mirror of
https://github.com/vladmandic/automatic
synced 2026-08-27 07:31:01 +02:00
CalamitousFelicitousness
9d1d7c839a
The generate-time gate compared the stored processor name against the sdp_overrides list, which can never be equal, so the check reduced to the processor name alone and a changed override set was never applied until the next model load. set_diffusers_attention now stamps the override set it applied beside the processor name, the gate compares both, and pipe switches carry the new attribute with the old one.
1706 lines
88 KiB
Python
1706 lines
88 KiB
Python
from enum import Enum
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import sys
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import time
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import copy
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import inspect
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import logging
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import os
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import os.path
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import diffusers
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import diffusers.loaders.single_file_utils
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import torch
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import huggingface_hub as hf
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from modules.logger import log
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from modules import timer, paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_unet, errors, sd_models_compile, sd_detect, model_quant, sd_hijack_te, sd_hijack_vae, sd_hijack_accelerate, sd_hijack_safetensors, sd_hijack_transformers, sd_hijack_hfhub, attention
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from modules.memstats import memory_stats, gpu_stats
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from modules.shared_helpers import walk_files
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from modules.modeldata import model_data
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from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoint_titles, get_closest_checkpoint_match, update_model_hashes, write_metadata, checkpoints_list # pylint: disable=unused-import
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from modules.sd_offload import get_module_names, disable_offload, set_diffuser_offload, apply_balanced_offload, set_accelerate, offload_ondemand, reapply_offload # pylint: disable=unused-import
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from modules.sd_models_utils import NoWatermark, get_signature, get_call, path_to_repo, apply_function_to_model, read_state_dict, get_state_dict_from_checkpoint # pylint: disable=unused-import
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model_dir = "Stable-diffusion"
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model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
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sd_metadata = None
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sd_metadata_pending = 0
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sd_metadata_timer = 0
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loaded_te = None # tracks the text-encoder selection currently loaded, to detect sd_text_encoder changes
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debug_move = log.trace if os.environ.get('SD_MOVE_DEBUG', None) is not None else lambda *args, **kwargs: None
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debug_load = os.environ.get('SD_LOAD_DEBUG', None)
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debug_process = log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
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diffusers_version = int(diffusers.__version__.split('.')[1])
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get_closet_checkpoint_match = get_closest_checkpoint_match # legacy compatibility
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checkpoint_tiles = checkpoint_titles # legacy compatibility
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allow_post_quant = None
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pipe_switch_task_exclude = [
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'AnimateDiffPipeline', 'AnimateDiffSDXLPipeline',
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'FluxControlPipeline',
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'FluxFillPipeline',
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'InstantIRPipeline',
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'LTXConditionPipeline',
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'OmniGenPipeline', 'OmniGen2Pipeline',
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'PhotoMakerStableDiffusionXLPipeline',
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'PixelSmithXLPipeline',
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'StableDiffusion3ControlNetPipeline',
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'StableDiffusionAdapterPipeline', 'StableDiffusionXLAdapterPipeline',
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'StableDiffusionControlNetXSPipeline', 'StableDiffusionXLControlNetXSPipeline',
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'StableDiffusionReferencePipeline',
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'StableDiffusionXLInstantIDPipeline',
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'XOmniPipeline',
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'HunyuanImagePipeline',
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'NucleusMoEImagePipeline',
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'AuraFlowPipeline',
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'ChronoEditPipeline',
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'Kandinsky5I2IPipeline',
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'GoogleNanoBananaPipeline',
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'Step1XEditPipeline',
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'BooguImagePipeline',
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'BooguImageTurboPipeline',
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]
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i2i_pipes = [
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'LEditsPPPipelineStableDiffusion', 'LEditsPPPipelineStableDiffusionXL',
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'OmniGenPipeline', 'OmniGen2Pipeline',
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'StableDiffusionAdapterPipeline', 'StableDiffusionXLAdapterPipeline',
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'StableDiffusionControlNetXSPipeline', 'StableDiffusionXLControlNetXSPipeline',
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'Step1XEditPipeline',
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]
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def set_huggingface_options(quiet=False):
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if shared.opts.diffusers_to_gpu: # and model_type.startswith('Stable Diffusion'):
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sd_hijack_accelerate.hijack_accelerate()
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else:
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sd_hijack_accelerate.restore_accelerate()
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if (shared.opts.runai_streamer_diffusers or shared.opts.runai_streamer_transformers) and (sys.platform == 'linux'):
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if not quiet:
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log.debug(f'Loader: runai enabled chunk={os.environ.get("RUNAI_STREAMER_CHUNK_BYTESIZE", "N/A")} limit={os.environ.get("RUNAI_STREAMER_MEMORY_LIMIT", "N/A")}')
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sd_hijack_safetensors.hijack_safetensors(shared.opts.runai_streamer_diffusers, shared.opts.runai_streamer_transformers)
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else:
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sd_hijack_safetensors.restore_safetensors()
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sd_hijack_hfhub.init_hijack()
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sd_hijack_transformers.hijack_transformers()
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def set_caption_load_options():
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if shared.opts.caption_to_gpu:
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sd_hijack_accelerate.hijack_accelerate()
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else:
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sd_hijack_accelerate.restore_accelerate()
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if (shared.opts.runai_streamer_diffusers or shared.opts.runai_streamer_transformers) and (sys.platform == 'linux'):
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log.debug(f'LLM loader: gpu={shared.opts.caption_to_gpu} runai=True chunk={os.environ.get("RUNAI_STREAMER_CHUNK_BYTESIZE", "N/A")} limit={os.environ.get("RUNAI_STREAMER_MEMORY_LIMIT", "N/A")}')
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sd_hijack_safetensors.hijack_safetensors(shared.opts.runai_streamer_diffusers, shared.opts.runai_streamer_transformers)
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else:
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if shared.opts.caption_to_gpu:
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log.debug(f'LLM loader: gpu={shared.opts.caption_to_gpu}')
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sd_hijack_safetensors.restore_safetensors()
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sd_hijack_hfhub.init_hijack()
