Awesome
<p align="center"> <img src="https://github.com/TencentARC/T2I-Adapter/assets/17445847/452793ae-dcf7-4c1a-b2ee-f465f8f85236" height=120> </p><div align="center">👉 T2I-Adapter for <b><a href="https://github.com/TencentARC/T2I-Adapter/tree/SD">[SD-1.4/1.5]</a>, for <a href="https://github.com/TencentARC/T2I-Adapter">[SDXL]</a></b></div>
<div align="center"> </div>Official implementation of T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models based on Stable Diffusion-XL.
The diffusers team and the T2I-Adapter authors have been collaborating to bring the support of T2I-Adapters for Stable Diffusion XL (SDXL) in diffusers! It achieves impressive results in both performance and efficiency.
🚩 New Features/Updates
-
✅ Sep. 8, 2023. We collaborate with the diffusers team to bring the support of T2I-Adapters for Stable Diffusion XL (SDXL) in diffusers! It achieves impressive results in both performance and efficiency. We release T2I-Adapter-SDXL models for sketch, canny, lineart, openpose, depth-zoe, and depth-mid. We release two online demos: and .
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✅ Aug. 21, 2023. We release T2I-Adapter-SDXL, including sketch, canny, and keypoint. We still use the original recipe (77M parameters, a single inference) to drive StableDiffusion-XL. Due to the limited computing resources, those adapters still need further improvement. We are collaborating with HuggingFace, and a more powerful adapter is in the works.
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✅ Jul. 13, 2023. Stability AI release Stable Doodle, a groundbreaking sketch-to-image tool based on T2I-Adapter and SDXL. It makes drawing easier.
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✅ Mar. 16, 2023. We add CoAdapter (Composable Adapter). The online Huggingface Gadio has been updated . You can also try the local gradio demo.
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✅ Mar. 16, 2023. We have shrunk the git repo with bfg. If you encounter any issues when pulling or pushing, you can try re-cloning the repository. Sorry for the inconvenience.
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✅ Mar. 3, 2023. Add a color adapter (spatial palette), which has only 17M parameters.
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✅ Mar. 3, 2023. Add four new adapters style, color, openpose and canny. See more info in the Adapter Zoo.
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✅ Feb. 23, 2023. Add the depth adapter t2iadapter_depth_sd14v1.pth. See more info in the Adapter Zoo.
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✅ Feb. 15, 2023. Release T2I-Adapter.
🔥🔥🔥 Why T2I-Adapter-SDXL?
The Original Recipe Drives Larger SD.
SD-V1.4/1.5 | SD-XL | T2I-Adapter | T2I-Adapter-SDXL | |
---|---|---|---|---|
Parameters | 860M | 2.6B | 77 M | 77/79 M |
Inherit High-quality Generation from SDXL.
- Lineart-guided
Model from TencentARC/t2i-adapter-lineart-sdxl-1.0
<p align="center"> <img src="https://huggingface.co/Adapter/t2iadapter/resolve/main/t_lineart.PNG" height=420> </p>- Keypoint-guided
Model from openpose_sdxl_1.0
<p align="center"> <img src="https://huggingface.co/Adapter/t2iadapter/resolve/main/t_pose.PNG" height=520> </p>- Sketch-guided
Model from TencentARC/t2i-adapter-sketch-sdxl-1.0
<p align="center"> <img src="https://huggingface.co/Adapter/t2iadapter/resolve/main/t_sketch.PNG" height=520> </p>- Canny-guided Model from TencentARC/t2i-adapter-canny-sdxl-1.0
- Depth-guided
Depth guided models from TencentARC/t2i-adapter-depth-midas-sdxl-1.0 and TencentARC/t2i-adapter-depth-zoe-sdxl-1.0 respectively
<p align="center"> <img src="https://huggingface.co/Adapter/t2iadapter/resolve/main/t_depth.PNG" height=520> </p>🔧 Dependencies and Installation
- Python >= 3.8 (Recommend to use Anaconda or Miniconda)
- PyTorch >= 2.0.1
pip install -r requirements.txt
⏬ Download Models
All models will be automatically downloaded. You can also choose to download manually from this url.
