| --- |
| license: other |
| pipeline_tag: image-to-image |
| --- |
| |
| # FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation |
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| [Paper](https://huggingface.co/papers/2610.36545) · [Project Page](https://lyne1.github.io/FrameForge-page/) · [Code](https://github.com/Lyne1/FrameForge) |
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| FrameForge adapts the temporal priors of [HunyuanVideo 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5) for three conditional image-generation tasks with one model: **subject-driven generation**, **Canny-to-image generation**, and **instruction-guided image editing**. The condition image is treated as an earlier frame and the output image as a target frame. This repository provides the inference code for the three-expert, one-reasoning-latent configuration. |
|
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| ## Usage |
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| For installation and checkpoint setup, please refer to the [GitHub repository](https://github.com/Lyne1/FrameForge). Run the commands from the repository root. Replace the example paths with your own reference or condition image, prepared at **512 × 512 pixels**. All three tasks generate a single **512 × 512** output image and use 50 denoising steps. |
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| ### Subject-driven generation |
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| ```bash |
| CUDA_VISIBLE_DEVICES=0 MODEL_PATH=./ckpts bash scripts/infer.sh \ |
| --lora_path ./checkpoints/pytorch_lora_weights.safetensors \ |
| --input_image ./inputs/subject.jpg \ |
| --reference_size 512 \ |
| --prompt "the dog playing on a beach" \ |
| --output_path ./outputs/subject.png \ |
| --num_inference_steps 50 |
| ``` |
|
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| ### Canny-to-image generation |
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| Pass a Canny edge map as the input image. The manuscript uses guidance scale `2.0` for this task: |
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| ```bash |
| CUDA_VISIBLE_DEVICES=0 MODEL_PATH=./ckpts bash scripts/infer.sh \ |
| --lora_path ./checkpoints/pytorch_lora_weights.safetensors \ |
| --input_image ./inputs/edges.png \ |
| --reference_size 512 \ |
| --prompt "A red car parked beside a lake at sunset" \ |
| --output_path ./outputs/canny.png \ |
| --num_inference_steps 50 --guidance_scale 2.0 |
| ``` |
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| ### Instruction-guided image editing |
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| Pass the source image and an editing instruction. The default guidance scale is `6.0`: |
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| ```bash |
| CUDA_VISIBLE_DEVICES=0 MODEL_PATH=./ckpts bash scripts/infer.sh \ |
| --lora_path ./checkpoints/pytorch_lora_weights.safetensors \ |
| --input_image ./inputs/edit.png \ |
| --reference_size 512 \ |
| --prompt "Change the sky to sunset while preserving the rest of the scene" \ |
| --output_path ./outputs/edited.png \ |
| --num_inference_steps 50 |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{lin2026frameforge, |
| title = {FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation}, |
| author = {Yijing Lin and Zhendong Mao and Mengqi Huang and Yuxin Wang and Yongdong Zhang}, |
| year = {2026}, |
| eprint = {2610.36545}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CV}, |
| url = {https://arxiv.org/abs/2610.36545} |
| } |
| ``` |
|
|
| ## License |
|
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| This project is derived from HunyuanVideo 1.5 and retains its [Tencent Hunyuan Community License](https://github.com/Lyne1/FrameForge/blob/main/LICENSE) and [third-party notices](https://github.com/Lyne1/FrameForge/blob/main/NOTICE). Please review those terms before redistributing code or weights. |
