FLUX MCP Server & CLI

A Model Context Protocol (MCP) server and command-line tool for generating high-quality images using FLUX.1-dev and FLUX.2-dev. Runs on NVIDIA GPUs (CUDA), AMD GPUs (ROCm), and Apple Silicon (MPS).

Features

• 🎨 Dual Model Support - FLUX.1-dev (faster quality, 4-8min on CUDA) and FLUX.2-dev (maximum quality, 30-40min on CUDA)

• 🔴 AMD GPU Support - Runs on ROCm (RX 6000/7000 series and RDNA2+ recommended), same VRAM tiers as CUDA

• 🍎 Apple Silicon Support - Runs on MPS (M1/M2/M3/M4), automatically uses FLUX.1-dev as default

• ⚡ Smart Memory Management - Automatically selects best strategy based on available VRAM (CUDA) or unified memory (MPS)

• 🔄 Auto-Unload - Automatically unloads model after configurable inactivity period (MCP mode)

• 💾 Memory Efficient - Sequential CPU offload for 16GB CUDA GPUs, full GPU mode for 24GB+

• 🎲 Reproducible - Seed-based generation for consistent results

• 📊 Status Monitoring - Check model status and memory usage

• 🔧 Runtime Configuration - Adjust timeout and switch models without restarting

• 🖥️ Dual Interface - Use via MCP-compatible applications or command-line (CLI)

• 🖼️ Preview Tool - Retrieve generated images by ID after background generation completes

Related MCP server: FLUX MCP Server

Requirements

NVIDIA GPU (Linux/Windows)

• Python 3.10+

• NVIDIA GPU with 12GB+ VRAM (16GB recommended, 24GB+ for maximum speed)

• CUDA toolkit installed

• PyTorch with CUDA support

AMD GPU / ROCm (Linux)

• Python 3.10+

• AMD GPU with ROCm support (RDNA2+ / RX 6000 series or newer recommended), 12GB+ VRAM

• ROCm toolkit installed (5.7+)

• PyTorch with ROCm support (see AMD/ROCm Installation below)

Apple Silicon (macOS)

• Python 3.10+

• Mac with Apple Silicon (M1 or later), 16GB+ unified memory

• PyTorch 2.4+ (standard PyPI wheel, no CUDA needed)

• Install with Mac extras: uv sync --extra mac

Quick Start

Get started with FLUX MCP in minutes:

# 1. Install dependencies (using UV - recommended)
uv sync

# 2. Configure environment
cp .env.example .env
# Edit .env to set FLUX_OUTPUT_DIR and other preferences

# 3. Add to your MCP client config (example for Claude Desktop)
# Add to ~/.config/Claude/claude_desktop_config.json (Linux)
# See "MCP Configuration" section below for full details

# 4. Generate your first image (CLI mode)
flux generate "a beautiful sunset over mountains"

# Or use via MCP client (e.g., Claude Desktop)
# Just ask: "Generate an image of a beautiful sunset over mountains"

For detailed setup and configuration, see the sections below.

MCP Configuration

Add the server to your MCP client configuration. Below is an example for Claude Desktop:

Claude Desktop configuration file locations:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "flux": {
      "command": "uv",
      "args": [
        "--directory",
        "/absolute/path/to/flux-mcp",
        "run",
        "flux-mcp"
      ]
    }
  }
}

Security note: Keep FLUX_OUTPUT_DIR and other settings in .env, not inlined into MCP client configs. The .env file is loaded automatically at startup.

Or if installed globally with pip:

{
  "mcpServers": {
    "flux": {
      "command": "python",
      "args": [
        "-m",
        "flux_mcp.server"
      ]
    }
  }
}

After adding the configuration, restart your MCP client (e.g., Claude Desktop).

Available MCP Tools

The following tools are available when using this server with any MCP-compatible client. Examples below show usage with Claude Desktop.

Note on FLUX.2-dev timeouts: MCP clients enforce a timeout (typically 1-5 min) on tool calls. FLUX.2-dev takes 30-40 min on a 16GB GPU — far exceeding any MCP timeout. The server is non-blocking: generation continues in the background after the client timeout. Use get_preview with the returned image_id to retrieve the result when ready. See Known Behavior: MCP Timeouts for full details.

1. generate_image

Generate an image from a text prompt using FLUX.1-dev (fast) or FLUX.2-dev (quality).

