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  1. README.md +157 -0
  2. config.json +42 -0
  3. control_net_inpaint.py +55 -0
  4. diffusion_pytorch_model.bin +3 -0
  5. diffusion_pytorch_model.fp16.bin +3 -0
  6. diffusion_pytorch_model.fp16.safetensors +3 -0
  7. diffusion_pytorch_model.safetensors +3 -0
README.md ADDED
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+ ---
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+ license: openrail
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+ base_model: runwayml/stable-diffusion-v1-5
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+ tags:
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+ - art
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+ - controlnet
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+ - stable-diffusion
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+ - controlnet-v1-1
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+ - image-to-image
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+ duplicated_from: ControlNet-1-1-preview/control_v11p_sd15_inpaint
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+ ---
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+
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+ # Controlnet - v1.1 - *InPaint Version*
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+
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+ **Controlnet v1.1** was released in [lllyasviel/ControlNet-v1-1](https://huggingface.co/lllyasviel/ControlNet-v1-1) by [Lvmin Zhang](https://huggingface.co/lllyasviel).
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+
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+ This checkpoint is a conversion of [the original checkpoint](https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_inpaint.pth) into `diffusers` format.
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+ It can be used in combination with **Stable Diffusion**, such as [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5).
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+
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+
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+ For more details, please also have a look at the [🧨 Diffusers docs](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/controlnet).
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+
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+
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+ ControlNet is a neural network structure to control diffusion models by adding extra conditions.
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+
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+ ![img](./sd.png)
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+
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+ This checkpoint corresponds to the ControlNet conditioned on **inpaint images**.
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+
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+ ## Model Details
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+ - **Developed by:** Lvmin Zhang, Maneesh Agrawala
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+ - **Model type:** Diffusion-based text-to-image generation model
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+ - **Language(s):** English
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+ - **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.
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+ - **Resources for more information:** [GitHub Repository](https://github.com/lllyasviel/ControlNet), [Paper](https://arxiv.org/abs/2302.05543).
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+ - **Cite as:**
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+
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+ @misc{zhang2023adding,
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+ title={Adding Conditional Control to Text-to-Image Diffusion Models},
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+ author={Lvmin Zhang and Maneesh Agrawala},
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+ year={2023},
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+ eprint={2302.05543},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CV}
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+ }
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+ ## Introduction
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+
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+ Controlnet was proposed in [*Adding Conditional Control to Text-to-Image Diffusion Models*](https://arxiv.org/abs/2302.05543) by
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+ Lvmin Zhang, Maneesh Agrawala.
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+
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+ The abstract reads as follows:
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+
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+ *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions.
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+ 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).
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+ Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices.
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+ Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data.
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+ 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.
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+ This may enrich the methods to control large diffusion models and further facilitate related applications.*
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+
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+ ## Example
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+
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+ It is recommended to use the checkpoint with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5) as the checkpoint
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+ has been trained on it.
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+ Experimentally, the checkpoint can be used with other diffusion models such as dreamboothed stable diffusion.
