Official PyTorch codes for paper Enhancing Diffusion Models with Text-Encoder Reinforcement Learning
- Clone the repo and install required packages with
# git clone this repository
git clone https://github.com/chaofengc/TexForce.git
cd TexForce
# create new anaconda env
conda create -n texforce python=3.8
source activate texforce
# install python dependencies
pip3 install -r requirements.txt
We also applied our method to the recent model sdxl-turbo. The model is trained with ImageReward feedback through direct back-propagation to save training time. Test with the following codes
## Note: sdturboxl requires latest diffusers installed from source with the following command
git clone https://github.com/huggingface/diffusers
cd diffusers
pip install -e .
from diffusers import AutoPipelineForText2Image
import torch
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
pipe = pipe.to("cuda")
pipe.load_lora_weights('chaofengc/sdxl-turbo_texforce')
pt = ['a photo of a cat.']
img = pipe(prompt=pt, num_inference_steps=1, guidance_scale=0.0).images[0]
Here are some example results:
| sdxl-turbo | sdxl-turbo + TexForce |
|---|---|
| A photo of a cat. | |
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| An astronaut riding a horse. | |
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| water bottle. | |
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We applied our method to the recent model sdturbo. The model is trained with Q-Instruct feedback through direct back-propagation to save training time. Test with the following codes
## Note: sdturbo requires latest diffusers>=0.24.0 with AutoPipelineForText2Image class
from diffusers import AutoPipelineForText2Image
from peft import PeftModel
import torch
pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sd-turbo", torch_dtype=torch.float16, variant="fp16")
pipe = pipe.to("cuda")
PeftModel.from_pretrained(pipe.text_encoder, 'chaofengc/sd-turbo_texforce')
pt = ['a photo of a cat.']
img = pipe(prompt=pt, num_inference_steps=1, guidance_scale=0.0).images[0]
Here are some example results:
| sd-turbo | sd-turbo + TexForce |
|---|---|
| A photo of a cat. | |
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| A photo of a dog. | |
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| A photo of a boy, colorful. | |
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Due to code compatibility, you need to install the following diffusers first:
pip uninstall diffusers
pip install diffusers==0.16.0
You may simply load the pretrained lora weights with the following code block to improve performance of original stable diffusion model:
from diffusers import StableDiffusionPipeline
from diffusers import DDIMScheduler
from peft import PeftModel
import torch
def load_model_weights(pipe, weight_path, model_type):
if model_type == 'text+lora':
text_encoder = pipe.text_encoder
PeftModel.from_pretrained(text_encoder, weight_path)
elif model_type == 'unet+lora':
pipe.unet.load_attn_procs(weight_path)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_id = "CompVis/stable-diffusion-v1-4"
pipe = StableDiffusionPipeline.from_pretrained(model_id, dtype=torch.float16)
pipe = pipe.to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
load_model_weights(pipe, './lora_weights/sd14_refl/', 'unet+lora')
load_model_weights(pipe, './lora_weights/sd14_texforce/', 'text+lora')
prompt = ['a painting of a dog.']
img = pipe(prompt).images[0]
Here are some example results:
| SDv1.4 | ReFL | TexForce | ReFL+TexForce |
|---|---|---|---|
| astronaut drifting afloat in space, in the darkness away from anyone else, alone, black background dotted with stars, realistic | |||
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| portrait of a cute cyberpunk cat, realistic, professional | |||
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| a coffee mug made of cardboard | |||
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We rewrite the training codes based on trl with the latest diffusers library.
Note
The latest diffusers support simple loading of lora weights with pipeline.load_lora_weights after training.
You may train the model with the following command:
accelerate launch --num_processes 2 src/train_ddpo.py \
--mixed_precision="fp16" \
--sample_num_steps 50 --train_timestep_fraction 0.5 \
--num_epochs 40 \
--sample_batch_size 4 --sample_num_batches_per_epoch 64 \
--train_batch_size 4 --train_gradient_accumulation_steps 1 \
--prompt="single" --single_prompt_type="hand" --reward_list="handdetreward" \
--per_prompt_stat_tracking=True \
--tracker_project_name="texforce_hand" \
--log_with="tensorboard"
The supported prompts and reward functions are listed below:
- prompts:
hand,face,color,count,comp,location - rewards:
handdetreward,topiq_nr-face,imagereward
accelerate launch --num_processes 2 src/train_ddpo.py \
--mixed_precision="fp16" \
--sample_num_steps 50 --train_timestep_fraction 0.5 \
--num_epochs 50 \
--sample_batch_size 4 --sample_num_batches_per_epoch 128 \
--train_batch_size 4 --train_gradient_accumulation_steps 4 \
--prompt="imagereward" --reward_list="imagereward" \
--per_prompt_stat_tracking=True \
--tracker_project_name="texforce_imgreward" \
--log_with="tensorboard"
The supported prompts and reward functions are:
- prompts:
imagereward,hps - rewards:
imagereward,hpsreward,laion_aes
If you find this code useful for your research, please cite our paper:
@inproceedings{chen2024texforce,
title={Enhancing Diffusion Models with Text-Encoder Reinforcement Learning},
author={Chaofeng Chen and Annan Wang and Haoning Wu and Liang Liao and Wenxiu Sun and Qiong Yan and Weisi Lin},
year={2024},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
}
This work is licensed under NTU S-Lab License 1.0 and a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This project is largely based on trl. The hand detection codes are taken from Unified-Gesture-and-Fingertip-Detection. Many thanks to their great work 🤗!