Upload layer_diff_dataset/test_inp.py with huggingface_hub

layer_diff_dataset/test_inp.py

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+from diffusers import AutoPipelineForInpainting
+from diffusers.utils import load_image
+import torch
+import os
+from tqdm import tqdm
+import cv2
+import PIL.Image
+import json
+
+pipe = AutoPipelineForInpainting.from_pretrained("/mnt/workspace/workgroup/sihui.jsh/alpha_work/diffusers/stable-diffusion-xl-1.0-inpainting_", torch_dtype=torch.float16, variant="fp16").to("cuda:1")
+generator = torch.Generator(device="cuda:1").manual_seed(0)
+# 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((1024, 1024))
+# mask_image = load_image(mask_url).resize((1024, 1024))
+
+folder_path_0 = '/mnt/workspace/workgroup/sihui.jsh/layer_diff_dataset/try/im'
+folder_path = '/mnt/workspace/workgroup/sihui.jsh/layer_diff_dataset/try/gt_resized'
+folder_path_ = '/mnt/workspace/workgroup/sihui.jsh/layer_diff_dataset/try/gt_inp_1'
+os.makedirs(folder_path_,exist_ok=True)
+file_list = os.listdir(folder_path)
+file_list = [i for i in file_list if i.endswith('.png')]
+file_list.sort()
+
+pbar = tqdm(enumerate(file_list),total=len(file_list))
+with open('/mnt/workspace/workgroup/sihui.jsh/layer_diff_dataset/try/im_rgba_base.json', 'r') as file:
+    data = json.load(file)
+for i, image_name in pbar:
+    # if i==1:
+    #     break
+    # gt_name = data[i]["images"].split('/')[-1]
+    # print(image_name,gt_name)
+    # prompt = data[i]["prompt_fg"]
+    prompt = 'hazy background with nothing on'
+    # print(prompt)
+    mask_image = load_image(os.path.join(folder_path,image_name)).resize((512, 512))
+    image = load_image(os.path.join(folder_path_0,image_name.split('.png')[0]+'.jpg')).resize((512, 512))
+    image = pipe(
+        prompt=prompt,
+        image=image,
+        mask_image=mask_image,
+        guidance_scale=8.0,
+        num_inference_steps=20,  # steps between 15 and 30 work well for us
+        strength=1.0,  # make sure to use `strength` below 1.0
+        generator=generator,
+    ).images[0]
+    image.save(os.path.join(folder_path_,image_name))
+    # cv2.imwrite(os.path.join(folder_path_,image_name),image)
+
+# mask_image = cv2.imread('/mnt/workspace/workgroup/sihui.jsh/layer_diff_dataset/try/gt_reverse/1#Accessories#1#Bag#2339506821_83cf9f1d22_o.png')
+
+
+
+
+