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README.md

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+---
+base_model: Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+library_name: peft
+license: apache-2.0
+pipeline_tag: text-to-video
+tags:
+- text-to-video
+- wan
+- lora
+- peft
+- flow-grpo
+- solace
+- reinforcement-learning
+---
+
+# Wan2.1-T2V-1.3B-SOLACE
+
+LoRA adapter from **SOLACE** (**S**elf-c**O**nfidence reward for a**L**igning text-to-im**A**ge models via **C**onfidenc**E** optimization), CVPR 2026.
+
+SOLACE applied to the **Wan2.1-T2V-1.3B** text-to-video model, using the model's own denoising confidence as an intrinsic reward (no external reward model at training time).
+
+- **Base model:** [`Wan-AI/Wan2.1-T2V-1.3B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers)
+- **Method:** SOLACE intrinsic self-confidence reward (built on Flow-GRPO)
+- **Code:** https://github.com/wookiekim/SOLACE
+- **Adapter type:** PEFT LoRA (rank 32) on the Wan 3D transformer
+
+## Usage
+
+```python
+import torch
+from diffusers import WanPipeline
+from diffusers.utils import export_to_video
+from peft import PeftModel
+
+model_id = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+lora_ckpt_path = "wookiekim/Wan2.1-T2V-1.3B-SOLACE"
+device = "cuda"
+
+pipe = WanPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+pipe.transformer = PeftModel.from_pretrained(pipe.transformer, lora_ckpt_path)
+pipe.transformer = pipe.transformer.merge_and_unload()
+pipe = pipe.to(device)
+
+frames = pipe(
+    "a cat walking across a sunlit kitchen floor",
+    height=480, width=832, num_frames=81,
+    num_inference_steps=50, guidance_scale=5.0,
+).frames[0]
+export_to_video(frames, "solace_wan.mp4", fps=16)
+```
+
+## Citation
+
+```bibtex
+@inproceedings{kim2026solace,
+    title={Improving Text-to-Image Generation with Intrinsic Self-Confidence Rewards},
+    author={Kim, Wookyoung and others},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year={2026}
+}
+```
+
+## Acknowledgments
+
+This work builds upon [Flow-GRPO](https://github.com/yifan123/flow_grpo) by Jie Liu et al.