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allow baking in VAE in checkpoint merger tab

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do not save config if it's the default for checkpoint merger tab
change file naming scheme for checkpoint merger tab
allow just saving A without any merging for checkpoint merger tab
some stylistic changes for UI in checkpoint merger tab
This commit is contained in:
AUTOMATIC 2023-01-19 10:39:51 +03:00
parent c7e50425f6
commit 0f5dbfffd0
7 changed files with 102 additions and 59 deletions
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1
javascript/hints.js
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@ -92,6 +92,7 @@ titles = {
"Weighted sum": "Result = A * (1 - M) + B * M",
"Add difference": "Result = A + (B - C) * M",
"No interpolation": "Result = A",
"Initialization text": "If the number of tokens is more than the number of vectors, some may be skipped.\nLeave the textbox empty to start with zeroed out vectors",
"Learning rate": "How fast should training go. Low values will take longer to train, high values may fail to converge (not generate accurate results) and/or may break the embedding (This has happened if you see Loss: nan in the training info textbox. If this happens, you need to manually restore your embedding from an older not-broken backup).\n\nYou can set a single numeric value, or multiple learning rates using the syntax:\n\n rate_1:max_steps_1, rate_2:max_steps_2, ...\n\nEG: 0.005:100, 1e-3:1000, 1e-5\n\nWill train with rate of 0.005 for first 100 steps, then 1e-3 until 1000 steps, then 1e-5 for all remaining steps.",

2
javascript/ui.js
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@ -176,8 +176,6 @@ function modelmerger(){
var id = randomId()
requestProgress(id, gradioApp().getElementById('modelmerger_results_panel'), null, function(){})
gradioApp().getElementById('modelmerger_result').innerHTML = ''
var res = create_submit_args(arguments)
res[0] = id
return res

