Update README.md

4e2e5b3 about 1 year ago

No virus

5.8 kB

	---
	license: openrail
	base_model: runwayml/stable-diffusion-v1-5
	tags:
	- art
	- controlnet
	- stable-diffusion
	---

	# Controlnet - v1.1 - lineart Version

	Controlnet v1.1 is the successor model of [Controlnet v1.0](https://huggingface.co/lllyasviel/ControlNet)
	and was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel).

	This checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_lineart.pth) into `diffusers` format.
	It can be used in combination with Stable Diffusion, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).


	For more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet).


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

	![img](./sd.png)

	This checkpoint corresponds to the ControlNet conditioned on lineart images.

	## 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](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
	- Resources for more information: [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543).
	- 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](https://arxiv.org/abs/2302.05543) 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](https://huggingface.co/runwayml/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

	```sh
	$ pip install controlnet_aux==0.3.0
	```

	2. Let's install `diffusers` and related packages:

	```
	$ pip install diffusers transformers accelerate
	```

	3. 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 LineartDetector

	from diffusers import (
	ControlNetModel,
	StableDiffusionControlNetPipeline,
	UniPCMultistepScheduler,
	)

	checkpoint = "ControlNet-1-1-preview/control_v11p_sd15_lineart"

	image = load_image(
	"https://huggingface.co/ControlNet-1-1-preview/control_v11p_sd15_lineart/resolve/main/images/input.png"
	)
	image = image.resize((512, 512))

	prompt = "michael jackson concert"
	processor = LineartDetector.from_pretrained("lllyasviel/Annotators")

	control_image = processor(image)
	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')

	```

	![bird](./images/input.png)

	![bird_canny](./images/control.png)

	![bird_canny_out](./images/image_out.png)

	## Other released checkpoints v1-1

	The authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
	on a different type of conditioning:

	\| Model Name \| Control Image Overview\| Control Image Example \| Generated Image Example \|
	\|---\|---\|---\|---\|
	TODO

	### Training

	TODO

	### Blog post

	For more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet).