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name: dreamomni2 description: "Routes DreamOmni2 image generation, image editing, and Gradio demo workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DreamOmni2

Use this skill when the task names DreamOmni2, FLUX.1-Kontext, two-image instruction-based image editing, two-image image generation, or the bundled Gradio demos.

What this skill covers

• Two-image editing and generation on a CUDA GPU
• The VLM prompt stage that converts instructions into a diffusion prompt
• Gradio launchers for the editing and generation demos
• Shared model-path, environment, and troubleshooting guidance

Prerequisites

• NVIDIA CUDA hardware with enough VRAM for the DreamOmni2 / Qwen2.5-VL / FLUX.1-Kontext stack
• Python 3.11 or another supported modern CPython
• Runtime dependencies from requirements.txt plus Gradio
• Local model assets or hub IDs for the VLM and LoRA weights

Start here

1. Read references/model-setup.md for the expected model layout.
2. Run scripts/check_env.py to verify the Python, torch, and GPU setup.
3. Run scripts/check_models.py to confirm the model paths you intend to use.
4. Choose the matching sub-skill:
   • sub-skills/inference/ for CLI generation and editing.
   • sub-skills/web-demo/ for the Gradio launchers.

Shared runtime helpers

• scripts/dreamomni2_common.py contains the bundled image-resize and prompt-format helpers used by both sub-skills.
• references/troubleshooting.md collects the shared install, GPU, model-path, and import failures.

Route map

sub-skills/inference/

Use this route for:

• editing one source image with a second reference image
• generating a new image from two references
• prompt-stage and diffusion-pipeline questions
• inference_edit.py and inference_gen.py-style command-line runs

sub-skills/web-demo/

Use this route for:

• launching the editing or generation Gradio UI
• choosing ports, server names, and upload order
• UI-specific troubleshooting such as port conflicts and browser launch issues

Do not use this skill for

• training, FSDP, or distributed optimization helpers
• dataset preparation or benchmark infrastructure
• generic diffusion research that is not DreamOmni2-specific

Routing notes

• The CLI and web demo workflows both depend on the same model layout and the same CUDA-capable stack.
• The editing workflow requires the source image to be first and the reference image to be second.
• The generation workflow uses the same two-image VLM prompt stage but a different LoRA adapter.
• If the user only needs installation or model-layout validation, start with the root helper scripts and the model-setup reference before going into a sub-skill.