_nvfp4_quantize copied every activation into a zero-padded buffer (F.pad
copies even when no rows are added) before FlashInfer quantization. For
the 128x4 swizzled layout FlashInfer already pads the scale factors to
whole row tiles and quantizes the logical rows exactly as it would the
padded copy, so quantize the input directly and keep the padded path as
a fallback when the returned scales do not span the padded tile.
On DGX Spark the pad accounted for about 2.7k device copies plus the
matching fills per FastH3 Trim denoise (about 6 s of 62 s). Quantized
bytes, scale factors and mm_fp4 outputs are bit-identical for the H3
projection shapes, and a FastH3 Trim 832x480x124 clip is bit-identical
with denoise at 72 s instead of 78 s.
The GB10 quantization fence stays: without it, identical seeded requests
still diverge with or without the pad copy, on FlashInfer 0.6.18 and
0.7.0. Its comment and the Spark guide now describe it without the
padded input. The off-Spark fence test skips on GB10, where the fence is
expected.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
For n > 1, the image endpoints returned one preview grid instead of n images.
The endpoints now run one model batch, save each decoded sample as its own file,
and return one URL or base64 item for each image. This applies to generations
and edits.
A keyword-only return_samples field (default False) is added to OutputConfig,
SamplingParam and ForwardBatch. Batched image requests set it and do not build
preview frames. The worker keeps the sample tensor for these requests, and
non-output ranks clear the field. Defaults and positional arguments do not
change.
Accept the causal-VAE-aligned 362-frame bucket (15.083 s) for 15-second H3 requests.
- Hoist MINIMAX_H3_MIN/MAX_ALIGNED_FRAMES next to align_num_frames and reuse them in the CUDA stage, the MLX runtime, and the LoRA example so the bound is consistent across entry points.
- State the accepted frame range in the error messages.
- Cover seconds="15" -> 362 at OpenAI admission and the MLX resolve_geometry bound.
- Update the Spark/openai cookbook frame caps from 345 to 362.
Known gaps: no GPU-lane coverage for the 362 bucket (SSIM runs 124 frames; the golden gate is a fixed-geometry fingerprint), and explicit num_frames=360 still hits the pre-existing grid gate.