Parameters:

• prompt (required): Text description of the image

• model (optional): "flux1-dev" (faster quality, 40 steps) or "flux2-dev" (maximum quality, 50 steps, default)

• steps (optional): Number of inference steps (auto: FLUX.1=40, FLUX.2=50, range: 20-100)

• guidance_scale (optional): Guidance scale (default: 7.5 for both models, range: 1.0-10.0)

• width (optional): Image width in pixels (default: 1024, range: 256-2048)

• height (optional): Image height in pixels (default: 1024, range: 256-2048)

• seed (optional): Random seed for reproducibility (random if not provided)

Returns: File path, seed, generation settings, inline thumbnail preview, and an image_id you can pass to get_preview later.

Example Usage (natural language with MCP client):

Generate a high quality image with flux1-dev of a futuristic cyberpunk city at sunset with neon lights

Generate maximum quality image with flux2-dev and seed 42 of a serene mountain landscape

2. get_preview

Retrieve a preview (thumbnail) of a generated image. Especially useful after a FLUX.2-dev background generation where the MCP client timed out.

Parameters:

• image_id (optional): Image ID returned by generate_image (e.g. 20250126_143052_42). Omit to get the last generated image.

Returns: Inline thumbnail and full-size image path.

Example Usage:

Get the preview for image 20250126_143052_42

Show me the last generated image

3. get_status

Check the current status of the FLUX generator.

Returns:

• Model load status

• Time remaining until auto-unload

• Current VRAM usage

• Last access time

Example Usage:

Check the FLUX model status

4. unload_model

Immediately unload the FLUX model from GPU memory.

Example Usage:

Unload the FLUX model to free up VRAM

5. set_timeout

Change the auto-unload timeout at runtime.

Parameters:

• timeout_seconds (required): New timeout in seconds (0 to disable)

Example Usage:

Set FLUX auto-unload timeout to 600 seconds

Disable FLUX auto-unload

CLI Usage

In addition to the MCP server mode, you can use FLUX directly from the command line for completely offline and private image generation.

Quick Start

# Basic usage
flux generate "a beautiful sunset over mountains"

# With custom parameters
flux generate "portrait of a cat" --steps 35 --guidance 4.0 --seed 42

# Interactive mode for batch generation
flux generate --interactive

# Check system status
flux status

# View configuration
flux config

# Open output directory
flux open-output

Generate Command

The main command for image generation:

flux generate [OPTIONS] PROMPT

Options:

• --steps, -s INTEGER - Number of inference steps (default: 50 on CUDA, 40 on MPS)

• --guidance, -g FLOAT - Guidance scale (default: 7.5)

• --width, -w INTEGER - Image width in pixels, must be multiple of 8 (default: 1024)

• --height, -h INTEGER - Image height in pixels, must be multiple of 8 (default: 1024)

• --seed INTEGER - Random seed for reproducibility

• --output, -o PATH - Custom output path (default: auto-generated)

• --output-dir PATH - Override output directory

• --interactive, -i - Interactive mode

• --fast, -f - Quick generation with FLUX.1-dev (no env var needed)

• --verbose, -v - Verbose output with debug info

Examples:

# Simple generation
flux generate "a cozy cabin in snowy mountains"

# High quality with more steps
flux generate "professional portrait" --steps 40 --guidance 7.5

# Custom resolution
flux generate "wide landscape" --width 1536 --height 1024

# Reproducible generation
flux generate "cute robot" --seed 42

# Fast generation with FLUX.1-dev
flux generate "sunset" --fast

# Save to specific location
flux generate "sunset" --output ~/Pictures/my-sunset.png

# Interactive mode (best for multiple images)
flux generate --interactive

Interactive Mode

Interactive mode allows you to generate multiple images without reloading the model:

flux generate --interactive

Interactive workflow:

1. Enter your prompt

2. Configure parameters (steps, guidance, dimensions, seed)

3. Image generates and saves

4. Choose to generate another or exit

5. Model stays loaded between generations for faster subsequent images

Other Commands

Status Command:

flux status

Shows:

• Model information

• Output directory

• CUDA availability

• GPU name and VRAM usage

• Model cache location

Config Command:

flux config

Displays current configuration from environment variables.

Open Output:

flux open-output

Opens the output directory in your file manager (Linux: xdg-open, macOS: open, Windows: explorer).