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def set_vae_options(sd_model, vae=None, op:str='model', quiet:bool=False):
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ops = {}
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if hasattr(sd_model, "vae"):
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if vae is not None:
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sd_model.vae = vae
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ops['name'] = f"{sd_vae.loaded_vae_file}"
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if shared.opts.diffusers_vae_upcast != 'default':
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sd_model.vae.config.force_upcast = True if shared.opts.diffusers_vae_upcast == 'true' else False
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ops['upcast'] = sd_model.vae.config.force_upcast
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if shared.opts.no_half_vae and op not in {'decode', 'encode'}:
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devices.dtype_vae = torch.float32
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sd_model.vae.to(devices.dtype_vae)
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ops['no-half'] = True
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'enable_slicing') and hasattr(sd_model.vae, 'disable_slicing'):
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ops['slicing'] = shared.opts.diffusers_vae_slicing
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try:
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if shared.opts.diffusers_vae_slicing:
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sd_model.vae.enable_slicing()
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else:
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sd_model.vae.disable_slicing()
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except Exception:
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pass
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'enable_tiling') and hasattr(sd_model.vae, 'disable_tiling'):
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ops['tiling'] = shared.opts.diffusers_vae_tiling
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try:
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if shared.opts.diffusers_vae_tiling:
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'config') and hasattr(sd_model.vae.config, 'sample_size') and isinstance(sd_model.vae.config.sample_size, int):
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if getattr(sd_model.vae, "tile_sample_min_size_backup", None) is None:
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sd_model.vae.tile_sample_min_size_backup = sd_model.vae.tile_sample_min_size
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sd_model.vae.tile_latent_min_size_backup = sd_model.vae.tile_latent_min_size
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sd_model.vae.tile_overlap_factor_backup = sd_model.vae.tile_overlap_factor
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if shared.opts.diffusers_vae_tile_size > 0:
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sd_model.vae.tile_sample_min_size = int(shared.opts.diffusers_vae_tile_size)
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sd_model.vae.tile_latent_min_size = int(shared.opts.diffusers_vae_tile_size / (2 ** (len(sd_model.vae.config.block_out_channels) - 1)))
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else:
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sd_model.vae.tile_sample_min_size = getattr(sd_model.vae, "tile_sample_min_size_backup", sd_model.vae.tile_sample_min_size)
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sd_model.vae.tile_latent_min_size = getattr(sd_model.vae, "tile_latent_min_size_backup", sd_model.vae.tile_latent_min_size)
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if shared.opts.diffusers_vae_tile_overlap != 0.25:
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sd_model.vae.tile_overlap_factor = float(shared.opts.diffusers_vae_tile_overlap)
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else:
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sd_model.vae.tile_overlap_factor = getattr(sd_model.vae, "tile_overlap_factor_backup", sd_model.vae.tile_overlap_factor)
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ops['tile'] = sd_model.vae.tile_sample_min_size
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ops['overlap'] = sd_model.vae.tile_overlap_factor
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sd_model.vae.enable_tiling()
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else:
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sd_model.vae.disable_tiling()
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except Exception:
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pass
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if hasattr(sd_model, "vqvae"):
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ops['upcast'] = True
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sd_model.vqvae.to(torch.float32) # vqvae is producing nans in fp16
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if not quiet and len(ops) > 0:
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fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
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log.quiet(quiet, f'Setting {op}: component=vae {ops} fn={fn}')
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def set_diffuser_options(sd_model, vae=None, op:str='model', offload:bool=True, quiet:bool=False):
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if sd_model is None:
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log.warning(f'{op} is not loaded')
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return
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if hasattr(sd_model, "watermark"):
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sd_model.watermark = NoWatermark()
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if not (hasattr(sd_model, "has_accelerate") and sd_model.has_accelerate):
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sd_model.has_accelerate = False
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clear_caches()
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set_vae_options(sd_model, vae, op, quiet)
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attention.set_diffusers_attention(sd_model, quiet)
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if shared.opts.diffusers_fuse_projections and hasattr(sd_model, 'fuse_qkv_projections'):
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try:
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sd_model.fuse_qkv_projections()
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log.quiet(quiet, f'Setting {op}: fused-qkv=True')
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except Exception as e:
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log.error(f'Setting {op}: fused-qkv=True {e}')
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if shared.opts.diffusers_fuse_projections and hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'fuse_qkv_projections'):
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try:
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sd_model.transformer.fuse_qkv_projections()
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log.quiet(quiet, f'Setting {op}: fused-qkv=True')
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except Exception as e:
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log.error(f'Setting {op}: fused-qkv=True {e}')
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if shared.opts.diffusers_eval:
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log.debug(f'Setting {op}: eval=True')
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def eval_model(model, op=None, sd_model=None): # pylint: disable=unused-argument
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if hasattr(model, "requires_grad_"):
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model.requires_grad_(False)
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model.eval()
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return model
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sd_model = apply_function_to_model(sd_model, eval_model, ["Model", "VAE", "TE"], op="eval")
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if shared.opts.opt_channelslast and hasattr(sd_model, 'unet'):
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log.quiet(quiet, f'Setting {op}: channels-last=True')
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sd_model.unet.to(memory_format=torch.channels_last)
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for module_name in get_module_names(sd_model):
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module = getattr(sd_model, module_name, None)
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if hasattr(module, "quantization_config") and getattr(module.quantization_config, "quant_method", None) == "sdnq":
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if module_name.startswith("text_encoder"):
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if shared.opts.sdnq_quantize_matmul_mode_te == "Same as model":
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sdnq_use_quantized_matmul = shared.opts.sdnq_quantize_matmul_mode != "disabled"
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else:
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sdnq_use_quantized_matmul = shared.opts.sdnq_quantize_matmul_mode_te != "disabled"
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else:
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sdnq_use_quantized_matmul = shared.opts.sdnq_quantize_matmul_mode != "disabled"
