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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# Model Summary
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This model is trained using [UnifiedReward-Flex](https://huggingface.co/collections/CodeGoat24/unifiedreward-flex) as reward on the training dataset of [UniGenBench](https://github.com/CodeGoat24/UniGenBench).
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🚀 The inference code is available at [Github](https://github.com/CodeGoat24/Pref-GRPO/blob/main/inference/flux_dist_infer.sh).
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# Model Summary
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This model is GRPO trained using [UnifiedReward-Flex](https://huggingface.co/collections/CodeGoat24/unifiedreward-flex) as reward on the training dataset of [UniGenBench](https://github.com/CodeGoat24/UniGenBench).
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🚀 The inference code is available at [Github](https://github.com/CodeGoat24/Pref-GRPO/blob/main/inference/flux_dist_infer.sh).
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