README
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
What this is
Image-to-video generation from thornmaze/WAMU_v3_WAN2.2_I2V_LIGHTNING
via WanImageToVideoPipeline, running on Hugging Face ZeroGPU.
Post-processing
Two optional steps can run after generation:
| step | what | runs on |
|---|---|---|
| Frame interpolation ("Video Fluidity") | RIFE v4.26 (thornmaze/RIFE), half precision. 2x/4x/8x the native 16 fps. | GPU, inside the generation call |
| Upscaling ("Upscale 4×") | 4xLSDIRCompact (SRVGGNetCompact, num_conv=16), fp16 on GPU (fp32 CPU fallback), tiled to bound peak memory. Fixed 4x. | GPU, inside the generation call |
Upscaling runs after interpolation, inside the same @spaces.GPU call as generation, so it bids for
ZeroGPU worker time like the rest of the pipeline (moved off the Space's shared CPU — see issue #11 —
to avoid CPU contention across concurrent visitors).
RIFE support code (model/warplayer.py, model/loss.py, model/pytorch_msssim/) is vendored from
hzwer/Practical-RIFE (MIT, see
LICENSES/RIFE-LICENSE) — required as sibling-import targets for the
train_log/RIFE_HDv3.py module downloaded at runtime from thornmaze/RIFE.
The upscaling code (postprocess/upscale/) is vendored/adapted from
xinntao/Real-ESRGAN (BSD-3-Clause License, see
LICENSES/REAL-ESRGAN-LICENSE). The weights are
Phips/4xLSDIRCompact (CC BY 4.0, see
LICENSES/4xLSDIRCompact-LICENSE), not Real-ESRGAN's own — see the
comment at the top of postprocess/upscale/upscale.py for why.
Debug logging (optional)
Each inference call can optionally be logged (prompt, seed, generation/interpolation/upscale settings, input image, pre-upscale output video, timing, success/error) to a private Hugging Face Hub dataset repo. Disabled by default — nothing is logged until both secrets below are set. Logging is based on legitimate interest (GDPR Art. 6(1)(f)), not consent — see the notice and Privacy Policy shown in the app for details, including how to request access to or deletion of your data. Configure independently per Space under Settings → Variables and secrets:
| variable | type | notes |
|---|---|---|
LOG_HF_TOKEN | Secret | fine-grained token, write-only on the target dataset repo. Do not reuse the deploy HF_TOKEN. |
LOG_DATASET_REPO | Secret | target dataset repo id; auto-created (private) on first log. |
LOG_STORAGE_CAP_GB | Variable | total-storage retention cap; oldest entries pruned first once exceeded. Defaults to 10GB if unset — dev should set 50, prod 450. |
LOG_BATCH_INTERVAL | Variable | seconds between batched log commits. Default 60. |
Logs are committed asynchronously in batches and never add latency to a generation request. The logged video is the pre-upscale result (post-interpolation, before 4x super-resolution); metadata references the image/video files by path rather than embedding them, so both stay browsable/playable in the Hub dataset viewer.
Status
Baseline (non-AOT) pipeline. AOT-compiled inference (faster, but requires a compatible precompiled package for WAMU_v3) is tracked separately — see the repo's open issues.
LoRA loading is out of scope for this version (see SRS FR-8/C-4).
Contributors
20 commits
18 commits
Similar repos
README
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
What this is
Image-to-video generation from thornmaze/WAMU_v3_WAN2.2_I2V_LIGHTNING
via WanImageToVideoPipeline, running on Hugging Face ZeroGPU.
Post-processing
Two optional steps can run after generation:
| step | what | runs on |
|---|---|---|
| Frame interpolation ("Video Fluidity") | RIFE v4.26 (thornmaze/RIFE), half precision. 2x/4x/8x the native 16 fps. | GPU, inside the generation call |
| Upscaling ("Upscale 4×") | 4xLSDIRCompact (SRVGGNetCompact, num_conv=16), fp16 on GPU (fp32 CPU fallback), tiled to bound peak memory. Fixed 4x. | GPU, inside the generation call |
Upscaling runs after interpolation, inside the same @spaces.GPU call as generation, so it bids for
ZeroGPU worker time like the rest of the pipeline (moved off the Space's shared CPU — see issue #11 —
to avoid CPU contention across concurrent visitors).
RIFE support code (model/warplayer.py, model/loss.py, model/pytorch_msssim/) is vendored from
hzwer/Practical-RIFE (MIT, see
LICENSES/RIFE-LICENSE) — required as sibling-import targets for the
train_log/RIFE_HDv3.py module downloaded at runtime from thornmaze/RIFE.
The upscaling code (postprocess/upscale/) is vendored/adapted from
xinntao/Real-ESRGAN (BSD-3-Clause License, see
LICENSES/REAL-ESRGAN-LICENSE). The weights are
Phips/4xLSDIRCompact (CC BY 4.0, see
LICENSES/4xLSDIRCompact-LICENSE), not Real-ESRGAN's own — see the
comment at the top of postprocess/upscale/upscale.py for why.
Debug logging (optional)
Each inference call can optionally be logged (prompt, seed, generation/interpolation/upscale settings, input image, pre-upscale output video, timing, success/error) to a private Hugging Face Hub dataset repo. Disabled by default — nothing is logged until both secrets below are set. Logging is based on legitimate interest (GDPR Art. 6(1)(f)), not consent — see the notice and Privacy Policy shown in the app for details, including how to request access to or deletion of your data. Configure independently per Space under Settings → Variables and secrets:
| variable | type | notes |
|---|---|---|
LOG_HF_TOKEN | Secret | fine-grained token, write-only on the target dataset repo. Do not reuse the deploy HF_TOKEN. |
LOG_DATASET_REPO | Secret | target dataset repo id; auto-created (private) on first log. |
LOG_STORAGE_CAP_GB | Variable | total-storage retention cap; oldest entries pruned first once exceeded. Defaults to 10GB if unset — dev should set 50, prod 450. |
LOG_BATCH_INTERVAL | Variable | seconds between batched log commits. Default 60. |
Logs are committed asynchronously in batches and never add latency to a generation request. The logged video is the pre-upscale result (post-interpolation, before 4x super-resolution); metadata references the image/video files by path rather than embedding them, so both stay browsable/playable in the Hub dataset viewer.
Status
Baseline (non-AOT) pipeline. AOT-compiled inference (faster, but requires a compatible precompiled package for WAMU_v3) is tracked separately — see the repo's open issues.
LoRA loading is out of scope for this version (see SRS FR-8/C-4).
Similar repos
Linked in READMEs
Contributors
20 commits
18 commits