Output Files

Generated images are saved with embedded metadata:

• Image: YYYYMMDD_HHMMSS_SEED.png

• Thumbnail: YYYYMMDD_HHMMSS_SEED_thumb.png (512x512 preview)

All generation parameters are embedded directly in the PNG metadata. You can view them with tools like exiftool:

exiftool image.png

Embedded metadata includes:

• Prompt: The text prompt used

• Seed: Random seed for reproducibility

• Steps: Number of inference steps

• Guidance Scale: Guidance scale value

• Width / Height: Image dimensions

• Model: FLUX model used

• Generation Time Seconds: How long generation took

• Timestamp: When the image was created

CLI vs MCP Server

CLI Mode:

• ✓ Completely offline and private (no MCP client needed)

• ✓ Direct control from terminal

• ✓ Batch generation with interactive mode

• ✓ No auto-unload (process terminates after generation)

• ✓ Generates thumbnails for quick preview

• ✓ Rich terminal UI with progress bars

• ✓ No timeout restrictions for long FLUX.2-dev generations

MCP Server Mode:

• ✓ Integrated with MCP-compatible applications (like Claude Desktop)

• ✓ Natural language interface

• ✓ Auto-unload after timeout (saves power)

• ✓ Persistent background process

• ✓ Access from conversational AI interfaces

• ✓ Preview retrieval via get_preview after background generation

Both modes share the same configuration, model cache, and output directory.

Configuration

Edit .env to customize (copy from .env.example):

# Auto-unload timeout in seconds (default: 300 = 5 minutes)
FLUX_UNLOAD_TIMEOUT=300

# Output directory for generated images
FLUX_OUTPUT_DIR=/path/to/flux_output

# Optional: Custom HuggingFace cache directory
# FLUX_MODEL_CACHE=/path/to/cache

# Model selection (choose default model)
# FLUX_MODEL_ID=black-forest-labs/FLUX.1-dev   # Faster quality (4-8 min, 40 steps default)
# FLUX_MODEL_ID=black-forest-labs/FLUX.2-dev   # Maximum quality (default, 30-40 min, 50 steps)

# Default generation parameters (model-specific smart defaults apply automatically)
# FLUX_DEFAULT_STEPS=50        # Override auto defaults: FLUX.1-dev=40, FLUX.2-dev=50
# FLUX_DEFAULT_GUIDANCE=7.5    # Both models use 7.5 for optimal quality

Environment Variable Reference

Advanced Configuration

Custom Model Cache (share across projects or save space):

# In .env
FLUX_MODEL_CACHE=/mnt/data/huggingface/cache

Disable Auto-Unload (keep model loaded permanently):

# In .env
FLUX_UNLOAD_TIMEOUT=0

Or at runtime:

Set FLUX timeout to 0

Logging (capture server logs):

{
  "mcpServers": {
    "flux": {
      "command": "sh",
      "args": [
        "-c",
        "cd /path/to/flux-mcp && uv run flux-mcp 2>> /tmp/flux-mcp.log"
      ]
    }
  }
}

Installation

1. Clone the repository (or navigate to the project directory):

cd /path/to/flux-mcp

2. Install with UV (recommended):

uv sync

Or install with pip:

pip install -e .

3. Configure environment variables:

cp .env.example .env
# Edit .env with your preferred settings

How It Works

Smart VRAM Detection

On model load, the server:

1. Detects total VRAM available on your GPU

2. Selects optimal mode automatically:

   • 24GB+: Full GPU mode (all components stay on GPU, fastest for both models)

   • 20-24GB: Model CPU offload mode (balanced, components moved between CPU/GPU as needed)

   • 12-20GB: Sequential CPU offload mode (stable, slower but fits in 16GB VRAM)

3. Logs the decision so you know which mode is active

Sequential CPU Offload moves entire model components (text encoder, transformer, VAE) to CPU when not actively being used. This is the most stable approach for limited VRAM systems and works reliably with both FLUX.1-dev and FLUX.2-dev.

For GPUs with <24GB VRAM, the server also enables:

• Memory-efficient attention (xFormers or PyTorch SDPA)

• bfloat16 precision for ~50% VRAM savings vs float32

These optimizations allow both models to run on 12-16GB VRAM. Note: The experimental group offload with CUDA streams has been removed due to stability issues - sequential CPU offload is the recommended stable configuration.

