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Previewof vllm-project/vllm#64586 at 90781b3.
NVIDIA

nvidia/Cosmos3-Super-Text2Image

64B Cosmos3-Super specialization for high-fidelity text-to-image generation

High-fidelity text-to-image — 8×H100 with CFG-parallel + ulysses + HSDP

View on HuggingFace
dense64B262,144 ctxvLLM 0.21.0+vLLM-Omninightlyomni
Default configuration: 8× H200 · BF16
vllm serve nvidia/Cosmos3-Super-Text2Image \
  --host 0.0.0.0 \
  --port 8000 \
  --cfg-parallel-size 2 \
  --ulysses-degree 4 \
  --tensor-parallel-size 1 \
  --use-hsdp \
  --hsdp-shard-size 8 \
  --init-timeout 1800 \
  --omni

The interactive builder loads with JavaScript. Append .md to this page's URL, query string included, for any configuration's command as Markdown — e.g. /nvidia/Cosmos3-Super-Text2Image.md.

Guide

Overview

Cosmos3-Super-Text2Image is a 64B specialization of NVIDIA's Cosmos3-Super omnimodal world model, tuned for high-fidelity text-to-image generation. It is served via vLLM-Omni.

For best quality, text prompts should be JSON-upsampled — the model repo ships an agentic_upsampling/ package and an AGENTIC_UPSAMPLING.md guide.

Prerequisites

  • 8× H100 for the documented full-node profile (4×H200 / 4×GB200 alternative below)
  • The release-tested vllm/vllm-omni:cosmos3 container, or vLLM-Omni installed on top of final vllm==0.33.0 for this source checkout. The published Cosmos3 container and its historical results use an older runtime and do not qualify the current source. The separate Reasoner environment below, where present, retains its own plugin/runtime requirements.

Launch command (vLLM-Omni)

Recommended configuration on an 8×H100 node:

docker pull vllm/vllm-omni:cosmos3

vllm serve nvidia/Cosmos3-Super-Text2Image \
  --omni \
  --host 0.0.0.0 \
  --port 8000 \
  --cfg-parallel-size 2 \
  --ulysses-degree 4 \
  --tensor-parallel-size 1 \
  --use-hsdp \
  --hsdp-shard-size 8 \
  --init-timeout 1800

For 4×H200 or 4×GB200, use --cfg-parallel-size 2 --ulysses-degree 2 --tensor-parallel-size 1. --enable-layerwise-offload can reduce VRAM usage on smaller GPUs, but for text-to-image it incurs a significant performance penalty.

Example: text-to-image

curl -X POST http://localhost:8000/v1/images/generations \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "a dragon flying over the Green Mountains at sunset",
    "size": "1024x1024",
    "seed": 42
  }' | jq -r '.data[0].b64_json' | base64 -d > output.png

References