| --- |
| title: PlanPalette |
| emoji: 🎨 |
| colorFrom: green |
| colorTo: blue |
| sdk: gradio |
| sdk_version: "4.44.1" |
| python_version: "3.10" |
| app_file: app.py |
| pinned: false |
| --- |
| |
| # PlanPalette |
|
|
| PlanPalette is a Hugging Face Spaces Gradio app for fast architectural floor-plan visualization. It accepts a colored reference floor plan and a black-and-white CAD floor plan, extracts the reference palette, and uses a fast text-to-image model to generate a furnished architectural plan render. |
|
|
| ## Hackathon Description |
|
|
| Architectural visualization artists often need quick mood-board style studies before a full rendering pass. PlanPalette is an MVP for that workflow: it transfers the visual language of one plan onto another with a small, controllable image-generation model instead of a giant multimodal model or manual masking. |
|
|
| The app currently performs: |
|
|
| - Reference image upload |
| - Raw CAD floor-plan upload |
| - Side-by-side input display |
| - Dominant palette extraction |
| - DreamShaper XL Lightning image generation by default |
| - AI-first furnished architectural rendering |
| - Optional CAD linework compositing |
| - Final PNG output |
| - Extracted palette and material-style legend |
|
|
| ## Small-Model Constraint |
|
|
| This project stays under the 32B-parameter hackathon constraint by using a small/medium image generation model. The default is `Lykon/dreamshaper-xl-lightning`, a fast SDXL-style model. FLUX.1-schnell can be used by setting `PLANPALETTE_BASE_MODEL=black-forest-labs/FLUX.1-schnell`, but that repo may require accepting gated model terms on Hugging Face. |
|
|
| The MVP uses: |
|
|
| - DreamShaper XL Lightning for fast text-to-image generation |
| - Prompt guidance derived from the reference image palette and CAD canvas shape |
| - OpenCV thresholding to prepare optional CAD line masks |
| - K-means color clustering through OpenCV for the reference palette |
| - Pillow and NumPy image handling |
| - Gradio for the interactive UI |
|
|
| A GPU or ZeroGPU Space is recommended. CPU inference is not practical for the AI mode. |
|
|
| ## Codex Usage |
|
|
| Codex was used to scaffold the Hugging Face Space structure, implement the palette and linework preprocessing pipeline, add the text-to-image generation path, add custom Gradio CSS, and document setup and limitations. |
|
|
| Suggested future Codex tasks: |
|
|
| - Add example floor-plan assets |
| - Add model presets for fast/quality GPU tiers |
| - Add export metadata with palette hex codes |
| - Add optional room-type labels or manual prompt regions |
| - Add before/after comparison controls |
|
|
| ## Hugging Face Space Setup |
|
|
| 1. Create a new Hugging Face Space. |
| 2. Select **Gradio** as the Space SDK. |
| 3. Upload or commit these files: |
| - `app.py` |
| - `requirements.txt` |
| - `README.md` |
| 4. Let the Space build automatically. |
| 5. Upload a colored reference floor plan and a black-and-white CAD floor plan. |
| 6. Click **Generate Colorized Plan**. |
|
|
| Use a GPU or ZeroGPU Space for generation. The app raises a clear error if it starts on CPU-only hardware. |
|
|
| ## Local Development |
|
|
| Install dependencies: |
|
|
| ```powershell |
| python -m venv .venv |
| .\.venv\Scripts\python.exe -m pip install torch==2.8.0 --index-url https://download.pytorch.org/whl/cpu |
| .\.venv\Scripts\python.exe -m pip install -r requirements-local.txt |
| ``` |
|
|
| Run the app: |
|
|
| ```powershell |
| $env:HF_HOME="$PWD\.cache\huggingface" |
| $env:TRANSFORMERS_CACHE="$PWD\.cache\huggingface\transformers" |
| $env:PLANPALETTE_ALLOW_CPU="1" |
| $env:PLANPALETTE_MAX_SIDE="640" |
| .\.venv\Scripts\python.exe app.py |
| ``` |
|
|
| Then open the local Gradio URL printed in the terminal. |
|
|
| Local CPU inference is supported for debugging, but it is very slow and downloads large image model weights. Use a CUDA GPU or Hugging Face GPU/ZeroGPU hardware for practical generation speed. |
|
|
| ## Limitations |
|
|
| - Text-to-image models can hallucinate rooms, fills, textures, or plan styling, especially from dense CAD sheets. |
| - The generated render may not perfectly understand every room, label, or wall in a dense CAD sheet. |
| - CAD linework overlay is optional. Set it to 0 for a pure AI render, or increase it for readability. |
| - Dense text, hatch patterns, and low-contrast scans may weaken prompt and overlay quality. |
| - Material names are inferred from color families, not from semantic understanding. |
| - The colorization pass is presentation-oriented, not physically based rendering. |
| - The MVP preserves black CAD linework but does not reconstruct missing or damaged CAD geometry. |
|
|
