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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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## Semantic Segmentation |
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### Diffusers |
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Semantic segmentation relies on transformers. Transformers is a |
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dependency of diffusers for running controlnet, so you should |
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have it installed already. |
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```py |
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from transformers import AutoImageProcessor, UperNetForSemanticSegmentation |
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from PIL import Image |
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import numpy as np |
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from controlnet_utils import ade_palette |
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import torch |
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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler |
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image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small") |
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image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small") |
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image = Image.open("./images/house.png").convert('RGB') |
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pixel_values = image_processor(image, return_tensors="pt").pixel_values |
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with torch.no_grad(): |
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outputs = image_segmentor(pixel_values) |
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seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] |
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color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3 |
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palette = np.array(ade_palette()) |
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for label, color in enumerate(palette): |
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color_seg[seg == label, :] = color |
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color_seg = color_seg.astype(np.uint8) |
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image = Image.fromarray(color_seg) |
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controlnet = ControlNetModel.from_pretrained( |
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"fusing/stable-diffusion-v1-5-controlnet-seg", 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("house", image, num_inference_steps=20).images[0] |
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image.save('./images/house_seg_out.png') |
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``` |
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### Training |
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The semantic segmentation model was trained on 164K segmentation-image, caption pairs from ADE20K. The model was trained for 200 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model. |
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