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fix lora default scales

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This commit is contained in:
Vladimir Mandic
2024-09-16 17:27:50 -04:00
parent e0904fbc43
commit cea5b57c80
5 changed files with 38 additions and 32 deletions
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CHANGELOG.md
+3 -2
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@@ -1,6 +1,6 @@
# Change Log for SD.Next
## Update for 2024-09-15
## Update for 2024-09-16
- **flux**
- avoid unet load if unchanged
@@ -23,11 +23,12 @@
- update logging
- **lora** auto-apply tags to prompt
- controlled via *settings -> networks -> lora_apply_tags*
0:disable, -1:all-tags, n:top-n-tags
*0:disable, -1:all-tags, n:top-n-tags*
- uses tags from both model embedded data and civitai downloaded data
- if lora contains no tags, lora name itself will be used as a tag
- if prompt contains `_tags_` it will be used as placeholder for replacement, otherwise tags will be appended
- used tags are also logged and registered in image metadata
- correct using of `extra_networks_default_multiplier` if not scale is specified
- **hf** force logout/login on token change
- **backend=original** is now marked as in maintenance-only mode
- minor ui optimizations
extensions-builtin/Lora/extra_networks_lora.py
+4 -4
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@@ -91,7 +91,7 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
for params in params_list:
assert params.items
names.append(params.positional[0])
te_multiplier = params.named.get("te", params.positional[1] if len(params.positional) > 1 else 1.0)
te_multiplier = params.named.get("te", params.positional[1] if len(params.positional) > 1 else shared.opts.extra_networks_default_multiplier)
if isinstance(te_multiplier, str) and "@" in te_multiplier:
te_multiplier = get_stepwise(te_multiplier, step, p.steps)
else:
@@ -125,10 +125,10 @@ class ExtraNetworkLora(extra_networks.ExtraNetwork):
names, te_multipliers, unet_multipliers, dyn_dims = self.parse(p, params_list, step)
networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
t2 = time.time()
self.infotext(p)
self.prompt(p)
if len(names) > 0 and step == 0:
shared.log.info(f'LoRA apply: {names} patch={t1-t0:.2f} load={t2-t1:.2f}')
self.infotext(p)
self.prompt(p)
shared.log.info(f'LoRA apply: {names} patch={t1-t0:.2f} te={te_multipliers} unet={unet_multipliers} dims={dyn_dims} load={t2-t1:.2f}')
elif self.active:
self.active = False
extensions-builtin/Lora/network_lora.py
-1
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@@ -71,5 +71,4 @@ class NetworkModuleLora(network.NetworkModule):
self.down_model.to(device=devices.device)
if hasattr(y, "scale"):
return y(scale=1) + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
extensions-builtin/Lora/networks.py
+28 -25
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@@ -82,7 +82,7 @@ def assign_network_names_to_compvis_modules(sd_model):
sd_model.network_layer_mapping = network_layer_mapping
def load_diffusers(name, network_on_disk, lora_scale=1.0) -> network.Network:
def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> network.Network:
t0 = time.time()
name = name.replace(".", "_")
#cached = lora_cache.get(name, None)
@@ -92,16 +92,19 @@ def load_diffusers(name, network_on_disk, lora_scale=1.0) -> network.Network:
if not shared.native:
return None
if not hasattr(shared.sd_model, 'load_lora_weights'):
shared.log.error(f"LoRA load failed: class={shared.sd_model.__class__} does not implement load lora")
shared.log.error(f'LoRA load failed: class={shared.sd_model.__class__} does not implement load lora')
return None
try:
shared.sd_model.load_lora_weights(network_on_disk.filename, adapter_name=name)
except Exception as e:
if 'already in use' in str(e):
# shared.log.warning(f"LoRA load failed: file={network_on_disk.filename} {e}")
# shared.log.warning(f'LoRA load failed: file={network_on_disk.filename} {e}')
pass
else:
shared.log.error(f"LoRA load failed: file={network_on_disk.filename} {e}")
if 'The following keys have not been correctly renamed' in str(e):
