updata readme

README.md

@@ -0,0 +1,112 @@
+---
+license: mit
+language:
+- en
+base_model:
+- sd2-community/stable-diffusion-2-1
+pipeline_tag: image-to-image
+tags:
+- material-decomposition
+- diffusion
+---
+
+# Model Overview
+
+This repository hosts the pretrained parameters for the SuperMat project, as described in *"SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates"* (ICCV 2025)
+
+| Model | Description |
+| --- | --- |
+| supermat.ckpt | Base SuperMat model for material decomposition
+| supermat_mv.ckpt | Multi-view version of SuperMat processing six orthogonal views
+| uv_refine_bc | UV refinement network for albedo materials
+| uv_refine_rm | UV refinement network for roughness & metallic materials
+
+All models are built upon the base model `stabilityai/stable-diffusion-2-1`.
+Note: The official `stabilityai/stable-diffusion-2-1` model has been removed. You may need to obtain the base model parameters through alternative sources, such as `sd2-community/stable-diffusion-2-1`.
+
+# Model Details
+#### SuperMat (supermat.ckpt)
+The core model for material decomposition. It takes RGBA images as input and decomposes materials from the target object.
+#### SuperMat Multi-View (supermat_mv.ckpt)
+An extended version that processes six orthogonal views simultaneously. This model leverages multi-view consistency for improved material estimation. For each view, the camera-to-world (c2w) matrix is provided as camera embeddings.
+#### UV Refinement Networks
+Two specialized networks for refining UV maps:
+- uv_refine_bc: Refines the UV map for albedo materials
+- uv_refine_rm: Refines the UV map for roughness & metallic materials
+
+# Download & Usage
+Download the desired model(s) from this repository and place them in the checkpoints folder:
+
+```
+checkpoints/
+├── supermat.pth
+├── supermat_mv.pth
+├── uv_refine_bc.pth
+└── uv_refine_rm.pth
+```
+The models are independent of each other, so you only need to download those required for your specific inference task.
+
+# Input Requirements
+#### Image Format
+- SuperMat models expect RGBA images where only the target object appears as foreground, with alpha values set to `0` for all other regions
+- During inference, the input image is alpha-composited with a gray background `(0.5, 0.5, 0.5)`
+#### Resolution Preferences
+- SuperMat models: `512×512` resolution (recommended)
+- UV refinement networks: `1024×1024` resolution (recommended)
+#### Multi-View Specific Requirements
+For the multi-view model:
+- All inputs for a single case should be organized in one folder
+- Input images must follow the naming convention as shown in `examples/bag_rendered_6views`
+- Camera information is stored in `meta.json` (refer to the example for the required format with c2w matrices)
+
+# Quick Inference Examples
+#### SuperMat Single-Image
+```
+python inference_supermat.py \
+  --input examples/ring_rendered_2views \
+  --output-dir outputs \
+  --checkpoint checkpoints/supermat.pth \
+  --base-model sd2-community/stable-diffusion-2-1 \
+  --device cuda:0 \
+  --image-size 512
+```
+
+#### SuperMat Multi-View
+```
+python inference_supermat_mv.py \
+  --input examples/bag_rendered_6views \
+  --output-dir outputs_mv \
+  --checkpoint checkpoints/supermat_mv.pth \
+  --base-model sd2-community/stable-diffusion-2-1 \
+  --device cuda:0 \
+  --image-size 512 \
+  --num_views 6 \
+  --use-camera-embeds
+```
+
+#### UV Refinement (Albedo)
+```
+python inference_uv_refine.py \
+  --input-uv examples/axe_uv/uv_bc.png \
+  --input-uv-position examples/axe_uv/uv_position.png \
+  --input-uv-mask examples/axe_uv/uv_mask.png \
+  --output-dir outputs_uv_bc \
+  --checkpoint checkpoints/uv_refine_bc.pth \
+  --base-model sd2-community/stable-diffusion-2-1 \
+  --device cuda:0 \
+  --image-size 1024
+```
+
+For complete usage instructions, please refer to the [main repository](https://github.com/hyj542682306/SuperMat).
+
+# Citation
+If you find these models useful in your research, please cite:
+```
+@inproceedings{hong2025supermat,
+  title={Supermat: Physically consistent pbr material estimation at interactive rates},
+  author={Hong, Yijia and Guo, Yuan-Chen and Yi, Ran and Chen, Yulong and Cao, Yan-Pei and Ma, Lizhuang},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={25083--25093},
+  year={2025}
+}
+```