feat: remove GPU support and simplify to CPU-only architecture

CLAUDE.md

@@ -4,7 +4,7 @@ This file provides guidance to Claude Code when working with this repository.
 
 ## Overview
 
-This is the Real-ESRGAN API project - a sophisticated, full-featured REST API for image upscaling using Real-ESRGAN. The API supports both synchronous and asynchronous (job-based) processing with Docker containerization for CPU and GPU deployments.
+This is the Real-ESRGAN API project - a sophisticated, full-featured REST API for image upscaling using Real-ESRGAN. The API supports both synchronous and asynchronous (job-based) processing with Docker containerization for CPU deployments.
 
 ## Architecture
 
@@ -32,11 +32,11 @@ This is the Real-ESRGAN API project - a sophisticated, full-featured REST API fo
 
 ## Development Workflow
 
-### Local Setup (CPU)
+### Local Setup
 
 ```bash
 # Install dependencies
-pip install -r requirements.txt -r requirements-cpu.txt
+pip install -r requirements.txt
 
 # Run development server
 python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
@@ -49,7 +49,7 @@ python -m uvicorn app.main:app --reload --host 0.0.0.0 --port 8000
 ### Docker Development
 
 ```bash
-# Build CPU image
+# Build image
 docker compose build
 
 # Run container
@@ -62,19 +62,6 @@ docker compose logs -f api
 docker compose down
 ```
 
-### GPU Development
-
-```bash
-# Build GPU image
-docker compose -f docker-compose.gpu.yml build
-
-# Run with GPU
-docker compose -f docker-compose.gpu.yml up -d
-
-# Check GPU usage
-docker compose -f docker-compose.gpu.yml exec api nvidia-smi
-```
-
 ## Configuration
 
 ### Environment Variables (prefix: RSR_)
@@ -85,7 +72,7 @@ All settings from `app/config.py` can be configured via environment:
 RSR_UPLOAD_DIR=/data/uploads
 RSR_OUTPUT_DIR=/data/outputs
 RSR_MODELS_DIR=/data/models
-RSR_EXECUTION_PROVIDERS=["cpu"]  # or ["cuda"] for GPU
+RSR_EXECUTION_PROVIDERS=["cpu"]
 RSR_TILE_SIZE=400               # Tile size for large images
 RSR_MAX_UPLOAD_SIZE_MB=500
 RSR_SYNC_TIMEOUT_SECONDS=300
@@ -93,7 +80,7 @@ RSR_SYNC_TIMEOUT_SECONDS=300
 
 ### Docker Compose Environment
 
-Set in `docker-compose.yml` or `docker-compose.gpu.yml` `environment` section.
+Set in `docker-compose.yml` `environment` section.
 
 ## API Endpoints
 
@@ -152,7 +139,7 @@ This project follows similar patterns to facefusion-api:
 
 - **File Management**: Same `file_manager.py` utilities
 - **Worker Queue**: Similar async job processing architecture
-- **Docker Setup**: Multi-variant CPU/GPU builds
+- **Docker Setup**: CPU-only builds
 - **Configuration**: Environment-based settings with pydantic
 - **Gitea CI/CD**: Automatic Docker image building
 - **API Structure**: Organized routers and services
@@ -199,16 +186,6 @@ curl -X POST http://localhost:8000/api/v1/models/download \
   -d '{"models": ["RealESRGAN_x4plus"]}'
 ```
 
-### GPU Not Detected
-
-```bash
-# Check GPU availability
-docker compose -f docker-compose.gpu.yml exec api python -c "import torch; print(torch.cuda.is_available())"
-
-# Check system GPU
-nvidia-smi
-```
-
 ### Permission Issues with Volumes
 
 ```bash
@@ -231,14 +208,13 @@ git push gitea main
 ```
 
 Gitea workflows automatically:
-- Build Docker images (CPU and GPU)
+- Build Docker image
 - Run tests
 - Publish to Container Registry
 
 ## Important Notes
 
 - **Model Weights**: Downloaded from GitHub releases (~100MB each)
-- **GPU Support**: Requires NVIDIA Docker runtime
 - **Async Processing**: Uses thread pool (configurable workers)
 - **Tile Processing**: Handles large images by splitting into tiles
 - **Data Persistence**: Volumes recommended for production