shared.log.error(f'LoRA load failed: file="{network_on_disk.filename}" diffusers unsupported format')
else:
shared.log.error(f'LoRA load failed: file="{network_on_disk.filename}" {e}')
if debug:
errors.display(e, "LoRA")
return None
@@ -156,22 +159,23 @@ def load_network(name, network_on_disk) -> network.Network:
if k not in matched_networks:
matched_networks[k] = network.NetworkWeights(network_key=key_network, sd_key=k, w={}, sd_module=module)
matched_networks[k].w[network_part] = weight
network_types = []
for key, weights in matched_networks.items():
net_module = None
for nettype in module_types:
net_module = nettype.create_module(net, weights)
if net_module is not None:
network_types.append(nettype.__class__.__name__)
break
if net_module is None:
shared.log.error(f'LoRA unhandled: name={name} key={key} weights={weights.w.keys()}')
else:
net.modules[key] = net_module
if len(keys_failed_to_match) > 0:
shared.log.warning(f"LoRA file={network_on_disk.filename} unmatched={len(keys_failed_to_match)} matched={len(matched_networks)}")
shared.log.warning(f'LoRA file="{network_on_disk.filename}" unmatched={len(keys_failed_to_match)} matched={len(matched_networks)}')
if debug:
shared.log.debug(f"LoRA file={network_on_disk.filename} unmatched={keys_failed_to_match}")
elif debug:
shared.log.debug(f"LoRA file={network_on_disk.filename} unmatched={len(keys_failed_to_match)} matched={len(matched_networks)}")
shared.log.debug(f'LoRA file="{network_on_disk.filename}" unmatched={keys_failed_to_match}')
shared.log.debug(f'LoRA file="{network_on_disk.filename}" type={set(network_types)} keys={len(matched_networks)}')
lora_cache[name] = net
t1 = time.time()
net.bundle_embeddings = bundle_embeddings
@@ -192,7 +196,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
if shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled:
if len(names) == len(shared.compiled_model_state.lora_model):
for i, name in enumerate(names):
if shared.compiled_model_state.lora_model[i] != f"{name}:{te_multipliers[i] if te_multipliers else 1.0}":
if shared.compiled_model_state.lora_model[i] != f"{name}:{te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier}":
recompile_model = True
shared.compiled_model_state.lora_model = []
break
@@ -221,28 +225,28 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
shared.log.debug(f'LoRA load: name="{name}" file="{network_on_disk.filename}" hash="{shorthash}"')
try:
if recompile_model:
shared.compiled_model_state.lora_model.append(f"{name}:{te_multipliers[i] if te_multipliers else 1.0}")
shared.compiled_model_state.lora_model.append(f"{name}:{te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier}")
if shared.native and (shared.opts.lora_force_diffusers or network_overrides.check_override(shorthash)): # OpenVINO only works with Diffusers LoRa loading
net = load_diffusers(name, network_on_disk, lora_scale=te_multipliers[i] if te_multipliers else 1.0)
net = load_diffusers(name, network_on_disk, lora_scale=te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier)
else:
net = load_network(name, network_on_disk)
if net is not None:
net.mentioned_name = name
network_on_disk.read_hash()
except Exception as e:
shared.log.error(f"LoRA load failed: file={network_on_disk.filename} {e}")
shared.log.error(f'LoRA load failed: file="{network_on_disk.filename}" {e}')
if debug:
errors.display(e, f"LoRA load failed file={network_on_disk.filename}")
errors.display(e, 'LoRA')
continue
if net is None:
failed_to_load_networks.append(name)
shared.log.error(f"LoRA unknown type: network={name}")
shared.log.error(f'LoRA unknown type: network="{name}"')
continue
if shared.native:
shared.sd_model.embedding_db.load_diffusers_embedding(None, net.bundle_embeddings)
net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
net.te_multiplier = te_multipliers[i] if te_multipliers else shared.opts.extra_networks_default_multiplier
net.unet_multiplier = unet_multipliers[i] if unet_multipliers else shared.opts.extra_networks_default_multiplier
net.dyn_dim = dyn_dims[i] if dyn_dims else shared.opts.extra_networks_default_multiplier
