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

Downloads last month
2,077
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for TencentARC/t2iadapter_color_sd14v1

Adapter
(2349)
this model

Space using TencentARC/t2iadapter_color_sd14v1 1