v1.3.0

Added

• DFR can finish 4K in a tiled spatial epilogue instead of denoising the full canvas in stage 2. --spatial-upscalings 2 keeps stage 2 at half resolution and upscales in that epilogue, which is the path for better-quality 4K (use 3840x2176; 3840x2160 is not on the size grid). Default --spatial-upscalings 1 is unchanged.
• Added ltx_pipelines.dfr_mgpu, a multi-GPU DFR runner with the same flags as ltx_pipelines.dfr_pipeline.
• Added keyframe-aware diffusion-VAE decoding, which sharpens detail at the frames a generator anchored on. Pass keyframe latents into VideoDecoder.decode_video(..., keyframes=). Needs a keyframe-trained video VAE; older checkpoints decode as before.
• --compile max_video_tokens=N max_audio_tokens=N (and the matching CompilationConfig fields) cap CUDA-graph input memory when capture=true, so several captured shapes share one buffer sized for the largest instead of paying for each separately.
• DiffusionStage.with_model_wrapper() runs a callable on each built transformer before denoising, and still composes with block streaming and quantization.
• VideoDecoder.decode_single_frames() decodes independent one-frame latents without neighbouring frames bleeding into each other.
• DecodeKeyframes.for_frame_span() and .crop_spatial() crop keyframe planes to a decode window, keeping the nearest anchors outside the window so a tiled decode matches a whole one.
• DiffusionStage.__call__ accepts optional audio_fps= when audio should be sized from a different frame rate than the video RoPE time base.

Changed

• DFRPipeline and ltx_pipelines.dfr_mgpu take a distilled checkpoint (--distilled-checkpoint-path) instead of a full checkpoint plus a distilled LoRA. Detailing IC-LoRA and optional user LoRAs are unchanged.
• DFR temporal rounds use --temporal-upscalings instead of --temporal-upsample-rounds.
• DFR snaps transformer conditioning fps to 60 whenever playback fps is above 30 (previously only values above 60 were capped), so 48 and 50 fps stay in the trained range. Playback fps is unchanged.
• --diffvae-optimization combined_compile no longer requires the natten extra. Without it the mode falls back to Triton, or to PyTorch when Triton is unavailable.
• Automatic decode tiling sizes tiles for keyframe-aware decoding as well as plain decoding, so a keyframe decode is less likely to run out of memory on a clip a plain decode fits.
• Video pipelines now return a named PipelineOutput instead of a 3- or 4-tuple. Extra fields are keyframes and video_latent. Existing unpacking must switch to result.video, result.audio, and so on.
• VideoDecoder.decode_video() takes keyframes= on every implementation. The diffusion decoder uses them; conv logs a warning and decodes without them; distributed splits them across ranks. Callers that never pass keyframes are unaffected; decoder implementations must accept the keyword.
• NVFP4 quantization and CuTe DSL DiffVAE kernels (blackwell_dsl) now support Jetson Thor. Build NVFP4 with the arch-specific 110a target.

Removed

• DiffusionVideoDecoder.decode_video_with_keyframes; use decode_video(..., keyframes=).
• DFROutput; use PipelineOutput.
• --checkpoint-path and --distilled-lora on ltx_pipelines.dfr_pipeline and ltx_pipelines.dfr_mgpu; pass --distilled-checkpoint-path instead.
• --num-generated-keyframes on DFR; slot positions come from DFR's segment grid.
• --temporal-upsample-rounds on DFR; use --temporal-upscalings.

Fixed

• Automatic decode tiling no longer OOMs in colour conversion on clips that looked like they had spare VRAM. The budget now reserves encoder memory for each chunk, so long videos decode in temporal pieces instead of as one oversize frame dump.
• A pipelines-only install (ltx-core / ltx-pipelines, no trainer) no longer fails to load Gemma 4 for missing torchvision.
• uv sync on macOS no longer fails looking for CUDA-only torch wheels. Macs use PyPI (MPS on Apple Silicon); Linux and Windows still use the CUDA 13.2 indexes.
• --quantization fp8-cast with a LoRA no longer crashes on pre-Ada GPUs such as RTX 3060 or A100.
• Blockwise FP8/FP6 quantization no longer fails on very long sequences or token counts that are not a multiple of 4.
• Multi-GPU DFR no longer crashes at tiling resolve, during carry-keyframe decode, or by dropping context at a temporal tile boundary.
• DFR image-to-video stills land on the correct frame after temporal upsampling (opening-frame frame_idx=0 was already correct).
• AUTO_TILING is recognised after it crosses a process boundary, so multi-GPU pipelines resolve automatic tiling instead of rejecting the sentinel.