mirror of
https://github.com/vladmandic/automatic
synced 2026-09-19 17:24:32 +02:00
add meissonic (unstable)
Signed-off-by: Vladimir Mandic <mandic00@live.com>
This commit is contained in:
Vladimir Mandic
+108
-139
@@ -563,6 +563,7 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
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if guess == 'Autodetect':
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try:
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guess = 'Stable Diffusion XL' if 'XL' in f.upper() else 'Stable Diffusion'
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pipeline = None
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# guess by size
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if os.path.isfile(f) and f.endswith('.safetensors'):
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size = round(os.path.getsize(f) / 1024 / 1024)
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@@ -624,6 +625,9 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
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guess = 'AuraFlow'
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if 'cogview' in f.lower():
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guess = 'CogView'
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if 'meissonic' in f.lower():
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guess = 'Meissonic'
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pipeline = 'custom'
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if 'flux' in f.lower():
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guess = 'FLUX'
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if size > 11000 and size < 20000:
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@@ -638,18 +642,20 @@ def detect_pipeline(f: str, op: str = 'model', warning=True, quiet=False):
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elif guess == 'Stable Diffusion XL' and 'instruct' in f.lower():
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guess = 'Stable Diffusion XL Instruct'
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# get actual pipeline
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pipeline = shared_items.get_pipelines().get(guess, None)
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pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline
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if not quiet:
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shared.log.info(f'Autodetect {op}: detect="{guess}" class={pipeline.__name__} file="{f}" size={size}MB')
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shared.log.info(f'Autodetect {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB')
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except Exception as e:
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shared.log.error(f'Autodetect {op}: file="{f}" {e}')
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if debug_load:
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errors.display(e, f'Load {op}: {f}')
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return None, None
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else:
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try:
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size = round(os.path.getsize(f) / 1024 / 1024)
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pipeline = shared_items.get_pipelines().get(guess, None)
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pipeline = shared_items.get_pipelines().get(guess, None) if pipeline is None else pipeline
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if not quiet:
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shared.log.info(f'Load {op}: detect="{guess}" class={pipeline.__name__} file="{f}" size={size}MB')
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shared.log.info(f'Load {op}: detect="{guess}" class={getattr(pipeline, "__name__", None)} file="{f}" size={size}MB')
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except Exception as e:
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shared.log.error(f'Load {op}: detect="{guess}" file="{f}" {e}')
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@@ -1099,149 +1105,108 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
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files = shared.walk_files(checkpoint_info.path, ['.safetensors', '.bin', '.ckpt'])
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if 'variant' not in diffusers_load_config and any('diffusion_pytorch_model.fp16' in f for f in files): # deal with diffusers lack of variant fallback when loading
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diffusers_load_config['variant'] = 'fp16'
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if model_type in ['Stable Cascade']: # forced pipeline
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if sd_model is None:
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try:
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from modules.model_stablecascade import load_cascade_combined
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sd_model = load_cascade_combined(checkpoint_info, diffusers_load_config)
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if model_type in ['Stable Cascade']: # forced pipeline
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from modules.model_stablecascade import load_cascade_combined
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sd_model = load_cascade_combined(checkpoint_info, diffusers_load_config)
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elif model_type in ['InstaFlow']: # forced pipeline
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pipeline = diffusers.utils.get_class_from_dynamic_module('instaflow_one_step', module_file='pipeline.py')
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sd_model = pipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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elif model_type in ['SegMoE']: # forced pipeline
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from modules.segmoe.segmoe_model import SegMoEPipeline
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sd_model = SegMoEPipeline(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model = sd_model.pipe # segmoe pipe does its stuff in __init__ and __call__ is the original pipeline
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elif model_type in ['PixArt-Sigma']: # forced pipeline
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from modules.model_pixart import load_pixart
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sd_model = load_pixart(checkpoint_info, diffusers_load_config)
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elif model_type in ['Lumina-Next']: # forced pipeline
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from modules.model_lumina import load_lumina
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sd_model = load_lumina(checkpoint_info, diffusers_load_config)
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elif model_type in ['Kolors']: # forced pipeline
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from modules.model_kolors import load_kolors
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sd_model = load_kolors(checkpoint_info, diffusers_load_config)
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elif model_type in ['AuraFlow']: # forced pipeline
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from modules.model_auraflow import load_auraflow
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sd_model = load_auraflow(checkpoint_info, diffusers_load_config)
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elif model_type in ['FLUX']:
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from modules.model_flux import load_flux
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sd_model = load_flux(checkpoint_info, diffusers_load_config)
