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-rw-r--r--modules/generation_parameters_copypaste.py5
-rw-r--r--modules/launch_utils.py29
-rw-r--r--modules/sd_models.py3
-rw-r--r--modules/sd_vae.py71
-rw-r--r--modules/shared.py20
-rw-r--r--modules/ui.py2
-rw-r--r--modules/ui_extra_networks_checkpoints_user_metadata.py8
7 files changed, 108 insertions, 30 deletions
diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py
index e71c9601..5758e6f3 100644
--- a/modules/generation_parameters_copypaste.py
+++ b/modules/generation_parameters_copypaste.py
@@ -414,10 +414,15 @@ def connect_paste(button, paste_fields, input_comp, override_settings_component,
return res
if override_settings_component is not None:
+ already_handled_fields = {key: 1 for _, key in paste_fields}
+
def paste_settings(params):
vals = {}
for param_name, setting_name in infotext_to_setting_name_mapping:
+ if param_name in already_handled_fields:
+ continue
+
v = params.get(param_name, None)
if v is None:
continue
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index f77b577a..5be30a18 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -139,6 +139,27 @@ def check_run_python(code: str) -> bool:
return result.returncode == 0
+def git_fix_workspace(dir, name):
+ run(f'"{git}" -C "{dir}" fetch --refetch --no-auto-gc', f"Fetching all contents for {name}", f"Couldn't fetch {name}", live=True)
+ run(f'"{git}" -C "{dir}" gc --aggressive --prune=now', f"Pruning {name}", f"Couldn't prune {name}", live=True)
+ return
+
+
+def run_git(dir, name, command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live, autofix=True):
+ try:
+ return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
+ except RuntimeError:
+ pass
+
+ if not autofix:
+ return None
+
+ print(f"{errdesc}, attempting autofix...")
+ git_fix_workspace(dir, name)
+
+ return run(f'"{git}" -C "{dir}" {command}', desc=desc, errdesc=errdesc, custom_env=custom_env, live=live)
+
+
def git_clone(url, dir, name, commithash=None):
# TODO clone into temporary dir and move if successful
@@ -146,12 +167,14 @@ def git_clone(url, dir, name, commithash=None):
if commithash is None:
return
- current_hash = run(f'"{git}" -C "{dir}" rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
+ current_hash = run_git(dir, name, 'rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}", live=False).strip()
if current_hash == commithash:
return
- run(f'"{git}" -C "{dir}" fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
- run(f'"{git}" -C "{dir}" checkout {commithash}', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+ run_git('fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
+
+ run_git('checkout', f"Checking out commit for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}", live=True)
+
return
run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}", live=True)
diff --git a/modules/sd_models.py b/modules/sd_models.py
index f6051604..53c1df54 100644
--- a/modules/sd_models.py
+++ b/modules/sd_models.py
@@ -356,7 +356,7 @@ def load_model_weights(model, checkpoint_info: CheckpointInfo, state_dict, timer
sd_vae.delete_base_vae()
sd_vae.clear_loaded_vae()
- vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
+ vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename).tuple()
sd_vae.load_vae(model, vae_file, vae_source)
timer.record("load VAE")
@@ -699,6 +699,7 @@ def reload_model_weights(sd_model=None, info=None):
print(f"Weights loaded in {timer.summary()}.")
model_data.set_sd_model(sd_model)
+ sd_unet.apply_unet()
return sd_model
diff --git a/modules/sd_vae.py b/modules/sd_vae.py
index 0bd5e19b..38bcb840 100644
--- a/modules/sd_vae.py
+++ b/modules/sd_vae.py
@@ -1,5 +1,7 @@
import os
import collections
+from dataclasses import dataclass
+
from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks
import glob
from copy import deepcopy
@@ -97,37 +99,74 @@ def find_vae_near_checkpoint(checkpoint_file):
return None
-def resolve_vae(checkpoint_file):
- if shared.cmd_opts.vae_path is not None:
- return shared.cmd_opts.vae_path, 'from commandline argument'
+@dataclass
+class VaeResolution:
+ vae: str = None
+ source: str = None
+ resolved: bool = True
+
+ def tuple(self):
+ return self.vae, self.source
+
+
+def is_automatic():
+ return shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config
+
+
+def resolve_vae_from_setting() -> VaeResolution:
+ if shared.opts.sd_vae == "None":
+ return VaeResolution()
+
+ vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
+ if vae_from_options is not None:
+ return VaeResolution(vae_from_options, 'specified in settings')
+
+ if not is_automatic():
+ print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
+ return VaeResolution(resolved=False)
+
+
+def resolve_vae_from_user_metadata(checkpoint_file) -> VaeResolution:
metadata = extra_networks.get_user_metadata(checkpoint_file)
vae_metadata = metadata.get("vae", None)
if vae_metadata is not None and vae_metadata != "Automatic":
if vae_metadata == "None":
- return None, None
+ return VaeResolution()
vae_from_metadata = vae_dict.get(vae_metadata, None)
if vae_from_metadata is not None:
- return vae_from_metadata, "from user metadata"
+ return VaeResolution(vae_from_metadata, "from user metadata")
+
+ return VaeResolution(resolved=False)
- is_automatic = shared.opts.sd_vae in {"Automatic", "auto"} # "auto" for people with old config
+def resolve_vae_near_checkpoint(checkpoint_file) -> VaeResolution:
vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
if vae_near_checkpoint is not None and (shared.opts.sd_vae_as_default or is_automatic):
- return vae_near_checkpoint, 'found near the checkpoint'
+ return VaeResolution(vae_near_checkpoint, 'found near the checkpoint')
- if shared.opts.sd_vae == "None":
- return None, None
+ return VaeResolution(resolved=False)
- vae_from_options = vae_dict.get(shared.opts.sd_vae, None)
- if vae_from_options is not None:
- return vae_from_options, 'specified in settings'
- if not is_automatic:
- print(f"Couldn't find VAE named {shared.opts.sd_vae}; using None instead")
+def resolve_vae(checkpoint_file) -> VaeResolution:
+ if shared.cmd_opts.vae_path is not None:
+ return VaeResolution(shared.cmd_opts.vae_path, 'from commandline argument')
+
+ if shared.opts.sd_vae_overrides_per_model_preferences and not is_automatic():
+ return resolve_vae_from_setting()
+
+ res = resolve_vae_from_user_metadata(checkpoint_file)
+ if res.resolved:
+ return res
+
+ res = resolve_vae_near_checkpoint(checkpoint_file)
+ if res.resolved:
+ return res
+
+ res = resolve_vae_from_setting()
- return None, None
+ return res
def load_vae_dict(filename, map_location):
@@ -201,7 +240,7 @@ def reload_vae_weights(sd_model=None, vae_file=unspecified):
checkpoint_file = checkpoint_info.filename
if vae_file == unspecified:
- vae_file, vae_source = resolve_vae(checkpoint_file)
+ vae_file, vae_source = resolve_vae(checkpoint_file).tuple()
else:
vae_source = "from function argument"
diff --git a/modules/shared.py b/modules/shared.py
index 078e8135..4d854928 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -479,7 +479,7 @@ For img2img, VAE is used to process user's input image before the sampling, and
"""),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
- "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
+ "sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),
"auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
"sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to decode latent to image"),
@@ -608,14 +608,14 @@ options_templates.update(options_section(('sampler-params', "Sampler parameters"
"eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
"eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
- 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}),
- 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}).info("0 = inf"),
- 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
- 'k_sched_type': OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
+ 's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}).info('amount of stochasticity; only applies to Euler, Heun, and DPM2'),
+ 's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}).info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
+ 's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}).info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
+ 's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}).info('amount of additional noise to counteract loss of detail during sampling; only applies to Euler, Heun, and DPM2'),
+ 'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
- 'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
- 'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
+ 'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
+ 'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
@@ -733,6 +733,10 @@ class Options:
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
+ # 1.6.0 VAE defaults
+ if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
+ self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')
+
# 1.1.1 quicksettings list migration
if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
diff --git a/modules/ui.py b/modules/ui.py
index 4ffb9b82..5150dae4 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -408,7 +408,7 @@ def create_ui():
extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs")
extra_tabs.__enter__()
- with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, gr.Row().style(equal_height=False):
+ with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, gr.Row(equal_height=False):
with gr.Column(variant='compact', elem_id="txt2img_settings"):
scripts.scripts_txt2img.prepare_ui()
diff --git a/modules/ui_extra_networks_checkpoints_user_metadata.py b/modules/ui_extra_networks_checkpoints_user_metadata.py
index 2c69aab8..25df0a80 100644
--- a/modules/ui_extra_networks_checkpoints_user_metadata.py
+++ b/modules/ui_extra_networks_checkpoints_user_metadata.py
@@ -1,6 +1,6 @@
import gradio as gr
-from modules import ui_extra_networks_user_metadata, sd_vae
+from modules import ui_extra_networks_user_metadata, sd_vae, shared
from modules.ui_common import create_refresh_button
@@ -18,6 +18,10 @@ class CheckpointUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataE
self.write_user_metadata(name, user_metadata)
+ def update_vae(self, name):
+ if name == shared.sd_model.sd_checkpoint_info.name_for_extra:
+ sd_vae.reload_vae_weights()
+
def put_values_into_components(self, name):
user_metadata = self.get_user_metadata(name)
values = super().put_values_into_components(name)
@@ -58,3 +62,5 @@ class CheckpointUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataE
]
self.setup_save_handler(self.button_save, self.save_user_metadata, edited_components)
+ self.button_save.click(fn=self.update_vae, inputs=[self.edit_name_input])
+
generated by cgit v1.2.3 (git 2.25.1) at 2025-05-01 20:33:19 +0000