Add model card

This PR adds a model card for the paper [FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation](https://huggingface.co/papers/2610.36545). It adds the relevant `pipeline_tag` so the model can be found under image-to-image, includes license metadata, and provides usage examples from the official GitHub repository.

README.md

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+---
+license: other
+pipeline_tag: image-to-image
+---
+
+# FrameForge: Harnessing Priors of Video Foundation Models for Unified Image Generation
+
+[Paper](https://huggingface.co/papers/2610.36545) · [Project Page](https://lyne1.github.io/FrameForge-page/) · [Code](https://github.com/Lyne1/FrameForge)
+
+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.
+
+## Usage
+
+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.
+
+### Subject-driven generation
+
+```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
+```
+
+### Canny-to-image generation
+
+Pass a Canny edge map as the input image. The manuscript uses guidance scale `2.0` for this task:
+
+```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
+```
+
+### Instruction-guided image editing
+
+Pass the source image and an editing instruction. The default guidance scale is `6.0`:
+
+```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
+
+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.