Instructions to use diffusionstudio/sam2.1-tiny-video-onnx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use diffusionstudio/sam2.1-tiny-video-onnx-fp16 with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("diffusionstudio/sam2.1-tiny-video-onnx-fp16") 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("diffusionstudio/sam2.1-tiny-video-onnx-fp16") 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
Download onnx/memory_encoder.onnx from diffusionstudio/sam2.1-tiny-video-onnx-fp16: direct link, hf CLI and curl.
- Browser
- Download file 2.81 MB
-
https://huggingface.co/diffusionstudio/sam2.1-tiny-video-onnx-fp16/resolve/main/onnx/memory_encoder.onnx
- Command line
-
hf download hf://diffusionstudio/sam2.1-tiny-video-onnx-fp16/onnx/memory_encoder.onnx
-
curl -L -o memory_encoder.onnx https://huggingface.co/diffusionstudio/sam2.1-tiny-video-onnx-fp16/resolve/main/onnx/memory_encoder.onnx
2.81 MB
- Xet hash:
- aaf0b17419e0c724166c6f706954289f9e81d2793681f79ae0758ea333d7bf6c
- Size of remote file:
- 2.81 MB
- SHA256:
- 8b2bb6810444cee684ef938b7464c5645653f236014f5497c3db3654818cc7ce
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