vllm-omni/recipes/inclusionAI/Ming-Image.md

Ming-Image 0.1 Design

Text-to-image, image editing, and layer decomposition.

Summary

• Vendor: inclusionAI
• Models: inclusionAI/Ming-Image-0.1-Design and inclusionAI/Ming-Image-0.1-Design-Layer
• Runtime: vLLM-Omni two-stage online serving
• API: OpenAI-compatible chat completions

Architecture

Stage 0 uses a Qwen2.5-VL vision tower plus a 20-layer BailingMoeV2 language model. It appends 256 learned image-query tokens and exports the final query states plus direct VLM states from layers 5, 12, and 20. The query-token checkpoint is in the root sibling mlp/ directory, so remote stage-0 materialization downloads both mllm/ and mlp/.

Stage 1 projects those conditions through the checkpoint connector and runs a 30-layer Z-Image DiT with a Qwen-Image RGBA VAE.

For Design-Layer, the first returned image is the reconstructed composite and the remaining images are the requested layers.

CUDA

Environment

• OS: Linux
• Python: 3.10+
• CUDA: 13.0
• vLLM version: 0.29.0
• vLLM-Omni version or commit: 6daf5b30f
• 2x H100 80GB (1xH100 to be validated)

Commands

MODEL=inclusionAI/Ming-Image-0.1-Design
vllm serve "$MODEL" --omni --deploy-config vllm_omni/deploy/ming_image.yaml --port 8091

For layer decomposition, set MODEL to inclusionAI/Ming-Image-0.1-Design-Layer.

Text-to-image

Note that a prompt refiner is expected to describe the prompts with details; we will refine with more example inputs soon.

curl -s http://127.0.0.1:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "inclusionAI/Ming-Image-0.1-Design",
    "messages": [{"role": "user", "content": "A clean editorial botanical poster"}],
    "modalities": ["image"],
    "extra_body": {
      "height": 1024, "width": 1024,
      "num_inference_steps": 12, "guidance_scale": 1.0, "seed": 42
    }
  }' \
  | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
  | base64 -d > ming_design_smoke.png

Image editing

Pass one input image and an editing instruction:

MODEL=inclusionAI/Ming-Image-0.1-Design
INPUT_IMAGE=/path/to/input.png

jq -n \
  --arg model "$MODEL" \
  --rawfile image <(base64 -w0 "$INPUT_IMAGE") \
  '{
    model: $model,
    messages: [{role: "user", content: [
      {type: "image_url", image_url: {url: ("data:image/png;base64," + $image)}},
      {type: "text", text: "Change the background to blue"}
    ]}],
    modalities: ["image"],
    extra_body: {
      height: 1024, width: 1024,
      num_inference_steps: 12, guidance_scale: 1.0, seed: 42
    }
  }' |
curl -sS http://127.0.0.1:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  --data-binary @- \
  | jq -r '.choices[0].message.content[0].image_url.url | split(",")[1]' \
  | base64 -d > ming_edit.png

Layer decomposition

Use the Design-Layer checkpoint and set INPUT_IMAGE to a local flattened design image. Note that the prompt should better depict each layer to be decomposed, we will refine with more example inputs soon.

MODEL=inclusionAI/Ming-Image-0.1-Design-Layer
INPUT_IMAGE=/path/to/input.png
PROMPT="Decompose this image into 6 layers with the following specifications: Number of layers: 6 \nLayer 1: Central text ... Layer 2: ... Layer 6: ..."

jq -n \
  --arg model "$MODEL" \
  --rawfile image <(base64 -w0 "$INPUT_IMAGE") \
  --arg prompt "$PROMPT" \
  '{
    model: $model,
    messages: [{role: "user", content: [
      {type: "image_url", image_url: {url: ("data:image/png;base64," + $image)}},
      {type: "text", text: $prompt}
    ]}],
    modalities: ["image"],
    extra_body: {
      num_layers: 6,
      height: 1024, width: 1024,
      num_inference_steps: 12, guidance_scale: 2.0, seed: 42
    }
  }' |
curl -sS http://127.0.0.1:8091/v1/chat/completions \
  -H "Content-Type: application/json" \
  --data-binary @- > response.json

jq -r '.choices[0].message.content[].image_url.url | split(",")[1]' response.json |
  nl -v 0 |
  while read -r index data; do
    printf "%s" "$data" | base64 -d > "ming_layer_${index}.png"
  done

Notes

• The default deployment keeps Stage 0 eager and enables dynamic regional compilation with CUDA Graph Trees for the repeated Stage 1 DiT blocks. The first request for each new image shape or layer count pays compilation and graph-capture cost; warm up every production shape before measuring or serving latency-sensitive traffic.
• Only one reference image and one request at a time are currently supported.
• Design-Layer requires a reference image except during warmup.
• A non-empty negative_prompt is rejected; Ming-Image uses zero negative conditioning.
• Height and width must be divisible by 16.
• Returned images retain the checkpoint's four RGBA channels.