Fixes all P1/P2 items from the review of 7c7704da:
- E2E test's data_path pointed at a nonexistent combined_parquet_dataset/
subdir; preprocess_kandinsky5_overfit.py writes directly to its output
root.
- Stage 2 loaded the raw DCP checkpoint-N/ directly, which isn't a
diffusers model dir. Added dcp_to_diffusers --verify (strict-reload
the export immediately) and wired the conversion into the E2E test,
launcher scripts, and README between stage 1 and stage 2.
- Kandinsky5DmdDenoisingStage called the transformer without
set_forward_context, so LocalAttention's missing-context guard caught
the AssertionError and silently fell back to plain SDPA -- Stage 2
validation never actually exercised ATTN_QAT_TRAIN. Wrapped the call
to match the parent stage's contract and added a scoped backend
assertion (ATTN_QAT_TRAIN/ATTN_QAT_INFER only, since other backends
can legitimately fall back to auto-selection).
- Exported DMD checkpoints kept the base T2V model_index.json, so
VideoGenerator.from_pretrained resolved them to Kandinsky5T2VPipeline
instead of the DMD sampler. Added Kandinsky5DMDConfig and documented
the required override_pipeline_cls_name + pipeline_config combo.
- The FSDP-training dtype-detection special case (scan params, default
to bf16) applied unconditionally, so an explicit fp32 pipeline was
silently cast to bf16 for plain inference. Extracted
_resolve_target_dtype and gated the scan on isinstance(transformer,
FSDPModule).
- Preprocessing tokenized captions with a 512-token cap that doesn't
account for the 129-token system template runtime strips off; now
reads text_encoder_max_lengths from pipeline_config to match runtime.
Also replaced unconditional item["cap"][0] indexing (silently takes
the first character of a string caption) with a get_caption() helper
that accepts/validates both the documented string schema and the
list-of-variants schema some producers use.
- test_kandinsky5_qat_attention_engages.py set FASTVIDEO_ATTENTION_BACKEND
and cleared the attention backend selector cache without teardown,
leaking ATTN_QAT_TRAIN into later tests in the same pytest process.
Now uses monkeypatch + try/finally.
Added regression tests: test_kandinsky5_target_dtype.py,
test_kandinsky5_dmd_pipeline_resolution.py,
test_kandinsky5_overfit_caption.py.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
