Staging product storytelling experiment

Production stays on its existing Git and Hugging Face revisions. Changes reach handler mode selection, its strict request contract, model-cache provenance, selftest, staging endpoint revision and contract tests. No Django caller changes.

motion and union use the pinned native ICLoraPipeline, one full-resolution stage, eight distilled steps and the respective official ref0.5 LoRA. Reference scale comes from the adapter metadata. They accept video_url containing a prepared sparse-motion or depth/canny/pose guide, plus optional image_url or images identity anchors. conditioning_strength defaults to 1, range [0,1]. Guide frame count and FPS must equal the requested output; aspect ratio must be preserved. A normal RGB clip is not a motion-track or union guide. Guidance, negative prompts and arbitrary step counts are refused rather than ignored.

A storyboard is orchestration: approve product references for each shot, render short continuous shots, then assemble explicit cuts and a continuous soundtrack. Multi-keyframe conditioning is already supported by i2v / keyframe; it does not automatically create or validate a storyboard. Never ask the model to complete hidden product lettering. Judge source markings, both shoe count and silhouette across the actual output, including cut boundaries.

Refine Details already exists as refine and uses tiled native diffusion with 50 percent tile overlap. Native blending differs from the official ComfyUI recipe; GPU comparisons must precede enabling it for customers. A 4K output size is not evidence that low-resolution source lettering became accurate.

Official references:

Checks: LTX_SOURCE=/path/to/pinned/LTX-2 python3 -m unittest discover -s tests. Real render receipts and quality conclusions belong in the application research report; callable controls alone are not evidence of improved product fidelity.