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--- |
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license: apache-2.0 |
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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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**FOFPred** is a diffusion-based model that predicts future optical flow from a single image guided by natural language instructions. Given an input image and a text prompt describing a desired action (e.g., *"Moving the water bottle from right to left"*), FOFPred generates 4 sequential optical flow frames showing how objects would move. |
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## Usage |
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```python |
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import torch |
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from fofpred.pipelines.fofpred.pipeline_fofpred import FOFPredPipeline |
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from fofpred.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler |
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from PIL import Image |
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pipeline = FOFPredPipeline.from_pretrained( |
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"Salesforce/FOFPred", |
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torch_dtype=torch.bfloat16, |
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).to("cuda") |
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pipeline.scheduler = FlowMatchEulerDiscreteScheduler() |
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results = pipeline( |
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prompt="Moving the water bottle from right to left.", |
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input_images=[Image.open("your_image.jpg")], |
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width=256, |
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height=256, |
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num_inference_steps=1, |
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num_images_per_prompt=4, |
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frame_count=4, |
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generator=torch.Generator(device="cuda").manual_seed(42), |
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output_type="pt", |
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) |
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flow_frames = results.images # [B, F, C, H, W] |
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``` |
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## Architecture |
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| Component | Model | |
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|-----------|-------| |
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| **V-LLM** | Qwen2.5-VL-3B-Instruct | |
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| **DiT** | OmniGen2Transformer3DModel | |
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| **VAE** | FLUX.1-dev AutoencoderKL | |
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| **Scheduler** | FlowMatchEulerDiscreteScheduler | |
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## Acknowledgements |
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- [OmniGen2](https://github.com/VectorSpaceLab/OmniGen2) |
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- [Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL) |
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- [Flux VAE](https://huggingface.co/black-forest-labs/FLUX.1-dev) |
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## License |
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Apache 2.0 — Copyright (c) 2025 Salesforce, Inc. |
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