Linux command
mflux 命令
安全
权限或系统影响较大，执行前请核对目标。
常用示例
Generate an image from a text prompt
mflux-generate --model [schnell] --prompt "[a sunset over mountains]"
Generate with the higher-quality dev model and more steps
mflux-generate --model [dev] --prompt "[prompt]" --steps [25] --seed [42]
Specify image dimensions
mflux-generate --model [schnell] --prompt "[prompt]" --width [1024] --height [768]
Save to a specific output path
mflux-generate --model [schnell] --prompt "[prompt]" --output [output.png]
Use a quantized model for lower memory usage
mflux-generate --model [schnell] --prompt "[prompt]" --quantize [8]
Generate using image-to-image with an init image
mflux-generate --model [dev] --prompt "[prompt]" --image-path [input.png] --image-strength [0.4]
Apply LoRA adapter weights
mflux-generate --model [dev] --prompt "[prompt]" --lora-paths [adapter.safetensors]
Save a local quantized copy of model weights
mflux-save --model [schnell] --quantize [8] --path [path/to/save]
说明
mflux is a Flux image generation tool built natively on Apple's MLX framework, optimized for Apple Silicon (M1/M2/M3/M4). It generates images locally using Flux models without requiring a GPU server or cloud API. The package provides multiple CLI commands: mflux-generate for image generation, mflux-save for saving quantized model weights locally, and mflux-info for viewing image metadata. Installation is via pip (`pip install mflux`). Model weights are downloaded from HuggingFace on first use and cached locally. Custom models can also be loaded from local paths or HuggingFace repositories. schnell is faster with fewer steps needed (2-4 steps). dev produces higher quality but requires more steps (20-25). Quantization (4-bit or 8-bit) reduces memory usage for machines with limited unified memory. The --low-ram flag further reduces memory by releasing components after use. LoRA adapters allow fine-tuned styles and concepts to be applied on top of base models. Image-to-image generation is supported via --image-path and --image-strength.
参数
- --model, -m _NAME_
- Model to use (schnell, dev, or a HuggingFace repo/local path).
- --prompt _TEXT_
- Text prompt for image generation. Use - to read from stdin.
- --output _FILE_
- Output image path.
- --width _PX_
- Image width in pixels.
- --height _PX_
- Image height in pixels.
- --steps _N_
- Number of inference steps.
- --seed _INT_
- Random seed for reproducibility.
- --quantize, -q _BITS_
- Quantization level (4 or 8 bit).
- --guidance _FLOAT_
- Guidance scale.
- --negative-prompt _TEXT_
- Text prompt for what the model should not generate.
- --image-path _FILE_
- Path to an initial image for image-to-image generation.
- --image-strength _FLOAT_
- How strongly the initial image influences output (default: 0.4, 0.0 = no influence).
- --lora-paths _FILE_...
- Paths to one or more LoRA adapter weights.
- --lora-scales _FLOAT_...
- Scales for each LoRA adapter.
- --metadata
- Export a JSON file with generation metadata alongside the image.
- --low-ram
- Reduce GPU memory usage by limiting MLX cache and releasing components after use.
- --base-model _NAME_
- Specify architecture (schnell, dev) when loading from a local path.
FAQ
What is the mflux command used for?
mflux is a Flux image generation tool built natively on Apple's MLX framework, optimized for Apple Silicon (M1/M2/M3/M4). It generates images locally using Flux models without requiring a GPU server or cloud API. The package provides multiple CLI commands: mflux-generate for image generation, mflux-save for saving quantized model weights locally, and mflux-info for viewing image metadata. Installation is via pip (`pip install mflux`). Model weights are downloaded from HuggingFace on first use and cached locally. Custom models can also be loaded from local paths or HuggingFace repositories. schnell is faster with fewer steps needed (2-4 steps). dev produces higher quality but requires more steps (20-25). Quantization (4-bit or 8-bit) reduces memory usage for machines with limited unified memory. The --low-ram flag further reduces memory by releasing components after use. LoRA adapters allow fine-tuned styles and concepts to be applied on top of base models. Image-to-image generation is supported via --image-path and --image-strength.
How do I run a basic mflux example?
Run `mflux-generate --model [schnell] --prompt "[a sunset over mountains]"` in a terminal, then adjust file names, paths, flags, or remote targets for your system.
What does --model, -m _NAME_ do in mflux?
Model to use (schnell, dev, or a HuggingFace repo/local path).