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elif model_type in ['Stable Diffusion 3']:
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from modules.model_sd3 import load_sd3
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shared.log.debug(f'Load {op}: model="Stable Diffusion 3" variant=medium')
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shared.opts.scheduler = 'Default'
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sd_model = load_sd3(cache_dir=shared.opts.diffusers_dir, config=diffusers_load_config.get('config', None))
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elif model_type in ['Meissonic']: # forced pipeline
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from modules.model_meissonic import load_meissonic
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sd_model = load_meissonic(checkpoint_info, diffusers_load_config)
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['InstaFlow']: # forced pipeline
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try:
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pipeline = diffusers.utils.get_class_from_dynamic_module('instaflow_one_step', module_file='pipeline.py')
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sd_model = pipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['SegMoE']: # forced pipeline
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try:
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from modules.segmoe.segmoe_model import SegMoEPipeline
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sd_model = SegMoEPipeline(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model = sd_model.pipe # segmoe pipe does its stuff in __init__ and __call__ is the original pipeline
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['PixArt-Sigma']: # forced pipeline
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try:
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from modules.model_pixart import load_pixart
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sd_model = load_pixart(checkpoint_info, diffusers_load_config)
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['Lumina-Next']: # forced pipeline
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try:
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from modules.model_lumina import load_lumina
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sd_model = load_lumina(checkpoint_info, diffusers_load_config)
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['Kolors']: # forced pipeline
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try:
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from modules.model_kolors import load_kolors
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sd_model = load_kolors(checkpoint_info, diffusers_load_config)
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['AuraFlow']: # forced pipeline
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try:
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from modules.model_auraflow import load_auraflow
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sd_model = load_auraflow(checkpoint_info, diffusers_load_config)
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['FLUX']:
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try:
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from modules.model_flux import load_flux
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sd_model = load_flux(checkpoint_info, diffusers_load_config)
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type in ['Stable Diffusion 3']:
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try:
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from modules.model_sd3 import load_sd3
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shared.log.debug(f'Load {op}: model="Stable Diffusion 3" variant=medium')
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shared.opts.scheduler = 'Default'
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sd_model = load_sd3(cache_dir=shared.opts.diffusers_dir, config=diffusers_load_config.get('config', None))
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except Exception as e:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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elif model_type is not None and pipeline is not None and 'ONNX' in model_type: # forced pipeline
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try:
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sd_model = pipeline.from_pretrained(checkpoint_info.path)
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except Exception as e:
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shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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else:
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err1, err2, err3 = None, None, None
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if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path):
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if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')):
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shared.log.debug(f'Load {op}: type=pickle')
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diffusers_load_config['use_safetensors'] = False
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if debug_load:
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shared.log.debug(f'Load {op}: args={diffusers_load_config}')
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try: # 1 - autopipeline, best choice but not all pipelines are available
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if sd_model is None:
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if model_type is not None and pipeline is not None and 'ONNX' in model_type: # forced pipeline
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try:
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sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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except ValueError as e:
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if 'no variant default' in str(e):
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shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant')
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diffusers_load_config.pop('variant', None)
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sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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elif 'safetensors found in directory' in str(err1):
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shared.log.warning(f'Load {op}: type=pickle')
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sd_model = pipeline.from_pretrained(checkpoint_info.path)
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except Exception as e:
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shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}')
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if debug_load:
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errors.display(e, 'Load')
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return
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else:
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err1, err2, err3 = None, None, None
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if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path):
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if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')):
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shared.log.debug(f'Load {op}: type=pickle')
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diffusers_load_config['use_safetensors'] = False
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if debug_load:
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shared.log.debug(f'Load {op}: args={diffusers_load_config}')
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try: # 1 - autopipeline, best choice but not all pipelines are available
