Wan-AI/Wan2.2-I2V-A14B-Diffusers is the image-to-video sibling of the already
supported T2V-A14B: the same dual-expert WanTransformer3DModel pair (boundary_ratio
0.9 in the pipeline config) behind WanImageToVideoPipeline, conditioning through the
VAE latent (no CLIP-vision image encoder in this 2.2 variant).
- New wan2.2-i2v-a14b family: image_conditioned flag, card recipe defaults (40 steps,
CFG 3.5, 81 frames at 16 fps), the T2V memory table (57.2 GB both experts bf16),
fp32-pinned VAE, and a wan2.2-i2v generation-defaults key ahead of the generic wan
50/5.0 entry.
- Source-image plumbing: /video/generate takes init_image (base64/data URL);
begin_generate 400s synchronously when an image-to-video family has no image or a
text-only family is given one, and generate() decodes and resizes the image to the
snapped output size before threading it as the pipeline's image kwarg. status()
reports image_input so the UI can gate the control.
- Trust the official -Diffusers repo for pipeline loads and transfer the measured wan
quant recipes: fp8 keeps condition_embedder in bf16 (zero padding-row scale), mxfp8
and nvfp4 stay denied, the UMT5 auto TE quant resolves dense, and the balanced
FBCache pin carries over. All tables share the T2V DiT profile.
- Tests: family detection/aliases/defaults, the dual-DiT image pipeline load, the
image gates on both begin_generate and generate, init_image route pass-through, and
the quant exclude/deny/auto entries.
GPU-verified on a B200: bf16 resident load (offload none, 72.8 GB peak) animating a
conditioning image at 832x480/33f in 28.7 s with first-frame MAE 5.7 vs the source,
and an int8 load with both experts quantized (43.1 GB peak, clean output).
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