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if module.quantization_config.use_quantized_matmul != sdnq_use_quantized_matmul:
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from sdnq.loader import apply_sdnq_options_to_model
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# log.debug(f'Setting {op} {module_name}: sdnq_use_quantized_matmul={sdnq_use_quantized_matmul}')
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module = apply_sdnq_options_to_model(module, use_quantized_matmul=sdnq_use_quantized_matmul)
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setattr(sd_model, module_name, module)
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if offload:
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set_diffuser_offload(sd_model, op, quiet)
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def move_model(model, device=None, force=False):
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def set_execution_device(module, device):
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if device == torch.device('cpu'):
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return
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if hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device"): # pylint: disable=protected-access
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try:
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"""
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for k, v in module.named_parameters(recurse=True):
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if v.device == torch.device('meta'):
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from accelerate.utils import set_module_tensor_to_device
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set_module_tensor_to_device(module, k, device, tied_params_map=module._hf_hook.tied_params_map)
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"""
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module._hf_hook.execution_device = device # pylint: disable=protected-access
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# module._hf_hook.offload = True
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except Exception as e:
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if os.environ.get('SD_MOVE_DEBUG', None):
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log.error(f'Model move execution device: device={device} {e}')
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if model is None or device is None:
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return
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if getattr(model, 'sdnext_ondemand', False) and device == devices.device: # on-demand components onload at their entry points instead of pre-moves
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return
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if hasattr(model, 'pipe'):
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move_model(model.pipe, device, force)
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fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
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if getattr(model, 'vae', None) is not None and get_diffusers_task(model) != DiffusersTaskType.TEXT_2_IMAGE:
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if (device == devices.device) and (model.vae.device.type != "meta") and not getattr(model.vae, 'sdnext_ondemand', False): # force vae back to gpu if not in txt2img mode; on-demand vaes onload at their entry point instead
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model.vae.to(device)
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if hasattr(model.vae, '_hf_hook'):
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debug_move(f'Model move: to={device} class={model.vae.__class__} fn={fn}') # pylint: disable=protected-access
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model.vae._hf_hook.execution_device = device # pylint: disable=protected-access
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if hasattr(model, "components"): # accelerate patch
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for name, m in model.components.items():
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if not hasattr(m, "_hf_hook"): # not accelerate hook
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break
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if not isinstance(m, torch.nn.Module) or name in getattr(model, '_exclude_from_cpu_offload', []): # modular pipelines lack the attr
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continue
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for module in m.modules():
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set_execution_device(module, device)
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# set_execution_device(model, device)
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if getattr(model, 'has_accelerate', False) and not force:
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return
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if hasattr(model, "device") and devices.normalize_device(model.device) == devices.normalize_device(device) and not force:
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return
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try:
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t0 = time.time()
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try:
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if hasattr(model, 'device') and model.device == torch.device('meta'):
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set_execution_device(model, device)
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elif hasattr(model, 'to'):
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if device == devices.device and getattr(model, 'sdnext_ondemand_modules', None):
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pass # the group engine already placed every component; a pipe-level move would only drag on-demand components to the accelerator for the trailing eviction to undo
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else:
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model.to(device)
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if hasattr(model, "prior_pipe"):
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model.prior_pipe.to(device)
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if device == devices.device:
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offload_ondemand(model) # a bulk move must not strand on-demand components on the accelerator; their entry points onload them when needed
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except Exception as e0:
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if 'Cannot copy out of meta tensor' in str(e0) or 'must be Tensor, not NoneType' in str(e0):
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if hasattr(model, "components"):
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for _name, component in model.components.items():
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if hasattr(component, 'modules'):
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for module in component.modules():
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try:
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if hasattr(module, 'to'):
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module.to(device)
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except Exception as e2:
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if 'Cannot copy out of meta tensor' in str(e2):
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if os.environ.get('SD_MOVE_DEBUG', None):
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log.warning(f'Model move meta: module={module.__class__}')
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module.to_empty(device=device)
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elif 'enable_sequential_cpu_offload' in str(e0):
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pass # ignore model move if sequential offload is enabled
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elif 'Params4bit' in str(e0) or 'Params8bit' in str(e0):
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pass # ignore model move if quantization is enabled
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elif 'already been set to the correct devices' in str(e0):
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pass # ignore errors on pre-quant models
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elif 'Casting a quantized model to' in str(e0):
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pass # ignore errors on quantized models
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else:
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raise e0
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t1 = time.time()
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except Exception as e1:
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t1 = time.time()
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log.warning(f'Model move: device={device} {e1}')
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if 'move' not in timer.process.records:
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timer.process.records['move'] = 0
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timer.process.records['move'] += t1 - t0
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if os.environ.get('SD_MOVE_DEBUG', None) is not None or (t1-t0) > 2:
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log.debug(f'Model move: device={device} class={model.__class__.__name__} accelerate={getattr(model, "has_accelerate", False)} fn={fn} time={t1-t0:.2f}') # pylint: disable=protected-access
