UCLA-AGI/SPIN-Diffusion-iter3

Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation (https://huggingface.co/papers/2402.10210)

SPIN-Diffusion-iter3

This model is a self-play fine-tuned diffusion model at iteration 3 from runwayml/stable-diffusion-v1-5 using synthetic data based on the winner images of the yuvalkirstain/pickapic_v2 dataset. We have also made a Gradio Demo at UCLA-AGI/SPIN-Diffusion-demo-v1.

Model Details

Model Description

• —Model type: A diffusion model with unet fine-tuned, based on the structure of stable diffusion 1.5
• —Language(s) (NLP): Primarily English
• —License: Apache-2.0
• —Finetuned from model: runwayml/stable-diffusion-v1-5

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 2.0e-05
• —trainbatchsize: 8
• —distributed_type: multi-GPU
• —num_devices: 8
• —traingradientaccumulation_steps: 32
• —totaltrainbatch_size: 2048
• —optimizer: AdamW
• —lr_scheduler: "linear"
• —lrwarmupsteps: 200
• —numtrainingsteps: 500

Usage

To use the model, you must first load the SD1.5 base model and then substitute its unet with our fine-tuned version.

from diffusers import StableDiffusionPipeline, UNet2DConditionModel
import torch

model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)

unet_id = "UCLA-AGI/SPIN-Diffusion-iter3"
unet = UNet2DConditionModel.from_pretrained(unet_id, subfolder="unet", torch_dtype=torch.float16)
pipe.unet = unet

###The rest of your generation code

Evaluation Results on Pick-a-pic test set

Citation

@misc{yuan2024self,
      title={Self-Play Fine-Tuning of Diffusion Models for Text-to-Image Generation}, 
      author={Yuan, Huizhuo and Chen, Zixiang and Ji, Kaixuan and Gu, Quanquan},
      year={2024},
      eprint={2402.10210},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}