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[Community] ControlNet Reference #3508

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May 23, 2023
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[Community] ControlNet Reference #3508

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1adeaa4
add controlnet reference and bugfix
okotaku May 23, 2023
0fac497
Merge branch 'main' into feature/controlnet_reference_control
patrickvonplaten May 23, 2023
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51 changes: 49 additions & 2 deletions examples/community/README.md
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Expand Up @@ -1324,7 +1324,7 @@ image.save('tensorrt_img2img_new_zealand_hills.png')

### Stable Diffusion Reference

This pipeline uses the Reference only Control. Refer to the [sd-webui-controlnet discussion](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236).
This pipeline uses the Reference Control. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).


```py
Expand Down Expand Up @@ -1365,6 +1365,54 @@ Output Image of `reference_attn=True` and `reference_adain=True`

![output_image](https://github.com/huggingface/diffusers/assets/24734142/3c5255d6-867d-4d35-b202-8dfd30cc6827)

### Stable Diffusion ControlNet Reference

This pipeline uses the Reference Control with ControlNet. Refer to the [sd-webui-controlnet discussion: Reference-only Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236)[sd-webui-controlnet discussion: Reference-adain Control](https://github.com/Mikubill/sd-webui-controlnet/discussions/1280).


```py
import cv2
import torch
import numpy as np
from PIL import Image
from diffusers import UniPCMultistepScheduler
from diffusers.utils import load_image

input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")

# get canny image
image = cv2.Canny(np.array(input_image), 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)

controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
).to('cuda:0')

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

result_img = pipe(ref_image=input_image,
prompt="1girl",
image=canny_image,
num_inference_steps=20,
reference_attn=True,
reference_adain=True).images[0]
```

Reference Image

![reference_image](https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png)

Output Image

![output_image](https://github.com/huggingface/diffusers/assets/24734142/7b9a5830-f173-4b92-b0cf-73d0e9c01d60)


### Stable Diffusion on IPEX

This diffusion pipeline aims to accelarate the inference of Stable-Diffusion on Intel Xeon CPUs with BF16/FP32 precision using [IPEX](https://github.com/intel/intel-extension-for-pytorch).
Expand Down Expand Up @@ -1462,4 +1510,3 @@ latency = elapsed_time(pipe4)
print("Latency of StableDiffusionPipeline--fp32",latency)

```

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