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devices.torch_gc()
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def move_base(model, device):
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if hasattr(model, 'transformer'):
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key = 'transformer'
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elif hasattr(model, 'unet'):
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key = 'unet'
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else:
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log.warning(f'Model move: model={model.__class__} device={device} key=unknown')
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return None
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log.debug(f'Model move: module={key} device={device}')
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model = getattr(model, key)
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R = model.device
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move_model(model, device)
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return R
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def load_diffuser_initial(diffusers_load_config: dict, op='model'):
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sd_model = None
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checkpoint_info = None
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ckpt_basename = os.path.basename(shared.cmd_opts.ckpt)
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model_name = modelloader.find_diffuser(ckpt_basename)
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if model_name is not None:
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log.info(f'Load model {op}: path="{model_name}"')
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model_file = modelloader.download_diffusers_model(hub_id=model_name, variant=diffusers_load_config.get('variant', None))
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try:
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log.debug(f'Load {op}: config={diffusers_load_config}')
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diffusers_load_config.pop('cache_dir', None)
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sd_model = diffusers.DiffusionPipeline.from_pretrained(model_file, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
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except Exception as e:
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log.error(f'Failed loading model: {model_file} {e}')
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errors.display(e, f'Load {op}: path="{model_file}"')
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return None, None
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list_models() # rescan for downloaded model
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checkpoint_info = CheckpointInfo(model_name)
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return sd_model, checkpoint_info
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def hf_prefetch_configs(checkpoint_info: CheckpointInfo | str, diffusers_load_config: dict, op='model'):
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# diffusers pipeline downloads build subfolder config allow-patterns with os.path.join, and huggingface_hub>=1.22
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# matches patterns with fnmatchcase which does not normalize separators (huggingface/huggingface_hub#4435),
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# so on windows component config.json files are never downloaded and the incomplete snapshot
|
|
# still passes the diffusers cache-completeness check so it is never repaired on load
|
|
# prefetching configs with forward-slash patterns both avoids and repairs such snapshots
|
|
if os.name != 'nt' or shared.opts.offline_mode:
|
|
return
|
|
repo_id = path_to_repo(checkpoint_info)
|
|
if repo_id is None or '/' not in repo_id or os.path.exists(repo_id): # local folder or single-file model
|
|
return
|
|
try:
|
|
t0 = time.time()
|
|
hf.snapshot_download(repo_id, cache_dir=shared.opts.diffusers_dir, revision=diffusers_load_config.get('revision', None), allow_patterns=['*/config.json'])
|
|
log.debug(f'Load {op}: repo="{repo_id}" prefetch=configs time={time.time()-t0:.2f}')
|
|
except Exception as e:
|
|
if debug_load:
|
|
log.debug(f'Load {op}: repo="{repo_id}" prefetch=configs {e}')
|
|
|
|
|
|
def load_diffuser_force(detected_model_type: str, checkpoint_info: CheckpointInfo, diffusers_load_config: dict, op='model'):
|
|
import sdnq # pylint: disable=unused-import
|
|
sd_model = None
|
|
global allow_post_quant # pylint: disable=global-statement
|
|
unload_model_weights(op=op)
|
|
shared.sd_model = None
|
|
model_type = detected_model_type.removesuffix('SDNQ').strip()
|
|
try:
|
|
if model_type in ['Stable Cascade']:
|
|
from pipelines.model_stablecascade import load_cascade_combined
|
|
sd_model = load_cascade_combined(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['InstaFlow']:
|
|
from pipelines.model_instaflow import load_instaflow
|
|
sd_model = load_instaflow(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['SegMoE']:
|
|
from pipelines.model_segmoe import load_segmoe
|
|
sd_model = load_segmoe(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['PixArtAlpha']:
|
|
from pipelines.model_pixart import load_pixart_alpha
|
|
sd_model = load_pixart_alpha(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['PixArtSigma']:
|
|
from pipelines.model_pixart import load_pixart_sigma
|
|
sd_model = load_pixart_sigma(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Sana']:
|
|
from pipelines.model_sana import load_sana
|
|
sd_model = load_sana(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['LuminaNext']:
|
|
from pipelines.model_lumina import load_lumina
|
|
sd_model = load_lumina(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['LuminaDiMOO']:
|
|
from pipelines.model_lumina import load_lumina_dimoo
|
|
sd_model = load_lumina_dimoo(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Kolors']:
|
|
from pipelines.model_kolors import load_kolors
|
|
sd_model = load_kolors(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['AuraFlow']:
|
|
from pipelines.model_auraflow import load_auraflow
|
|
sd_model = load_auraflow(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['UltraFlux']:
|
|
from pipelines.model_ultraflux import load_ultraflux
|
|
sd_model = load_ultraflux(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['FLUX']:
|
|
from pipelines.model_flux import load_flux
|
|
sd_model = load_flux(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['FLUX2']:
|
|
from pipelines.model_flux2 import load_flux2
|
|
sd_model = load_flux2(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['FLUX2Klein']:
|
|
from pipelines.model_flux2_klein import load_flux2_klein
|
|
sd_model = load_flux2_klein(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['FLEX']:
|
|
from pipelines.model_flex import load_flex
|
|
sd_model = load_flex(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['ZetaChroma']:
|
|
from pipelines.model_zetachroma import load_zetachroma
|
|
sd_model = load_zetachroma(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Chroma']:
|
|
from pipelines.model_chroma import load_chroma
|
|
sd_model = load_chroma(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Lumina2']:
|
|
from pipelines.model_lumina import load_lumina2
|
|
sd_model = load_lumina2(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Stable Diffusion 3']:
|
|
from pipelines.model_sd3 import load_sd3
|
|
sd_model = load_sd3(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['CogView3']:
|
|
from pipelines.model_cogview import load_cogview3
|
|
sd_model = load_cogview3(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['CogView4']:
|
|
from pipelines.model_cogview import load_cogview4
|
|
sd_model = load_cogview4(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Meissonic']:
|
|
from pipelines.model_meissonic import load_meissonic
|
|
sd_model = load_meissonic(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['OmniGen2']:
|
|
from pipelines.model_omnigen import load_omnigen2
|
|
sd_model = load_omnigen2(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['OmniGen']:
|
|
from pipelines.model_omnigen import load_omnigen
|
|
sd_model = load_omnigen(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['HiDreamO1']:
|
|
from pipelines.model_hidream import load_hidream_o1
|
|
sd_model = load_hidream_o1(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['HiDream']:
|
|
from pipelines.model_hidream import load_hidream
|
|
sd_model = load_hidream(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Cosmos']:
|
|
from pipelines.model_cosmos import load_cosmos_t2i
|
|
sd_model = load_cosmos_t2i(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Anima']:
|
|
from pipelines.model_anima import load_anima
|
|
sd_model = load_anima(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['FLite']:
|
|
from pipelines.model_flite import load_flite
|
|
sd_model = load_flite(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['WanAI']:
|
|
from pipelines.model_wanai import load_wan
|
|
sd_model = load_wan(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['MiniMaxH3']:
|
|
from pipelines.model_minimax import load_minimax
|
|
sd_model = load_minimax(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['ChronoEdit']:
|
|
from pipelines.model_chrono import load_chrono
|
|
sd_model = load_chrono(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Bria']:
|
|
from pipelines.model_bria import load_bria
|
|
sd_model = load_bria(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Step1XEdit']:
|
|
from pipelines.model_step1x_edit import load_step1x_edit
|
|
sd_model = load_step1x_edit(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['VIBE']:
|
|
from pipelines.model_vibe import load_vibe
|
|
sd_model = load_vibe(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['JoyEdit']:
|
|
from pipelines.model_joy import load_joyedit
|
|
sd_model = load_joyedit(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Qwen']:
|
|
from pipelines.model_qwen import load_qwen
|
|
sd_model = load_qwen(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Boogu']:
|
|
from pipelines.model_boogu import load_boogu
|
|
sd_model = load_boogu(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['HunyuanDiT']:
|
|
from pipelines.model_hunyuandit import load_hunyuandit
|
|
sd_model = load_hunyuandit(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Lens']:
|
|
from pipelines.model_lens import load_lens
|
|
sd_model = load_lens(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Kandinsky21']:
|
|
from pipelines.model_kandinsky import load_kandinsky21
|
|
sd_model = load_kandinsky21(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['Kandinsky22']:
|
|
from pipelines.model_kandinsky import load_kandinsky22
|
|
sd_model = load_kandinsky22(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
elif model_type in ['Kandinsky30']:
|
|
from pipelines.model_kandinsky import load_kandinsky3
|
|
sd_model = load_kandinsky3(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Kandinsky50']:
|
|
from pipelines.model_kandinsky import load_kandinsky5
|
|
sd_model = load_kandinsky5(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['NextStep']:
|
|
from pipelines.model_nextstep import load_nextstep
|
|
sd_model = load_nextstep(checkpoint_info, diffusers_load_config) # pylint: disable=assignment-from-none
|
|
allow_post_quant = False
|
|
elif model_type in ['HunyuanImage']:
|
|
from pipelines.model_hyimage import load_hyimage
|
|
sd_model = load_hyimage(checkpoint_info, diffusers_load_config) # pylint: disable=assignment-from-none
|
|
allow_post_quant = False
|
|
elif model_type in ['HunyuanImage3']:
|
|
from pipelines.model_hyimage import load_hyimage3
|
|
sd_model = load_hyimage3(checkpoint_info, diffusers_load_config) # pylint: disable=assignment-from-none
|
|
allow_post_quant = False
|
|
elif model_type in ['XOmni']:
|
|
from pipelines.model_xomni import load_xomni
|
|
sd_model = load_xomni(checkpoint_info, diffusers_load_config) # pylint: disable=assignment-from-none
|
|
allow_post_quant = False
|
|
elif model_type in ['NanoBanana']:
|
|
from pipelines.model_google import load_nanobanana
|
|
sd_model = load_nanobanana(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type == 'PRXPixel':
|
|
from pipelines.model_prx_pixel import load_prx_pixel
|
|
sd_model = load_prx_pixel(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type == 'PRX':
|
|
from pipelines.model_prx import load_prx
|
|
sd_model = load_prx(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['ERNIEImage']:
|
|
from pipelines.model_ernie import load_ernie_image
|
|
sd_model = load_ernie_image(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['NucleusImage']:
|
|
from pipelines.model_nucleus import load_nucleus
|
|
sd_model = load_nucleus(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['ZImage']:
|
|
from pipelines.model_z_image import load_z_image
|
|
sd_model = load_z_image(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Krea2']:
|
|
from pipelines.model_krea2 import load_krea2
|
|
sd_model = load_krea2(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['Ideogram4']:
|
|
from pipelines.model_ideogram4 import load_ideogram4
|
|
sd_model = load_ideogram4(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['LongCat']:
|
|
from pipelines.model_longcat import load_longcat
|
|
sd_model = load_longcat(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['OvisImage']:
|
|
from pipelines.model_ovis import load_ovis
|
|
sd_model = load_ovis(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['GLMImage']:
|
|
from pipelines.model_glm import load_glm_image
|
|
sd_model = load_glm_image(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['SDXS']:
|
|
from pipelines.model_sdxs import load_sdxs
|
|
sd_model = load_sdxs(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['SeFi']:
|
|
from pipelines.model_sefi import load_sefi
|
|
sd_model = load_sefi(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = False
|
|
elif model_type in ['MageFlow']:
|
|
from pipelines.model_mageflow import load_mageflow
|
|
sd_model = load_mageflow(checkpoint_info, diffusers_load_config)
|
|
allow_post_quant = True
|
|
except Exception as e:
|
|
log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
|
|
errors.display(e, 'Load')
|
|
return None, True
|
|
if sd_model is not None:
|
|
return sd_model, True
|
|
else:
|
|
return sd_model, False
|
|
|
|
|
|
def load_diffuser_folder(model_type: str, pipeline, checkpoint_info: CheckpointInfo, diffusers_load_config: dict, op='model'):
|
|
sd_model = None
|
|
files = walk_files(checkpoint_info.path, ['.safetensors', '.bin', '.ckpt'])
|
|
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
|
|
diffusers_load_config['variant'] = 'fp16'
|
|
|
|
err0, err1, err2, err3 = None, None, None, None
|
|
if os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path):
|
|
if os.path.exists(os.path.join(checkpoint_info.path, 'unet', 'diffusion_pytorch_model.bin')):
|
|
log.debug(f'Load {op}: type=pickle')
|
|
diffusers_load_config['use_safetensors'] = False
|
|
if debug_load:
|
|
log.debug(f'Load {op}: args={diffusers_load_config}')
|
|
|
|
try: #0 - using detected model type and pipeline
|
|
if (model_type is not None) and (pipeline is not None):
|
|
if ('sdnq' in model_type.lower()) or ('sdnq' in checkpoint_info.path.lower()):
|
|
global allow_post_quant # pylint: disable=global-statement
|
|
allow_post_quant = False
|
|
sd_model = pipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
|
|
sd_model.model_type = sd_model.__class__.__name__
|
|
except Exception as e:
|
|
err0 = e
|
|
if debug_load:
|
|
errors.display(e, 'Load Detected')
|
|
|
|
try: # 1 - autopipeline, best choice but not all pipelines are available
|
|
try:
|
|
if err0 is not None:
|
|
sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
|
|
sd_model.model_type = sd_model.__class__.__name__
|
|
except ValueError as e:
|
|
if 'no variant default' in str(e):
|
|
log.warning(f'Load {op}: variant={diffusers_load_config["variant"]} model="{checkpoint_info.path}" using default variant')
|
|
diffusers_load_config.pop('variant', None)
|
|
sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
|
|
sd_model.model_type = sd_model.__class__.__name__
|
|
elif 'safetensors found in directory' in str(err1):
|
|
log.warning(f'Load {op}: type=pickle')
|
|
diffusers_load_config['use_safetensors'] = False
|
|
sd_model = diffusers.AutoPipelineForText2Image.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
|
|
sd_model.model_type = sd_model.__class__.__name__
|
|
else:
|
|
raise ValueError from e # reraise
|
|
except Exception as e:
|
|
err1 = e
|
|
if debug_load:
|
|
errors.display(e, 'Load AutoPipeline')
|
|
|
|
try: # 2 - diffusion pipeline, works for most non-linked pipelines
|
|
if err1 is not None:
|
|
sd_model = diffusers.DiffusionPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
|
|
sd_model.model_type = sd_model.__class__.__name__
|
|
except Exception as e:
|
|
err2 = e
|
|
if debug_load:
|
|
errors.display(e, "Load DiffusionPipeline")
|
|
|
|
try: # 3 - try basic pipeline just in case
|
|
if err2 is not None:
|
|
sd_model = diffusers.StableDiffusionXLPipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
|
|
sd_model.model_type = sd_model.__class__.__name__
|
|
except Exception as e:
|
|
err3 = e
|
|
if debug_load:
|
|
errors.display(e, "Load StableDiffusionPipeline")
|
|
|
|
if sd_model is None:
|
|
log.error(f'Load {op}: path="{checkpoint_info.path}" pipeline={pipeline.__class__.__name__} type="{model_type}"')
|
|
log.error(f'Load {op}: attempt detected: {err0}')
|
|
log.error(f'Load {op}: attempt auto: {err1}')
|
|
log.error(f'Load {op}: attempt diffusion: {err2}')
|
|
log.error(f'Load {op}: attempt base: {err3}')
|
|
return None
|
|
|
|
return sd_model
|
|
|
|
|
|
def load_diffuser_file(model_type: str, pipeline, checkpoint_info: CheckpointInfo, diffusers_load_config: dict, op='model'):
|
|
sd_model = None
|
|
diffusers_load_config["extract_ema"] = shared.opts.diffusers_extract_ema
|
|
if pipeline is None:
|
|
log.error(f'Load {op}: pipeline={shared.opts.diffusers_pipeline} not initialized')
|
|
return None
|
|
try:
|
|
if model_type.startswith('Stable Diffusion'):
|
|
if shared.opts.diffusers_force_zeros:
|
|
diffusers_load_config['force_zeros_for_empty_prompt '] = shared.opts.diffusers_force_zeros
|
|
else:
|
|
model_config = sd_detect.get_load_config(checkpoint_info.path, model_type, config_type='json')
|
|
if model_config is not None:
|
|
if debug_load:
|
|
log.debug(f'Load {op}: config="{model_config}"')
|
|
diffusers_load_config['config'] = model_config
|
|
if model_type.startswith('Stable Diffusion 3'):
|
|
from pipelines.model_sd3 import load_sd3
|
|
sd_model = load_sd3(checkpoint_info, diffusers_load_config)
|
|
elif hasattr(pipeline, 'from_single_file'):
|
|
diffusers.loaders.single_file_utils.CHECKPOINT_KEY_NAMES["clip"] = "cond_stage_model.transformer.text_model.embeddings.position_embedding.weight" # patch for diffusers==0.28.0
|
|
diffusers_load_config['safety_checker'] = None # sd15 specific but we cant know ahead of time
|
|
diffusers_load_config['requires_safety_checker'] = False # sd15 specific but we cant know ahead of time
|
|
diffusers_load_config['use_safetensors'] = True
|
|
diffusers_load_config.pop('cache_dir', None)
|
|
if shared.opts.stream_load:
|
|
diffusers_load_config['disable_mmap'] = True
|
|
if shared.opts.disable_accelerate:
|
|
