T2I-Adapter-SDXL - Lineart

T2I Adapter is a network providing additional conditioning to stable diffusion. Each t2i checkpoint takes a different type of conditioning as input and is used with a specific base stable diffusion checkpoint.

This checkpoint provides conditioning on lineart for the StableDiffusionXL checkpoint. This was a collaboration between Tencent ARC and Hugging Face.

Model Details

• Developed by: T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models

• Model type: Diffusion-based text-to-image generation model

• Language(s): English

• License: Apache 2.0

• Resources for more information: GitHub Repository, Paper.

• Model complexity:

  	SD-V1.4/1.5	SD-XL	T2I-Adapter	T2I-Adapter-SDXL
  Parameters	860M	2.6B	77 M	77/79 M
  Cite as:
  @misc{
  title={T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models},
  author={Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, Xiaohu Qie},
  year={2023},
  eprint={2302.08453},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
  }
  Checkpoints

  Model Name	Control Image Overview	Control Image Example	Generated Image Example
  TencentARC/t2i-adapter-canny-sdxl-1.0 Trained with canny edge detection	A monochrome image with white edges on a black background.
  TencentARC/t2i-adapter-sketch-sdxl-1.0 Trained with PidiNet edge detection	A hand-drawn monochrome image with white outlines on a black background.
  TencentARC/t2i-adapter-lineart-sdxl-1.0 Trained with lineart edge detection	A hand-drawn monochrome image with white outlines on a black background.
  TencentARC/t2i-adapter-depth-midas-sdxl-1.0 Trained with Midas depth estimation	A grayscale image with black representing deep areas and white representing shallow areas.
  TencentARC/t2i-adapter-depth-zoe-sdxl-1.0 Trained with Zoe depth estimation	A grayscale image with black representing deep areas and white representing shallow areas.
  TencentARC/t2i-adapter-openpose-sdxl-1.0 Trained with OpenPose bone image	A OpenPose bone image.

  Example

  To get started, first install the required dependencies:

  pip install -U git+https://github.com/huggingface/diffusers.git
  pip install -U controlnet_aux==0.0.7 # for conditioning models and detectors  
  pip install transformers accelerate safetensors
  

  1. Images are first downloaded into the appropriate control image format.
  2. The control image and prompt are passed to the StableDiffusionXLAdapterPipeline.

  Let's have a look at a simple example using the Canny 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
  )
  

  

  • 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')
  

  

  Training

  Our training script was built on top of the official training script that we provide here.

  The model is trained on 3M high-resolution image-text pairs from LAION-Aesthetics V2 with

  • Training steps: 20000
  • Batch size: Data parallel with a single gpu batch size of 16 for a total batch size of 256.
  • Learning rate: Constant learning rate of 1e-5.
  • Mixed precision: fp16