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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2024-10-20 09:26:07 -04:00
parent 2570d87ad3
commit 64f363283f
4 changed files with 80 additions and 37 deletions
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extensions-builtin/Lora/lora_extract.py
+72 -29
View File
@@ -78,20 +78,36 @@ def loaded_lora():
return ", ".join(list(loaded))
def make_lora(filename, maxrank, auto_rank, rank_ratio):
def make_meta():
return {
'todo': 'extra-lora'
}
def make_lora(fn, maxrank, auto_rank, rank_ratio, modules, overwrite):
if not shared.sd_loaded or not shared.native:
msg = "LoRA extract: model not loaded"
shared.log.warning(msg)
yield msg
return
if loaded_lora() == "":
shared.log.warning("LoRA extract: no LoRA detected")
msg = "LoRA extract: no LoRA detected"
shared.log.warning(msg)
yield msg
return
if not filename:
shared.log.warning("LoRA extract: target filename required")
if not fn:
msg = "LoRA extract: target filename required"
shared.log.warning(msg)
yield msg
return
t0 = time.time()
maxrank = int(maxrank)
rank_ratio = 1 if not auto_rank else rank_ratio
shared.state.begin('LoRA extract')
if hasattr(shared.sd_model, 'text_encoder') and shared.sd_model.text_encoder is not None:
shared.log.debug(f'LoRA extract: modules={modules} maxrank={maxrank} auto={auto_rank} ratio={rank_ratio} fn="{fn}"')
if 'te' in modules and getattr(shared.sd_model, 'text_encoder', None) is not None:
yield "LoRA extract: extracting TE-1"
for name, module in shared.sd_model.text_encoder.named_modules():
weights_backup = getattr(module, "network_weights_backup", None)
if weights_backup is None or getattr(module, "network_current_names", None) is None:
@@ -101,8 +117,10 @@ def make_lora(filename, maxrank, auto_rank, rank_ratio):
module.svdhandler.network_name = prefix + name.replace(".", "_")
with devices.inference_context():
module.svdhandler.decompose(module.weight, weights_backup)
t1 = time.time()
if hasattr(shared.sd_model, 'text_encoder_2'):
if 'te' in modules and getattr(shared.sd_model, 'text_encoder_2', None) is not None:
yield "LoRA extract: extracting TE-2"
for name, module in shared.sd_model.text_encoder_2.named_modules():
weights_backup = getattr(module, "network_weights_backup", None)
if weights_backup is None or getattr(module, "network_current_names", None) is None:
@@ -111,8 +129,10 @@ def make_lora(filename, maxrank, auto_rank, rank_ratio):
module.svdhandler.network_name = "lora_te2_" + name.replace(".", "_")
with devices.inference_context():
module.svdhandler.decompose(module.weight, weights_backup)
t2 = time.time()
if hasattr(shared.sd_model, 'unet'):
if 'unet' in modules and getattr(shared.sd_model, 'unet', None) is not None:
yield "LoRA extract: extracting UNet"
for name, module in shared.sd_model.unet.named_modules():
weights_backup = getattr(module, "network_weights_backup", None)
if weights_backup is None or getattr(module, "network_current_names", None) is None:
@@ -121,9 +141,10 @@ def make_lora(filename, maxrank, auto_rank, rank_ratio):
module.svdhandler.network_name = "lora_unet_" + name.replace(".", "_")
with devices.inference_context():
module.svdhandler.decompose(module.weight, weights_backup)
t3 = time.time()
# TODO: Handle quant for Flux
# if hasattr(shared.sd_model, 'transformer'):
# if 'te' in modules and getattr(shared.sd_model, 'transformer', None) is not None:
# for name, module in shared.sd_model.transformer.named_modules():
# if "norm" in name and "linear" not in name:
# continue
@@ -135,29 +156,48 @@ def make_lora(filename, maxrank, auto_rank, rank_ratio):
# module.svdhandler.decompose(module.weight, weights_backup)
# module.svdhandler.findrank(rank, rank_ratio)
submodelname = ['text_encoder', 'text_encoder_2', 'unet', 'transformer']
lora_state_dict = {}
for sub in submodelname:
for sub in ['text_encoder', 'text_encoder_2', 'unet', 'transformer']:
submodel = getattr(shared.sd_model, sub, None)
if submodel is not None:
yield f"LoRA extract: creating {sub}"
for _name, module in submodel.named_modules():
if not hasattr(module, "svdhandler"):
continue
lora_state_dict.update(module.svdhandler.makeweights())
del module.svdhandler
shared.log.debug('LoRA extract: create done')
t4 = time.time()
suffix = []
if maxrank and auto_rank and rank_ratio != 1:
suffix.append(f'maxrank{str(maxrank).replace(".","-")}')
else:
suffix.append(f'rank{str(maxrank).replace(".","-")}')