from diffusers.utils import import_utils
|
|
import_utils._accelerate_available = False # pylint: disable=protected-access
|
|
sd_model = pipeline.from_single_file(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
|
|
# sd_model = patch_diffuser_config(sd_model, checkpoint_info.path)
|
|
elif hasattr(pipeline, 'from_ckpt'):
|
|
diffusers_load_config['cache_dir'] = shared.opts.hfcache_dir
|
|
sd_model = pipeline.from_ckpt(checkpoint_info.path, **diffusers_load_config)
|
|
else:
|
|
log.error(f'Load {op}: file="{checkpoint_info.path}" {shared.opts.diffusers_pipeline} cannot load safetensor model')
|
|
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 sd_model is not None:
|
|
diffusers_load_config.pop('vae', None)
|
|
diffusers_load_config.pop('safety_checker', None)
|
|
diffusers_load_config.pop('requires_safety_checker', None)
|
|
diffusers_load_config.pop('config_files', None)
|
|
diffusers_load_config.pop('local_files_only', None)
|
|
log.debug(f'Setting {op}: pipeline={sd_model.__class__.__name__} config={diffusers_load_config}') # pylint: disable=protected-access
|
|
except Exception as e:
|
|
log.error(f'Load {op}: file="{checkpoint_info.path}" pipeline={shared.opts.diffusers_pipeline} config={diffusers_load_config} {e}')
|
|
if 'Weights for this component appear to be missing in the checkpoint' in str(e):
|
|
log.error(f'Load {op}: file="{checkpoint_info.path}" is not a complete model')
|
|
else:
|
|
errors.display(e, 'Load')
|
|
return None
|
|
return sd_model
|
|
|
|
|
|
def load_sdnq_module(fn: str, module_name: str, load_method: str):
|
|
t0 = time.time()
|
|
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)
|
|
try:
|
|
import sdnq
|
|
module = sdnq.load_sdnq_model(
|
|
model_path=model_name,
|
|
quantization_config=quantization_config,
|
|
device=devices.device if shared.opts.diffusers_to_gpu else devices.cpu,
|
|
dtype=devices.dtype,
|
|
load_method=load_method,
|
|
)
|
|
t1 = time.time()
|
|
return module, module_name, t1 - t0
|
|
except Exception as e:
|
|
log.error(f'Load sdnq: model="{fn}" module="{module_name}" {e}')
|
|
errors.display(e, 'Load')
|
|
return None, module_name, 0
|
|
|
|
|
|
def load_sdnq_model(checkpoint_info: CheckpointInfo, pipeline, diffusers_load_config: dict, op: str):
|
|
modules = {}
|
|
global allow_post_quant # pylint: disable=global-statement
|
|
allow_post_quant = False
|
|
t0 = time.time()
|
|
|
|
if shared.opts.runai_streamer_diffusers and (sys.platform == 'linux'):
|
|
load_method = 'streamer'
|
|
from installer import install
|
|
install('runai_model_streamer>=0.15.1')
|
|
elif shared.opts.sd_parallel_load:
|
|
load_method = 'threaded'
|
|
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}')
|
|
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(
|
|
checkpoint_info.path,
|
|
cache_dir=shared.opts.diffusers_dir,
|
|
**modules,
|
|
**diffusers_load_config,
|
|
)
|
|
return sd_model
|
|
|
|
|
|
def set_overrides(sd_model, checkpoint_info: CheckpointInfo, model_type: str):
|
|
checkpoint_info_name = checkpoint_info.name.lower()
|
|
if "Kandinsky" in sd_model.__class__.__name__:
|
|
sd_model.scheduler.name = 'DDIM'
|
|
elif (
|
|
checkpoint_info.path.lower().endswith('.safetensors')
|
|
and model_type.startswith("Stable Diffusion") and model_type != "Stable Diffusion 3"
|
|
): # SDXL and SD 1.5
|
|
scheduler_config = sd_model.scheduler.config
|
|
# scheduler_config['beta_schedule'] = 'scaled_linear'
|
|
# scheduler_config['timestep_spacing'] = 'trailing'
|
|
sd_model.scheduler = diffusers.EulerAncestralDiscreteScheduler.from_config(scheduler_config)
|
|
if 'bigaspv25' in checkpoint_info_name or 'noobai-rf' in checkpoint_info_name or ('flow' in checkpoint_info_name and 'flower' not in checkpoint_info_name):
|
|
scheduler_config = sd_model.scheduler.config
|
|
scheduler_config['prediction_type'] = 'flow_prediction'
|
|
scheduler_config['beta_schedule'] = 'linear'
|
|
scheduler_config['use_flow_sigmas'] = True
|
|
scheduler_config["flow_shift"] = 2.5
|
|
sd_model.scheduler = diffusers.UniPCMultistepScheduler.from_config(scheduler_config)
|
|
log.info(f'Setting override: model="{checkpoint_info.name}" component=scheduler prediction="flow-prediction"')
|
|
elif 'vpred' in checkpoint_info_name or 'v-pred' in checkpoint_info_name or 'v_pred' in checkpoint_info_name:
|
|
scheduler_config = sd_model.scheduler.config
|
|
scheduler_config['prediction_type'] = 'v_prediction'
|
|
scheduler_config['beta_schedule'] = 'scaled_linear'
|
|
scheduler_config['rescale_betas_zero_snr'] = True
|
|
sd_model.scheduler = diffusers.EulerAncestralDiscreteScheduler.from_config(scheduler_config)
|
|
log.info(f'Setting override: model="{checkpoint_info.name}" component=scheduler prediction="v-prediction" rescale=True')
|
|
else:
|
|
try:
|
|
from safetensors import safe_open
|
|
with safe_open(checkpoint_info.path, framework='pt') as f:
|
|
keys = f.keys()
|
|
if 'v_pred' in keys: # NoobAI VPred models added empty v_pred and ztsnr keys
|
|
scheduler_config = sd_model.scheduler.config
|
|
scheduler_config['prediction_type'] = 'v_prediction'
|
|
scheduler_config['beta_schedule'] = 'scaled_linear'
|
|
if 'ztsnr' in keys:
|
|
scheduler_config['rescale_betas_zero_snr'] = True
|
|
sd_model.scheduler = diffusers.EulerAncestralDiscreteScheduler.from_config(scheduler_config)
|
|
log.info(f'Setting override: model="{checkpoint_info.name}" component=scheduler prediction="v-prediction" rescale={scheduler_config.get("rescale_betas_zero_snr", False)}')
|
|
except Exception as e:
|
|
log.debug(f'Setting override from keys failed: {e}')
|
|
|
|
|
|
def set_defaults(sd_model, checkpoint_info: CheckpointInfo):
|
|
sd_model.sd_model_hash = checkpoint_info.calculate_shorthash() # pylint: disable=attribute-defined-outside-init
|
|
sd_model.sd_checkpoint_info = checkpoint_info # pylint: disable=attribute-defined-outside-init
|
|
sd_model.sd_model_checkpoint = checkpoint_info.filename # pylint: disable=attribute-defined-outside-init
|
|
if hasattr(sd_model, "prior_pipe"):
|
|
sd_model.default_scheduler = copy.deepcopy(sd_model.prior_pipe.scheduler) if hasattr(sd_model.prior_pipe, "scheduler") else None
|
|
else:
|
|
sd_model.default_scheduler = copy.deepcopy(sd_model.scheduler) if hasattr(sd_model, "scheduler") else None
|
|
sd_model.is_sdxl = False # a1111 compatibility item
|
|
sd_model.is_sd2 = hasattr(sd_model, 'cond_stage_model') and hasattr(sd_model.cond_stage_model, 'model') # a1111 compatibility item
|
|
sd_model.is_sd1 = not sd_model.is_sd2 # a1111 compatibility item
|
|
sd_model.logvar = sd_model.logvar.to(devices.device) if hasattr(sd_model, 'logvar') else None # fix for training
|
|
shared.opts.data["sd_checkpoint_hash"] = checkpoint_info.sha256
|
|
if hasattr(sd_model, "set_progress_bar_config"):
|
|
sd_model.set_progress_bar_config(bar_format='Progress {rate_fmt}{postfix} {bar:15} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining}', ncols=120, colour='#327fba')
|
|
|
|
|
|
def load_diffuser(checkpoint_info: CheckpointInfo | None = None, op='model', revision=None): # pylint: disable=unused-argument
|
|
global allow_post_quant # pylint: disable=global-statement
|
|
allow_post_quant = True # assume default
|
|
logging.getLogger("diffusers").setLevel(logging.ERROR)
|
|
timer.load.record("diffusers")
|
|
diffusers_load_config = {
|
|
"low_cpu_mem_usage": True,
|
|
"torch_dtype": devices.dtype,
|
|
"load_connected_pipeline": True,
|
|
}
|
|
if shared.opts.stream_load:
|
|
diffusers_load_config['disable_mmap'] = True
|
|
if shared.opts.offload_state_dict:
|
|
diffusers_load_config['offload_state_dict'] = True
|
|
diffusers_load_config['offload_folder'] = paths.temp_dir
|
|
if revision is not None:
|
|
diffusers_load_config['revision'] = revision
|
|
if shared.opts.diffusers_model_load_variant != 'default':
|
|
diffusers_load_config['variant'] = shared.opts.diffusers_model_load_variant
|
|
if shared.opts.diffusers_pipeline == 'Custom Diffusers Pipeline' and len(shared.opts.custom_diffusers_pipeline) > 0:
|
|
log.debug(f'Model pipeline: pipeline="{shared.opts.custom_diffusers_pipeline}"')
|
|
diffusers_load_config['custom_pipeline'] = shared.opts.custom_diffusers_pipeline
|
|
if shared.opts.data.get('sd_model_checkpoint', '') == 'model.safetensors' or shared.opts.data.get('sd_model_checkpoint', '') == '':
|
|
shared.opts.data['sd_model_checkpoint'] = "stabilityai/stable-diffusion-xl-base-1.0"
|
|
|
|
if (op == 'model' or op == 'dict'):
|
|
if (model_data.sd_model is not None) and (checkpoint_info is not None) and (getattr(model_data.sd_model, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_model.sd_checkpoint_info.hash): # trying to load the same model
|
|
return
|
|
else:
|
|
if (model_data.sd_refiner is not None) and (checkpoint_info is not None) and (getattr(model_data.sd_refiner, 'sd_checkpoint_info', None) is not None) and (checkpoint_info.hash == model_data.sd_refiner.sd_checkpoint_info.hash): # trying to load the same model
|
|
return
|
|
|
|
sd_model = None
|
|
handled = False
|
|
try:
|
|
# initial load only
|
|
if sd_model is None:
|
|
if shared.cmd_opts.ckpt is not None and os.path.isdir(shared.cmd_opts.ckpt) and model_data.initial:
|
|
sd_model, checkpoint_info = load_diffuser_initial(diffusers_load_config, op)
|
|
|
|
# unload current model
|
|
checkpoint_info = checkpoint_info or select_checkpoint(op=op)
|
|
if checkpoint_info is None:
|
|
unload_model_weights(op=op)
|
|
return
|
|
|
|
# handle offline mode
|
|
if shared.opts.offline_mode:
|
|
log.info(f'Load {op}: offline=True')
|
|
diffusers_load_config["local_files_only"] = True
|
|
os.environ['HF_HUB_OFFLINE'] = '1'
|
|
else:
|
|
os.environ.pop('HF_HUB_OFFLINE', None)
|
|
os.unsetenv('HF_HUB_OFFLINE')
|
|
|
|
# detect pipeline
|
|
pipeline, model_type = sd_detect.detect_pipeline(checkpoint_info, op)
|
|
set_huggingface_options()
|
|
|
|
# preload vae so it can be used as param
|
