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controlnet_img2img.py

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+#!/usr/bin/env python3
+import torch
+import os
+from huggingface_hub import HfApi
+from pathlib import Path
+from diffusers.utils import load_image
+import cv2
+from PIL import Image
+import numpy as np
+
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionControlNetImg2ImgPipeline,
+    StableDiffusionControlNetInpaintPipeline,
+    DiffusionPipeline,
+    UniPCMultistepScheduler,
+)
+import sys
+
+checkpoint = sys.argv[1]
+
+# image = load_image(
+#     "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
+# )
+
+img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
+mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
+image = load_image(img_url).resize((512, 512))
+mask_image = load_image(mask_url).resize((512, 512))
+
+np_image = np.array(image)
+
+low_threshold = 100
+high_threshold = 200
+
+np_image = cv2.Canny(np_image, low_threshold, high_threshold)
+np_image = np_image[:, :, None]
+np_image = np.concatenate([np_image, np_image, np_image], axis=2)
+canny_image = Image.fromarray(np_image)
+
+controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
+# pipe = DiffusionPipeline.from_pretrained(
+#    "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, custom_pipeline="stable_diffusion_controlnet_inpaint"
+# )
+pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+    "runwayml/stable-diffusion-inpainting",
+    controlnet=controlnet,
+    torch_dtype=torch.float16,
+)
+
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
+pipe.enable_model_cpu_offload()
+
+generator = torch.manual_seed(0)
+text_prompt="a blue dog"
+# out_image = pipe("A blue dog", num_inference_steps=50, generator=generator, image=image, mask_image=mask_image, controlnet_conditioning_image=canny_image).images[0]
+out_image = pipe(
+    text_prompt,
+    num_inference_steps=20,
+    generator=generator,
+    image=image,
+    mask_image=mask_image,
+    control_image=canny_image,
+).images[0]
+
+path = os.path.join(Path.home(), "images", "aa.png")
+out_image.save(path)
+
+api = HfApi()
+
+api.upload_file(
+    path_or_fileobj=path,
+    path_in_repo=path.split("/")[-1],
+    repo_id="patrickvonplaten/images",
+    repo_type="dataset",
+)
+print("https://huggingface.co/datasets/patrickvonplaten/images/blob/main/aa.png")