lishunyang
69bcc6416d
Adds Lance (bytedance-research/Lance), a 3B unified AR+diffusion multimodal model on a Qwen2.5-VL backbone. Lance is BAGEL-lineage: it reuses vLLM-Omni's BAGEL Mixture-of-Transformers core verbatim and specializes only the ViT (Qwen2.5-VL vision), the VAE (Wan2.2) and the checkpoint layout. All six single-stage Lance modalities verified end-to-end on 1x B300: t2i, t2v, image_edit, video_edit, x2t_image (image understanding) and x2t_video (video understanding). 3-D mRoPE is wired through BagelRotaryEmbedding for the x2t / video / edit paths. Includes the upstream gap-analysis quality fixes (task-specific system prompts, full Qwen chat template, upstream inference defaults and aspect-ratio buckets). Ships an offline example (examples/offline_inference/lance/), an OpenAI-compatible online-serving example (examples/online_serving/lance/) and an e2e test (tests/e2e/online_serving/test_lance.py). See LANCE_PR.md for the architecture rationale, per-modality status and the two remaining follow-ups (two-stage AR+DiT, video_edit polish). Closes #3697. Signed-off-by: lishunyang <lishunyang03@gmail.com>
51 lines
2.1 KiB
Markdown
51 lines
2.1 KiB
Markdown
# Lance (ByteDance) — offline inference
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[Lance](https://huggingface.co/bytedance-research/Lance) is a 3B unified
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autoregressive + diffusion multimodal model on a Qwen2.5-VL backbone. It is
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**BAGEL-lineage** (ByteDance Mixture-of-Transformers): the released `Lance_3B`
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checkpoint uses the same `*_moe_gen` MoT weight layout as BAGEL, so vLLM-Omni
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implements it by reusing the BAGEL transformer core and specializing only the
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ViT (Qwen2.5-VL vision), the VAE (Wan2.2) and the checkpoint layout.
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This example covers all six Lance modalities from the upstream HF model card:
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`t2i`, `t2v`, `image_edit`, `video_edit`, `x2t_image` (image understanding) and
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`x2t_video` (video understanding).
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## Hardware
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Single NVIDIA GPU with 16 GB+ VRAM in BF16 (we test on B300 / A100). CUDA ≥ 12.4.
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## Run
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```bash
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# Text-to-image
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python examples/offline_inference/lance/end2end.py \
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--model bytedance-research/Lance \
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--prompts "a corgi astronaut on the moon, cinematic" \
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--steps 30 --cfg-text-scale 4.0 --timestep-shift 3.5 \
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--output ./out
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# Text-to-video (uses the Lance_3B_Video subfolder; see ``--modality``
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# choices for all six task variants)
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python examples/offline_inference/lance/end2end.py \
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--model bytedance-research/Lance/Lance_3B_Video --modality text2video \
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--num-frames 25 --video-height 480 --video-width 768 \
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--prompts "a cat playing piano, cinematic" \
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--steps 30 --fps 8 --output ./out
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```
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`video_edit` requires `--model bytedance-research/Lance/Lance_3B_Video` so
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the 3-D `latent_pos_embed` table is loaded; the other paths can point at
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the top-level `bytedance-research/Lance` repo and resolve the right
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sub-checkpoint automatically.
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The HF repo bundles everything (`Lance_3B/`, `Lance_3B_Video/`,
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`Qwen2.5-VL-ViT/`, `Wan2.2_VAE.pth`); no separate downloads are required.
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## Defaults
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Matches upstream `inference_lance.sh`: 30 denoising steps, timestep-shift 3.5,
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text CFG 4.0, seed 42, 768×768. For the understanding paths (`img2text` /
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`video2text`), sampling is enabled by default at `--text-temperature 0.8`
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because Lance's greedy decoder emits an immediate EOS for many prompts.
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