stable-diffusion.cpp/docs/ming_image.md

Ming-Image

Ming-Image 0.1 Design uses a 6B diffusion transformer (DiT), Ling-mini-2.0 for text conditioning, and the Ming-Image VAE. Text-to-image generation with RGBA output is supported.

Download weights

• Download Ming-Image 0.1 Design DiT
  • safetensors: https://huggingface.co/Comfy-Org/Ming-Image/tree/main/diffusion_models
• Download Ling-mini-2.0 BF16
  • safetensors: https://huggingface.co/Comfy-Org/Ming-Image/tree/main/text_encoders
• Download Ming-Image VAE
  • safetensors: https://huggingface.co/Comfy-Org/Ming-Image/tree/main/vae
• Download Ling tokenizer
  • tokenizer.json: https://huggingface.co/inclusionAI/Ming-Image-0.1-Design/blob/main/mllm/tokenizer.json

The example below uses ming_image_0.1_design_bf16.safetensors for the DiT. You can also use ming_image_0.1_design_int8_convrot.safetensors with INT8 convrot support. Use the BF16 text encoder.

Text-to-image

Pass the Ling tokenizer.json with --tokenizer and use the matching Ming-Image VAE. Image dimensions must be multiples of 16. Save the output as PNG to preserve the alpha channel.

.\bin\Release\sd-cli.exe --diffusion-model ..\models\diffusion_models\ming_image_0.1_design_bf16.safetensors --llm ..\models\text_encoders\ming_image_0.1_ling_mini_2.0_bf16.safetensors --vae ..\models\vae\ming_image_vae_bf16.safetensors --tokenizer ..\models\text_encoders\tokenizer.json -p "A cheerful orange cat sticker, transparent background" --width 1024 --height 1024 --steps 12 --cfg-scale 1 --sampling-method euler --diffusion-fa -v --offload-to-cpu -o ming_image.png