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license: cc-by-nc-sa-4.0 |
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--- |
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# Enhancing Diffusion Models with Text-Encoder Reinforcement Learning |
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Official PyTorch codes for paper [Enhancing Diffusion Models with Text-Encoder Reinforcement Learning](https://arxiv.org/abs/2311.15657) |
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## Results on SD-Turbo |
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We applied our method to the recent model [sdturbo](https://huggingface.co/stabilityai/sd-turbo). The model is trained with [Q-Instruct](https://github.com/Q-Future/Q-Instruct) feedback through direct back-propagation to save training time. Test with the following codes |
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``` |
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## Note: sdturbo requires latest diffusers>=0.24.0 with AutoPipelineForText2Image class |
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from diffusers import AutoPipelineForText2Image |
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from peft import PeftModel |
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import torch |
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pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sd-turbo", torch_dtype=torch.float16, variant="fp16") |
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pipe = pipe.to("cuda") |
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PeftModel.from_pretrained(pipe.text_encoder, 'chaofengc/sd-turbo_texforce') |
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pt = ['a photo of a cat.'] |
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img = pipe(prompt=pt, num_inference_steps=1, guidance_scale=0.0).images[0] |
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``` |
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