This checkpoint is a conversion of the original checkpoint into diffusers format. It can be used in combination with Stable Diffusion, such as runwayml/stable-diffusion-v1-5.

For more details, please also have a look at the 🧨 Diffusers docs.

ControlNet is a neural network structure to control diffusion models by adding extra conditions.

This checkpoint corresponds to the ControlNet conditioned on Soft edges.

Model Details

Developed by: Lvmin Zhang, Maneesh Agrawala
Model type: Diffusion-based text-to-image generation model
Language(s): English
License: The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based.
Resources for more information: GitHub Repository, Paper.
Cite as:

@misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }

Introduction

Controlnet was proposed in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Maneesh Agrawala.

The abstract reads as follows:

We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.

Example

It is recommended to use the checkpoint with Stable Diffusion v1-5 as the checkpoint has been trained on it. Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion.

Note: If you want to process an image to create the auxiliary conditioning, external dependencies are required as shown below:

Install https://github.com/patrickvonplaten/controlnet_aux

$ pip install controlnet_aux==0.3.0

Let's install diffusers and related packages:

$ pip install diffusers transformers accelerate

Run code:

python

import torch
import os
from huggingface_hub import HfApi
from pathlib import Path
from diffusers.utils import load_image
from PIL import Image
import numpy as np
from controlnet_aux import PidiNetDetector, HEDdetector

from diffusers import (
    ControlNetModel,
    StableDiffusionControlNetPipeline,
    UniPCMultistepScheduler,
)

checkpoint = "lllyasviel/control_v11p_sd15_softedge"

image = load_image(
    "https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/input.png"
)

prompt = "royal chamber with fancy bed"

processor = HEDdetector.from_pretrained('lllyasviel/Annotators')
processor = PidiNetDetector.from_pretrained('lllyasviel/Annotators')
control_image = processor(image, safe=True)
control_image.save("./images/control.png")

controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)

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

generator = torch.manual_seed(0)
image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0]

image.save('images/image_out.png')

Other released checkpoints v1-1

The authors released 14 different checkpoints, each trained with Stable Diffusion v1-5 on a different type of conditioning:

Model Name	Control Image Overview	Condition Image
lllyasviel/control_v11p_sd15_canny	Trained with canny edge detection	A monochrome image with white edges on a black background.
lllyasviel/control_v11e_sd15_ip2p	Trained with pixel to pixel instruction	No condition .
lllyasviel/control_v11p_sd15_inpaint	Trained with image inpainting	No condition.
lllyasviel/control_v11p_sd15_mlsd	Trained with multi-level line segment detection	An image with annotated line segments.
lllyasviel/control_v11f1p_sd15_depth	Trained with depth estimation	An image with depth information, usually represented as a grayscale image.
lllyasviel/control_v11p_sd15_normalbae	Trained with surface normal estimation	An image with surface normal information, usually represented as a color-coded image.
lllyasviel/control_v11p_sd15_seg	Trained with image segmentation	An image with segmented regions, usually represented as a color-coded image.
lllyasviel/control_v11p_sd15_lineart	Trained with line art generation	An image with line art, usually black lines on a white background.
lllyasviel/control_v11p_sd15s2_lineart_anime	Trained with anime line art generation	An image with anime-style line art.
lllyasviel/control_v11p_sd15_openpose	Trained with human pose estimation	An image with human poses, usually represented as a set of keypoints or skeletons.
lllyasviel/control_v11p_sd15_scribble	Trained with scribble-based image generation	An image with scribbles, usually random or user-drawn strokes.
lllyasviel/control_v11p_sd15_softedge	Trained with soft edge image generation	An image with soft edges, usually to create a more painterly or artistic effect.
lllyasviel/control_v11e_sd15_shuffle	Trained with image shuffling	An image with shuffled patches or regions.
lllyasviel/control_v11f1e_sd15_tile	Trained with image tiling	A blurry image or part of an image .

Improvements in Soft Edge 1.1:

Soft Edge 1.1 was called HED 1.0 in previous ControlNet.
The training dataset of previous cnet 1.0 has several problems including (1) a small group of greyscale human images are duplicated thousands of times (!!), causing the previous model somewhat likely to generate grayscale human images; (2) some images has low quality, very blurry, or significant JPEG artifacts; (3) a small group of images has wrong paired prompts caused by a mistake in our data processing scripts. The new model fixed all problems of the training dataset and should be more reasonable in many cases.
The Soft Edge 1.1 is significantly (in nealy 100% cases) better than HED 1.0. This is mainly because HED or PIDI estimator tend to hide a corrupted greyscale version of original image inside the soft edge map and the previous model HED 1.0 is over-fitted to restore that hidden corrupted image rather than perform boundary-aware diffusion. The training of Soft Edge 1.1 used 75% "safe" filtering to remove such hidden corrupted greyscale images insider control maps. This makes the Soft Edge 1.1 very robust. In out test, Soft Edge 1.1 is as usable as the depth model and has potential to be more frequently used.

More information

For more information, please also have a look at the Diffusers ControlNet Blog Post and have a look at the official docs.