Total runs:
17.8K
Run Growth:
-14.6K
Growth Rate:
-82.36%
Sponsored by
YouArt
TencentARC / t2iadapter_depth_sd15v2
Model's Last Updated: July 31 2023
image-to-image
Introduction of t2iadapter_depth_sd15v2
Model Details of t2iadapter_depth_sd15v2
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 1.5 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:
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_sd15v2", torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", adapter=adapter, safety_checker=None, torch_dtype=torch.float16, variant="fp16"
)
pipe.to('cuda')
generator = torch.Generator().manual_seed(1)
depth_out = pipe(prompt="storm trooper giving a speech", image=image, generator=generator).images[0]
depth_out.save('./images/depth_output.png')
Runs of TencentARC t2iadapter_depth_sd15v2 on huggingface.co
796
Total runs
-3
24-hour runs
-4
3-day runs
13
7-day runs
-61
30-day runs
More Information About t2iadapter_depth_sd15v2 huggingface.co Model
Url of t2iadapter_depth_sd15v2
t2iadapter_depth_sd15v2 huggingface.co Url
Provider of t2iadapter_depth_sd15v2 huggingface.co
Other API from TencentARC
Updated:April 11 2024
Create photos, paintings and avatars for anyone in any style within seconds.
Total runs:
5.3K
Run Growth:
-1.4K
Growth Rate:
-26.94%
Total runs:
2.8K
Run Growth:
525
Growth Rate:
19.06%
Total runs:
2.6K
Run Growth:
575
Growth Rate:
22.21%
Total runs:
2.2K
Run Growth:
786
Growth Rate:
35.26%
Total runs:
2.2K
Run Growth:
-59
Growth Rate:
-2.70%
Total runs:
1.9K
Run Growth:
256
Growth Rate:
13.60%
Total runs:
1.8K
Run Growth:
542
Growth Rate:
30.76%
Total runs:
1.8K
Run Growth:
230
Growth Rate:
13.13%
Total runs:
1.6K
Run Growth:
63
Growth Rate:
3.87%
Total runs:
1.1K
Run Growth:
-51
Growth Rate:
-4.67%
Total runs:
799
Run Growth:
-83
Growth Rate:
-10.39%
Total runs:
657
Run Growth:
-6
Growth Rate:
-0.95%
Total runs:
116
Run Growth:
-189
Growth Rate:
-162.93%
Total runs:
71
Run Growth:
33
Growth Rate:
46.48%
Total runs:
43
Run Growth:
-1
Growth Rate:
-2.33%
Total runs:
39
Run Growth:
20
Growth Rate:
51.28%
Total runs:
38
Run Growth:
-73
Growth Rate:
-192.11%
Total runs:
37
Run Growth:
-14
Growth Rate:
-37.84%
Total runs:
34
Run Growth:
18
Growth Rate:
52.94%
Total runs:
25
Run Growth:
-90
Growth Rate:
-360.00%
Total runs:
24
Run Growth:
9
Growth Rate:
37.50%
Total runs:
24
Run Growth:
14
Growth Rate:
58.33%
Total runs:
16
Run Growth:
-2
Growth Rate:
-12.50%
Total runs:
12
Run Growth:
3
Growth Rate:
27.27%
Total runs:
10
Run Growth:
-5
Growth Rate:
-50.00%
Total runs:
10
Run Growth:
2
Growth Rate:
22.22%
Total runs:
9
Run Growth:
-1
Growth Rate:
-14.29%
Total runs:
9
Run Growth:
0
Growth Rate:
0.00%
Total runs:
8
Run Growth:
-12
Growth Rate:
-150.00%
Total runs:
7
Run Growth:
-7
Growth Rate:
-116.67%
Total runs:
7
Run Growth:
-10
Growth Rate:
-142.86%
Total runs:
7
Run Growth:
1
Growth Rate:
14.29%
Total runs:
6
Run Growth:
-4
Growth Rate:
-80.00%
Total runs:
6
Run Growth:
0
Growth Rate:
0.00%
Total runs:
5
Run Growth:
0
Growth Rate:
0.00%
Total runs:
4
Run Growth:
0
Growth Rate:
0.00%
Total runs:
4
Run Growth:
-3
Growth Rate:
-75.00%
Total runs:
3
Run Growth:
2
Growth Rate:
66.67%
Total runs:
3
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%
Total runs:
0
Run Growth:
-1
Growth Rate:
0.00%
Total runs:
0
Run Growth:
0
Growth Rate:
0.00%