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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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correctly list and display model names for #1261
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43e273004b
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@ -6,7 +6,7 @@ from PIL import Image
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import torch
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import tqdm
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from modules import processing, shared, images, devices
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from modules import processing, shared, images, devices, sd_models
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from modules.shared import opts
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import modules.gfpgan_model
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from modules.ui import plaintext_to_html
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@ -156,17 +156,8 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
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alpha = 0.5 - math.sin(math.asin(1.0 - 2.0 * alpha) / 3.0)
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return theta0 + ((theta1 - theta0) * alpha)
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if os.path.exists(primary_model_name):
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primary_model_filename = primary_model_name
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primary_model_name = os.path.splitext(os.path.basename(primary_model_name))[0]
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else:
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primary_model_filename = 'models/' + primary_model_name + '.ckpt'
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if os.path.exists(secondary_model_name):
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secondary_model_filename = secondary_model_name
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secondary_model_name = os.path.splitext(os.path.basename(secondary_model_name))[0]
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else:
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secondary_model_filename = 'models/' + secondary_model_name + '.ckpt'
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primary_model_filename = sd_models.checkpoints_list[primary_model_name].filename
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secondary_model_filename = sd_models.checkpoints_list[secondary_model_name].filename
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print(f"Loading {primary_model_filename}...")
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primary_model = torch.load(primary_model_filename, map_location='cpu')
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@ -180,7 +171,7 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
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theta_funcs = {
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"Weighted Sum": weighted_sum,
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"Sigmoid": sigmoid,
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"Inverse Sigmoid": inv_sigmoid
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"Inverse Sigmoid": inv_sigmoid,
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}
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theta_func = theta_funcs[interp_method]
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@ -193,9 +184,11 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
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if 'model' in key and key not in theta_0:
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theta_0[key] = theta_1[key]
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output_modelname = 'models/' + primary_model_name + '_' + str(round(interp_amount,2)) + '-' + secondary_model_name + '_' + str(round((float(1.0) - interp_amount),2)) + '-' + interp_method.replace(" ", "_") + '-merged.ckpt'
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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'
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output_modelname = os.path.join(shared.cmd_opts.ckpt_dir, filename)
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print(f"Saving to {output_modelname}...")
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torch.save(primary_model, output_modelname)
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print(f"Checkpoint saved.")
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return "Checkpoint saved to " + output_modelname
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return "Checkpoint saved to " + output_modelname
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@ -872,8 +872,8 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
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gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
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with gr.Row():
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ckpt_name_list = sorted([x.model_name for x in modules.sd_models.checkpoints_list.values()])
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primary_model_name = gr.Dropdown(ckpt_name_list, elem_id="modelmerger_primary_model_name", label="Primary Model Name")
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ckpt_name_list = sorted([x.title for x in modules.sd_models.checkpoints_list.values()])
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primary_model_name = gr.Dropdown(ckpt_name_list, elem_id="modelmerger_primary_model_name", label="Primary Model Name")
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secondary_model_name = gr.Dropdown(ckpt_name_list, elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
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interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
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interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
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