Updated temporalvideo.py to work with the dif checkpoint.

temporalvideo.py

@@ -23,47 +23,47 @@ def get_image_paths(folder):
 
 y_paths = get_image_paths(y_folder)
 
-def send_request(init_image_path, temp_path,controlnet_input_image_path):
+def send_request(last_image_path, temp_path,current_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(last_image_path, "rb") as f:
+        last_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")
+    with open(current_image_path, "rb") as b:
+        current_image = base64.b64encode(b.read()).decode("utf-8")
 
     data = {
-        "init_images": [init_image],
+        "init_images": [current_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",
+        "denoising_strength": 0.45,
+        "prompt": "pop art, painting, highly detailed,",
         "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,
+                        "input_image": current_image,
                         "module": "hed",
                         "model": "control_hed-fp16 [13fee50b]",
-                        "weight": 1,
-                        "guidance": 0.6,
-                    },
+                        "weight": 1.5,
+                        "guidance": 1,
+                   },
                     {
-                        "input_image": init_image,
-                        "model": "temporalnetv3 [b146ac48]",
+                        "input_image": last_image,
+                        "model": "diff_control_sd15_temporalnet_fp16 [adc6bd97]",
                         "module": "none",
-                        "weight": 1,
-                        "guidance": 0.8,
+                        "weight": 0.7,
+                        "guidance": 1,
                     }
                   
                 ]
             }
         },
-        "seed": 770169050,
+        "seed": 3189343382,
         "subseed": -1,
         "subseed_strength": -1,
         "sampler_index": "Euler a",
@@ -71,8 +71,8 @@ def send_request(init_image_path, temp_path,controlnet_input_image_path):
         "n_iter": 1,
         "steps": 20,
         "cfg_scale": 6,
-        "width": 1152,
-        "height": 640,
+        "width": 512,
+        "height": 512,
         "restore_faces": True,
         "include_init_images": True,
         "override_settings": {},
@@ -103,7 +103,7 @@ for i in range(1, len(y_paths)):
      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)
+     result = send_request(temp_image_path, y_folder, y_paths[i])
      output_images.append(result)
      print(f"Written data for frame {i}:")