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try:
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sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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else:
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raise ValueError from e # reraise
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except Exception as e:
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err1 = e
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if debug_load:
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errors.display(e, 'Load AutoPipeline')
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# shared.log.error(f'AutoPipeline: {e}')
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try: # 2 - diffusion pipeline, works for most non-linked pipelines
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if err1 is not None:
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sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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except Exception as e:
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err2 = e
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if debug_load:
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errors.display(e, "Load DiffusionPipeline")
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# shared.log.error(f'DiffusionPipeline: {e}')
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try: # 3 - try basic pipeline just in case
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if err2 is not None:
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sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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except Exception as e:
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err3 = e # ignore last error
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shared.log.error(f"StableDiffusionPipeline: {e}")
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if debug_load:
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errors.display(e, "Load StableDiffusionPipeline")
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if err3 is not None:
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shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}')
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return
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except ValueError as e:
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if 'no variant default' in str(e):
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shared.log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant')
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diffusers_load_config.pop('variant', None)
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sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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elif 'safetensors found in directory' in str(err1):
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shared.log.warning(f'Load {op}: type=pickle')
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diffusers_load_config['use_safetensors'] = False
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sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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else:
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raise ValueError from e # reraise
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except Exception as e:
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err1 = e
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if debug_load:
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errors.display(e, 'Load AutoPipeline')
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# shared.log.error(f'AutoPipeline: {e}')
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try: # 2 - diffusion pipeline, works for most non-linked pipelines
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if err1 is not None:
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sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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except Exception as e:
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err2 = e
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if debug_load:
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errors.display(e, "Load DiffusionPipeline")
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# shared.log.error(f'DiffusionPipeline: {e}')
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try: # 3 - try basic pipeline just in case
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if err2 is not None:
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sd_model = diffusers.StableDiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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sd_model.model_type = sd_model.__class__.__name__
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except Exception as e:
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err3 = e # ignore last error
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shared.log.error(f"StableDiffusionPipeline: {e}")
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if debug_load:
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errors.display(e, "Load StableDiffusionPipeline")
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if err3 is not None:
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shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}')
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return
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elif os.path.isfile(checkpoint_info.path) and checkpoint_info.path.lower().endswith('.safetensors'):
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diffusers_load_config["local_files_only"] = diffusers_version < 28 # must be true for old diffusers, otherwise false but we override config for sd15/sdxl
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diffusers_load_config["extract_ema"] = shared.opts.diffusers_extract_ema
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@@ -1297,7 +1262,7 @@ def load_diffuser(checkpoint_info=None, already_loaded_state_dict=None, timer=No
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errors.display(e, f'loading {op}={checkpoint_info.path} pipeline={shared.opts.diffusers_pipeline}/{sd_model.__class__.__name__}')
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return
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else:
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" failed')
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shared.log.error(f'Load {op}: path="{checkpoint_info.path}" not found')
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return
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if "StableDiffusion" in sd_model.__class__.__name__:
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@@ -1981,7 +1946,11 @@ def remove_token_merging(sd_model):
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def path_to_repo(fn: str = ''):
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repo_id = fn.replace('\\', '/').split('/')
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repo_id = fn.replace('\\', '/')
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if 'models--' in repo_id:
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repo_id = repo_id.split('models--')[-1]
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repo_id = repo_id.split('/')[0]
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repo_id = repo_id.split('/')
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repo_id = '/'.join(repo_id[-2:] if len(repo_id) > 1 else repo_id)
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repo_id = repo_id.replace('models--', '').replace('--', '/')
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return repo_id
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