Instructions to use CodeGoat24/FLUX.1-dev-UnifiedReward-Flex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use CodeGoat24/FLUX.1-dev-UnifiedReward-Flex with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("CodeGoat24/FLUX.1-dev-UnifiedReward-Flex", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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- 🤗 Dataset: https://huggingface.co/datasets/CodeGoat24/UnifiedReward-Flex-SFT-90K
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- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
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- 🤗 Dataset: https://huggingface.co/datasets/CodeGoat24/UnifiedReward-Flex-SFT-90K
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- 👋 Point of Contact: [Yibin Wang](https://codegoat24.github.io)
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# Qualitative Results
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# Quantitative Results
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