108
modules/extras.py
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@ -15,7 +15,7 @@ from typing import Callable, List, OrderedDict, Tuple
from functools import partial
from dataclasses import dataclass
from modules import processing, shared, images, devices, sd_models, sd_samplers
from modules import processing, shared, images, devices, sd_models, sd_samplers, sd_vae
from modules.shared import opts
import modules.gfpgan_model
from modules.ui import plaintext_to_html
@ -251,7 +251,8 @@ def run_pnginfo(image):
def create_config(ckpt_result, config_source, a, b, c):
def config(x):
return sd_models.find_checkpoint_config(x) if x else None
res = sd_models.find_checkpoint_config(x) if x else None
return res if res != shared.sd_default_config else None
if config_source == 0:
cfg = config(a) or config(b) or config(c)
@ -274,10 +275,12 @@ def create_config(ckpt_result, config_source, a, b, c):
shutil.copyfile(cfg, checkpoint_filename)
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source):
chckpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]
def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae):
shared.state.begin()
shared.state.job = 'model-merge'
shared.state.job_count = 1
def fail(message):
shared.state.textinfo = message
@ -293,21 +296,42 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
def add_difference(theta0, theta1_2_diff, alpha):
return theta0 + (alpha * theta1_2_diff)
def filename_weighed_sum():
a = primary_model_info.model_name
b = secondary_model_info.model_name
Ma = round(1 - multiplier, 2)
Mb = round(multiplier, 2)
return f"{Ma}({a}) + {Mb}({b})"
def filename_add_differnece():
a = primary_model_info.model_name
b = secondary_model_info.model_name
c = tertiary_model_info.model_name
M = round(multiplier, 2)
return f"{a} + {M}({b} - {c})"
def filename_nothing():
return primary_model_info.model_name
theta_funcs = {
"Weighted sum": (filename_weighed_sum, None, weighted_sum),
"Add difference": (filename_add_differnece, get_difference, add_difference),
"No interpolation": (filename_nothing, None, None),
}
filename_generator, theta_func1, theta_func2 = theta_funcs[interp_method]
shared.state.job_count = (1 if theta_func1 else 0) + (1 if theta_func2 else 0)
if not primary_model_name:
return fail("Failed: Merging requires a primary model.")
primary_model_info = sd_models.checkpoints_list[primary_model_name]
if not secondary_model_name:
if theta_func2 and not secondary_model_name:
return fail("Failed: Merging requires a secondary model.")
secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
theta_funcs = {
"Weighted sum": (None, weighted_sum),
"Add difference": (get_difference, add_difference),
}
theta_func1, theta_func2 = theta_funcs[interp_method]
secondary_model_info = sd_models.checkpoints_list[secondary_model_name] if theta_func2 else None
if theta_func1 and not tertiary_model_name:
return fail(f"Failed: Interpolation method ({interp_method}) requires a tertiary model.")
@ -316,18 +340,24 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
result_is_inpainting_model = False
shared.state.textinfo = f"Loading {secondary_model_info.filename}..."
if theta_func2:
shared.state.textinfo = f"Loading B"
print(f"Loading {secondary_model_info.filename}...")
theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
else:
theta_1 = None
if theta_func1:
shared.state.job_count += 1
shared.state.textinfo = f"Loading C"
print(f"Loading {tertiary_model_info.filename}...")
theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
shared.state.textinfo = 'Merging B and C'
shared.state.sampling_steps = len(theta_1.keys())
for key in tqdm.tqdm(theta_1.keys()):
if key in chckpoint_dict_skip_on_merge:
continue
if 'model' in key:
if key in theta_2:
t2 = theta_2.get(key, torch.zeros_like(theta_1[key]))
@ -345,12 +375,10 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
print("Merging...")
chckpoint_dict_skip_on_merge = ["cond_stage_model.transformer.text_model.embeddings.position_ids"]
shared.state.textinfo = 'Merging A and B'
shared.state.sampling_steps = len(theta_0.keys())
for key in tqdm.tqdm(theta_0.keys()):
if 'model' in key and key in theta_1:
if theta_1 and 'model' in key and key in theta_1:
if key in chckpoint_dict_skip_on_merge:
continue
@ -358,7 +386,6 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
a = theta_0[key]
b = theta_1[key]
shared.state.textinfo = f'Merging layer {key}'
# this enables merging an inpainting model (A) with another one (B);
# where normal model would have 4 channels, for latenst space, inpainting model would
# have another 4 channels for unmasked picture's latent space, plus one channel for mask, for a total of 9
@ -378,34 +405,31 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
shared.state.sampling_step += 1
# 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:
if key in chckpoint_dict_skip_on_merge:
continue
theta_0[key] = theta_1[key]
if save_as_half:
theta_0[key] = theta_0[key].half()
del theta_1
bake_in_vae_filename = sd_vae.vae_dict.get(bake_in_vae, None)
if bake_in_vae_filename is not None:
print(f"Baking in VAE from {bake_in_vae_filename}")
shared.state.textinfo = 'Baking in VAE'
vae_dict = sd_vae.load_vae_dict(bake_in_vae_filename, map_location='cpu')
for key in vae_dict.keys():
theta_0_key = 'first_stage_model.' + key
if theta_0_key in theta_0:
theta_0[theta_0_key] = vae_dict[key].half() if save_as_half else vae_dict[key]
del vae_dict
ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
filename = \
primary_model_info.model_name + '_' + str(round(1-multiplier, 2)) + '-' + \
secondary_model_info.model_name + '_' + str(round(multiplier, 2)) + '-' + \
interp_method.replace(" ", "_") + \
'-merged.' + \
("inpainting." if result_is_inpainting_model else "") + \
checkpoint_format
filename = filename if custom_name == '' else (custom_name + '.' + checkpoint_format)
filename = filename_generator() if custom_name == '' else custom_name
filename += ".inpainting" if result_is_inpainting_model else ""
filename += "." + checkpoint_format
output_modelname = os.path.join(ckpt_dir, filename)
shared.state.nextjob()
shared.state.textinfo = f"Saving to {output_modelname}..."
shared.state.textinfo = "Saving"
print(f"Saving to {output_modelname}...")
_, extension = os.path.splitext(output_modelname)
@ -418,8 +442,8 @@ def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_
create_config(output_modelname, config_source, primary_model_info, secondary_model_info, tertiary_model_info)
print("Checkpoint saved.")
shared.state.textinfo = "Checkpoint saved to " + output_modelname
print(f"Checkpoint saved to {output_modelname}.")
shared.state.textinfo = "Checkpoint saved"
shared.state.end()
return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname]

