diff options
Diffstat (limited to 'modules/extras.py')
| -rw-r--r-- | modules/extras.py | 27 |
1 files changed, 17 insertions, 10 deletions
diff --git a/modules/extras.py b/modules/extras.py index dcc0148c..9a825530 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -13,6 +13,7 @@ from modules.ui import plaintext_to_html import modules.codeformer_model
import piexif
import piexif.helper
+import gradio as gr
cached_images = {}
@@ -140,7 +141,7 @@ def run_pnginfo(image): return '', geninfo, info
-def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount):
+def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half):
# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
@@ -156,14 +157,14 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
return theta0 + ((theta1 - theta0) * alpha)
- primary_model_filename = sd_models.checkpoints_list[primary_model_name].filename
- secondary_model_filename = sd_models.checkpoints_list[secondary_model_name].filename
+ primary_model_info = sd_models.checkpoints_list[primary_model_name]
+ secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
- print(f"Loading {primary_model_filename}...")
- primary_model = torch.load(primary_model_filename, map_location='cpu')
+ print(f"Loading {primary_model_info.filename}...")
+ primary_model = torch.load(primary_model_info.filename, map_location='cpu')
- print(f"Loading {secondary_model_filename}...")
- secondary_model = torch.load(secondary_model_filename, map_location='cpu')
+ print(f"Loading {secondary_model_info.filename}...")
+ secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
theta_0 = primary_model['state_dict']
theta_1 = secondary_model['state_dict']
@@ -178,17 +179,23 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int 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
+ 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
+ if save_as_half:
+ theta_0[key] = theta_0[key].half()
for key in theta_1.keys():
if 'model' in key and key not in theta_0:
theta_0[key] = theta_1[key]
+ if save_as_half:
+ theta_0[key] = theta_0[key].half()
- filename = primary_model_name + '_' + str(round(interp_amount,2)) + '-' + secondary_model_name + '_' + str(round((float(1.0) - interp_amount),2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
+ 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'
output_modelname = os.path.join(shared.cmd_opts.ckpt_dir, filename)
print(f"Saving to {output_modelname}...")
torch.save(primary_model, output_modelname)
+ sd_models.list_models()
+
print(f"Checkpoint saved.")
- return "Checkpoint saved to " + output_modelname
+ return ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]
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