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--- |
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license: creativeml-openrail-m |
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base_model: runwayml/stable-diffusion-v1-5 |
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tags: |
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- stable-diffusion |
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- stable-diffusion-diffusers |
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- text-to-image |
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- diffusers |
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- controlnet |
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inference: true |
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--- |
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# SDXL-controlnet: Canny |
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These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with canny conditioning. You can find some example images in the following. |
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prompt: a couple watching a romantic sunset, 4k photo |
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![images_0)](./out_couple.png) |
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prompt: ultrarealistic shot of a furry blue bird |
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![images_1)](./out_bird.png) |
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prompt: a woman, close up, detailed, beautiful, street photography, photorealistic, detailed, Kodak ektar 100, natural, candid shot |
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![images_2)](./out_women.png) |
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prompt: Cinematic, neoclassical table in the living room, cinematic, contour, lighting, highly detailed, winter, golden hour |
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![images_3)](./out_room.png) |
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prompt: a tornado hitting grass field, 1980's film grain. overcast, muted colors. |
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![images_0)](./out_tornado.png) |
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## Usage |
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Make sure to first install the libraries: |
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```bash |
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pip install accelerate transformers opencv-python diffusers |
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``` |
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And then we're ready to go: |
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```python |
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from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL |
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from diffusers.utils import load_image |
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from PIL import Image |
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import torch |
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import numpy as np |
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import cv2 |
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prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting" |
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negative_prompt = 'low quality, bad quality, sketches' |
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image = load_image("https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png") |
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controlnet_conditioning_scale = 0.5 # recommended for good generalization |
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controlnet = ControlNetModel.from_pretrained( |
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"diffusers/controlnet-sdxl-1.0", |
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torch_dtype=torch.float16 |
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) |
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vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16) |
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pipe = StableDiffusionXLControlNetPipeline.from_pretrained( |
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"stabilityai/stable-diffusion-xl-base-1.0", |
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controlnet=controlnet, |
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torch_dtype=torch.float16, |
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) |
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pipe.enable_model_cpu_offload() |
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image = np.array(image) |
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image = cv2.Canny(image, 100, 200) |
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image = image[:, :, None] |
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image = np.concatenate([image, image, image], axis=2) |
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image = Image.fromarray(image) |
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images = pipe( |
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prompt, image=image, controlnet_conditioning_scale=controlnet_conditioning_scale, |
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).images |
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images[0].save(f"hug_lab.png") |
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``` |
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![images_10)](./out_hug_lab_7.png) |
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To more details, check out the official documentation of [`StableDiffusionXLControlNetPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet_sdxl). |
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### Training |
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Our training script was built on top of the official training script that we provide [here](https://github.com/huggingface/diffusers/blob/main/examples/controlnet/README_sdxl.md). |
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#### Training data |
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This checkpoint was first trained for 20,000 steps on laion 6a resized to a max minimum dimension of 384. |
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It was then further trained for 20,000 steps on laion 6a resized to a max minimum dimension of 1024 and |
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then filtered to contain only minimum 1024 images. We found the further high resolution finetuning was |
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necessary for image quality. |
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#### Compute |
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one 8xA100 machine |
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#### Batch size |
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Data parallel with a single gpu batch size of 8 for a total batch size of 64. |
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#### Hyper Parameters |
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Constant learning rate of 1e-4 scaled by batch size for total learning rate of 64e-4 |
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#### Mixed precision |
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fp16 |