Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 09:14:35 +02:00
Code Issues Packages Projects Releases Wiki Activity

new lora handler and remove lyco

Browse Source
This commit is contained in:
Vladimir Mandic
2023-10-05 14:30:58 -04:00
parent 7dc098182a
commit 3727bf3d02
29 changed files with 1260 additions and 656 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/lora_diffusers.py
+32 -23
View File
@@ -1,28 +1,35 @@
import os
import time
import diffusers
import diffusers.models.lora as diffusers_lora
# from modules import shared
import modules.shared as shared
import modules.errors
lora_state = { # TODO Lora state for Diffusers
debug_output = os.environ.get('SD_LORA_DEBUG', None)
debug = shared.log.info if debug_output is not None else lambda *args, **kwargs: None
lora_state = { # Lora state for Diffusers
'multiplier': [],
'active': False,
'loaded': [],
'all_loras': [],
}
def unload_diffusers_lora():
try:
pipe = shared.sd_model
if shared.opts.diffusers_lora_loader == "diffusers":
if len(lora_state['loaded']) > 1 and hasattr(pipe, "unfuse_lora"):
debug(f'LoRA unfuse: loader={shared.opts.diffusers_lora_loader}')
pipe.unfuse_lora()
pipe.unload_lora_weights()
pipe._remove_text_encoder_monkey_patch() # pylint: disable=W0212
proc_cls_name = next(iter(pipe.unet.attn_processors.values())).__class__.__name__
non_lora_proc_cls = getattr(diffusers.models.attention_processor, proc_cls_name)#[len("LORA"):])
pipe.unet.set_attn_processor(non_lora_proc_cls())
# shared.log.debug('Diffusers LoRA unloaded')
else:
lora_state['all_loras'].reverse()
lora_state['multiplier'].reverse()
@@ -35,29 +42,30 @@ def unload_diffusers_lora():
lora_state['loaded'].clear()
lora_state['all_loras'] = []
lora_state['multiplier'] = []
debug(f'LoRA unloaded: loader={shared.opts.diffusers_lora_loader}')
except Exception as e:
shared.log.error(f"LoRA unload failed: {e}")
def load_diffusers_lora(name, lora, strength = 1.0, num_loras = 1):
if f'{lora.filename}:{strength}' in lora_state['loaded']:
shared.log.info(f'LoRA cached: {name} strength={strength}')
def load_diffusers_lora(name, lora, te_multiplier = 1.0, unet_multiplier = 1.0, dyn_dim = None): # TODO: te_multiplier is used as strength and unet_multiplier is ignored
if f'{lora.filename}:{te_multiplier}' in lora_state['loaded']:
debug(f'LoRA cached: {name} te-strength={te_multiplier} unet-strength={unet_multiplier} dyn-dim={dyn_dim}')
return
try:
t0 = time.time()
pipe = shared.sd_model
lora_state['active'] = True
lora_state['multiplier'].append(strength)
lora_state['multiplier'].append(te_multiplier)
fuse = 0
if shared.opts.diffusers_lora_loader.startswith("diffusers"):
pipe.load_lora_weights(lora.filename, cache_dir=shared.opts.diffusers_dir, local_files_only=True, lora_scale=strength, low_cpu_mem_usage=True)
if num_loras > 1 and hasattr(pipe, "fuse_lora"):
pipe.load_lora_weights(lora.filename, cache_dir=shared.opts.diffusers_dir, local_files_only=True, lora_scale=te_multiplier, low_cpu_mem_usage=True)
if hasattr(pipe, "fuse_lora"):
t2 = time.time()
pipe.fuse_lora(lora_scale=strength)
pipe.fuse_lora(lora_scale=te_multiplier)
fuse = time.time() - t2
lora_state['loaded'].append(f'{lora.filename}:{strength}')
lora_state['loaded'].append(f'{lora.filename}:{te_multiplier}')
if shared.compiled_model_state is not None: #filename breaks caching