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+
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+
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+ 1. Let's install `diffusers` and related packages:
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+
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+ ```
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+ $ pip install diffusers transformers accelerate
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+ ```
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+
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+ 2. Run code:
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+
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+ ```python
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+ # !pip install transformers accelerate
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+ from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel
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+ from diffusers.utils import load_image
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+ import numpy as np
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+ import torch
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+
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+ init_image = load_image(
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+ "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"
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+ )
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+ init_image = init_image.resize((512, 512))
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+
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+ generator = torch.Generator(device="cpu").manual_seed(1)
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+
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+ mask_image = load_image(
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+ "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"
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+ )
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+ mask_image = mask_image.resize((512, 512))
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+
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+
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+ def make_inpaint_condition(image, image_mask):
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+ image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
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+ image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
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+
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+ assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
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+ image[image_mask > 0.5] = -1.0 # set as masked pixel
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+ image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
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+ image = torch.from_numpy(image)
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+ return image
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+
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+
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+ control_image = make_inpaint_condition(init_image, mask_image)
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+
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+ controlnet = ControlNetModel.from_pretrained(
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+ "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
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+ )
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+ pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
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+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
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+ )
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+
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+ pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
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+ pipe.enable_model_cpu_offload()
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+
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+ # generate image
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+ image = pipe(
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+ "a handsome man with ray-ban sunglasses",
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+ num_inference_steps=20,
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+ generator=generator,
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+ eta=1.0,
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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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+
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+ ![original](https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png)
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+ ![img](https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_with_mask.png)
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+ ![img](https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.png)
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+
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+ ## Other released checkpoints v1-1
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+ The authors released 14 different checkpoints, each trained with [Stable Diffusion v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)
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+ on a different type of conditioning:
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+
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+ | Model Name | Control Image Overview| Condition Image | Control Image Example | Generated Image Example |
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+ |---|---|---|---|---|
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+ |[lllyasviel/control_v11p_sd15_canny](https://huggingface.co/lllyasviel/control_v11p_sd15_canny)<br/> | *Trained with canny edge detection* | A monochrome image with white edges on a black background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/main/images/image_out.png"/></a>|
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+ |[lllyasviel/control_v11e_sd15_ip2p](https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p)<br/> | *Trained with pixel to pixel instruction* | No condition .|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_ip2p/resolve/main/images/image_out.png"/></a>|
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+ |[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint)<br/> | Trained with image inpainting | No condition.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint/resolve/main/images/output.png"/></a>|
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+ |[lllyasviel/control_v11p_sd15_mlsd](https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd)<br/> | Trained with multi-level line segment detection | An image with annotated line segments.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_mlsd/resolve/main/images/image_out.png"/></a>|
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+ |[lllyasviel/control_v11f1p_sd15_depth](https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth)<br/> | Trained with depth estimation | An image with depth information, usually represented as a grayscale image.|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/main/images/image_out.png"/></a>|
145
+ |[lllyasviel/control_v11p_sd15_normalbae](https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae)<br/> | Trained with surface normal estimation | An image with surface normal information, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_normalbae/resolve/main/images/image_out.png"/></a>|
146
+ |[lllyasviel/control_v11p_sd15_seg](https://huggingface.co/lllyasviel/control_v11p_sd15_seg)<br/> | Trained with image segmentation | An image with segmented regions, usually represented as a color-coded image.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_seg/resolve/main/images/image_out.png"/></a>|
147
+ |[lllyasviel/control_v11p_sd15_lineart](https://huggingface.co/lllyasviel/control_v11p_sd15_lineart)<br/> | Trained with line art generation | An image with line art, usually black lines on a white background.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_lineart/resolve/main/images/image_out.png"/></a>|
148
+ |[lllyasviel/control_v11p_sd15s2_lineart_anime](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with anime line art generation | An image with anime-style line art.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime/resolve/main/images/image_out.png"/></a>|
149
+ |[lllyasviel/control_v11p_sd15_openpose](https://huggingface.co/lllyasviel/control_v11p_sd15s2_lineart_anime)<br/> | Trained with human pose estimation | An image with human poses, usually represented as a set of keypoints or skeletons.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_openpose/resolve/main/images/image_out.png"/></a>|
150
+ |[lllyasviel/control_v11p_sd15_scribble](https://huggingface.co/lllyasviel/control_v11p_sd15_scribble)<br/> | Trained with scribble-based image generation | An image with scribbles, usually random or user-drawn strokes.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_scribble/resolve/main/images/image_out.png"/></a>|
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+ |[lllyasviel/control_v11p_sd15_softedge](https://huggingface.co/lllyasviel/control_v11p_sd15_softedge)<br/> | Trained with soft edge image generation | An image with soft edges, usually to create a more painterly or artistic effect.|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11p_sd15_softedge/resolve/main/images/image_out.png"/></a>|
152
+ |[lllyasviel/control_v11e_sd15_shuffle](https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle)<br/> | Trained with image shuffling | An image with shuffled patches or regions.|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/image_out.png"/></a>|
153
+ |[lllyasviel/control_v11f1e_sd15_tile](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile)<br/> | Trained with image tiling | A blurry image or part of an image .|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"><img width="64" style="margin:0;padding:0;" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/original.png"/></a>|<a href="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"><img width="64" src="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/main/images/output.png"/></a>|
154
+
155
+ ## More information
156
+
157
+ For more information, please also have a look at the [Diffusers ControlNet Blog Post](https://huggingface.co/blog/controlnet) and have a look at the [official docs](https://github.com/lllyasviel/ControlNet-v1-1-nightly).
