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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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## Scribble |
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Install the additional controlnet models package. |
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```sh |
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$ pip install git+https://github.com/patrickvonplaten/controlnet_aux.git |
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``` |
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```py |
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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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from controlnet_aux import HEDdetector |
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hed = HEDdetector.from_pretrained('lllyasviel/ControlNet') |
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image = Image.open('images/bag.png') |
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image = hed(image, scribble=True) |
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controlnet = ControlNetModel.from_pretrained( |
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"fusing/stable-diffusion-v1-5-controlnet-scribble", 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("bag", image, num_inference_steps=20).images[0] |
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image.save('images/bag_scribble_out.png') |
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``` |
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### Training |
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The scribble model was trained on 500k scribble-image, caption pairs. The scribble images were generated with HED boundary detection and a set of data augmentations — thresholds, masking, morphological transformations, and non-maximum suppression. The model was trained for 150 GPU-hours with Nvidia A100 80G using the canny model as a base model. |
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