Instructions to use t8star/WeVisOne-VL-Comfy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use t8star/WeVisOne-VL-Comfy with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("t8star/WeVisOne-VL-Comfy") with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained("t8star/WeVisOne-VL-Comfy") with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>) # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
WeVisOne-VL for ComfyUI · T8
Original Tencent WeVisOne-VL checkpoints and Mask2Alpha assets, organized for ComfyUI-WeVisOne-VL-T8. T8 maintains the ComfyUI integration and this distribution; upstream model authors and licenses are preserved.
Files
| Path | Contents |
|---|---|
2B/ |
WeVisOne-VL 2B, tokenizer, processor and required pixel decoder |
4B/ |
WeVisOne-VL 4B, tokenizer, processor and required pixel decoder |
sam2.1_hiera_base_plus.pt |
Official SAM2.1 Hiera Base+ checkpoint |
mask2alpha_sam2_bplus.pt |
Tencent Mask2Alpha refinement head |
mask2alpha_head.onnx |
Optional fixed-shape ONNX export of the refinement head |
provenance.json, SHA256SUMS |
Source revisions, file sizes and SHA-256 checksums |
The pretrained weights are copied unchanged. TensorRT engines are built locally for the target GPU/runtime.
Use in ComfyUI
Install the node package and follow its setup instructions. The downloader selects only the requested model size and the shared Mask2Alpha checkpoints.
For manual download:
from huggingface_hub import snapshot_download
snapshot_download(
"t8star/WeVisOne-VL-Comfy",
local_dir="ComfyUI/models/wevisone",
allow_patterns=["2B/*", "sam2.1_hiera_base_plus.pt", "mask2alpha_sam2_bplus.pt"],
)
Set WEVISONE_MODELS to that directory, or select the model path in the loader. Replace 2B/* with 4B/* for 4B. mask_decoder.pt is required for text-guided segmentation and matting.
Sources and license
Apache-2.0. Original notices are retained in each model directory and LICENSES/. Pinned provenance is recorded in provenance.json.
T8 links
Bilibili · YouTube · API · Free gallery · Online AI apps · ComfyUI pack · Hugging Face
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Model tree for t8star/WeVisOne-VL-Comfy
Base model
tencent/WeVisOne-VL-2B