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fix lora with nested pipeline

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Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2025-09-11 09:03:00 -04:00
parent 4f2b829450
commit 16670636d7
4 changed files with 42 additions and 29 deletions
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CHANGELOG.md
+4 -3
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@@ -1,8 +1,8 @@
# Change Log for SD.Next
## Update for 2025-09-10
## Update for 2025-09-11
### Highlights for 2025-09-10
### Highlights for 2025-09-11
*What's new*? Big one is that we're (finally) switching the default UI to **ModernUI**!
StandardUI is still available and can be selected in settings, but ModernUI is now the default for new installs
@@ -12,7 +12,7 @@ Also, there are quite a few offloading improvements and many quality-of-life cha
[ReadMe](https://github.com/vladmandic/automatic/blob/master/README.md) | [ChangeLog](https://github.com/vladmandic/automatic/blob/master/CHANGELOG.md) | [Docs](https://vladmandic.github.io/sdnext-docs/) | [WiKi](https://github.com/vladmandic/automatic/wiki) | [Discord](https://discord.com/invite/sd-next-federal-batch-inspectors-1101998836328697867) | [Sponsor](https://github.com/sponsors/vladmandic)
### Details for 2025-09-10
### Details for 2025-09-11
- **Models**
- **Chroma** final versions: [Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD), [Chroma1-Base](https://huggingface.co/lodestones/Chroma1-Base) and [Chroma1-Flash](https://huggingface.co/lodestones/Chroma1-Flash)
@@ -107,6 +107,7 @@ Also, there are quite a few offloading improvements and many quality-of-life cha
- fix reprocess workflow for control with hires
- fix samplers set timesteps vs sigmas
- fix `detailer` missing metadata
- fix `infiniteyou` lora load with
## Update for 2025-08-20
extensions-builtin/sdnext-modernui
+1 -1
Submodule extensions-builtin/sdnext-modernui updated: 4d107f6869...21a7e3cfcf
modules/loader.py
+8 -6
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@@ -118,17 +118,23 @@ errors.install([gradio])
import pydantic # pylint: disable=W0611,C0411
timer.startup.record("pydantic")
# patch different progress bars
import tqdm as tqdm_lib # pylint: disable=C0411
from tqdm.rich import tqdm # pylint: disable=W0611,C0411
import diffusers.utils.import_utils # pylint: disable=W0611,C0411
diffusers.utils.import_utils._k_diffusion_available = True # pylint: disable=protected-access # monkey-patch since we use k-diffusion from git
diffusers.utils.import_utils._k_diffusion_version = '0.0.12' # pylint: disable=protected-access
import diffusers # pylint: disable=W0611,C0411
import diffusers.loaders.single_file # pylint: disable=W0611,C0411
import huggingface_hub # pylint: disable=W0611,C0411
diffusers.loaders.single_file.logging.tqdm = partial(tqdm, unit='C')
logging.getLogger("diffusers.loaders.single_file").setLevel(logging.ERROR)
timer.startup.record("diffusers")
import huggingface_hub # pylint: disable=W0611,C0411
timer.startup.record("hfhub")
try:
import pillow_jxl # pylint: disable=W0611,C0411
except Exception:
@@ -136,10 +142,6 @@ except Exception:
from PIL import Image # pylint: disable=W0611,C0411
timer.startup.record("pillow")
# patch different progress bars
import tqdm as tqdm_lib # pylint: disable=C0411
from tqdm.rich import tqdm # pylint: disable=W0611,C0411
diffusers.loaders.single_file.logging.tqdm = partial(tqdm, unit='C')
class _tqdm_cls():
def __call__(self, *args, **kwargs):
modules/lora/lora_load.py
+29 -19
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@@ -20,12 +20,13 @@ dump_lora_keys = os.environ.get('SD_LORA_DUMP', None) is not None
