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Image generation · MiniMax-H3-Image 33B · diffusers · GB10 · 88 GB VRAM

published by @dh7 on

m64 m64_a970a7a75864
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MiniMax-H3-Image text-to-image on GB10 (diffusers). MiniMax's 33B open-weight image model (MiniMax-H3 family, guidance-distilled, 6 steps) with its Qwen3-VL-32B text encoder cut to the 51 layers it uses and quantized to 4-bit, at 2048x2048. MiniMax-H3-Image Community License: not licensed in the EU, UK, US or South Korea. Runs fully offline.

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AGENT.md

MiniMax-H3-Image 33B text-to-image

This service turns a text prompt into a 2048x2048 PNG. It runs MiniMax's MiniMax-H3-Image (33B DiT, released November 2026 under the MiniMax-H3-Image Community License, which excludes the EU, UK, US and South Korea) through diffusers 0.43's modular pipeline on one GPU, with its Qwen3-VL-32B text encoder cut to the 51 layers the pipeline reads and quantized to 4-bit (nf4) so both fit in the GB10's memory. Prompts are used verbatim: long, complete descriptions work best. Generation settings are fixed by this capsule (size "2048x2048", num_inference_steps 6). You send a prompt and, optionally, a seed.

Base URL: {{BASE_URL}}

Routes

POST /v1/images/generations

OpenAI-style image generation. Returns one image as base64 PNG.

curl -s {{BASE_URL}}/v1/images/generations {{AUTH}} \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a lighthouse at sunset", "seed": 42}' \
  | python3 -c 'import sys,json,base64; d=json.load(sys.stdin); open("out.png","wb").write(base64.b64decode(d["data"][0]["b64_json"])); print(d["params_used"])'

Request fields:

  • prompt (string, required): what to draw.
  • seed (integer >= 0, optional): the random seed. The same prompt and seed give the same image. Leave it out for a random one.

Also accepted, for stock OpenAI clients: model (any string), n (must be 1), response_format (must be "b64_json"), user, and size if it is "2048x2048". Any other field, or another value for a fixed one, is rejected with 400.

The response is {"created": ..., "data": [{"b64_json": "<PNG, base64>"}], "params_used": {...}}.

The OpenAI Python SDK works too:

from openai import OpenAI
client = OpenAI(base_url="{{BASE_URL}}/v1", api_key="unused")
r = client.images.generate(model="minimax-h3-image", prompt="a lighthouse at sunset",
                           response_format="b64_json", extra_body={"seed": 7})

GET /v1/config

The server's settings and what they mean.

GET /status

Where the service is in its start-up: phase is one of building, downloading, starting, loading, ready, error, with eta_seconds.

Timings and concurrency

  • First start: builds a container image and downloads about 135 GB of weights. After that, loading takes about 12 minutes (budgeted at 2400 s).
  • Each image takes about 30 seconds (2048x2048, 6 steps) on a DGX Spark (GB10).
  • Send requests one at a time, and allow at least 2 minutes per request in your client timeout.

Errors

  • 503 with Retry-After while the service is still starting.
  • 400: a field was rejected; the body's error says which and why.
  • 422: prompt missing or a field of the wrong type.
  • 5xx otherwise: retry once, then check GET /status.

capsule.toml

schema = "m64/capsule@1"description = "MiniMax-H3-Image text-to-image on GB10 (diffusers). MiniMax's 33B open-weight image model (MiniMax-H3 family, guidance-distilled, 6 steps) with its Qwen3-VL-32B text encoder cut to the 51 layers it uses and quantized to 4-bit, at 2048x2048. MiniMax-H3-Image Community License: not licensed in the EU, UK, US or South Korea. Runs fully offline."kinds = ["image.generate"]authors = ["dh7"]license = "other"[hardware]platform = "linux-aarch64"gpu = "NVIDIA GB10"gpu_count = 1vram_gb = 88driver = ">=580"disk_gb = 140[runtime]sandbox = "container"# No image: the Dockerfile builds on nvcr.io/nvidia/pytorch:25.09-py3 + diffusers 0.41.0, pinned by digest.gpu = trueshm_gb = 8internal_port = 8000health = "/health"expected_load_seconds = 2400model_name = "minimax-h3-image"user = "65534:65534"[[artifacts]]type = "hf"repo = "MiniMaxAI/MiniMax-H3-Image"revision = "5f90473023975e6271b0ed1dd84c4e88e4bbc6d3"include = [  ".gitattributes",  "LICENSE",  "README.md",  "model_index.json",  "modular_model_index.json",  "processor/*",  "scheduler/*",  "text_encoder/*",  "tokenizer/*",  "transformer_diffusers/*",  "vae_diffusers/*",]mount = "/weights/minimax-h3-image"# The server's own defaults (minimax_h3_image_server.py), which every request gets: callers can only send# prompt and seed. Returned as params_used.[params]size = "2048x2048"num_inference_steps = 6[routes]extra = ["/v1/config"]