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MODELS/controlnet-canny-sdxl-1.0/README.md

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