Handle conversions back to ldm for saving

testing/test_vae_cycle.py

@@ -0,0 +1,112 @@
+import os
+
+import torch
+from safetensors.torch import load_file
+from collections import OrderedDict
+from toolkit.kohya_model_util import load_vae, convert_diffusers_back_to_ldm, vae_keys_squished_on_diffusers
+import json
+# this was just used to match the vae keys to the diffusers keys
+# you probably wont need this. Unless they change them.... again... again
+# on second thought, you probably will
+
+device = torch.device('cpu')
+dtype = torch.float32
+vae_path = '/mnt/Models/stable-diffusion/models/VAE/vae-ft-mse-840000-ema-pruned/vae-ft-mse-840000-ema-pruned.safetensors'
+
+find_matches = False
+
+state_dict_ldm = load_file(vae_path)
+diffusers_vae = load_vae(vae_path, dtype=torch.float32).to(device)
+
+ldm_keys = state_dict_ldm.keys()
+
+matched_keys = {}
+duplicated_keys = {
+
+}
+
+if find_matches:
+    # find values that match with a very low mse
+    for ldm_key in ldm_keys:
+        ldm_value = state_dict_ldm[ldm_key]
+        for diffusers_key in list(diffusers_vae.state_dict().keys()):
+            diffusers_value = diffusers_vae.state_dict()[diffusers_key]
+            if diffusers_key in vae_keys_squished_on_diffusers:
+                diffusers_value = diffusers_value.clone().unsqueeze(-1).unsqueeze(-1)
+            # if they are not same shape, skip
+            if ldm_value.shape != diffusers_value.shape:
+                continue
+            mse = torch.nn.functional.mse_loss(ldm_value, diffusers_value)
+            if mse < 1e-6:
+                if ldm_key in list(matched_keys.keys()):
+                    print(f'{ldm_key} already matched to {matched_keys[ldm_key]}')
+                    if ldm_key in duplicated_keys:
+                        duplicated_keys[ldm_key].append(diffusers_key)
+                    else:
+                        duplicated_keys[ldm_key] = [diffusers_key]
+                    continue
+                matched_keys[ldm_key] = diffusers_key
+                is_matched = True
+                break
+
+    print(f'Found {len(matched_keys)} matches')
+
+dif_to_ldm_state_dict = convert_diffusers_back_to_ldm(diffusers_vae)
+dif_to_ldm_state_dict_keys = list(dif_to_ldm_state_dict.keys())
+keys_in_both = []
+
+keys_not_in_diffusers = []
+for key in ldm_keys:
+    if key not in dif_to_ldm_state_dict_keys:
+        keys_not_in_diffusers.append(key)
+
+keys_not_in_ldm = []
+for key in dif_to_ldm_state_dict_keys:
+    if key not in ldm_keys:
+        keys_not_in_ldm.append(key)
+
+keys_in_both = []
+for key in ldm_keys:
+    if key in dif_to_ldm_state_dict_keys:
+        keys_in_both.append(key)
+
+# sort them
+keys_not_in_diffusers.sort()
+keys_not_in_ldm.sort()
+keys_in_both.sort()
+
+# print(f'Keys in LDM but not in Diffusers: {len(keys_not_in_diffusers)}{keys_not_in_diffusers}')
+# print(f'Keys in Diffusers but not in LDM: {len(keys_not_in_ldm)}{keys_not_in_ldm}')
+# print(f'Keys in both: {len(keys_in_both)}{keys_in_both}')
+
+json_data = {
+    "both": keys_in_both,
+    "ldm": keys_not_in_diffusers,
+    "diffusers": keys_not_in_ldm
+}
+json_data = json.dumps(json_data, indent=4)
+
+remaining_diffusers_values = OrderedDict()
+for key in keys_not_in_ldm:
+    remaining_diffusers_values[key] = dif_to_ldm_state_dict[key]
+
+# print(remaining_diffusers_values.keys())
+
+remaining_ldm_values = OrderedDict()
+for key in keys_not_in_diffusers:
+    remaining_ldm_values[key] = state_dict_ldm[key]
+
+# print(json_data)
+
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+json_save_path = os.path.join(project_root, 'config', 'keys.json')
+json_matched_save_path = os.path.join(project_root, 'config', 'matched.json')
+json_duped_save_path = os.path.join(project_root, 'config', 'duped.json')
+
+with open(json_save_path, 'w') as f:
+    f.write(json_data)
+if find_matches:
+    with open(json_matched_save_path, 'w') as f:
+        f.write(json.dumps(matched_keys, indent=4))
+    with open(json_duped_save_path, 'w') as f:
+        f.write(json.dumps(duplicated_keys, indent=4))