|
vae = None
|
|
sd_vae.loaded_vae_file = None
|
|
if model_type is None:
|
|
log.error(f'Load {op}: pipeline={shared.opts.diffusers_pipeline} not detected')
|
|
return
|
|
|
|
hf_prefetch_configs(checkpoint_info, diffusers_load_config, op)
|
|
|
|
vae_file = None
|
|
if model_type.startswith('Stable Diffusion') and (op == 'model' or op == 'refiner'): # preload vae for sd models
|
|
vae_file, vae_source = sd_vae.resolve_vae(checkpoint_info.filename)
|
|
vae = sd_vae.load_vae(checkpoint_info.path, vae_file, vae_source)
|
|
if vae is not None:
|
|
diffusers_load_config["vae"] = vae
|
|
timer.load.record("vae")
|
|
|
|
# load with custom loader
|
|
if sd_model is None and not handled:
|
|
sd_model, handled = load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op)
|
|
if sd_model is not None and not sd_model:
|
|
log.error(f'Load {op}: type="{model_type}" pipeline="{pipeline}" not loaded')
|
|
return
|
|
|
|
# load sdnq-prequantized model
|
|
if sd_model is None and not handled:
|
|
if model_type.endswith('SDNQ'):
|
|
sd_model = load_sdnq_model(checkpoint_info, pipeline, diffusers_load_config, op)
|
|
model_type = model_type.replace(' SDNQ', '')
|
|
|
|
# load from single-file
|
|
if sd_model is None and not handled:
|
|
if os.path.isfile(checkpoint_info.path) and checkpoint_info.path.lower().endswith('.safetensors'):
|
|
sd_model = load_diffuser_file(model_type, pipeline, checkpoint_info, diffusers_load_config, op)
|
|
|
|
# load from hf folder-style
|
|
if sd_model is None and not handled:
|
|
if os.path.isdir(checkpoint_info.path) or (checkpoint_info.type == 'huggingface') or (checkpoint_info.type == 'transformer') or (checkpoint_info.type == 'reference'):
|
|
sd_model = load_diffuser_folder(model_type, pipeline, checkpoint_info, diffusers_load_config, op)
|
|
|
|
if sd_model is None:
|
|
log.error(f'Load {op}: name="{checkpoint_info.name if checkpoint_info is not None else None}" not loaded')
|
|
return
|
|
|
|
# a family loader that returns None falls through to the generic folder loader, and for a modular pipeline that
|
|
# yields an object holding nothing but its from_config helpers, since a modular load registers the fetched
|
|
# components empty until load_components runs. it reports as a loaded model and then fails on the first
|
|
# component something reaches, far from the cause
|
|
specs = getattr(sd_model, '_component_specs', None) # pylint: disable=protected-access
|
|
if isinstance(specs, dict):
|
|
fetched = [name for name, spec in specs.items() if getattr(spec, 'default_creation_method', None) == 'from_pretrained']
|
|
if len(fetched) > 0 and all(getattr(sd_model, name, None) is None for name in fetched):
|
|
log.error(f'Load {op}: name="{checkpoint_info.name if checkpoint_info is not None else None}" cls={sd_model.__class__.__name__} no components loaded')
|
|
return
|
|
|
|
set_overrides(sd_model, checkpoint_info, model_type)
|
|
set_defaults(sd_model, checkpoint_info)
|
|
|
|
if hasattr(sd_model, "unet") and model_type not in ['Stable Cascade']: # others calls load_diffuser again
|
|
sd_unet.load_unet(sd_model, checkpoint_info.path)
|
|
|
|
add_noise_pred_to_diffusers_callback(sd_model)
|
|
|
|
timer.load.record("load")
|
|
|
|
if op == 'refiner':
|
|
model_data.sd_refiner = sd_model
|
|
else:
|
|
model_data.sd_model = sd_model
|
|
|
|
reload_text_encoder(initial=True) # must be before embeddings
|
|
timer.load.record("te")
|
|
|
|
if debug_load:
|
|
log.trace(f'Model components: {list(get_signature(sd_model).values())}')
|
|
|
|
from modules import textual_inversion
|
|
sd_model.embedding_db = textual_inversion.EmbeddingDatabase()
|
|
sd_model.embedding_db.add_embedding_dir(shared.opts.embeddings_dir)
|
|
sd_model.embedding_db.load_textual_inversion_embeddings(force_reload=True)
|
|
timer.load.record("embeddings")
|
|
|
|
from modules import prompt_parser_diffusers
|
|
prompt_parser_diffusers.insert_parser_highjack(sd_model.__class__.__name__)
|
|
prompt_parser_diffusers.cache.clear()
|
|
|
|
set_diffuser_options(sd_model, vae, op, offload=False)
|
|
attention.set_attention_dispatcher(sd_model)
|
|
sd_model = model_quant.do_post_load_quant(sd_model, allow=allow_post_quant) # run this before move model so it can be compressed in CPU
|
|
timer.load.record("options")
|
|
|
|
set_diffuser_offload(sd_model, op)
|
|
|
|
if op == 'model' and not (os.path.isdir(checkpoint_info.path) or checkpoint_info.type == 'huggingface'):
|
|
if getattr(shared.sd_model, 'sd_checkpoint_info', None) is not None and vae_file is not None:
|
|
sd_vae.apply_vae_config(shared.sd_model.sd_checkpoint_info.filename, vae_file, sd_model)
|
|
# move_model(sd_model, devices.device)
|
|
timer.load.record("move")
|
|
|
|
except Exception as e:
|
|
log.error(f"Load {op}: {e}")
|
|
errors.display(e, "Model")
|
|
|
|
try:
|
|
if shared.opts.ipex_optimize:
|
|
sd_model = sd_models_compile.ipex_optimize(sd_model)
|
|
|
|
if (shared.opts.cuda_compile_backend != 'none') and len(shared.opts.cuda_compile) > 0:
|
|
if 'components' in shared.opts.cuda_compile_options:
|
|
sd_model = sd_models_compile.compile_diffusers(sd_model, apply_to_components=True)
|
|
else:
|
|
if 'Model' in shared.opts.cuda_compile:
|
|
if hasattr(sd_model, "unet"):
|
|
sd_model.unet = sd_models_compile.compile_diffusers(sd_model.unet, apply_to_components=False)
|
|
if hasattr(sd_model, "transformer"):
|
|
sd_model.transformer = sd_models_compile.compile_diffusers(sd_model.transformer, apply_to_components=False)
|
|
if 'TE' in shared.opts.cuda_compile:
|
|
if hasattr(sd_model, "text_encoder"):
|
|
sd_model.text_encoder = sd_models_compile.compile_diffusers(sd_model.text_encoder, apply_to_components=False)
|
|
if hasattr(sd_model, "text_encoder_2"):
|
|
sd_model.text_encoder_2 = sd_models_compile.compile_diffusers(sd_model.text_encoder_2, apply_to_components=False)
|
|
if hasattr(sd_model, "text_encoder_3"):
|
|
sd_model.text_encoder_3 = sd_models_compile.compile_diffusers(sd_model.text_encoder_3, apply_to_components=False)
|
|
if 'VAE' in shared.opts.cuda_compile:
|
|
if hasattr(sd_model, "vae"):
|
|
sd_model.vae = sd_models_compile.compile_diffusers(sd_model.vae, apply_to_components=False)
|
|
|
|
timer.load.record("compile")
|
|
except Exception as e:
|
|
log.error(f"Compile {op}: {e}")
|
|
errors.display(e, "Compile")
|
|
|
|
if shared.opts.diffusers_offload_mode != 'balanced':
|
|
devices.torch_gc(force=True, reason='load')
|
|
|
|
if sd_model is not None:
|
|
script_callbacks.model_loaded_callback(sd_model)
|
|
|
|
if debug_load:
|
|
from modules import modelstats
|
|
modelstats.analyze()
|
|
|
|
log.info(f"Load {op}: type={shared.sd_model_type} time={timer.load.dct()} native={get_native(sd_model)} memory={memory_stats()}")
|
|
|
|
from modules.platform import cleanup
|
|
cleanup()
|
|
|
|
shared.opts.save(silent=True)
|
|
|
|
|
|
class DiffusersTaskType(Enum):
|
|
TEXT_2_IMAGE = 1
|
|
IMAGE_2_IMAGE = 2
|
|
INPAINTING = 3
|
|
INSTRUCT = 4
|
|
MODULAR = 5
|
|
|
|
|
|
def get_diffusers_task(pipe: diffusers.DiffusionPipeline) -> DiffusersTaskType:
|
|
cls = pipe.__class__.__name__
|
|
if cls in i2i_pipes: # special case
|
|
return DiffusersTaskType.IMAGE_2_IMAGE
|
|
elif 'ImageToVideo' in cls or cls in ['LTXConditionPipeline', 'StableVideoDiffusionPipeline']: # i2v pipelines
|
|
return DiffusersTaskType.IMAGE_2_IMAGE
|
|
elif 'Instruct' in cls:
|
|
return DiffusersTaskType.INSTRUCT
|
|
elif 'Modular' in cls:
|
|
return DiffusersTaskType.MODULAR
|
|
elif pipe.__class__ in diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING.values():
|
|
return DiffusersTaskType.IMAGE_2_IMAGE
|
|
elif pipe.__class__ in diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING.values():
|
|
return DiffusersTaskType.INPAINTING
|
|
else:
|
|
return DiffusersTaskType.TEXT_2_IMAGE
|
|
|
|
|
|
def switch_pipe(cls: type[diffusers.DiffusionPipeline] | str, pipeline: diffusers.DiffusionPipeline | None = None, force = False, args: dict | None = None):
|
|
"""
|
|
args:
|
|
- cls: can be pipeline class or a string from custom pipelines
|
|
for example: diffusers.StableDiffusionPipeline or 'mixture_tiling'
|
|
- pipeline: source model to be used, if not provided currently loaded model is used
|
|
- args: any additional components to load into the pipeline
|
|
for example: { 'vae': None }
|
|
"""
|
|
try:
|
|
if args is None:
|
|
args = {}
|
|
if isinstance(cls, str):
|
|
log.debug(f'Pipeline switch: custom={cls}')
|
|
cls_object = diffusers.utils.get_class_from_dynamic_module(cls, module_file='pipeline.py')
|
|
if not cls_object:
|
|
log.error(f"Pipeline switch: Failed to get class for '{cls}'")
|
|
if shared.sd_model is not None:
|
|
return shared.sd_model
|
|
raise RuntimeError("Pipeline switch: No existing pipeline to fall back to")
|
|
else:
|
|
cls_object = cls
|
|
if pipeline is None:
|
|
if shared.sd_model is None:
|
|
raise RuntimeError("Pipeline switch: No existing pipeline to use as default")
|
|
pipeline = shared.sd_model
|
|
new_pipe = None
|
|
signature = get_signature(cls_object)
|
|
possible = signature.keys()
|
|
if not force and isinstance(pipeline, cls_object) and args == {}:
|
|
return pipeline
|
|
pipe_dict = {}
|
|
components_used = []
|
|
components_skipped = []
|
|
components_missing = []
|
|
switch_mode = 'none'
|
|
if hasattr(pipeline, '_internal_dict'):
|
|
for item in pipeline._internal_dict.keys(): # pylint: disable=protected-access
|
|
if item in possible:
|
|
pipe_dict[item] = getattr(pipeline, item, None)
|
|
components_used.append(item)
|
|
else:
|
|
components_skipped.append(item)
|
|
for item in possible:
|
|
if item in ['self', 'args', 'kwargs']: # skip
|
|
continue
|
|
if signature[item].default != inspect._empty: # has default value so we dont have to worry about it # pylint: disable=protected-access
|
|
continue
|
|
if item not in components_used:
|
|
