32 lines
1.1 KiB
Markdown
32 lines
1.1 KiB
Markdown
# Omni Examples
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Examples showcasing multi-stage AR + DiT pipelines powered by vLLM-omni.
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## AR ➜ DiT with Diffusers Backend
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```bash
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# Optional: customise models
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export AR_MODEL=Qwen/Qwen3-0.6B
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export DIT_MODEL=./models/stable-diffusion-2-1/
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# Run with defaults (20 steps @ 512x512)
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python examples/omni/ar_dit_diffusers.py
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# Faster run
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python examples/omni/ar_dit_diffusers.py --steps 14 --height 384 --width 384 --guidance 4.5
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```
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Arguments:
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- `--ar-model`: Override AR stage model (default `Qwen/Qwen3-0.6B`).
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- `--dit-model`: Override DiT stage model (default env `DIT_MODEL`, else diffusers repo).
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- `--steps`: Number of diffusion steps (default 20).
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- `--guidance`: Guidance scale (default 5.0).
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- `--height` / `--width`: Output resolution (default 512).
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- `--seed`: Optional RNG seed.
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- `--prompt`, `--temperature`, `--max-tokens`: Control AR stage generation.
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- `--output`: Destination path for the generated image file.
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Generated images are saved next to the script as `omni_dit_output.png` by default.
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See the script for additional tweaks (scheduler, prompt chaining, etc.).
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