9
modules/sd_vae.py
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@ -120,6 +120,12 @@ def resolve_vae(checkpoint_file):
return None, None
def load_vae_dict(filename, map_location):
vae_ckpt = sd_models.read_state_dict(filename, map_location=map_location)
vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
return vae_dict_1
def load_vae(model, vae_file=None, vae_source="from unknown source"):
global vae_dict, loaded_vae_file
# save_settings = False
@ -137,8 +143,7 @@ def load_vae(model, vae_file=None, vae_source="from unknown source"):
print(f"Loading VAE weights {vae_source}: {vae_file}")
store_base_vae(model)
vae_ckpt = sd_models.read_state_dict(vae_file, map_location=shared.weight_load_location)
vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
vae_dict_1 = load_vae_dict(vae_file, map_location=shared.weight_load_location)
_load_vae_dict(model, vae_dict_1)
if cache_enabled:

3
modules/shared.py
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@ -20,10 +20,11 @@ from modules.paths import models_path, script_path, sd_path
demo = None
sd_default_config = os.path.join(script_path, "configs/v1-inference.yaml")
sd_model_file = os.path.join(script_path, 'model.ckpt')
default_sd_model_file = sd_model_file
parser = argparse.ArgumentParser()
parser.add_argument("--config", type=str, default=os.path.join(script_path, "configs/v1-inference.yaml"), help="path to config which constructs model",)
parser.add_argument("--config", type=str, default=sd_default_config, help="path to config which constructs model",)
parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",)
parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints")
parser.add_argument("--vae-dir", type=str, default=None, help="Path to directory with VAE files")

15
modules/ui.py
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@ -20,7 +20,7 @@ import numpy as np
from PIL import Image, PngImagePlugin
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
from modules.paths import script_path
@ -1185,7 +1185,7 @@ def create_ui():
with gr.Column(variant='compact'):
gr.HTML(value="<p style='margin-bottom: 2.5em'>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
with FormRow():
with FormRow(elem_id="modelmerger_models"):
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
@ -1197,14 +1197,21 @@ def create_ui():
custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
with FormRow():
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
with FormRow():
with gr.Column():
config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
with gr.Column():
with FormRow():
bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
with gr.Row():
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
@ -1757,6 +1764,7 @@ def create_ui():
return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
return results
modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
modelmerger_merge.click(
fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
_js='modelmerger',
@ -1771,6 +1779,7 @@ def create_ui():
custom_name,
checkpoint_format,
config_source,
bake_in_vae,
],
outputs=[
primary_model_name,

15
style.css
View File

@ -641,6 +641,16 @@ canvas[key="mask"] {
margin: 0.6em 0em 0.55em 0;
}
#modelmerger_results_container{
margin-top: 1em;
overflow: visible;
}
#modelmerger_models{
gap: 0;
}
#quicksettings .gr-button-tool{
margin: 0;
}
@ -737,11 +747,6 @@ footer {
line-height: 2.4em;
}
#modelmerger_results_container{
margin-top: 1em;
overflow: visible;
}
/* The following handles localization for right-to-left (RTL) languages like Arabic.
The rtl media type will only be activated by the logic in javascript/localization.js.
If you change anything above, you need to make sure it is RTL compliant by just running