Wire up and verify the Cosmos3 image-to-video path. The denoise stage already
handled the I2V conditioning math (encode -> condition mask -> keep frame 0
clean -> velocity zeroed on condition frames, all matching the framework), but
the conditioning pixel clip was built wrong.
Bug: `_image_to_video_tensor` zero-filled every frame after frame 0. The
framework (`cosmos_framework.inference.vision.build_conditioned_video_batch`)
instead fills frame 0 with the image and **repeats the last conditioning frame**
for the rest of the clip (a static video) before VAE-encoding. Because the Wan
VAE is temporal (4x), latent frame 0 (the kept-clean condition frame) depends on
several pixel frames, so zero-filling produces a wrong conditioning latent.
Fix `_image_to_video_tensor` to be framework-faithful:
- aspect-preserving resize + center crop + uint8 quantization, then `/127.5 - 1`
(mirrors `load_conditioning_image` / `_resize_and_center_crop`);
- static-repeat fill across all frames (mirrors `build_conditioned_video_batch`).
Parity: add `test_cosmos3_i2v_conditioning_parity.py` comparing the FastVideo
conditioning clip against the framework's `load_conditioning_image` +
repeat-fill across aspect/size/frame-count cases — bit-exact (max abs diff 0.0).
Example: `examples/inference/basic/basic_cosmos3_i2v_new_api.py` (new
VideoGenerator API, image via `InputConfig(image_path=...)`,
`assets/images/cyclist.jpg` default).
Verified on B200 (1280x704, 29f, 35 steps, real weights): frame 0 reproduces
the conditioning image and the clip generates coherent forward motion following
the prompt. Full cosmos3 suite: 98 passed, 0 skipped.
SolitaryThinker
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