edit readme color

README.md

@@ -1,83 +1,83 @@
----
-title: TEXTure CPU Lite
-emoji: 🎨
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 6.2.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# TEXTure CPU Lite - Text-Guided 3D Texturing
-
-Generate **proper UV texture maps** for 3D meshes using text prompts.
-Uses SD-2-Depth (same as original TEXTure paper) for depth-conditioned generation and xatlas for UV unwrapping.
-
-## Features
-
-- **Proper UV Texture Output**: Creates UV atlas that can be applied to the mesh
-- **SD-2-Depth**: Native depth conditioning (same model as original TEXTure)
-- **OBJ Export**: Outputs OBJ + MTL + texture PNG ready to use
-- **xatlas UV Unwrapping**: Automatic UV coordinate generation
-
-## How it works
-
-1. Upload a 3D mesh (.obj, .stl, .ply, .glb)
-2. Enter a text prompt describing the desired texture
-3. xatlas generates UV coordinates
-4. Depth is rendered from multiple viewpoints
-5. SD-2-Depth generates textures conditioned on depth
-6. Textures are projected back to UV space
-7. Download textured mesh (OBJ + MTL + PNG)
-
-## Models Used
-
-| Component | Model | Size |
-|-----------|-------|------|
-| SD-2-Depth | radames/stable-diffusion-2-depth-img2img | ~5GB |
-
-**Same architecture as original TEXTure paper** (SD-2-Depth) - has native depth conditioning built-in.
-Uses public community copy since official `stabilityai/stable-diffusion-2-depth` is gated.
-
-**Runtime:**
-- First run: Downloads ~5GB model (cached in `~/.cache/huggingface/`)
-- CPU uses INT8 quantization via `optimum.quanto` for 3-5x speedup
-- ONNX not supported (SD-2-Depth has 5-channel UNet)
-
-**Debug logs:** Check build logs for `[OK]`, `[ERROR]`, `[WARN]` messages if something fails.
-
-## Performance
-
-| Device | Steps | Time per View | 4 Views Total |
-|--------|-------|---------------|---------------|
-| GPU (CUDA) | 20 | ~2-3 sec | ~12 sec |
-| CPU (INT8) | 10 | ~1.5 min | ~6 min |
-| CPU (INT8) | 20 | ~3 min | ~14 min |
-
-**Tip:** For faster results on CPU, use fewer steps (5-10) or fewer views (2-3).
-
-## Local Development
-
-```bash
-pip install -r requirements.txt
-python app.py
-```
-
-## Files Structure
-
-```
-├── app.py              # Single-file implementation
-├── requirements.txt
-├── README.md
-└── shapes/
-    └── bunny.obj       # Sample mesh
-```
-
-## Credits
-
-- [TEXTure Paper](https://texturepaper.github.io/TEXTurePaper/) - Yael Vinker et al.
-- [ControlNet](https://github.com/lllyasviel/ControlNet) - Lvmin Zhang
-- [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5) - RunwayML
-- [xatlas](https://github.com/jpcy/xatlas) - UV unwrapping
+---
+title: TEXTure CPU Lite
+emoji: 🎨
+colorFrom: green
+colorTo: pink
+sdk: gradio
+sdk_version: 6.2.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# TEXTure CPU Lite - Text-Guided 3D Texturing
+
+Generate **proper UV texture maps** for 3D meshes using text prompts.
+Uses SD-2-Depth (same as original TEXTure paper) for depth-conditioned generation and xatlas for UV unwrapping.
+
+## Features
+
+- **Proper UV Texture Output**: Creates UV atlas that can be applied to the mesh
+- **SD-2-Depth**: Native depth conditioning (same model as original TEXTure)
+- **OBJ Export**: Outputs OBJ + MTL + texture PNG ready to use
+- **xatlas UV Unwrapping**: Automatic UV coordinate generation
+
+## How it works
+
+1. Upload a 3D mesh (.obj, .stl, .ply, .glb)
+2. Enter a text prompt describing the desired texture
+3. xatlas generates UV coordinates
+4. Depth is rendered from multiple viewpoints
+5. SD-2-Depth generates textures conditioned on depth
+6. Textures are projected back to UV space
+7. Download textured mesh (OBJ + MTL + PNG)
+
+## Models Used
+
+| Component | Model | Size |
+|-----------|-------|------|
+| SD-2-Depth | radames/stable-diffusion-2-depth-img2img | ~5GB |
+
+**Same architecture as original TEXTure paper** (SD-2-Depth) - has native depth conditioning built-in.
+Uses public community copy since official `stabilityai/stable-diffusion-2-depth` is gated.
+
+**Runtime:**
+- First run: Downloads ~5GB model (cached in `~/.cache/huggingface/`)
+- CPU uses INT8 quantization via `optimum.quanto` for 3-5x speedup
+- ONNX not supported (SD-2-Depth has 5-channel UNet)
+
+**Debug logs:** Check build logs for `[OK]`, `[ERROR]`, `[WARN]` messages if something fails.
+
+## Performance
+
+| Device | Steps | Time per View | 4 Views Total |
+|--------|-------|---------------|---------------|
+| GPU (CUDA) | 20 | ~2-3 sec | ~12 sec |
+| CPU (INT8) | 10 | ~1.5 min | ~6 min |
+| CPU (INT8) | 20 | ~3 min | ~14 min |
+
+**Tip:** For faster results on CPU, use fewer steps (5-10) or fewer views (2-3).
+
+## Local Development
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+## Files Structure
+
+```
+├── app.py              # Single-file implementation
+├── requirements.txt
+├── README.md
+└── shapes/
+    └── bunny.obj       # Sample mesh
+```
+
+## Credits
+
+- [TEXTure Paper](https://texturepaper.github.io/TEXTurePaper/) - Yael Vinker et al.
+- [ControlNet](https://github.com/lllyasviel/ControlNet) - Lvmin Zhang
+- [Stable Diffusion](https://huggingface.co/runwayml/stable-diffusion-v1-5) - RunwayML
+- [xatlas](https://github.com/jpcy/xatlas) - UV unwrapping