def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_default_multiplier) -> Union[network.Network, None]:
t0 = time.time()
name = name.replace(".", "_")
sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
shared.log.debug(f'Network load: type=LoRA name="{name}" file="{network_on_disk.filename}" detected={network_on_disk.sd_version} method=diffusers scale={lora_scale} fuse={shared.opts.lora_fuse_diffusers}')
if not hasattr(shared.sd_model, 'load_lora_weights'):
shared.log.error(f'Network load: type=LoRA class={shared.sd_model.__class__} does not implement load lora')
if not hasattr(sd_model, 'load_lora_weights'):
shared.log.error(f'Network load: type=LoRA class={sd_model.__class__} does not implement load lora')
return None
try:
shared.sd_model.load_lora_weights(network_on_disk.filename, adapter_name=name)
sd_model.load_lora_weights(network_on_disk.filename, adapter_name=name)
except Exception as e:
if 'already in use' in str(e):
pass
@@ -38,7 +39,7 @@ def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_
errors.display(e, "LoRA")
return None
if name not in diffuser_loaded:
list_adapters = shared.sd_model.get_list_adapters()
list_adapters = sd_model.get_list_adapters()
list_adapters = [adapter for adapters in list_adapters.values() for adapter in adapters]
if name not in list_adapters:
shared.log.error(f'Network load: type=LoRA name="{name}" adapters={list_adapters} not loaded')
@@ -53,8 +54,9 @@ def load_diffusers(name, network_on_disk, lora_scale=shared.opts.extra_networks_
def lora_dump(lora, dct):
import tempfile
sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
ty = shared.sd_model_type
cn = shared.sd_model.__class__.__name__
cn = sd_model.__class__.__name__
shared.log.trace(f'LoRA dump: type={ty} model={cn} fn="{lora}"')
bn = os.path.splitext(os.path.basename(lora))[0]
fn = os.path.join(tempfile.gettempdir(), f'LoRA-{ty}-{cn}-{bn}.txt')
@@ -65,7 +67,7 @@ def lora_dump(lora, dct):
f.write(line + "\n")
fn = os.path.join(tempfile.gettempdir(), f'Model-{ty}-{cn}.txt')
with open(fn, 'w', encoding='utf8') as f:
keys = shared.sd_model.network_layer_mapping.keys()
keys = sd_model.network_layer_mapping.keys()
shared.log.trace(f'LoRA dump: type=Mapping fn="{fn}" keys={len(keys)}')
for line in keys:
f.write(line + "\n")
@@ -75,6 +77,7 @@ def load_safetensors(name, network_on_disk) -> Union[network.Network, None]:
if not shared.sd_loaded:
return None
sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
cached = lora_cache.get(name, None)
if l.debug:
shared.log.debug(f'Network load: type=LoRA name="{name}" file="{network_on_disk.filename}" type=lora {"cached" if cached else ""}')
@@ -90,7 +93,7 @@ def load_safetensors(name, network_on_disk) -> Union[network.Network, None]:
state_dict = lora_convert._convert_kohya_sd3_lora_to_diffusers(state_dict) or state_dict # pylint: disable=protected-access
except ValueError: # EAFP for diffusers PEFT keys
pass
lora_convert.assign_network_names_to_compvis_modules(shared.sd_model)
lora_convert.assign_network_names_to_compvis_modules(sd_model)
keys_failed_to_match = {}
matched_networks = {}
bundle_embeddings = {}
@@ -168,6 +171,7 @@ def load_safetensors(name, network_on_disk) -> Union[network.Network, None]:
def maybe_recompile_model(names, te_multipliers):
sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
recompile_model = False
skip_lora_load = False
if shared.compiled_model_state is not None and shared.compiled_model_state.is_compiled:
@@ -188,13 +192,13 @@ def maybe_recompile_model(names, te_multipliers):
shared.compiled_model_state.lora_model = []
if recompile_model:
backup_cuda_compile = shared.opts.cuda_compile