Auto-Unload Mechanism

1. Lazy Loading: The model is NOT loaded when the server starts

2. On-Demand Loading: Model loads automatically on first generation request

3. Timer Reset: Each generation resets the auto-unload timer

4. Automatic Cleanup: After the configured timeout with no activity:

   • Model is removed from memory

   • GPU cache is cleared (torch.cuda.empty_cache())

   • Python garbage collection runs

5. Seamless Reload: Model automatically reloads on next request

Memory Management

The server uses several strategies to optimize VRAM:

• Smart mode selection based on detected VRAM (24GB+: full GPU, 20-24GB: model CPU offload, <20GB: sequential CPU offload)

• bfloat16 precision instead of float32 (saves ~50% VRAM)

• Sequential CPU offload for <20GB GPUs (stable, moves entire model components to CPU when idle)

• Memory-efficient attention (xFormers or PyTorch SDPA for reduced memory usage)

• TF32 acceleration on Ampere+ GPUs for faster matrix operations

• Explicit cache clearing when unloading

• Threading for non-blocking auto-unload

• Lock-based synchronization for thread-safe operation

• Dynamic model switching - can switch between FLUX.1-dev and FLUX.2-dev without restart

Output Files

Generated images are saved as:

{FLUX_OUTPUT_DIR}/{timestamp}_{seed}.png

Example: 20250126_143052_42.png

Performance Tips

Model Comparison

Both models are optimized for high-quality output with tested parameters:

When to use which:

• FLUX.1-dev: When you need high quality faster (~4-8 min), batch generation, iterating on ideas

• FLUX.2-dev: When you need absolute maximum quality and time isn't critical (~30-40 min)

VRAM Modes (automatic selection):

• 24GB+ - Full GPU mode (fastest, both models < 5 min)

• 16-24GB - Model CPU offload mode (balanced)

• 12-16GB - Sequential CPU offload mode (slower, FLUX.1-dev recommended)

Optimal Settings by GPU VRAM

RTX 4090 / A6000 (24GB+)

• Resolution: Up to 1536x1536 comfortably

• Mode: Full GPU (automatic)

• FLUX.1-dev: ~1-2 min (40 steps, quality)

• FLUX.2-dev: ~2-4 min (50 steps, maximum quality)

• Guidance: 7.5 (both models)

RTX 4070 Ti Super / 3090 (16GB-24GB)

• Resolution: Up to 1024x1024 comfortably

• Mode: Sequential CPU offload (automatic for 16GB)

• FLUX.1-dev: ~4-8 min (40 steps, quality) ← Recommended for faster workflow

• FLUX.2-dev: ~30-40 min (50 steps, maximum quality)

• Guidance: 7.5 (both models)

RTX 3060 / 4060 Ti (12GB-16GB)

• Resolution: 1024x1024 (FLUX.1) or 768x768 (FLUX.2 for safety)

• Mode: Sequential CPU offload (automatic)

• FLUX.1-dev: ~6-10 min (40 steps) ← Highly recommended

• FLUX.2-dev: ~40-50 min (50 steps) or reduce to 768x768

• Tip: Use FLUX.1-dev for better speed/quality balance on lower VRAM

All GPUs:

• Guidance Scale: 7.5 (optimal for both models)

• Batch size: 1 (models don't support batching)

• Timeout: 300s for occasional use, 600s for active sessions

Generation Time Expectations

Full GPU Mode (24GB+ VRAM):

• FLUX.1-dev (40 steps): ~1-2 min for 1024x1024

• FLUX.2-dev (50 steps): ~2-4 min for 1024x1024

• First generation: +15-30 seconds for model loading

Sequential CPU Offload (16GB VRAM - RTX 4070 Ti Super):

• FLUX.1-dev (40 steps): ~4-8 min for 1024x1024 ← Recommended

• FLUX.2-dev (50 steps): ~30-40 min for 1024x1024

• First generation: +2-3 seconds for model loading

• Optimization: Use FLUX.1-dev for 5-8x faster generation with excellent quality

Known Behavior: MCP Timeouts During FLUX.2-dev Generation

When using generate_image with flux2-dev via an MCP client (e.g. Claude Desktop, Claude Code), the client will likely report a timeout error during generation. This is expected and normal — it is not a server error.