loaded_networks.append(net)
while len(lora_cache) > shared.opts.lora_in_memory_limit:
@@ -252,7 +256,7 @@ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=No
shared.log.debug(f'LoRA loaded={diffuser_loaded} scales={diffuser_scales}')
shared.sd_model.set_adapters(adapter_names=diffuser_loaded, adapter_weights=diffuser_scales)
if shared.opts.lora_fuse_diffusers:
shared.sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True)
shared.sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # fuse uses fixed scale since later apply does the scaling
shared.sd_model.unload_lora_weights()
if len(loaded_networks) > 0 and debug:
shared.log.debug(f'LoRA loaded={len(loaded_networks)} cache={list(lora_cache)}')
@@ -356,8 +360,8 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
if debug:
module_name = net.modules.get(network_layer_name, None)
shared.log.error(f"LoRA apply weight name={net.name} module={module_name} layer={network_layer_name} {e}")
errors.display(e, 'LoRA apply weight')
shared.log.error(f'LoRA apply weight name="{net.name}" module="{module_name}" layer="{network_layer_name}" {e}')
errors.display(e, 'LoRA')
raise RuntimeError('LoRA apply weight') from e
continue
# alternative workflow looking at _*_proj layers
@@ -382,12 +386,12 @@ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn
self.out_proj.bias += ex_bias
except RuntimeError as e:
if debug:
shared.log.debug(f"LoRA network={net.name} layer={network_layer_name} {e}")
shared.log.debug(f'LoRA network="{net.name}" layer="{network_layer_name}" {e}')
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
continue
if module is None:
continue
shared.log.warning(f"LoRA network={net.name} layer={network_layer_name} unsupported operation")
shared.log.warning(f'LoRA network="{net.name}" layer="{network_layer_name}" unsupported operation')
extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
self.network_current_names = wanted_names
t1 = time.time()
@@ -509,11 +513,10 @@ def list_available_networks():
if not os.path.isfile(filename):
return
name = os.path.splitext(os.path.basename(filename))[0]
name = name.replace('.', '_')
try:
entry = network.NetworkOnDisk(name, filename)
available_networks[entry.name] = entry
if '.' in entry.name:
available_networks[entry.name.replace('.', '_')] = entry
if entry.alias in available_network_aliases:
forbidden_network_aliases[entry.alias.lower()] = 1
if shared.opts.lora_preferred_name == 'filename':
@@ -523,7 +526,7 @@ def list_available_networks():
if entry.shorthash:
available_network_hash_lookup[entry.shorthash] = entry
except OSError as e: # should catch FileNotFoundError and PermissionError etc.
shared.log.error(f"Failed to load network {name} from {filename} {e}")
shared.log.error(f'LoRA: filename="{filename}" {e}')
candidates = list(files_cache.list_files(*directories, ext_filter=[".pt", ".ckpt", ".safetensors"]))
with concurrent.futures.ThreadPoolExecutor(max_workers=shared.max_workers) as executor:
scripts/xyz_grid_classes.py
+3
View File
@@ -35,6 +35,7 @@ class SharedSettingsStackHelper(object):
sd_vae = None
sd_unet = None
sd_text_encoder = None
extra_networks_default_multiplier = None
def __enter__(self):
#Save overridden settings so they can be restored later.
@@ -48,6 +49,7 @@ class SharedSettingsStackHelper(object):
self.sd_vae = shared.opts.sd_vae
self.sd_unet = shared.opts.sd_unet
self.sd_text_encoder = shared.opts.sd_text_encoder
self.extra_networks_default_multiplier = shared.opts.extra_networks_default_multiplier
def __exit__(self, exc_type, exc_value, tb):
#Restore overriden settings after plot generation.
@@ -55,6 +57,7 @@ class SharedSettingsStackHelper(object):
shared.opts.data["schedulers_solver_order"] = self.schedulers_solver_order
shared.opts.data["tome_ratio"] = self.tome_ratio
shared.opts.data["todo_ratio"] = self.todo_ratio
shared.opts.data["extra_networks_default_multiplier"] = self.extra_networks_default_multiplier
if self.sd_model_checkpoint != shared.opts.sd_model_checkpoint:
shared.opts.data["sd_model_checkpoint"] = self.sd_model_checkpoint
sd_models.reload_model_weights(op='model')