backup_scheduler = getattr(shared.sd_model, "scheduler", None)
backup_scheduler = getattr(sd_model, "scheduler", None)
sd_models.unload_model_weights(op='model')
shared.opts.cuda_compile = []
sd_models.reload_model_weights(op='model')
shared.opts.cuda_compile = backup_cuda_compile
if backup_scheduler is not None:
shared.sd_model.scheduler = backup_scheduler
sd_model.scheduler = backup_scheduler
return recompile_model, skip_lora_load
@@ -261,6 +265,7 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
networks_on_disk = gather_networks(names)
failed_to_load_networks = []
recompile_model, skip_lora_load = maybe_recompile_model(names, te_multipliers)
sd_model = getattr(shared.sd_model, "pipe", shared.sd_model)
l.loaded_networks.clear()
diffuser_loaded.clear()
@@ -295,8 +300,8 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
failed_to_load_networks.append(name)
shared.log.error(f'Network load: type=LoRA name="{name}" detected={network_on_disk.sd_version if network_on_disk is not None else None} failed')
continue
if hasattr(shared.sd_model, 'embedding_db'):
shared.sd_model.embedding_db.load_diffusers_embedding(None, net.bundle_embeddings)
if hasattr(sd_model, 'embedding_db'):
sd_model.embedding_db.load_diffusers_embedding(None, net.bundle_embeddings)
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
@@ -307,19 +312,24 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
lora_cache.pop(name, None)
if not skip_lora_load and len(diffuser_loaded) > 0:
shared.log.debug(f'Network load: type=LoRA loaded={diffuser_loaded} available={shared.sd_model.get_list_adapters()} active={shared.sd_model.get_active_adapters()} scales={diffuser_scales}')
shared.log.debug(f'Network load: type=LoRA loaded={diffuser_loaded} available={sd_model.get_list_adapters()} active={sd_model.get_active_adapters()} scales={diffuser_scales}')
try:
t1 = time.time()
if l.debug:
shared.log.trace(f'Network load: type=LoRA list={shared.sd_model.get_list_adapters()}')
shared.log.trace(f'Network load: type=LoRA active={shared.sd_model.get_active_adapters()}')
shared.sd_model.set_adapters(adapter_names=diffuser_loaded, adapter_weights=diffuser_scales)
shared.log.trace(f'Network load: type=LoRA list={sd_model.get_list_adapters()}')
shared.log.trace(f'Network load: type=LoRA active={sd_model.get_active_adapters()}')
sd_model.set_adapters(adapter_names=diffuser_loaded, adapter_weights=diffuser_scales)
except Exception as e:
shared.log.error(f'Network load: type=LoRA action=set {e}')
if l.debug:
errors.display(e, 'LoRA')
try:
if shared.opts.lora_fuse_diffusers and not lora_overrides.disable_fuse():
shared.sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # diffusers with fuse uses fixed scale since later apply does the scaling
shared.sd_model.unload_lora_weights()
sd_model.fuse_lora(adapter_names=diffuser_loaded, lora_scale=1.0, fuse_unet=True, fuse_text_encoder=True) # diffusers with fuse uses fixed scale since later apply does the scaling
sd_model.unload_lora_weights()
l.timer.activate += time.time() - t1
except Exception as e:
shared.log.error(f'Network load: type=LoRA {e}')
shared.log.error(f'Network load: type=LoRA action=fuse {e}')
if l.debug:
errors.display(e, 'LoRA')
@@ -330,7 +340,7 @@ def network_load(names, te_multipliers=None, unet_multipliers=None, dyn_dims=Non
shared.log.info("Network load: type=LoRA recompiling model")
backup_lora_model = shared.compiled_model_state.lora_model
if 'Model' in shared.opts.cuda_compile:
shared.sd_model = sd_models_compile.compile_diffusers(shared.sd_model)
sd_model = sd_models_compile.compile_diffusers(sd_model)
shared.compiled_model_state.lora_model = backup_lora_model
l.timer.load = time.time() - t0