Why the timeout occurs

MCP clients enforce a client-side timeout on tool calls, typically in the range of 1-5 minutes. FLUX.2-dev generation on a 16GB GPU takes 30-40 minutes, which far exceeds any standard MCP client timeout. The server itself does not time out — only the client's wait for a response does.

The generate_image tool description explicitly informs the LLM about this behavior so it does not treat the timeout as a failure and does not retry unnecessarily.

What actually happens

1. The MCP client sends the generate_image request to the server

2. The server starts the generation in a background thread and returns immediately (non-blocking)

3. The client-side timeout fires after 1-5 minutes — the client shows an error

4. Generation continues running in the background — the server process is unaffected

5. When generation completes (30-40 min later), the image is saved to the configured output directory automatically

Retrieving the result after timeout

Use the get_preview tool with the image_id shown in the generate_image output (or omit image_id to get the last generated image):

Get the preview for image 20250126_143052_42

Or check the output directory directly:

# Watch for new files appearing
watch -n 10 ls -lht ~/flux_output | head

# Or list files sorted by modification time
ls -lht ~/flux_output | head

How to verify generation is still running

# Check GPU utilization — should show ~100% during generation
watch -n 2 nvidia-smi

# Check CPU and process activity
htop

If nvidia-smi shows near-100% GPU utilization, generation is actively running.

Recommended alternatives for long generations

• Use FLUX.1-dev (flux1-dev) — completes in 4-8 min on 16GB GPUs, well within MCP timeout limits, and produces excellent quality

• Use CLI mode — no timeout restrictions at all:

  flux generate "your prompt" --model flux2-dev

  The CLI runs the generation synchronously and shows a progress bar.

Troubleshooting

CUDA Out of Memory

Problem: Error during generation: "CUDA out of memory"

Note: The server automatically detects available VRAM and selects the best mode:

• 24GB+: Full GPU (fastest)

• 16GB: Model CPU offload (balanced)

• 12GB+: Model CPU offload with reduced resolution

If you still get OOM errors:

1. Close other GPU applications:

   # Check what's using VRAM
   nvidia-smi

2. Reduce image dimensions:

   flux generate "prompt" --width 768 --height 768
   # Or even smaller for 12GB cards
   flux generate "prompt" --width 512 --height 512

3. Reduce inference steps (minimal memory impact):

   flux generate "prompt" --steps 28  # Default is 50

4. Check logs to see which mode was selected:

   # Look for "Using full GPU mode" or "Using model CPU offload"
   tail -f /tmp/flux-mcp.log

5. Restart the process if VRAM isn't fully freed:

   # CLI: Just run again (process exits after generation)
   # MCP: Restart your MCP client

Model Download Issues

Problem: Model download fails or times out

Solutions:

1. Check internet connection

2. Set a custom cache directory with more space:

   FLUX_MODEL_CACHE=/path/to/large/disk/cache

3. Download manually with HuggingFace CLI:

   huggingface-cli download black-forest-labs/FLUX.2-dev

Server Not Responding

Problem: MCP client doesn't see the tools

Solutions:

1. Check your MCP client's logs for errors

2. Verify the configuration path is absolute

3. Ensure UV is in PATH or use full path to UV binary

4. Restart your MCP client after config changes

5. Test the server manually:

   cd /path/to/flux-mcp
   uv run flux-mcp

AMD/ROCm Installation

ROCm support works through PyTorch's ROCm build, which exposes itself via the standard CUDA API — no code changes are needed. The only requirement is installing the ROCm-enabled PyTorch wheel before running uv sync.

Step 1: Install ROCm PyTorch (replace rocm6.2 with your installed ROCm version):

pip install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.2

Step 2: Install remaining dependencies:

uv sync --extra rocm

Verify ROCm is detected:

python -c "import torch; print(torch.cuda.is_available(), torch.version.hip)"
# Should print: True  <rocm-version>

Check GPU activity on AMD:

rocm-smi

Notes:

• ROCm 5.7+ is recommended; earlier versions may have stability issues with bfloat16

• xFormers is generally not available on ROCm — the server falls back to PyTorch SDPA automatically

• TF32 is an NVIDIA-only feature and is automatically skipped on ROCm

• Random seed generation uses a CPU generator on ROCm (same as MPS) for compatibility

• VRAM tier selection (full GPU / model offload / sequential offload) works identically to CUDA

Apple Silicon (MPS)

Setup: Install with Mac extras to get psutil for unified memory reporting:

uv sync --extra mac

Default model on Mac: FLUX.1-dev (auto-selected — FLUX.2-dev requires more unified memory than most Macs have available alongside macOS).

