i

.gitignore

@@ -8,7 +8,7 @@ training/
 *.safetensors
 
 gradio_pose2image_private.py
-tool_transfer_control.py
+gradio_canny2image_private.py
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

tool_transfer_control.py

@@ -0,0 +1,59 @@
+path_sd15 = './models/v1-5-pruned.ckpt'
+path_sd15_with_control = './models/control_sd15_openpose.pth'
+path_input = './models/anything-v3-full.safetensors'
+path_output = './models/control_any3_openpose.pth'
+
+
+import os
+
+
+assert os.path.exists(path_sd15), 'Input path_sd15 does not exists!'
+assert os.path.exists(path_sd15_with_control), 'Input path_sd15_with_control does not exists!'
+assert os.path.exists(path_input), 'Input path_input does not exists!'
+assert os.path.exists(os.path.dirname(path_output)), 'Output folder not exists!'
+
+
+import torch
+from share import *
+from cldm.model import load_state_dict
+
+
+sd15_state_dict = load_state_dict(path_sd15)
+sd15_with_control_state_dict = load_state_dict(path_sd15_with_control)
+input_state_dict = load_state_dict(path_input)
+
+
+def get_node_name(name, parent_name):
+    if len(name) <= len(parent_name):
+        return False, ''
+    p = name[:len(parent_name)]
+    if p != parent_name:
+        return False, ''
+    return True, name[len(parent_name):]
+
+
+keys = sd15_with_control_state_dict.keys()
+
+final_state_dict = {}
+for key in keys:
+    is_first_stage, _ = get_node_name(key, 'first_stage_model')
+    is_cond_stage, _ = get_node_name(key, 'cond_stage_model')
+    if is_first_stage or is_cond_stage:
+        final_state_dict[key] = input_state_dict[key]
+        continue
+    p = sd15_with_control_state_dict[key]
+    is_control, node_name = get_node_name(key, 'control_')
+    if is_control:
+        sd15_key_name = 'model.diffusion_' + node_name
+    else:
+        sd15_key_name = key
+    if sd15_key_name in input_state_dict:
+        p_new = p + input_state_dict[sd15_key_name] - sd15_state_dict[sd15_key_name]
+        # print(f'Offset clone from [{sd15_key_name}] to [{key}]')
+    else:
+        p_new = p
+        # print(f'Direct clone to [{key}]')
+    final_state_dict[key] = p_new
+
+torch.save(final_state_dict, path_output)
+print('Transferred model saved at ' + path_output)