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Running on Zero
Running on Zero
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Fix print_frames_info logging garbage bound-method reprs for tensor frames
torch.Tensor has .size()/.mode() methods with the same names as PIL Image's .size/.mode attributes, which print_frames_info reads via getattr assuming a PIL Image. Since #53 the RIFE->upscale tensor handoff can pass a tensor frame list to this same logging call, producing e.g. "pil_size=<built-in method size of Tensor object at 0x...>" in production logs instead of a clean None. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>e7c7a36 -
Log the final served video, not the pre-upscale result (#46)
Unverified signature* Sync develop into main (#43) * Check Upscale 4x by default and drop model name from its label (#19) Simplifies the option to a plain checkbox toggle without exposing the underlying model name in the UI. Claude-Session: https://claude.ai/code/session_01W9GLmk1R3mFfb7otDoGiWX Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com> * Add detailed export/traceback logging to diagnose post-pipeline UI errors (#20) The Space logs cut off right after the pipeline stages finish, with no trace of what happens next in generate_video() (export_to_video and the run_inference call boundary), even though the UI shows a generic Error. Log frame metadata and full tracebacks at those points so the next failure is diagnosable from the Space logs alone. * Pre-download RIFE/upscale weights at startup instead of inside @spaces.GPU (#22) The RIFE and 4xLSDIRCompact weight fetches are pure network/disk I/O with no CUDA dependency, but were only downloaded lazily on first use inside the metered @sp…3d9fab2
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Prune log repo on file count, not just storage size (#44)
Unverified signatureThe Hub commit endpoint rejects pushes once a directory holds more than 10000 files. Storage-size-only retention didn't bound this: data/ and images/ files are tiny relative to videos/, so thousands of stems accumulated in those directories long before the byte cap ever triggered a prune, eventually hitting the Hub's per-directory limit. Add a LOG_MAX_FILES cap (default 8000 stems) that prunes alongside the existing LOG_STORAGE_CAP_GB, whichever is hit first. Claude-Session: https://claude.ai/code/session_01WHfM7C6kNJh15dWfYt8agd Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>4578f11
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Add detailed export/traceback logging to diagnose post-pipeline UI errors (#20)
Unverified signatureThe Space logs cut off right after the pipeline stages finish, with no trace of what happens next in generate_video() (export_to_video and the run_inference call boundary), even though the UI shows a generic Error. Log frame metadata and full tracebacks at those points so the next failure is diagnosable from the Space logs alone.ab3b519
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Add optional debug logging pipeline (LOG-1–LOG-9) (#13)
Unverified signatureEach inference call can optionally be logged to a private HF Hub dataset repo for debugging, gated entirely by LOG_HF_TOKEN/LOG_DATASET_REPO (silent no-op when unset). Adapted from the FireRed-Image-Edit-1.0-Fast reference pattern for video: logged media (input image, pre-upscale output video) is referenced by path rather than embedded as bytes, and retention is a total-storage-size cap (LOG_STORAGE_CAP_GB) rather than a file-count limit, with oldest entries pruned first once exceeded. Uploads are batched and committed asynchronously (~60s) so logging never adds latency to a generation request. The generation/interpolation/upscale stages of run_inference are each extracted into their own helper function, so the frame/fps state for each stage is assigned exactly once instead of being reassigned across branches. Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>e6a95be