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---
license: apache-2.0
library_name: diffusers
pipeline_tag: image-to-image
tags:
- optical-flow prediction
- motion prediction
- diffusion
---
# FOFPred: Language-Driven Future Optical Flow Prediction
**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.
## Usage
```python
import torch
from fofpred.pipelines.fofpred.pipeline_fofpred import FOFPredPipeline
from fofpred.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
from PIL import Image
pipeline = FOFPredPipeline.from_pretrained(
"Salesforce/FOFPred",
torch_dtype=torch.bfloat16,
).to("cuda")
pipeline.scheduler = FlowMatchEulerDiscreteScheduler()
results = pipeline(
prompt="Moving the water bottle from right to left.",
input_images=[Image.open("your_image.jpg")],
width=256,
height=256,
num_inference_steps=1,
num_images_per_prompt=4,
frame_count=4,
generator=torch.Generator(device="cuda").manual_seed(42),
output_type="pt",
)
flow_frames = results.images # [B, F, C, H, W]
```
## Architecture
| Component | Model |
|-----------|-------|
| **V-LLM** | Qwen2.5-VL-3B-Instruct |
| **DiT** | OmniGen2Transformer3DModel |
| **VAE** | FLUX.1-dev AutoencoderKL |
| **Scheduler** | FlowMatchEulerDiscreteScheduler |
## Acknowledgements
- [OmniGen2](https://github.com/VectorSpaceLab/OmniGen2)
- [Qwen2.5-VL](https://huggingface.co/Qwen/Qwen2.5-VL)
- [Flux VAE](https://huggingface.co/black-forest-labs/FLUX.1-dev)
## License
Apache 2.0 — Copyright (c) 2025 Salesforce, Inc.
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