update lists of models after merging them in checkpoints tab
support saving as half
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AUTOMATIC
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@ -13,6 +13,7 @@ from modules.ui import plaintext_to_html
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import modules.codeformer_model
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import piexif
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import piexif.helper
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import gradio as gr
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cached_images = {}
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@ -140,7 +141,7 @@ def run_pnginfo(image):
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return '', geninfo, info
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def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount):
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def run_modelmerger(primary_model_name, secondary_model_name, interp_method, interp_amount, save_as_half):
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# Linear interpolation (https://en.wikipedia.org/wiki/Linear_interpolation)
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def weighted_sum(theta0, theta1, alpha):
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return ((1 - alpha) * theta0) + (alpha * theta1)
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@ -156,14 +157,14 @@ 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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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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primary_model_info = sd_models.checkpoints_list[primary_model_name]
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secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
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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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print(f"Loading {primary_model_info.filename}...")
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primary_model = torch.load(primary_model_info.filename, map_location='cpu')
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print(f"Loading {secondary_model_filename}...")
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secondary_model = torch.load(secondary_model_filename, map_location='cpu')
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print(f"Loading {secondary_model_info.filename}...")
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secondary_model = torch.load(secondary_model_info.filename, map_location='cpu')
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theta_0 = primary_model['state_dict']
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theta_1 = secondary_model['state_dict']
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@ -178,17 +179,23 @@ def run_modelmerger(primary_model_name, secondary_model_name, interp_method, int
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print(f"Merging...")
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for key in tqdm.tqdm(theta_0.keys()):
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if 'model' in key and key in theta_1:
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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
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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
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if save_as_half:
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theta_0[key] = theta_0[key].half()
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for key in theta_1.keys():
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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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if save_as_half:
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theta_0[key] = theta_0[key].half()
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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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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'
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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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sd_models.list_models()
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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] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(3)]
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@ -23,6 +23,11 @@ except Exception:
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pass
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def checkpoint_tiles():
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print(sorted([x.title for x in checkpoints_list.values()]))
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return sorted([x.title for x in checkpoints_list.values()])
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def list_models():
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checkpoints_list.clear()
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@ -39,13 +44,14 @@ def list_models():
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if name.startswith("\\") or name.startswith("/"):
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name = name[1:]
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return f'{name} [{h}]'
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shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
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return f'{name} [{h}]', shortname
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cmd_ckpt = shared.cmd_opts.ckpt
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if os.path.exists(cmd_ckpt):
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h = model_hash(cmd_ckpt)
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title = modeltitle(cmd_ckpt, h)
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model_name = title.rsplit(".",1)[0] # remove extension if present
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title, model_name = modeltitle(cmd_ckpt, h)
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checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
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elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
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print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
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@ -53,8 +59,7 @@ def list_models():
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if os.path.exists(model_dir):
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for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
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h = model_hash(filename)
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title = modeltitle(filename, h)
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model_name = title.rsplit(".",1)[0] # remove extension if present
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title, model_name = modeltitle(filename, h)
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checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)
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@ -190,7 +190,7 @@ options_templates.update(options_section(('system', "System"), {
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}))
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options_templates.update(options_section(('sd', "Stable Diffusion"), {
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
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"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
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"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
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"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
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"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
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@ -872,29 +872,16 @@ 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.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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primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
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secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), 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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submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
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save_as_half = gr.Checkbox(value=False, label="Safe as float16")
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modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
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with gr.Column(variant='panel'):
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submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
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submit.click(
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fn=run_modelmerger,
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inputs=[
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primary_model_name,
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secondary_model_name,
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interp_method,
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interp_amount
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],
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outputs=[
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submit_result,
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]
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)
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def create_setting_component(key):
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def fun():
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return opts.data[key] if key in opts.data else opts.data_labels[key].default
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@ -918,6 +905,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
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return comp(label=info.label, value=fun, **(args or {}))
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components = []
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component_dict = {}
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def run_settings(*args):
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changed = 0
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@ -973,7 +961,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
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gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
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components.append(create_setting_component(k))
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component = create_setting_component(k)
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component_dict[k] = component
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components.append(component)
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items_displayed += 1
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request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
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@ -1024,6 +1014,22 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
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outputs=[result, text_settings],
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)
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modelmerger_merge.click(
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fn=run_modelmerger,
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inputs=[
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primary_model_name,
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secondary_model_name,
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interp_method,
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interp_amount,
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save_as_half,
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],
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outputs=[
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submit_result,
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primary_model_name,
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secondary_model_name,
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component_dict['sd_model_checkpoint'],
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]
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)
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paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
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txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
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img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
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