diff options
Diffstat (limited to 'modules/extras.py')
| -rw-r--r-- | modules/extras.py | 46 |
1 files changed, 25 insertions, 21 deletions
diff --git a/modules/extras.py b/modules/extras.py index b24d7de3..f2f5a7b0 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -159,48 +159,52 @@ def run_pnginfo(image): return '', geninfo, info
-def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half, custom_name):
- # Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
- def weighted_sum(theta0, theta1, alpha):
+def run_modelmerger(primary_model_name, secondary_model_name, teritary_model_name, interp_method, multiplier, save_as_half, custom_name):
+ def weighted_sum(theta0, theta1, theta2, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
- # Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
- def sigmoid(theta0, theta1, alpha):
- alpha = alpha * alpha * (3 - (2 * alpha))
- return theta0 + ((theta1 - theta0) * alpha)
-
- # Inverse Smoothstep (https://en.wikipedia.org/wiki/Smoothstep)
- def inv_sigmoid(theta0, theta1, alpha):
- import math
- alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
- return theta0 + ((theta1 - theta0) * alpha)
+ def add_difference(theta0, theta1, theta2, alpha):
+ return theta0 + (theta1 - theta2) * alpha
primary_model_info = sd_models.checkpoints_list[primary_model_name]
secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
+ teritary_model_info = sd_models.checkpoints_list.get(teritary_model_name, None)
print(f"Loading {primary_model_info.filename}...")
primary_model = torch.load(primary_model_info.filename, map_location='cpu')
+ theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
print(f"Loading {secondary_model_info.filename}...")
secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
-
- theta_0 = sd_models.get_state_dict_from_checkpoint(primary_model)
theta_1 = sd_models.get_state_dict_from_checkpoint(secondary_model)
+ if teritary_model_info is not None:
+ print(f"Loading {teritary_model_info.filename}...")
+ teritary_model = torch.load(teritary_model_info.filename, map_location='cpu')
+ theta_2 = sd_models.get_state_dict_from_checkpoint(teritary_model)
+ else:
+ theta_2 = None
+
theta_funcs = {
- "Weighted Sum": weighted_sum,
- "Sigmoid": sigmoid,
- "Inverse Sigmoid": inv_sigmoid,
+ "Weighted sum": weighted_sum,
+ "Add difference": add_difference,
}
theta_func = theta_funcs[interp_method]
print(f"Merging...")
+
for key in tqdm.tqdm(theta_0.keys()):
if 'model' in key and key in theta_1:
- theta_0[key] = theta_func(theta_0[key], theta_1[key], (float(1.0) - interp_amount)) # Need to reverse the interp_amount to match the desired mix ration in the merged checkpoint
+ t2 = (theta_2 or {}).get(key)
+ if t2 is None:
+ t2 = torch.zeros_like(theta_0[key])
+
+ theta_0[key] = theta_func(theta_0[key], theta_1[key], t2, multiplier)
+
if save_as_half:
theta_0[key] = theta_0[key].half()
+ # I believe this part should be discarded, but I'll leave it for now until I am sure
for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
@@ -209,7 +213,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
- filename = primary_model_info.model_name + '_' + str(round(interp_amount, 2)) + '-' + secondary_model_info.model_name + '_' + str(round((float(1.0) - interp_amount), 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
+ filename = primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
filename = filename if custom_name == '' else (custom_name + '.ckpt')
output_modelname = os.path.join(ckpt_dir, filename)
@@ -219,4 +223,4 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int sd_models.list_models()
print(f"Checkpoint saved.")
- return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]
+ return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
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