update lists of models after merging them in checkpoints tab

support saving as half
This commit is contained in:
AUTOMATIC 2022-09-29 00:59:44 +03:00
parent 0dc904aa3d
commit 7acfaca05a
4 changed files with 52 additions and 34 deletions

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@ -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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@ -23,6 +23,11 @@ except Exception:
pass
def checkpoint_tiles():
print(sorted([x.title for x in checkpoints_list.values()]))
return sorted([x.title for x in checkpoints_list.values()])
def list_models():
checkpoints_list.clear()
@ -39,13 +44,14 @@ def list_models():
if name.startswith("\\") or name.startswith("/"):
name = name[1:]
return f'{name} [{h}]'
shortname = os.path.splitext(name.replace("/", "_").replace("\\", "_"))[0]
return f'{name} [{h}]', shortname
cmd_ckpt = shared.cmd_opts.ckpt
if os.path.exists(cmd_ckpt):
h = model_hash(cmd_ckpt)
title = modeltitle(cmd_ckpt, h)
model_name = title.rsplit(".",1)[0] # remove extension if present
title, model_name = modeltitle(cmd_ckpt, h)
checkpoints_list[title] = CheckpointInfo(cmd_ckpt, title, h, model_name)
elif cmd_ckpt is not None and cmd_ckpt != shared.default_sd_model_file:
print(f"Checkpoint in --ckpt argument not found: {cmd_ckpt}", file=sys.stderr)
@ -53,8 +59,7 @@ def list_models():
if os.path.exists(model_dir):
for filename in glob.glob(model_dir + '/**/*.ckpt', recursive=True):
h = model_hash(filename)
title = modeltitle(filename, h)
model_name = title.rsplit(".",1)[0] # remove extension if present
title, model_name = modeltitle(filename, h)
checkpoints_list[title] = CheckpointInfo(filename, title, h, model_name)

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@ -190,7 +190,7 @@ options_templates.update(options_section(('system', "System"), {
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": [x.title for x in modules.sd_models.checkpoints_list.values()]}),
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Radio, lambda: {"choices": modules.sd_models.checkpoint_tiles()}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),

View File

@ -872,29 +872,16 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>/models</b> directory.</p>")
with gr.Row():
ckpt_name_list = sorted([x.title for x in modules.sd_models.checkpoints_list.values()])
primary_model_name = gr.Dropdown(ckpt_name_list, elem_id="modelmerger_primary_model_name", label="Primary Model Name")
secondary_model_name = gr.Dropdown(ckpt_name_list, elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary Model Name")
secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary Model Name")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Interpolation Amount', value=0.3)
interp_method = gr.Radio(choices=["Weighted Sum", "Sigmoid", "Inverse Sigmoid"], value="Weighted Sum", label="Interpolation Method")
submit = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
save_as_half = gr.Checkbox(value=False, label="Safe as float16")
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
with gr.Column(variant='panel'):
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
submit.click(
fn=run_modelmerger,
inputs=[
primary_model_name,
secondary_model_name,
interp_method,
interp_amount
],
outputs=[
submit_result,
]
)
def create_setting_component(key):
def fun():
return opts.data[key] if key in opts.data else opts.data_labels[key].default
@ -918,6 +905,7 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
return comp(label=info.label, value=fun, **(args or {}))
components = []
component_dict = {}
def run_settings(*args):
changed = 0
@ -973,7 +961,9 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
components.append(create_setting_component(k))
component = create_setting_component(k)
component_dict[k] = component
components.append(component)
items_displayed += 1
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
@ -1024,6 +1014,22 @@ def create_ui(txt2img, img2img, run_extras, run_pnginfo, run_modelmerger):
outputs=[result, text_settings],
)
modelmerger_merge.click(
fn=run_modelmerger,
inputs=[
primary_model_name,
secondary_model_name,
interp_method,
interp_amount,
save_as_half,
],
outputs=[
submit_result,
primary_model_name,
secondary_model_name,
component_dict['sd_model_checkpoint'],
]
)
paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]