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lllyasviel/ control_v11p_sd15_scribble
Controlnet - v1.1 - Scribble Version Controlnet v1.1 is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. 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 Scribble 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 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: 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_scribble" image = load_image( "https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/input.png" ) prompt = "royal chamber with fancy bed" processor = HEDdetector.from_pretrained('lllyasviel/Annotators') control_image = processor(image, scribble=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 Control Image Example Generated Image Example 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 Scribble 1.1: 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. We find out that users sometimes like to draw very thick scribbles. Because of that, we used more aggressive random morphological transforms to synthesize scribbles. This model should work well even when the scribbles are relatively thick (the maximum width of training data is 24-pixel-width scribble in a 512 canvas, but it seems to work well even for a bit wider scribbles; the minimum width is 1 pixel). Resumed from Scribble 1.0, continued with 200 GPU hours of A100 80G. More information For more information, please also have a look at the Diffusers ControlNet Blog Post and have a look at the official docs.
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