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T2I Adapter - Color

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 color palettes 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

  1. Dependencies
pip install diffusers transformers
  1. Run code:
from PIL import Image
import torch
from diffusers import StableDiffusionAdapterPipeline, T2IAdapter

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

color_palette = image.resize((8, 8))
color_palette = color_palette.resize((512, 512), resample=Image.Resampling.NEAREST)

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

adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_color_sd14v1", torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    adapter=adapter,
    torch_dtype=torch.float16,
)
pipe.to("cuda")

generator = torch.manual_seed(0)

out_image = pipe(
    "At night, glowing cubes in front of the beach",
    image=color_palette,
    generator=generator,
).images[0]

out_image.save('./images/color_out_image.png')

color_ref color_palette color_out_image

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