Controlnet - Scribble Version

ControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on Scribble images.

It can be used in combination with Stable Diffusion.

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.

Released Checkpoints

The authors released 8 different checkpoints, each trained with Stable Diffusion v1-5 on a different type of conditioning:

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:

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

$ pip install controlnet_aux

2. Let's install diffusers and related packages:

$ pip install diffusers transformers accelerate

3. Run code:

from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
import torch
from controlnet_aux import HEDdetector
from diffusers.utils import load_image

hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')

image = load_image("https://huggingface.co/lllyasviel/sd-controlnet-scribble/resolve/main/images/bag.png")

image = hed(image, scribble=True)

controlnet = ControlNetModel.from_pretrained(
    "lllyasviel/sd-controlnet-scribble", torch_dtype=torch.float16
)

pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
)

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

# Remove if you do not have xformers installed
# see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
# for installation instructions
pipe.enable_xformers_memory_efficient_attention()

pipe.enable_model_cpu_offload()

image = pipe("bag", image, num_inference_steps=20).images[0]

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

Training

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.

Blog post

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