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# SD3 Controlnet
| control image | weight=0.0 | weight=0.3 | weight=0.5 | weight=0.7 | weight=0.9 |
|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|
|<img src="./tile.jpg" width = "400" /> | <img src="./demo_0.jpg" width = "400" /> | <img src="./demo_3.jpg" width = "400" /> | <img src="./demo_5.jpg" width = "400" /> | <img src="./demo_7.jpg" width = "400" /> | <img src="./demo_9.jpg" width = "400" /> |
**Please ensure that the version of diffusers >= 0.30.0.dev0.**
# Demo
```python
import torch
from diffusers import StableDiffusion3ControlNetPipeline
from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
from diffusers.utils import load_image
# load pipeline
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Tile")
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers",
controlnet=controlnet
)
pipe.to("cuda", torch.float16)
# config
control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Tile/resolve/main/tile.jpg")
prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
n_prompt = 'NSFW, nude, naked, porn, ugly'
image = pipe(
prompt,
negative_prompt=n_prompt,
control_image=control_image,
controlnet_conditioning_scale=0.5,
).images[0]
image.save('image.jpg')
```
## Limitation
Due to the fact that only 1024*1024 pixel resolution was used during the training phase,
the inference performs best at this size, with other sizes yielding suboptimal results.
We will initiate multi-resolution training in the future, and at that time, we will open-source the new weights.
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