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update installer and add sd_model_dict

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Vladimir Mandic
2023-06-07 13:26:19 -04:00
parent 8e3a8fb474
commit aaa0d46286
15 changed files with 110 additions and 103 deletions
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modules/extensions.py
+1 -1
View File
@@ -139,7 +139,7 @@ def list_extensions():
shared.log.info(f'Skipping conflicting extension: {path}')
continue
extension_names.append(extension_dirname)
extension_paths.append((extension_dirname, path, dirname == dirname))
extension_paths.append((extension_dirname, path, dirname == extensions_builtin_dir))
for dirname, path, is_builtin in extension_paths:
extension = Extension(name=dirname, path=path, enabled=dirname not in shared.opts.disabled_extensions, is_builtin=is_builtin)
extensions.append(extension)
modules/processing.py
+2 -2
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@@ -933,7 +933,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
shared.state.nextjob()
img2img_sampler_name = self.sampler_name
force_latent_upscaler = shared.opts.data.get('xyz_fallback_sampler')
force_latent_upscaler = shared.opts.data.get('force_latent_sampler')
if force_latent_upscaler != 'None' and force_latent_upscaler != 'PLMS':
img2img_sampler_name = force_latent_upscaler
if img2img_sampler_name == 'PLMS':
@@ -981,7 +981,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_conditioning = None
def init(self, all_prompts, all_seeds, all_subseeds):
force_latent_upscaler = shared.opts.data.get('xyz_fallback_sampler')
force_latent_upscaler = shared.opts.data.get('force_latent_sampler')
if self.sampler_name in ['PLMS']:
self.sampler_name = force_latent_upscaler if force_latent_upscaler != 'None' else shared.opts.fallback_sampler # PLMS does not support img2img, use fallback instead
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
modules/sd_models.py
+22 -10
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@@ -190,8 +190,8 @@ def model_hash(filename):
return 'NOHASH'
def select_checkpoint():
model_checkpoint = shared.opts.sd_model_checkpoint
def select_checkpoint(model=True):
model_checkpoint = shared.opts.sd_model_checkpoint if model else shared.opts.sd_model_dict
checkpoint_info = checkpoint_aliases.get(model_checkpoint, None)
if checkpoint_info is not None:
shared.log.debug(f'Select checkpoint: {checkpoint_info.title if checkpoint_info is not None else None}')
@@ -300,7 +300,8 @@ def load_model_weights(model: torch.nn.Module, checkpoint_info: CheckpointInfo,
shared.log.debug(f'Model weights loading: {memory_stats()}')
sd_model_hash = checkpoint_info.calculate_shorthash()
timer.record("hash")
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
if model_data.sd_dict == 'None':
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
if state_dict is None:
state_dict = get_checkpoint_state_dict(checkpoint_info, timer)
model.load_state_dict(state_dict, strict=False)
@@ -398,6 +399,7 @@ sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_w
class SdModelData:
def __init__(self):
self.sd_model = None
self.sd_dict = 'None'
self.initial = True
self.lock = threading.Lock()
@@ -406,7 +408,7 @@ class SdModelData:
with self.lock:
try:
if shared.backend == shared.Backend.ORIGINAL:
load_model()
reload_model_weights()
elif shared.backend == shared.Backend.DIFFUSERS:
load_diffuser()
else:
@@ -552,15 +554,21 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None, timer=None)
shared.log.info(f'Model load finished: {memory_stats()}')
def reload_model_weights(sd_model=None, info=None):
def reload_model_weights(sd_model=None, info=None, reuse_dict=False):
load_dict = shared.opts.sd_model_dict != model_data.sd_dict
global skip_next_load # pylint: disable=global-statement
if skip_next_load:
shared.log.debug('Reload model weights skip')
skip_next_load = False
return
shared.log.debug(f'Reload model weights: {sd_model is not None} {info}')
from modules import lowvram, sd_hijack
checkpoint_info = info or select_checkpoint()
checkpoint_info = info or select_checkpoint(model=not load_dict) # are we selecting model or dictionary
next_checkpoint_info = info or select_checkpoint(model=load_dict) if load_dict else None
if load_dict:
shared.log.debug(f'Model dict: existing={sd_model is not None} target={checkpoint_info.filename} info={info}')
else:
model_data.sd_dict = 'None'
shared.log.debug(f'Reload model weights: existing={sd_model is not None} target={checkpoint_info.filename} info={info}')
if not sd_model:
sd_model = model_data.sd_model
if sd_model is None: # previous model load failed
@@ -573,7 +581,7 @@ def reload_model_weights(sd_model=None, info=None):
lowvram.send_everything_to_cpu()
else:
sd_model.to(devices.cpu)
if shared.opts.model_reuse_dict and sd_model is not None:
if reuse_dict or (shared.opts.model_reuse_dict and sd_model is not None):
