metadata

license: apache-2.0
base_model: runwayml/stable-diffusion-v1-5
tags:
  - art
  - t2i-adapter
  - controlnet
  - stable-diffusion
  - image-to-image

T2I Adapter - Sketch

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 sketches for the stable diffusion 1.4 checkpoint.

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.
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/t2iadapter_color_sd14v1 Trained with spatial color palette	A image with 8x8 color palette.
TencentARC/t2iadapter_canny_sd14v1 Trained with canny edge detection	A monochrome image with white edges on a black background.
TencentARC/t2iadapter_sketch_sd14v1 Trained with PidiNet edge detection	A hand-drawn monochrome image with white outlines on a black background.
TencentARC/t2iadapter_depth_sd14v1 Trained with Midas depth estimation	A grayscale image with black representing deep areas and white representing shallow areas.
TencentARC/t2iadapter_openpose_sd14v1 Trained with OpenPose bone image	A OpenPose bone image.
TencentARC/t2iadapter_keypose_sd14v1 Trained with mmpose skeleton image	A mmpose skeleton image.
TencentARC/t2iadapter_seg_sd14v1 Trained with semantic segmentation	An custom segmentation protocol image.
TencentARC/t2iadapter_canny_sd15v2
TencentARC/t2iadapter_depth_sd15v2
TencentARC/t2iadapter_sketch_sd15v2
TencentARC/t2iadapter_zoedepth_sd15v1

Example

Dependencies

pip install diffusers transformers controlnet_aux

Run code:

import torch
from PIL import Image
from controlnet_aux import PidiNetDetector

from diffusers import (
    T2IAdapter,
    StableDiffusionAdapterPipeline
)

image = Image.open('./images/sketch_in.png')

processor = PidiNetDetector.from_pretrained('lllyasviel/Annotators')

sketch_image = processor(image)

sketch_image.save('./images/sketch.png')

adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_zoedepth_sd15v1", torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16"
)

pipe.to('cuda')

generator = torch.Generator().manual_seed(0)

sketch_image_out = pipe(prompt="royal chamber with fancy bed", image=sketch_image, generator=generator).images[0]

sketch_image_out.save('./images/sketch_image_out.png')