config.json ADDED
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+ {
2
+ "_class_name": "ControlNetModel",
3
+ "_diffusers_version": "0.16.0.dev0",
4
+ "_name_or_path": "/home/patrick/controlnet_v1_1/control_v11p_sd15_inpaint",
5
+ "act_fn": "silu",
6
+ "attention_head_dim": 8,
7
+ "block_out_channels": [
8
+ 320,
9
+ 640,
10
+ 1280,
11
+ 1280
12
+ ],
13
+ "class_embed_type": null,
14
+ "conditioning_embedding_out_channels": [
15
+ 16,
16
+ 32,
17
+ 96,
18
+ 256
19
+ ],
20
+ "controlnet_conditioning_channel_order": "rgb",
21
+ "cross_attention_dim": 768,
22
+ "down_block_types": [
23
+ "CrossAttnDownBlock2D",
24
+ "CrossAttnDownBlock2D",
25
+ "CrossAttnDownBlock2D",
26
+ "DownBlock2D"
27
+ ],
28
+ "downsample_padding": 1,
29
+ "flip_sin_to_cos": true,
30
+ "freq_shift": 0,
31
+ "in_channels": 4,
32
+ "layers_per_block": 2,
33
+ "mid_block_scale_factor": 1,
34
+ "norm_eps": 1e-05,
35
+ "norm_num_groups": 32,
36
+ "num_class_embeds": null,
37
+ "only_cross_attention": false,
38
+ "projection_class_embeddings_input_dim": null,
39
+ "resnet_time_scale_shift": "default",
40
+ "upcast_attention": false,
41
+ "use_linear_projection": false
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+ }
control_net_inpaint.py ADDED
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+ #!/usr/bin/env python3
2
+ import torch
3
+ import os
4
+ from huggingface_hub import HfApi
5
+ from pathlib import Path
6
+ from diffusers.utils import load_image
7
+ from PIL import Image
8
+ import numpy as np
9
+
10
+ from diffusers import (
11
+ ControlNetModel,
12
+ StableDiffusionControlNetPipeline,
13
+ DDIMScheduler,
14
+ )
15
+ import sys
16
+
17
+ checkpoint = sys.argv[1]
18
+
19
+
20
+ # pre-process image and mask
21
+ image = load_image("https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png").convert('RGB')
22
+ mask_image = load_image("https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png").convert("L")
23
+
24
+ # convert to float32
25
+ image = np.asarray(image, dtype=np.float32)
26
+ mask_image = np.asarray(mask_image, dtype=np.float32)
27
+
28
+ image[mask_image > 127] = -255.0
29
+ image = torch.from_numpy(image)[None].permute(0, 3, 1, 2) / 255.0
30
+
31
+ prompt = "A blue cat sitting on a park bench"
32
+
33
+ controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
34
+ pipe = StableDiffusionControlNetPipeline.from_pretrained(
35
+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
36
+ )
37
+
38
+ pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
39
+ pipe.enable_model_cpu_offload()
40
+
41
+ generator = torch.manual_seed(0)
42
+ out_image = pipe(prompt, num_inference_steps=20, generator=generator, image=image, guidance_scale=9.0).images[0]
43
+
44
+ path = os.path.join(Path.home(), "images", "aa.png")
45
+ out_image.save(path)
46
+
47
+ api = HfApi()
48
+
49
+ api.upload_file(
50
+ path_or_fileobj=path,
51
+ path_in_repo=path.split("/")[-1],
52
+ repo_id="patrickvonplaten/images",
53
+ repo_type="dataset",
54
+ )
55
+ print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png")
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