Buckets:
| # creuthecat/Open-Sora-v2.1 — Hugging Face Cloud Training Package | |
| Cloud-first training package for **creuthecat/Open-Sora-v2.1**. | |
| This package does not fabricate model weights. It starts from the real | |
| `hpcai-tech/Open-Sora-v2` checkpoint, trains on real video-caption pairs, | |
| and persists checkpoints to the read/write Storage Bucket: | |
| `creuthecat/opensora-v21` | |
| ## What this trains | |
| ### Stage A — real video I2V/T2V fine-tuning | |
| Uses the official Open-Sora v2 training code and its `stage1_i2v.py` recipe. | |
| The upstream project documents this recipe for joint T2V/I2V training at | |
| 256px and documents fine-tuning from `Open_Sora_v2.safetensors` on 8 GPUs. | |
| ### Stage B — reference-image adapter | |
| A separate trainable reference encoder/projector is trained from real video | |
| first frames. This is an **adapter artifact**, not a claim that the upstream | |
| 11B transformer magically gained multi-reference support without model | |
| changes. | |
| ### Stage C — audio/SFX extension | |
| The package includes an interface and event-timeline format for a later SFX | |
| training stage. It is kept separate because Open-Sora v2 itself is a video | |
| model and does not ship a native SFX generator in its checkpoint. | |
| ## Cloud mounts | |
| The Job mounts: | |
| - `hpcai-tech/Open-Sora-v2` → `/base-model` (read-only) | |
| - `google/t5-v1_1-xxl` → `/ckpts/google/t5-v1_1-xxl` (read-only) | |
| - `openai/clip-vit-large-patch14` → `/ckpts/openai/clip-vit-large-patch14` (read-only) | |
| - your real training data → `/data` (read-only) | |
| - `creuthecat/opensora-v21` → `/training-outputs` (read/write) | |
| Hugging Face Jobs supports model/dataset/bucket volume mounts and buckets | |
| are read-write by default. The Job disk is ephemeral, so checkpoints must | |
| be written to the mounted bucket as they are created. | |
| ## Data format | |
| The real-video CSV should contain at least: | |
| ```text | |
| path,text,num_frames,height,width,aspect_ratio,resolution,fps | |
| ``` | |
| The `path` column should point to the video file as visible inside the Job, | |
| for example: | |
| ```text | |
| /data/videos/000001.mp4 | |
| ``` | |
| or a relative path resolved by the preprocessing step. | |
| ## Recommended first run | |
| Start with a curated real-video subset instead of attempting the entire | |
| internet-scale corpus on the first run. The OpenVid-1M dataset is a real | |
| video-text dataset and is licensed CC BY 4.0, but its Hub release is about | |
| 12.4 TB and the data are distributed in large ZIP shards; additionally, its | |
| card says downstream users must follow the licenses of the source datasets. | |
| For a first run, put only the data you are legally permitted to train on in | |
| `/data` or in a dedicated private dataset/bucket. | |
| ## Submit on Hugging Face | |
| From the Hugging Face environment/Jobs interface, run the equivalent of: | |
| ```bash | |
| hf jobs run \ | |
| --flavor a100x8 \ | |
| --timeout 7d \ | |
| -s HF_TOKEN \ | |
| -v hf://hpcai-tech/Open-Sora-v2:/base-model:ro \ | |
| -v hf://google/t5-v1_1-xxl:/ckpts/google/t5-v1_1-xxl:ro \ | |
| -v hf://openai/clip-vit-large-patch14:/ckpts/openai/clip-vit-large-patch14:ro \ | |
| -v hf://datasets/YOUR_ORG/YOUR_DATASET:/data:ro \ | |
| -v hf://buckets/creuthecat/opensora-v21:/training-outputs \ | |
| python:3.12 \ | |
| bash /workspace/job/entrypoint.sh | |
| ``` | |
| Replace the dataset volume with your own dataset. If the training package is | |
| stored in a Git repository, clone it in the Job first or use the packaged | |
| `job/entrypoint.sh` as the entrypoint. | |
| ## Outputs | |
| During training, the bucket will contain: | |
| ```text | |
| /checkpoints/ | |
| /logs/ | |
| /reference_encoder/ | |
| /release/ | |
| ``` | |
| The release step places the final/selected model artifact and metadata into | |
| separate folders without pretending that all components were newly trained | |
| from scratch. | |
| ## Licensing | |
| The Open-Sora-v2 base checkpoint is listed on Hugging Face as Apache-2.0. | |
| Always verify the licenses/terms for every dataset, pretrained encoder, and | |
| any audio dataset before publishing derived weights. | |
| ## Why this is real training | |
| The video stage does not synthesize toy shapes or random weights. It fine-tunes | |
| the published 11B Open-Sora v2 checkpoint on a real video-caption dataset. | |
| The reference component is separately trained on real video first frames. | |
| The T5 and CLIP text/image encoders are pretrained components and are frozen | |
| by default; they are not falsely presented as newly trained v2.1 encoders. | |
| The published Open-Sora v2 checkpoint is 23.8 GB and its model repository is | |
| about 69.2 GB, so the Job mounts it rather than copying it into the temporary | |
| Job filesystem. | |
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