Instructions to use Salesforce/FOFPred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Salesforce/FOFPred with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Salesforce/FOFPred", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Upload FOFPred pipeline
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by kahnchana - opened
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- model_index.json +1 -1
README.md
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library_name: diffusers
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pipeline_tag: image-to-image
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tags:
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---
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# FOFPred: Language-Driven Future Optical Flow Prediction
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library_name: diffusers
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pipeline_tag: image-to-image
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tags:
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- optical-flow prediction
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- motion prediction
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- diffusion
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---
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# FOFPred: Language-Driven Future Optical Flow Prediction
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model_index.json
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{
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"_class_name": "FOFPredPipeline",
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"_diffusers_version": "0.34.0",
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"_name_or_path": "
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"mllm": [
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"transformers",
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"Qwen2_5_VLForConditionalGeneration"
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{
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"_class_name": "FOFPredPipeline",
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"_diffusers_version": "0.34.0",
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"_name_or_path": "/export/home/public_repo/FOFPred/pretrained_models/hf_upload",
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"mllm": [
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"transformers",
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"Qwen2_5_VLForConditionalGeneration"
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