shared.log.info('Reusing previous model dictionary')
sd_hijack.model_hijack.undo_hijack(sd_model)
else:
@@ -590,6 +598,10 @@ def reload_model_weights(sd_model=None, info=None):
load_model(checkpoint_info, already_loaded_state_dict=state_dict, timer=timer)
else:
load_diffuser(checkpoint_info, already_loaded_state_dict=state_dict, timer=timer)
if load_dict and next_checkpoint_info is not None:
model_data.sd_dict = shared.opts.sd_model_dict
shared.opts.data["sd_model_checkpoint"] = next_checkpoint_info.title
reload_model_weights(reuse_dict=True) # ok we loaded dict now lets redo and load model on top of it
return model_data.sd_model
try:
load_model_weights(sd_model, checkpoint_info, state_dict, timer)
@@ -615,8 +627,8 @@ def unload_model_weights(sd_model=None, _info=None):
sd_hijack.model_hijack.undo_hijack(model_data.sd_model)
model_data.sd_model = None
sd_model = None
devices.torch_gc(force=True)
shared.log.debug(f'Model weights unloaded: {memory_stats()}')
devices.torch_gc(force=True)
shared.log.debug(f'Model weights unloaded: {memory_stats()}')
return sd_model
modules/shared.py
+3 -2
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@@ -291,6 +291,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_checkpoint_cache": OptionInfo(0, "Number of cached model checkpoints", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAE checkpoints", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "Select VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
"sd_model_dict": OptionInfo('None', "Stable Diffusion checkpoint dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_vae_sliced_encode": OptionInfo(False, "Enable splitting of hires batch processing"),
"stream_load": OptionInfo(False, "When loading models attempt stream loading optimized for slow or network storage"),
"model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary"),
@@ -440,7 +441,7 @@ options_templates.update(options_section(('ui', "Live previews"), {
options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
"show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
"fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
"xyz_fallback_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
"force_latent_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
"eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
@@ -504,7 +505,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
}))
options_templates.update(options_section(('lora', "Lora"), {
"lyco_patch_lora": OptionInfo(False, "Use LyCoris handler for all Lora types", gr.Checkbox, { "visible": True }), # TODO: lyco-patch-lora
"lyco_patch_lora": OptionInfo(False, "Use LyCoris handler for all Lora types", gr.Checkbox, { "visible": True }),
"lora_disable": OptionInfo(False, "Disable built-in Lora handler", gr.Checkbox, { "visible": True }, onchange=lora_disable),
"lora_functional": OptionInfo(False, "Use Kohya method for handling multiple Loras", gr.Checkbox, { "visible": True }),
}))
modules/textual_inversion/textual_inversion.py
+1 -1
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@@ -243,7 +243,7 @@ class EmbeddingDatabase:
self.previously_displayed_embeddings = displayed_embeddings
shared.log.info(f"Embeddings loaded: {len(self.word_embeddings)} {[k for k in self.word_embeddings.keys()]}")
if len(self.skipped_embeddings) > 0:
shared.log.info(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")
shared.log.info(f"Embeddings skipped: {len(self.skipped_embeddings)} {[k for k in self.skipped_embeddings.keys()]}")
def find_embedding_at_position(self, tokens, offset):
token = tokens[offset]
modules/ui.py
+2 -2
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@@ -1638,7 +1638,7 @@ def html_head():
head += f'<script type="module" src="{webpath(script.path)}"></script>\n'
added.append(script.path)
added = [a.replace(script_path, '').replace('\\', '/') for a in added]
modules.shared.log.debug(f'Adding JS scripts: {added}')
# modules.shared.log.debug(f'Adding JS scripts: {added}')
return head
@@ -1671,7 +1671,7 @@ def html_css():
if os.path.exists(os.path.join(data_path, "user.css")):
head += stylesheet(os.path.join(data_path, "user.css"))
added = [a.replace(script_path, '').replace('\\', '/') for a in added]
modules.shared.log.debug(f'Adding CSS stylesheets: {added}')
# modules.shared.log.debug(f'Adding CSS stylesheets: {added}')
return head
modules/ui_extensions.py
+1 -1
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@@ -261,7 +261,7 @@ def search_extensions(search_text, sort_column):
def refresh_extensions_list_from_data(search_text, sort_column):
shared.log.debug(f'Extensions manager: refresh list search="{search_text}" sort="{sort_column}"')
# shared.log.debug(f'Extensions manager: refresh list search="{search_text}" sort="{sort_column}"')
code = """
<table id="extensions">
<colgroup>