🔥 How to Train
Here we take sketch guidance as an example, but of course, you can also prepare your own dataset following this method.
accelerate launch train_sketch.py --pretrained_model_name_or_path stabilityai/stable-diffusion-xl-base-1.0 --output_dir experiments/adapter_sketch_xl --config configs/train/Adapter-XL-sketch.yaml --mixed_precision="fp16" --resolution=1024 --learning_rate=1e-5 --max_train_steps=60000 --train_batch_size=1 --gradient_accumulation_steps=4 --report_to="wandb" --seed=42 --num_train_epochs 100
We train with FP16
data precision on 4
NVIDIA A100
GPUs.
💻 How to Test
Inference requires at least 15GB
of GPU memory.
Quick start with diffusers
To get started, first install the required dependencies:
pip install git+https://github.com/huggingface/diffusers.git@t2iadapterxl # for now
pip install -U controlnet_aux==0.0.7 # for conditioning models and detectors
pip install transformers accelerate safetensors
- Images are first downloaded into the appropriate control image format.
- The control image and prompt are passed to the
StableDiffusionXLAdapterPipeline
.
Let's have a look at a simple example using the LineArt Adapter.
- Dependency
from diffusers import StableDiffusionXLAdapterPipeline, T2IAdapter, EulerAncestralDiscreteScheduler, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
from controlnet_aux.lineart import LineartDetector
import torch
# load adapter
adapter = T2IAdapter.from_pretrained(
"TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16, varient="fp16"
).to("cuda")
# load euler_a scheduler
model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
euler_a = EulerAncestralDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
vae=AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16",
).to("cuda")
pipe.enable_xformers_memory_efficient_attention()
line_detector = LineartDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
- Condition Image
url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_lin.jpg"
image = load_image(url)
image = line_detector(
image, detect_resolution=384, image_resolution=1024
)
<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"/></a>
- Generation
prompt = "Ice dragon roar, 4k photo"
negative_prompt = "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured"
gen_images = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=image,
num_inference_steps=30,
adapter_conditioning_scale=0.8,
guidance_scale=7.5,
).images[0]
gen_images.save('out_lin.png')
<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/cond_lin.png"><img width="480" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/res_lin.png"/></a>
Online Demo
<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/demo_dragon.png"><img width="520" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/demo_dragon.png"/></a>
Online Doodly Demo
<a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/demo_tree.png"><img width="520" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/demo_tree.png"/></a> <a href="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/demo_doodle_dragon.png"><img width="520" style="margin:0;padding:0;" src="https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/demo_doodle_dragon.png"/></a>
Tutorials on HuggingFace:
- Sketch: https://huggingface.co/TencentARC/t2i-adapter-sketch-sdxl-1.0
- Canny: https://huggingface.co/TencentARC/t2i-adapter-canny-sdxl-1.0
- Lineart: https://huggingface.co/TencentARC/t2i-adapter-lineart-sdxl-1.0
- Openpose: https://huggingface.co/TencentARC/t2i-adapter-openpose-sdxl-1.0
- Depth-mid: https://huggingface.co/TencentARC/t2i-adapter-depth-midas-sdxl-1.0
- Depth-zoe: https://huggingface.co/TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
...
Other Source
Jul. 13, 2023. Stability AI release Stable Doodle, a groundbreaking sketch-to-image tool based on T2I-Adapter and SDXL. It makes drawing easier.
🤗 Acknowledgements
- Thanks to HuggingFace for their support of T2I-Adapter.
- T2I-Adapter is co-hosted by Tencent ARC Lab and Peking University VILLA.
BibTeX
@article{mou2023t2i,
title={T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models},
author={Mou, Chong and Wang, Xintao and Xie, Liangbin and Wu, Yanze and Zhang, Jian and Qi, Zhongang and Shan, Ying and Qie, Xiaohu},
journal={arXiv preprint arXiv:2302.08453},
year={2023}
}