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https://github.com/vladmandic/automatic
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add tokenizers to explict requirements
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@@ -20,7 +20,7 @@ import tomesd
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from transformers import logging as transformers_logging
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import ldm.modules.midas as midas
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from ldm.util import instantiate_from_config
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from modules import paths, shared, shared_items, shared_state, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_models_compile, sd_hijack_inpainting
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from modules import paths, shared, shared_items, shared_state, modelloader, devices, script_callbacks, sd_vae, errors, hashes, sd_models_config, sd_models_compile, sd_hijack_inpainting
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from modules.timer import Timer
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from modules.memstats import memory_stats
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from modules.paths import models_path, script_path
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@@ -637,7 +637,7 @@ def detect_pipeline(f: str, op: str = 'model', warning=True):
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else:
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guess = 'Stable Diffusion'
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# guess by name
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if 'LCM_' in f or 'LCM-' in f:
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if 'LCM_' in f or 'LCM-' or '_LCM' or '-LCM' in f.upper():
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if shared.backend == shared.Backend.ORIGINAL:
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warn(f'Model detected as LCM model, but attempting to load using backend=original: {op}={f} size={size} MB')
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guess = 'Latent Consistency Model'
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