patrickvonplaten
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README.md
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@@ -79,33 +79,40 @@ $ pip install diffusers transformers accelerate
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3. Run code:
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```python
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#!/usr/bin/env python3
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import torch
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from diffusers.utils import load_image
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import cv2
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from diffusers import (
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ControlNetModel,
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StableDiffusionControlNetPipeline,
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UniPCMultistepScheduler,
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)
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import sys
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image = load_image(
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"https://huggingface.co/
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)
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low_threshold = 100
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high_threshold = 200
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image = cv2.Canny(image, low_threshold, high_threshold)
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image = image[:, :, None]
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image = np.concatenate([image, image, image], axis=2)
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controlnet = ControlNetModel.from_pretrained(
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pipe = StableDiffusionControlNetPipeline.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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@@ -114,16 +121,16 @@ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.enable_model_cpu_offload()
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generator = torch.manual_seed(33)
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image = pipe("a blue paradise bird in the jungle", num_inference_steps=20, generator=generator, image=
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image.save('images/
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```
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![bird](./images/
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![bird_canny](./images/
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![bird_canny_out](./images/
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## Other released checkpoints v1-1
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3. Run code:
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```python
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import torch
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import os
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from huggingface_hub import HfApi
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from pathlib import Path
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from diffusers.utils import load_image
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import numpy as np
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import cv2
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from PIL import Image
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from diffusers import (
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ControlNetModel,
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StableDiffusionControlNetPipeline,
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UniPCMultistepScheduler,
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)
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checkpoint = "ControlNet-1-1-preview/control_v11p_sd15_canny"
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image = load_image(
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"https://huggingface.co/ControlNet-1-1-preview/control_v11p_sd15_canny/resolve/main/images/input.png"
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)
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image = np.array(image)
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low_threshold = 100
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high_threshold = 200
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image = cv2.Canny(image, low_threshold, high_threshold)
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image = image[:, :, None]
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image = np.concatenate([image, image, image], axis=2)
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control_image = Image.fromarray(image)
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control_image.save("./images/control.png")
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controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
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pipe = StableDiffusionControlNetPipeline.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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pipe.enable_model_cpu_offload()
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generator = torch.manual_seed(33)
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image = pipe("a blue paradise bird in the jungle", num_inference_steps=20, generator=generator, image=control_image).images[0]
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image.save('images/image_out.png')
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```
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![bird](./images/input.png)
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![bird_canny](./images/control.png)
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![bird_canny_out](./images/image_out.png)
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## Other released checkpoints v1-1
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