diff options
Diffstat (limited to 'modules/extras.py')
| -rw-r--r-- | modules/extras.py | 150 |
1 files changed, 109 insertions, 41 deletions
diff --git a/modules/extras.py b/modules/extras.py index a03d558e..d03f976e 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -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 +275,25 @@ def create_config(ckpt_result, config_source, a, b, c): shutil.copyfile(cfg, checkpoint_filename)
-def run_modelmerger(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 to_half(tensor, enable):
+ if enable and tensor.dtype == torch.float:
+ return tensor.half()
+
+ return tensor
+
+
+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'
+ def fail(message):
+ shared.state.textinfo = message
+ shared.state.end()
+ return [*[gr.update() for _ in range(4)], message]
+
def weighted_sum(theta0, theta1, alpha):
return ((1 - alpha) * theta0) + (alpha * theta1)
@@ -287,51 +303,96 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam def add_difference(theta0, theta1_2_diff, alpha):
return theta0 + (alpha * theta1_2_diff)
- primary_model_info = sd_models.checkpoints_list[primary_model_name]
- secondary_model_info = sd_models.checkpoints_list[secondary_model_name]
- tertiary_model_info = sd_models.checkpoints_list.get(tertiary_model_name, None)
- result_is_inpainting_model = False
+ 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": (None, weighted_sum),
- "Add difference": (get_difference, add_difference),
+ "Weighted sum": (filename_weighed_sum, None, weighted_sum),
+ "Add difference": (filename_add_differnece, get_difference, add_difference),
+ "No interpolation": (filename_nothing, None, None),
}
- theta_func1, theta_func2 = theta_funcs[interp_method]
+ 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 theta_func1 and not tertiary_model_info:
- shared.state.textinfo = "Failed: Interpolation method requires a tertiary model."
- shared.state.end()
- return ["Failed: Interpolation method requires a tertiary model."] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
+ if not primary_model_name:
+ return fail("Failed: Merging requires a primary model.")
+
+ primary_model_info = sd_models.checkpoints_list[primary_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] 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.")
- shared.state.textinfo = f"Loading {secondary_model_info.filename}..."
- print(f"Loading {secondary_model_info.filename}...")
- theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
+ tertiary_model_info = sd_models.checkpoints_list[tertiary_model_name] if theta_func1 else None
+
+ result_is_inpainting_model = False
+
+ 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.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]))
theta_1[key] = theta_func1(theta_1[key], t2)
else:
theta_1[key] = torch.zeros_like(theta_1[key])
+
+ shared.state.sampling_step += 1
del theta_2
+ shared.state.nextjob()
+
shared.state.textinfo = f"Loading {primary_model_info.filename}..."
print(f"Loading {primary_model_info.filename}...")
theta_0 = sd_models.read_state_dict(primary_model_info.filename, map_location='cpu')
print("Merging...")
-
+ 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
+
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
@@ -346,32 +407,39 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam else:
theta_0[key] = theta_func2(a, b, multiplier)
- if save_as_half:
- theta_0[key] = theta_0[key].half()
+ theta_0[key] = to_half(theta_0[key], save_as_half)
+
+ 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:
- theta_0[key] = theta_1[key]
- if save_as_half:
- theta_0[key] = theta_0[key].half()
del theta_1
- ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
+ 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')
- 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
+ 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] = to_half(vae_dict[key], save_as_half)
+
+ del vae_dict
+
+ if save_as_half and not theta_func2:
+ for key in theta_0.keys():
+ theta_0[key] = to_half(theta_0[key], save_as_half)
+
+ ckpt_dir = shared.cmd_opts.ckpt_dir or sd_models.model_path
- 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.textinfo = f"Saving to {output_modelname}..."
+ shared.state.nextjob()
+ shared.state.textinfo = "Saving"
print(f"Saving to {output_modelname}...")
_, extension = os.path.splitext(output_modelname)
@@ -384,8 +452,8 @@ def run_modelmerger(primary_model_name, secondary_model_name, tertiary_model_nam 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 ["Checkpoint saved to " + output_modelname] + [gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)]
+ return [*[gr.Dropdown.update(choices=sd_models.checkpoint_tiles()) for _ in range(4)], "Checkpoint saved to " + output_modelname]
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