- Add CUDA sync before encoding to ensure RGB->NV12 kernel completes
- Add debug logging for frame data validation (sum check)
- Handle GPUFrame objects in GPUHLSOutput.write()
- Fix cv2.resize for CuPy arrays (use cupyx.scipy.ndimage.zoom)
- Fix fused pipeline parameter ordering (geometric first, color second)
- Add raindrop-style ripple with random position/freq/decay/amp
- Generate final VOD playlist with #EXT-X-ENDLIST
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Audio playback path was being resolved during parsing when database
may not be ready, causing fallback to non-existent path. Now resolves
lazily when stream starts, matching how audio analyzer works.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Replace slow scipy.ndimage operations with custom CUDA kernels:
- gpu_rotate: AFFINE_WARP_KERNEL (< 1ms vs 20ms for scipy)
- gpu_blend: BLEND_KERNEL for fast alpha blending
- gpu_brightness/contrast: BRIGHTNESS_CONTRAST_KERNEL
- Add gpu_zoom, gpu_hue_shift, gpu_invert, gpu_ripple
Preserve GPU arrays through pipeline:
- Updated _maybe_to_numpy() to keep CuPy arrays for GPU primitives
- Primitives detect CuPy arrays via __cuda_array_interface__
- No unnecessary CPU round-trips between operations
New jit_compiler.py contains all CUDA kernels with FastGPUOps
class using ping-pong buffer strategy for efficient in-place ops.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- New GPUHLSOutput class for direct GPU-to-NVENC encoding
- RGB→NV12 conversion via CUDA kernel (no CPU transfer)
- Uses PyNvVideoCodec for zero-copy GPU encoding
- ~220fps vs ~4fps with CPU pipe approach
- Automatically used when PyNvVideoCodec is available
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add on_playlist_update callback to IPFSHLSOutput
- Pass callback through StreamInterpreter to output
- Update database with playlist CID as segments are created
- Enables live HLS redirect to IPFS before rendering completes
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Convert GPU frames/CuPy arrays to numpy before calling primitives.
This fixes all CPU primitives without modifying each one individually.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
The background IPFS upload task was running on workers that don't have
the file locally, causing uploads to fail silently. Now uploads go to
IPFS synchronously so the IPFS CID is available immediately.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add IPFSHLSOutput class that uploads segments to IPFS as they're created
- Update streaming task to use IPFS HLS output for distributed streaming
- Add /ipfs-stream endpoint to get IPFS playlist URL
- Update /stream endpoint to redirect to IPFS when available
- Add GPU persistence mode (STREAMING_GPU_PERSIST=1) to keep frames on GPU
- Add hardware video decoding (NVDEC) support for faster video processing
- Add GPU-accelerated primitive libraries: blending_gpu, color_ops_gpu, geometry_gpu
- Add streaming_gpu module with GPUFrame class for tracking CPU/GPU data location
- Add Dockerfile.gpu for building GPU-enabled worker image
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Update save_run_cache to also update actor_id, recipe, inputs on conflict
- Add logging for actor_id when saving runs to run_cache
- Add admin endpoint DELETE /runs/admin/purge-failed to delete all failed runs
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Remove legacy_tasks.py, hybrid_state.py, render.py
- Remove old task modules (analyze, execute, execute_sexp, orchestrate)
- Add streaming interpreter from test repo
- Add sexp_effects with primitives and video effects
- Add streaming Celery task with CID-based asset resolution
- Support both CID and friendly name references for assets
- Add .dockerignore to prevent local clones from conflicting
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
