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## 🔥 ControlNet
We incorporate a ControlNet-like(https://github.com/lllyasviel/ControlNet) module enables fine-grained control over text-to-image diffusion models. We implement a ControlNet-Transformer architecture, specifically tailored for Transformers, achieving explicit controllability alongside high-quality image generation.
<p align="center">
<img src="https://raw.githubusercontent.com/NVlabs/Sana/refs/heads/page/asset/content/controlnet/sana_controlnet.jpg" height=480>
</p>
## Inference of `Sana + ControlNet`
### 1). Gradio Interface
```bash
python app/app_sana_controlnet_hed.py \
--config configs/sana_controlnet_config/Sana_1600M_1024px_controlnet_bf16.yaml \
--model_path hf://Efficient-Large-Model/Sana_1600M_1024px_BF16_ControlNet_HED/checkpoints/Sana_1600M_1024px_BF16_ControlNet_HED.pth
```
<p align="center" border-raduis="10px">
<img src="https://nvlabs.github.io/Sana/asset/content/controlnet/controlnet_app.jpg" width="90%" alt="teaser_page2"/>
</p>
### 2). Inference with JSON file
```bash
python tools/controlnet/inference_controlnet.py \
--config configs/sana_controlnet_config/Sana_1600M_1024px_controlnet_bf16.yaml \
--model_path hf://Efficient-Large-Model/Sana_1600M_1024px_BF16_ControlNet_HED/checkpoints/Sana_1600M_1024px_BF16_ControlNet_HED.pth \
--json_file asset/controlnet/samples_controlnet.json
```
### 3). Inference code snap
```python
import torch
from PIL import Image
from app.sana_controlnet_pipeline import SanaControlNetPipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = SanaControlNetPipeline("configs/sana_controlnet_config/Sana_1600M_1024px_controlnet_bf16.yaml")
pipe.from_pretrained("hf://Efficient-Large-Model/Sana_1600M_1024px_BF16_ControlNet_HED/checkpoints/Sana_1600M_1024px_BF16_ControlNet_HED.pth")
ref_image = Image.open("asset/controlnet/ref_images/A transparent sculpture of a duck made out of glass. The sculpture is in front of a painting of a la.jpg")
prompt = "A transparent sculpture of a duck made out of glass. The sculpture is in front of a painting of a landscape."
images = pipe(
prompt=prompt,
ref_image=ref_image,
guidance_scale=4.5,
num_inference_steps=10,
sketch_thickness=2,
generator=torch.Generator(device=device).manual_seed(0),
)
```
## Training of `Sana + ControlNet`
### Coming soon
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