Out of memory on Mac: Run sudo purge to free RAM after other heavy processes, then retry with reduced resolution:

flux generate "prompt" --width 768 --height 768

Check GPU activity on Mac:

sudo powermetrics --samplers gpu_power -n 1 2>/dev/null | grep "GPU HW active residency"

Slow Generation

Problem: Image generation takes too long

Solutions:

1. Reduce steps parameter (try 20-25 instead of 28)

2. Ensure GPU is being used (CUDA: nvidia-smi, MPS: see above)

3. Close background applications to free GPU/RAM resources

4. Check that CUDA is properly installed (Linux/Windows)

MCP Timeout False Positives

Problem: MCP client shows error during long FLUX.2-dev generations, but image still generates successfully

Solution: Use get_preview after the expected generation time to retrieve the result. See Known Behavior: MCP Timeouts for full details.

Permission Errors

Problem: Cannot write to output directory

Solutions:

1. Check directory permissions

2. Set a different output directory in .env:

   FLUX_OUTPUT_DIR=/home/$USER/flux_output

3. Create the directory manually:

   mkdir -p ~/flux_output
   chmod 755 ~/flux_output

Architecture

flux-mcp/
├── src/flux_mcp/
│   ├── __init__.py       # Package metadata
│   ├── config.py         # Environment configuration (shared)
│   ├── generator.py      # FluxGenerator class (shared)
│   ├── server.py         # MCP server (tool handlers)
│   └── cli.py            # CLI tool
├── pyproject.toml        # Project dependencies
├── .env                  # Local configuration (gitignored)
└── README.md            # This file

Key Components

• FluxGenerator: Manages model lifecycle, threading, and device memory — auto-detects CUDA/MPS/CPU (shared between CLI and MCP)

• Config: Loads environment variables and provides defaults (shared)

• MCP Server: Exposes tools via Model Context Protocol for MCP-compatible clients

• CLI Tool: Direct command-line interface for offline usage

Thread Safety

The generator uses a threading lock (threading.Lock) to ensure:

• Only one generation at a time

• Safe model loading/unloading

• No race conditions with auto-unload timer

License

MIT License - see LICENSE file for details

Contributing

Contributions welcome! Please:

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Submit a pull request

Support

For issues and questions:

• Check the Troubleshooting section above

• Review server logs for errors

• Open an issue on GitHub

Changelog

v1.2.0 (2026-03-12)

New Features

• ✨ Added get_preview MCP tool — retrieve last generated image (or by image_id) as inline thumbnail after background FLUX.2-dev generation completes

• ✨ generate_image now returns image_id for later reference via get_preview

• 📚 Restructured README for operator-first flow: MCP configuration and tools before CLI and installation details

v1.1.0 (2025-12-07)

Improvements & Fixes

• ✨ Added --fast / -f flag to CLI for quick FLUX.1-dev generation without environment variables

• 🐛 Fixed pipeline class selection - FLUX.1-dev now correctly uses FluxPipeline (was causing tokenizer errors)

• 📚 Added MCP timeout warning documentation for long FLUX.2-dev generations

v1.0.0 (2025-11-29)

Stable Release

• ✨ Dual model support: FLUX.1-dev (faster) and FLUX.2-dev (maximum quality)

• ✨ Model-specific quality defaults (FLUX.1=40 steps, FLUX.2=50 steps, both at 7.5 guidance)

• ✨ Smart VRAM optimization for 12-24GB GPUs with automatic mode selection

• ✨ CLI tool with interactive mode and thumbnail generation

• ✨ MCP server with auto-unload and progress reporting

• ✨ Clean metadata embedding in PNG (individual fields, no JSON blobs)

• ✨ Thumbnail generation for quick previews (512x512)

• 🐛 Fixed duplicate metadata in PNG files

• 🐛 Fixed .gitignore for PyTorch checkpoint files

• 📚 Comprehensive documentation with usage examples

v0.1.0 (2025-01-26)

• Initial development release

• FLUX.2-dev integration

• Auto-unload functionality (MCP mode)

• Four MCP tools (generate, unload, status, set_timeout)

• CLI tool with interactive mode (flux command)

• Shared architecture between CLI and MCP server