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This PR adds support for the Qwen-Image-Layered model, which decomposes an input image into semantically disentangled RGBA layers for layer-based editing workflows. ## Features - New CLI command: \`mflux-generate-qwen-layered\` - Decomposes images into N RGBA layers (default 4) - Supports 6-bit quantization for ~29GB memory usage (vs 55GB BF16) - Resolution buckets: 640 and 1024 ## Architecture - RGBA-VAE (4-channel) with 3D temporal convolutions for layer handling - Layer3D RoPE: 3D positional encoding [layer, height, width] - Uses base QwenTransformer with extended RoPE for multi-layer sequences - Condition image encoded as layer_index=-1 for proper decomposition ## New Files - \`src/mflux/models/qwen_layered/\` - Full model implementation - \`model/qwen_layered_vae/\` - RGBA-VAE encoder/decoder - \`model/qwen_layered_transformer/\` - Layer3D RoPE - \`weights/\` - Weight mapping and definitions - \`variants/i2l/\` - Image-to-Layers pipeline - \`cli/\` - Command-line interface ## Usage \`\`\`sh mflux-generate-qwen-layered \\ --image input.png \\ --layers 4 \\ --steps 50 \\ -q 6 \\ --output-dir ./layers \`\`\` ## Documentation Added comprehensive documentation to README.md including: - TOC entry - CLI argument reference - Usage examples and tips - Memory requirements Tested on M4 Max 48GB with 6-bit quantization. |
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| __init__.py | ||