if auto_rank and rank_ratio != 1:
suffix.append(f'autorank{str(rank_ratio).replace(".","-")}')
if not os.path.isabs(fn):
fn = os.path.join(shared.cmd_opts.lora_dir, fn)
if not fn.endswith('.safetensors'):
fn += '.safetensors'
if os.path.exists(fn):
if overwrite:
shared.log.warning(f'LoRA extract: fn="{fn}" overwriting existing file')
os.remove(fn)
else:
msg = f'LoRA extract: fn="{fn}" file exists'
shared.log.warning(msg)
yield msg
return
pathstr = str(os.path.join(shared.cmd_opts.lora_dir, filename+f'_{"_".join(suffix)}.safetensors'))
save_file(lora_state_dict, pathstr)
shared.log.info(f'LoRA extra: fn={pathstr} in {time.time()-t0} seconds')
shared.state.end()
meta = make_meta()
try:
save_file(tensors=lora_state_dict, metadata=meta, filename=fn)
except Exception as e:
msg = f'LoRA extract error: fn="{fn}" {e}'
shared.log.error(msg)
yield msg
return
t5 = time.time()
shared.log.debug(f'LoRA extract: te1={t1-t0:.2f} te2={t2-t1:.2f} unet={t3-t2:.2f} save={t5-t4:.2f}')
keys = list(lora_state_dict.keys())
msg = f'LoRA extract: fn="{fn}" keys={len(keys)} time={t5-t0:.2f}'
shared.log.info(msg)
yield msg
def create_ui():
@@ -168,16 +208,19 @@ def create_ui():
with gr.Row():
loaded = gr.Textbox(value="Press refresh to query loaded LoRA", label="Loaded LoRA", interactive=False)
create_refresh_button(loaded, lambda: None, lambda: {'value': loaded_lora()}, "testid")
with gr.Row():
rank = gr.Slider(label="Maximum rank", value=32, minimum=1, maximum=256)
with gr.Row():
auto_rank = gr.Checkbox(value=False, label="Automatically determine rank")
with gr.Row(visible=False) as rank_options:
rank_ratio = gr.Slider(label="Autorank ratio", value=1, minimum=0, maximum=1, step=0.05, visible=True)
with gr.Group():
with gr.Row():
modules = gr.CheckboxGroup(label="Modules to extract", value=['unet'], choices=['te', 'unet'])
with gr.Row():
auto_rank = gr.Checkbox(value=False, label="Automatically determine rank")
rank_ratio = gr.Slider(label="Autorank ratio", value=1, minimum=0, maximum=1, step=0.05, visible=False)
rank = gr.Slider(label="Maximum rank", value=32, minimum=1, maximum=256)
with gr.Row():
filename = gr.Textbox(label="LoRA target filename")
overwrite = gr.Checkbox(value=False, label="Overwrite existing file")
with gr.Row():
extract = gr.Button(value="Extract LoRA", variant='primary')
status = gr.HTML(value="", show_label=False)
auto_rank.change(fn=lambda x: gr_show(x), inputs=[auto_rank], outputs=[rank_options])
extract.click(fn=make_lora, inputs=[filename, rank, auto_rank, rank_ratio], outputs=[])
auto_rank.change(fn=lambda x: gr_show(x), inputs=[auto_rank], outputs=[rank_ratio])
extract.click(fn=make_lora, inputs=[filename, rank, auto_rank, rank_ratio, modules, overwrite], outputs=[status])
extensions-builtin/sdnext-modernui
+1 -1
Submodule extensions-builtin/sdnext-modernui updated: 74e12fb5e5...8afbad75d6
modules/devices.py
+6 -6
View File
@@ -508,12 +508,12 @@ def test_for_nans(x, where):
raise NansException(message)
def normalize_device(device):
if torch.device(device).type in {"cpu", "mps", "meta"}:
return torch.device(device)
if torch.device(device).index is None:
return torch.device(str(device), index=0)
return torch.device(device)
def normalize_device(dev):
if torch.device(dev).type in {"cpu", "mps", "meta"}:
return torch.device(dev)
if torch.device(dev).index is None:
return torch.device(str(dev), index=0)
return torch.device(dev)
def same_device(d1, d2):
modules/ui_sections.py
+1 -1
View File
@@ -270,7 +270,7 @@ def create_sampler_options(tabname):
sampler_options = gr.CheckboxGroup(label='Sampler options', elem_id=f"{tabname}_sampler_options", choices=options, value=values, type='value')
with gr.Row(elem_classes=['flex-break']):
shared.opts.data['schedulers_sigma'] = shared.opts.data.get('schedulers_sigma', 'default')
sampler_algo = gr.Radio(label='Sigma algorithm', elem_id=f"{tabname}_sigma_algo", choices=['default', 'karras', 'exponential', 'polyexponential'], value=shared.opts.data['schedulers_sigma'], type='value')
sampler_algo = gr.Radio(label='Sigma algorithm', elem_id=f"{tabname}_sigma_algo", choices=['default', 'karras', 'exponential', 'polyexponential'], value=shared.opts.data.schedulers_sigma, type='value')
sampler_options.change(fn=set_sampler_original_options, inputs=[sampler_options, sampler_algo], outputs=[])
sampler_algo.change(fn=set_sampler_original_options, inputs=[sampler_options, sampler_algo], outputs=[])