| extra_gated_fields: |
| "Name": text |
| "Affiliation (Organization / Company / University)": text |
| "Your role": |
| type: select |
| options: |
| - Researcher / Professor |
| - Student |
| - Engineer / Architect |
| - Independent Developer |
| - Other |
| "Intended use case": text |
| "I confirm that my use is non-commercial and complies with the CC BY-NC license terms": checkbox |
| "I commit not to redistribute or resell the model weights to any third party": checkbox |
| "I commit not to use this model for illegal, harmful, or unethical activities": checkbox |
| --- |
| |
| # Energy-Guided Flow Matching (EG-FM) |
|
|
| Official checkpoints for **Energy-Guided Flow Matching (EG-FM)** on class-conditional ImageNet-1K generation at 256×256 and 512×512 resolutions. |
|
|
| EG-FM introduces an image-specific, energy-guided moving endpoint to construct an explicit coarse-to-fine generation trajectory while requiring no changes to the backbone or training data. |
|
|
| - **Paper:** [Energy-Guided Flow Matching](https://huggingface.co/papers/2608.05811) |
| - **Code and usage:** [ysng123/EG-FM](https://github.com/ysng123/EG-FM) |
|
|
| ## Released models |
|
|
| | Model | Resolution | Training | FID | |
| | --- | ---: | ---: | ---: | |
| | [PixelDiT-200](./256/pixeldit200/checkpoint-200.pth) | 256×256 | 200 epochs | 1.55 | |
| | [PixelDiT-600](./256/pixeldit600/checkpoint-600.pth) | 256×256 | 600 epochs | 1.45 | |
| | [PixelDiT-220](./512/pixeldit220/checkpoint-220.pth) | 512×512 | 200+20 epochs | 1.72 | |
| | [PixelDiT-240](./512/pixeldit240/checkpoint-240.pth) | 512×512 | 200+40 epochs | 1.68 | |
|
|
| All FID values use the ADM evaluation suite. The 512×512 models continue from the 200-epoch 256×256 checkpoint; `200+20` and `200+40` denote the initial training followed by additional high-resolution adaptation epochs. |
|
|
| For installation, checkpoint loading, inference, and evaluation commands, see the [official GitHub repository](https://github.com/ysng123/EG-FM). |
|
|
|
|
| ## License |
|
|
| The model weights are released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Commercial use is not permitted under this license. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{tong2026energy, |
| title = {Energy-Guided Flow Matching}, |
| author = {Tong, Haoyang and He, Yu and Li, Fang and Ma, Lichen and Fu, Jingling and Chen, Dong and Chen, Zhen and Huang, Junshi and Cao, Jie}, |
| journal = {arXiv preprint arXiv:2608.05811}, |
| year = {2026} |
| } |
| ``` |
|
|