shared.compiled_model_state.lora_model.append(f'{name}:{strength}')
shared.compiled_model_state.lora_model.append(f'{name}:{te_multiplier}')
else:
from safetensors.torch import load_file
lora_sd = load_file(lora.filename)
@@ -65,23 +73,26 @@ def load_diffusers_lora(name, lora, strength = 1.0, num_loras = 1):
text_encoders = [pipe.text_encoder, pipe.text_encoder_2]
else:
text_encoders = pipe.text_encoder
lora_network: LoRANetwork = create_network_from_weights(text_encoders, pipe.unet, lora_sd, multiplier=strength)
lora_network: LoRANetwork = create_network_from_weights(text_encoders, pipe.unet, lora_sd, multiplier=te_multiplier)
lora_network.load_state_dict(lora_sd)
if shared.opts.diffusers_lora_loader == "merge and apply":
lora_network.merge_to(multiplier=strength)
lora_network.merge_to(multiplier=te_multiplier)
if shared.opts.diffusers_lora_loader == "sequential apply":
lora_network.to(shared.device, dtype=pipe.unet.dtype)
lora_network.apply_to(multiplier=strength)
lora_network.apply_to(multiplier=te_multiplier)
lora_state['all_loras'].append(lora_network)
lora_state['loaded'].append(f'{lora.filename}:{strength}')
lora_state['loaded'].append(f'{lora.filename}:{te_multiplier}')
if shared.compiled_model_state is not None: #filename breaks caching
shared.compiled_model_state.lora_model.append(f'{name}:{strength}')
shared.compiled_model_state.lora_model.append(f'{name}:{te_multiplier}')
t1 = time.time()
fuse = f'fuse={fuse:.2f}s' if fuse > 0 else ''
shared.log.info(f'LoRA loaded: {name} strength={strength} loader="{shared.opts.diffusers_lora_loader}" lora={t1-t0:.2f}s {fuse}')
shared.log.info(f'LoRA loaded: {name} strength={te_multiplier} loader="{shared.opts.diffusers_lora_loader}" lora={t1-t0:.2f}s {fuse}')
except Exception as e:
lines = str(e).splitlines()
shared.log.error(f'LoRA loading failed: {name} loader="{shared.opts.diffusers_lora_loader}" {lines[0]}')
if debug_output is None:
shared.log.error(f'LoRA load failed: {name} loader="{shared.opts.diffusers_lora_loader}" {lines[0]}')
else:
modules.errors.display(e, 'LoRA load failed')
# Diffusersで動くLoRA。このファイル単独で完結する。
@@ -249,7 +260,7 @@ class LoRAModule(torch.nn.Module):
self.org_module[0].forward = self.org_forward
# forward with lora
def forward(self, x, scale = 1.0):
def forward(self, x, scale = 1.0): # pylint: disable=unused-argument
if not self.enabled:
return self.org_forward(x)
return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
@@ -442,7 +453,7 @@ class LoRANetwork(torch.nn.Module): # pylint: disable=abstract-method
self.unet_loras: List[LoRAModule]
self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
shared.log.debug(f"LoRA modules loaded/skipped: te={len(self.text_encoder_loras)}/{len(skipped_te)} unet={len(self.unet_loras)}/skip={len(skipped_un)}")
debug(f"LoRA module: te_loaded={len(self.text_encoder_loras)} te_skipped={len(skipped_te)} unet_loaded={len(self.unet_loras)} unet_skipped={len(skipped_un)}")
# assertion
names = set()
@@ -475,9 +486,7 @@ class LoRANetwork(torch.nn.Module): # pylint: disable=abstract-method
converted_count += 1
else:
not_converted_count += 1
if not_converted_count > 0:
shared.log.warning(f'LoRA modules not converted: {not_converted_count}')
debug(f'LoRA module: unet converted={converted_count}/{not_converted_count}')
def set_multiplier(self, multiplier):
self.multiplier = multiplier