Initial Files

temporalnetv3.ckpt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0a5b9f31f6d7e11aed40da84399c3aeebe7129a9788c0199cfa5d579d9363607
+size 12688107909

temporalvideo.py

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+import os
+import glob
+import requests
+import json
+from pprint import pprint
+import base64
+from io import BytesIO
+
+
+# Replace with the actual path to your image file and folder
+x_path = "./init.png"
+y_folder = "./New folder"
+
+output_folder = "output"
+os.makedirs(output_folder, exist_ok=True)
+
+def get_image_paths(folder):
+    image_extensions = ("*.jpg", "*.jpeg", "*.png", "*.bmp")
+    files = []
+    for ext in image_extensions:
+        files.extend(glob.glob(os.path.join(folder, ext)))
+    return sorted(files)
+
+y_paths = get_image_paths(y_folder)
+
+def send_request(init_image_path, temp_path,controlnet_input_image_path):
+    url = "http://localhost:7860/sdapi/v1/img2img"
+
+    with open(init_image_path, "rb") as f:
+        init_image = base64.b64encode(f.read()).decode("utf-8")
+
+    with open(controlnet_input_image_path, "rb") as b:
+        controlnet_input_image = base64.b64encode(b.read()).decode("utf-8")
+
+    data = {
+        "init_images": [init_image],
+        "inpainting_fill": 0,
+        "inpaint_full_res": True,
+        "inpaint_full_res_padding": 1,
+        "inpainting_mask_invert": 1,
+        "resize_mode": 0,
+        "denoising_strength": 0.5,
+        "prompt": "anime man at computer",
+        "negative_prompt": "(ugly:1.3), (fused fingers), (too many fingers), (bad anatomy:1.5), (watermark:1.5), (words), letters, untracked eyes, asymmetric eyes, floating head, (logo:1.5), (bad hands:1.3), (mangled hands:1.2), (missing hands), (missing arms), backward hands, floating jewelry, unattached jewelry, floating head, doubled head, unattached head, doubled head, head in body, (misshapen body:1.1), (badly fitted headwear:1.2), floating arms, (too many arms:1.5), limbs fused with body, (facial blemish:1.5), badly fitted clothes, imperfect eyes, untracked eyes, crossed eyes, hair growing from clothes, partial faces, hair not attached to head",
+        "alwayson_scripts": {
+            "ControlNet":{
+                "args": [
+                    {
+                        "input_image": controlnet_input_image,
+                        "module": "hed",
+                        "model": "control_hed-fp16 [13fee50b]",
+                        "weight": 1,
+                        "guidance": 0.6,
+                    },
+                    {
+                        "input_image": init_image,
+                        "model": "temporalnetv3 [b146ac48]",
+                        "module": "none",
+                        "weight": 1,
+                        "guidance": 0.8,
+                    }
+                  
+                ]
+            }
+        },
+        "seed": 770169050,
+        "subseed": -1,
+        "subseed_strength": -1,
+        "sampler_index": "Euler a",
+        "batch_size": 1,
+        "n_iter": 1,
+        "steps": 20,
+        "cfg_scale": 6,
+        "width": 1152,
+        "height": 640,
+        "restore_faces": True,
+        "include_init_images": True,
+        "override_settings": {},
+        "override_settings_restore_afterwards": True
+    }
+    response = requests.post(url, json=data)
+    if response.status_code == 200:
+        return response.content
+    else:
+        try:
+            error_data = response.json()
+            print("Error:")
+            print(str(error_data))
+            
+        except json.JSONDecodeError:
+            print(f"Error: Unable to parse JSON error data.")
+        return None
+
+output_images = []
+output_images.append(send_request(x_path,y_folder, y_paths[0]))
+output_paths = []
+
+for i in range(1, len(y_paths)):
+     result_image = output_images[i-1]
+     temp_image_path = os.path.join(output_folder, f"temp_image_{i}.png")
+     data = json.loads(result_image)
+     encoded_image = data["images"][0]
+     with open(temp_image_path, "wb") as f:
+         f.write(base64.b64decode(encoded_image))
+     output_paths.append(temp_image_path)
+     result = send_request(y_paths[i], y_folder, temp_image_path)
+     output_images.append(result)
+     print(f"Written data for frame {i}:")
+
+