TencentARC / t2iadapter_depth_sd14v1

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Total runs: 57
24-hour runs: 0
7-day runs: 42
30-day runs: 35
Model's Last Updated: July 31 2023
image-to-image

Introduction of t2iadapter_depth_sd14v1

Model Details of t2iadapter_depth_sd14v1

T2I Adapter - Depth

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 depth 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 controlnet_aux
  1. Run code:
from controlnet_aux import MidasDetector
from PIL import Image
from diffusers import T2IAdapter, StableDiffusionAdapterPipeline
import torch

midas = MidasDetector.from_pretrained("lllyasviel/Annotators")

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

image = midas(image)

image.save('./images/depth.png')

adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_depth_sd14v1", 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(1)

openpose_out = pipe(prompt="storm trooper giving a speech", image=image, generator=generator).images[0]

openpose_out.save('./images/depth_output.png')

depth_input depth depth_output

Runs of TencentARC t2iadapter_depth_sd14v1 on huggingface.co

57
Total runs
0
24-hour runs
2
3-day runs
42
7-day runs
35
30-day runs

More Information About t2iadapter_depth_sd14v1 huggingface.co Model

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t2iadapter_depth_sd14v1 huggingface.co

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t2iadapter_depth_sd14v1 install

t2iadapter_depth_sd14v1 is an open source model from GitHub that offers a free installation service, and any user can find t2iadapter_depth_sd14v1 on GitHub to install. At the same time, huggingface.co provides the effect of t2iadapter_depth_sd14v1 install, users can directly use t2iadapter_depth_sd14v1 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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