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‎docs/source/en/using-diffusers/controlnet.md

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@@ -44,7 +44,7 @@ image = np.concatenate([image, image, image], axis=2)
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canny_image = Image.fromarray(image)
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```
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Pass the canny image to the pipeline.
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Pass the canny image to the pipeline. Use the `controlnet_conditioning_scale` parameter to determine how much weight to assign to the control.
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```py
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import torch
@@ -91,17 +91,220 @@ pipeline(
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</hfoption>
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<hfoption id="image-to-image">
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Generate a depth map with a depth estimation pipeline from Transformers.
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```py
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import torch
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import numpy as np
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from PIL import Image
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from transformers import DPTImageProcessor, DPTForDepthEstimation
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from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, AutoencoderKL
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from diffusers.utils import load_image
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depth_estimator = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to("cuda")
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feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
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def get_depth_map(image):
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image = feature_extractor(images=image, return_tensors="pt").pixel_values.to("cuda")
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with torch.no_grad(), torch.autocast("cuda"):
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depth_map = depth_estimator(image).predicted_depth
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depth_map = torch.nn.functional.interpolate(
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depth_map.unsqueeze(1),
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size=(1024, 1024),
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mode="bicubic",
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align_corners=False,
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)
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depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
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depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
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depth_map = (depth_map - depth_min) / (depth_max - depth_min)
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image = torch.cat([depth_map] * 3, dim=1)
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image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
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image = Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))
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return image
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depth_image = get_depth_map(image)
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```
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Pass the depth map to the pipeline. Use the `controlnet_conditioning_scale` parameter to determine how much weight to assign to the control.
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```py
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controlnet = ControlNetModel.from_pretrained(
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"diffusers/controlnet-depth-sdxl-1.0-small",
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torch_dtype=torch.float16,
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)
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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pipeline = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0",
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controlnet=controlnet,
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vae=vae,
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torch_dtype=torch.float16,
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).to("cuda")
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prompt = """
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A photorealistic overhead image of a cat reclining sideways in a flamingo pool floatie holding a margarita.
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The cat is floating leisurely in the pool and completely relaxed and happy.
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"""
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image = load_image(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
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).resize((1024, 1024))
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controlnet_conditioning_scale = 0.5
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pipeline(
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prompt,
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image=image,
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control_image=depth_image,
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controlnet_conditioning_scale=controlnet_conditioning_scale,
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strength=0.99,
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num_inference_steps=100,
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).images[0]
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```
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<div style="display: flex; gap: 10px; justify-content: space-around; align-items: flex-end;">
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png" width="300" alt="Generated image (prompt only)"/>
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<figcaption style="text-align: center;">original image</figcaption>
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</figure>
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_depth_image.png" width="300" alt="Control image (Canny edges)"/>
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<figcaption style="text-align: center;">depth map</figcaption>
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</figure>
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_depth_cat.png" width="300" alt="Generated image (ControlNet + prompt)"/>
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<figcaption style="text-align: center;">generated image</figcaption>
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</figure>
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</div>
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</hfoption>
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<hfoption id="inpainting">
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Generate a mask image and convert it to a tensor to mark the pixels in the original image as masked if the corresponding pixel in the mask image is over a certain threshold.
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```py
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import cv2
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import torch
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import numpy as np
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from PIL import Image
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from diffusers.utils import load_image
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from diffusers import StableDiffusionXLControlNetInpaintPipeline, ControlNetModel
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init_image = load_image(
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"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png"
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)
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init_image = init_image.resize((1024, 1024))
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mask_image = load_image(
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"/content/cat_mask.png"
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)
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mask_image = mask_image.resize((1024, 1024))
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def make_canny_condition(image):
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image = np.array(image)
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image = cv2.Canny(image, 100, 200)
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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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return image
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control_image = make_canny_condition(init_image)
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```
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Pass the mask and control image to the pipeline. Use the `controlnet_conditioning_scale` parameter to determine how much weight to assign to the control.
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```py
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controlnet = ControlNetModel.from_pretrained(
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"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16
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)
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pipeline = StableDiffusionXLControlNetInpaintPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16
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)
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pipeline(
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"a cute and fluffy bunny rabbit",
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num_inference_steps=100,
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strength=0.99,
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controlnet_conditioning_scale=0.5,
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image=init_image,
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mask_image=mask_image,
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control_image=control_image,
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).images[0]
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```
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<div style="display: flex; gap: 10px; justify-content: space-around; align-items: flex-end;">
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/non-enhanced-prompt.png" width="300" alt="Generated image (prompt only)"/>
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<figcaption style="text-align: center;">original image</figcaption>
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</figure>
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat_mask.png" width="300" alt="Control image (Canny edges)"/>
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<figcaption style="text-align: center;">mask image</figcaption>
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</figure>
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_rabbit_inpaint.png" width="300" alt="Generated image (ControlNet + prompt)"/>
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<figcaption style="text-align: center;">generated image</figcaption>
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</figure>
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</div>
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</hfoption>
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</hfoptions>
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## Multi-ControlNet
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You can compose multiple ControlNet conditionings, such as canny image and a depth map, to create a *MultiControlNet*. For the best rersults, you should mask conditionings so they don't overlap and experiment with different `controlnet_conditioning_scale` parameters to adjust how much weight is assigned to each control input.
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The example below composes a canny image and depth map.
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Pass the ControlNets as a list to the pipeline and resize the images to the expected input size.
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```py
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import torch
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from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
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controlnets = [
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ControlNetModel.from_pretrained(
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"diffusers/controlnet-depth-sdxl-1.0-small", torch_dtype=torch.float16
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),
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ControlNetModel.from_pretrained(
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"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16,
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),
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]
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
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pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
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"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnets, vae=vae, torch_dtype=torch.float16
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).to("cuda")
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prompt = """
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a relaxed rabbit sitting on a striped towel next to a pool with a tropical drink nearby,
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bright sunny day, vacation scene, 35mm photograph, film, professional, 4k, highly detailed
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"""
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negative_prompt = "lowres, bad anatomy, worst quality, low quality, deformed, ugly"
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images = [canny_image.resize((1024, 1024)), depth_image.resize((1024, 1024))]
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pipeline(
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prompt,
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negative_prompt=negative_prompt,
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image=images,
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num_inference_steps=100,
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controlnet_conditioning_scale=[0.5, 0.5],
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strength=0.7,
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).images[0]
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```
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<div style="display: flex; gap: 10px; justify-content: space-around; align-items: flex-end;">
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/canny-cat.png" width="300" alt="Generated image (prompt only)"/>
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<figcaption style="text-align: center;">canny image</figcaption>
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</figure>
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multicontrolnet_depth.png" width="300" alt="Control image (Canny edges)"/>
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<figcaption style="text-align: center;">depth map</figcaption>
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</figure>
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<figure>
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl_multi_controlnet.png" width="300" alt="Generated image (ControlNet + prompt)"/>
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<figcaption style="text-align: center;">generated image</figcaption>
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</figure>
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</div>
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## guess_mode
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[Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) generates an image from **only** the control input (canny edge, depth map, pose, etc.) and without guidance from a prompt. It adjusts the scale of the ControlNet's output residuals by a fixed ratio depending on block depth. The earlier `DownBlock` is only scaled by `0.1` and the `MidBlock` is fully scaled by `1.0`.

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