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--- |
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license: openrail |
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base_model: runwayml/stable-diffusion-v1-5 |
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tags: |
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- art |
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- controlnet |
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- stable-diffusion |
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--- |
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# Controlnet |
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Controlnet is an auxiliary model which augments pre-trained diffusion models with an additional conditioning. |
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Controlnet comes with multiple auxiliary models, each which allows a different type of conditioning |
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Controlnet's auxiliary models are trained with stable diffusion 1.5. Experimentally, the auxiliary models can be used with other diffusion models such as dreamboothed stable diffusion. |
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The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required. |
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Some of the additional conditionings can be extracted from images via additional models. We extracted these |
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additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/controlnet_aux.git). |
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## Canny edge detection |
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### Diffusers |
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Install opencv |
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```sh |
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$ pip install opencv-contrib-python |
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``` |
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```python |
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import cv2 |
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from PIL import Image |
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler |
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import torch |
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import numpy as np |
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image = Image.open('images/bird.png') |
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image = np.array(image) |
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low_threshold = 100 |
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high_threshold = 200 |
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image = cv2.Canny(image, low_threshold, high_threshold) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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image = Image.fromarray(image) |
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controlnet = ControlNetModel.from_pretrained( |
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"fusing/stable-diffusion-v1-5-controlnet-canny", torch_dtype=torch.float16 |
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) |
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pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 |
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) |
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pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) |
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# Remove if you do not have xformers installed |
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# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers |
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# for installation instructions |
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pipe.enable_xformers_memory_efficient_attention() |
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pipe.enable_model_cpu_offload() |
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image = pipe("bird", image, num_inference_steps=20).images[0] |
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image.save('images/bird_canny_out.png') |
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``` |
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### Training |
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The canny edge model was trained on 3M edge-image, caption pairs. The model was trained for 600 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model. |
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