Public/automatic
1
0
Fork 0
mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
Code Issues Packages Projects Releases Wiki Activity

logical-vs-physical param count

Browse Source 
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
2026-06-08 12:03:37 +02:00
parent d710b7d766
commit 8a85178f93
5 changed files with 34 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files
modules/sd_models.py
+12 -17
View File
@@ -566,7 +566,6 @@ def load_diffuser_force(detected_model_type: str, checkpoint_info: CheckpointInf
allow_post_quant = False
except Exception as e:
log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
# if debug_load:
errors.display(e, 'Load')
return None, True
if sd_model is not None:
@@ -691,8 +690,6 @@ def load_diffuser_file(model_type: str, pipeline, checkpoint_info: CheckpointInf
return None
if shared.opts.diffusers_vae_upcast != 'default' and model_type in ['Stable Diffusion', 'Stable Diffusion XL']:
diffusers_load_config['force_upcast'] = True if shared.opts.diffusers_vae_upcast == 'true' else False
# if debug_load:
# log.debug(f'Model args: {diffusers_load_config}')
if sd_model is not None:
diffusers_load_config.pop('vae', None)
diffusers_load_config.pop('safety_checker', None)
@@ -715,10 +712,17 @@ def load_sdnq_module(fn: str, module_name: str, load_method: str):
quantization_config = None
quantization_config_path = os.path.join(fn, module_name, 'quantization_config.json')
model_config_path = os.path.join(fn, module_name, 'config.json')
root_quantization_config_path = os.path.join(fn, 'quantization_config.json')
if os.path.exists(quantization_config_path):
quantization_config = shared.readfile(quantization_config_path, silent=True, as_type="dict")
elif os.path.exists(model_config_path):
quantization_config = shared.readfile(model_config_path, silent=True, as_type="dict").get("quantization_config", None)
elif os.path.exists(root_quantization_config_path):
quantization_config = shared.readfile(root_quantization_config_path, silent=True, as_type="dict")
if debug_load:
log.debug(f'SDNQ: load_sdnq_module fn={fn} module={module_name} quant_path={quantization_config_path} model_config={model_config_path} root_quant={root_quantization_config_path} found={quantization_config is not None}')
if isinstance(quantization_config, dict):
log.debug(f'SDNQ: quantization_config keys={list(quantization_config.keys())}')
if quantization_config is None:
return None, module_name, 0
model_name = os.path.join(fn, module_name)
@@ -754,25 +758,15 @@ def load_sdnq_model(checkpoint_info: CheckpointInfo, pipeline, diffusers_load_co
else:
load_method = 'safetensors'
if debug_load:
log.debug(f'SDNQ: load_sdnq_model path={checkpoint_info.path} method={load_method}')
for module_name in os.listdir(checkpoint_info.path):
module, name, t = load_sdnq_module(checkpoint_info.path, module_name, load_method=load_method)
if module is not None:
modules[name] = module
if debug_load:
log.debug(f'SDNQ: loaded module={name} path={os.path.join(checkpoint_info.path, module_name)} time={t:.2f}')
log.debug(f'Load {op}: module="{checkpoint_info.name}" module="{name}" gpu={shared.opts.diffusers_to_gpu} prequant=sdnq method={load_method} time={t:.2f}')
"""
futures = []
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
for module_name in os.listdir(checkpoint_info.path):
future = executor.submit(load_sdnq_module, checkpoint_info.path, module_name)
futures.append(future)
for future in futures:
loaded_module, name, t = future.result()
if loaded_module is not None:
log.debug(f'Load module: model="{checkpoint_info.name}" module="{name}" direct={shared.opts.diffusers_to_gpu} prequant=sdnq time={t:.2f}')
modules[name] = loaded_module
"""
t1 = time.time()
log.debug(f'Load {op}: model="{checkpoint_info.name}" modules={list(modules.keys())} prequant=sdnq time={t1-t0:.2f}')
sd_model = pipeline.from_pretrained(
@@ -1543,6 +1537,7 @@ def save_model(name: str, path: str | None = None, shard: str = "5GB", overwrite
shard = "5GB" # Guard against empty input
try:
t0 = time.time()
log.info(f'Save model: path="{model_name}" cls={shared.sd_model.__class__.__name__} start')
save_sdnq_model(
model=shared.sd_model,
model_path=model_name,