log.warning(f'Pipeling switch: missing component={item} type={signature[item].annotation}')
|
|
pipe_dict[item] = None # try but not likely to work
|
|
components_missing.append(item)
|
|
new_pipe = cls_object(**pipe_dict)
|
|
switch_mode = 'auto'
|
|
elif 'tokenizer_2' in possible and hasattr(pipeline, 'tokenizer_2'):
|
|
new_pipe = cls_object(
|
|
vae=pipeline.vae,
|
|
text_encoder=pipeline.text_encoder,
|
|
text_encoder_2=pipeline.text_encoder_2,
|
|
tokenizer=pipeline.tokenizer,
|
|
tokenizer_2=pipeline.tokenizer_2,
|
|
unet=pipeline.unet,
|
|
scheduler=pipeline.scheduler,
|
|
feature_extractor=getattr(pipeline, 'feature_extractor', None),
|
|
)
|
|
move_model(new_pipe, pipeline.device)
|
|
switch_mode = 'sdxl'
|
|
elif 'tokenizer' in possible and hasattr(pipeline, 'tokenizer'):
|
|
new_pipe = cls_object(
|
|
vae=pipeline.vae,
|
|
text_encoder=pipeline.text_encoder,
|
|
tokenizer=pipeline.tokenizer,
|
|
unet=pipeline.unet,
|
|
scheduler=pipeline.scheduler,
|
|
feature_extractor=getattr(pipeline, 'feature_extractor', None),
|
|
requires_safety_checker=False,
|
|
safety_checker=None,
|
|
)
|
|
move_model(new_pipe, pipeline.device)
|
|
switch_mode = 'sd'
|
|
else:
|
|
log.error(f'Pipeline switch error: {pipeline.__class__.__name__} unrecognized')
|
|
return pipeline
|
|
if new_pipe is not None:
|
|
for k, v in args.items():
|
|
if k in possible:
|
|
setattr(new_pipe, k, v)
|
|
components_used.append(k)
|
|
else:
|
|
log.warning(f'Pipeline switch skipping unknown: component={k}')
|
|
components_skipped.append(k)
|
|
if new_pipe is not None:
|
|
copy_diffuser_options(new_pipe, pipeline)
|
|
sd_hijack_te.init_hijack(new_pipe)
|
|
if hasattr(new_pipe, "watermark"):
|
|
new_pipe.watermark = NoWatermark()
|
|
if switch_mode == 'auto':
|
|
log.debug(f'Pipeline switch: from={pipeline.__class__.__name__} to={new_pipe.__class__.__name__} components={components_used} skipped={components_skipped} missing={components_missing}')
|
|
else:
|
|
log.debug(f'Pipeline switch: from={pipeline.__class__.__name__} to={new_pipe.__class__.__name__} mode={switch_mode}')
|
|
return new_pipe
|
|
else:
|
|
log.error(f'Pipeline switch error: from={pipeline.__class__.__name__} to={cls_object.__name__} empty pipeline')
|
|
except Exception as e:
|
|
log.error(f'Pipeline switch error: from={pipeline.__class__.__name__} to={cls if isinstance(cls, str) else cls.__name__} {e}')
|
|
errors.display(e, 'Pipeline switch')
|
|
return pipeline
|
|
|
|
|
|
def clean_diffuser_pipe(pipe):
|
|
if pipe is not None and shared.sd_model_type == 'sdxl' and hasattr(pipe, 'config') and 'requires_aesthetics_score' in pipe.config and hasattr(pipe, '_internal_dict'):
|
|
debug_process(f'Pipeline clean: {pipe.__class__.__name__}')
|
|
# diffusers adds requires_aesthetics_score with img2img and complains if requires_aesthetics_score exist in txt2img
|
|
internal_dict = dict(pipe._internal_dict) # pylint: disable=protected-access
|
|
internal_dict.pop('requires_aesthetics_score', None)
|
|
del pipe._internal_dict
|
|
pipe.register_to_config(**internal_dict)
|
|
|
|
|
|
def copy_diffuser_options(new_pipe, orig_pipe):
|
|
new_pipe.sd_checkpoint_info = getattr(orig_pipe, 'sd_checkpoint_info', None)
|
|
new_pipe.sd_model_checkpoint = getattr(orig_pipe, 'sd_model_checkpoint', None)
|
|
new_pipe.embedding_db = getattr(orig_pipe, 'embedding_db', None)
|
|
new_pipe.loaded_loras = getattr(orig_pipe, 'loaded_loras', {})
|
|
new_pipe.sd_model_hash = getattr(orig_pipe, 'sd_model_hash', None)
|
|
new_pipe.has_accelerate = getattr(orig_pipe, 'has_accelerate', False)
|
|
new_pipe.current_attn_name = getattr(orig_pipe, 'current_attn_name', None)
|
|
new_pipe.current_attn_overrides = getattr(orig_pipe, 'current_attn_overrides', None)
|
|
new_pipe.default_scheduler = getattr(orig_pipe, 'default_scheduler', None)
|
|
new_pipe.image_encoder = getattr(orig_pipe, 'image_encoder', None)
|
|
new_pipe.feature_extractor = getattr(orig_pipe, 'feature_extractor', None)
|
|
new_pipe.mask_processor = getattr(orig_pipe, 'mask_processor', None)
|
|
new_pipe.restore_pipeline = getattr(orig_pipe, 'restore_pipeline', None)
|
|
new_pipe.is_sdxl = getattr(orig_pipe, 'is_sdxl', False) # a1111 compatibility item
|
|
new_pipe.is_sd2 = getattr(orig_pipe, 'is_sd2', False)
|
|
new_pipe.is_sd1 = getattr(orig_pipe, 'is_sd1', True)
|
|
add_noise_pred_to_diffusers_callback(new_pipe)
|
|
if getattr(new_pipe, 'task_args', None) is None:
|
|
new_pipe.task_args = {}
|
|
new_pipe.task_args.update(getattr(orig_pipe, 'task_args', {}))
|
|
if new_pipe.has_accelerate:
|
|
set_accelerate(new_pipe)
|
|
|
|
|
|
def backup_pipe_components(pipe):
|
|
if pipe is None:
|
|
return {}
|
|
return {
|
|
'sd_checkpoint_info': getattr(pipe, "sd_checkpoint_info", None),
|
|
'sd_model_checkpoint': getattr(pipe, "sd_model_checkpoint", None),
|
|
'embedding_db': getattr(pipe, "embedding_db", None),
|
|
'loaded_loras': getattr(pipe, "loaded_loras", {}),
|
|
'sd_model_hash': getattr(pipe, "sd_model_hash", None),
|
|
'has_accelerate': getattr(pipe, "has_accelerate", None),
|
|
'current_attn_name': getattr(pipe, "current_attn_name", None),
|
|
'current_attn_overrides': getattr(pipe, "current_attn_overrides", None),
|
|
'default_scheduler': getattr(pipe, "default_scheduler", None),
|
|
'image_encoder': getattr(pipe, "image_encoder", None),
|
|
'feature_extractor': getattr(pipe, "feature_extractor", None),
|
|
'mask_processor': getattr(pipe, "mask_processor", None),
|
|
'restore_pipeline': getattr(pipe, "restore_pipeline", None),
|
|
'task_args': getattr(pipe, "task_args", None),
|
|
'hijack_prompt': hasattr(pipe, "orig_encode_prompt"),
|
|
'hijack_vae': hasattr(pipe, "vae") and hasattr(pipe.vae, "orig_decode")
|
|
}
|
|
|
|
|
|
def restore_pipe_components(pipe, components):
|
|
if pipe is None or components is None:
|
|
return
|
|
pipe.sd_checkpoint_info = components['sd_checkpoint_info']
|
|
pipe.sd_model_checkpoint = components['sd_model_checkpoint']
|
|
pipe.embedding_db = components['embedding_db']
|
|
pipe.loaded_loras = components['loaded_loras'] if components['loaded_loras'] is not None else {}
|
|
pipe.sd_model_hash = components['sd_model_hash']
|
|
pipe.has_accelerate = components['has_accelerate']
|
|
pipe.current_attn_name = components['current_attn_name']
|
|
pipe.current_attn_overrides = components.get('current_attn_overrides')
|
|
pipe.default_scheduler = components['default_scheduler']
|
|
|
|
if components['image_encoder'] is not None:
|
|
pipe.image_encoder = components['image_encoder']
|
|
if components['feature_extractor'] is not None:
|
|
pipe.feature_extractor = components['feature_extractor']
|
|
if components['mask_processor'] is not None:
|
|
pipe.mask_processor = components['mask_processor']
|
|
if components['restore_pipeline'] is not None:
|
|
pipe.restore_pipeline = components['restore_pipeline']
|
|
if components['task_args'] is not None:
|
|
pipe.task_args = components['task_args']
|
|
if components['hijack_prompt']:
|
|
sd_hijack_te.init_hijack(pipe)
|
|
if components['hijack_vae']:
|
|
sd_hijack_vae.init_hijack(pipe)
|
|
|
|
if pipe.__class__.__name__ in ['FluxPipeline', 'StableDiffusion3Pipeline']:
|
|
pipe.register_modules(image_encoder = components['image_encoder'])
|
|
pipe.register_modules(feature_extractor = components['feature_extractor'])
|
|
|
|
|
|
def set_diffuser_pipe(pipe, new_pipe_type):
|
|
has_errors = False
|
|
if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
|
|
clean_diffuser_pipe(pipe)
|
|
|
|
if hasattr(pipe, 'no_task_switch'):
|
|
del pipe.no_task_switch
|
|
return pipe
|
|
if get_diffusers_task(pipe) == new_pipe_type:
|
|
return pipe
|
|
|
|
if get_diffusers_task(pipe) == DiffusersTaskType.MODULAR:
|
|
return pipe
|
|
|
|
# skip specific pipelines
|
|
cls = pipe.__class__.__name__
|
|
if cls in pipe_switch_task_exclude:
|
|
return pipe
|
|
if 'Video' in cls:
|
|
return pipe
|
|
if 'Onnx' in cls:
|
|
return pipe
|
|
|
|
# in some cases we want to reset the pipeline to parent as they dont have their own variants
|
|
if (new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE) or (new_pipe_type == DiffusersTaskType.INPAINTING):
|
|
if cls == 'StableDiffusionPAGPipeline':
|
|
pipe = switch_pipe(diffusers.StableDiffusionPipeline, pipe)
|
|
if cls == 'StableDiffusionXLPAGPipeline':
|
|
pipe = switch_pipe(diffusers.StableDiffusionXLPipeline, pipe)
|
|
|
|
new_pipe = None
|
|
components_backup = backup_pipe_components(pipe)
|
|
|
|
if hasattr(pipe, 'config'): # real pipeline which can be auto-switched
|
|
try:
|
|
if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
|
|
new_pipe = diffusers.AutoPipelineForText2Image.from_pipe(pipe)
|
|
elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
|
|
new_pipe = diffusers.AutoPipelineForImage2Image.from_pipe(pipe)
|
|
elif new_pipe_type == DiffusersTaskType.INPAINTING:
|
|
new_pipe = diffusers.AutoPipelineForInpainting.from_pipe(pipe)
|
|
else:
|
|
log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={cls}')
|
|
return pipe
|
|
except Exception as e: # pylint: disable=unused-variable
|
|
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
|
|
log.trace(f"Pipeline class change requested: target={new_pipe_type} fn={fn}") # pylint: disable=protected-access
|
|
log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={cls} {e}')
|
|
if debug_load:
|
|
errors.display(e, 'Pipeline switch')
|
|
has_errors = True
|
|
if not hasattr(pipe, 'config') or has_errors:
|
|
try: # maybe a wrapper pipeline so just change the class
|
|
if new_pipe_type == DiffusersTaskType.TEXT_2_IMAGE:
|
|
pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING, cls) # pylint: disable=protected-access
|
|
new_pipe = pipe
|
|
elif new_pipe_type == DiffusersTaskType.IMAGE_2_IMAGE:
|
|
pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING, cls) # pylint: disable=protected-access
|
|
new_pipe = pipe
|
|
elif new_pipe_type == DiffusersTaskType.INPAINTING:
|
|
pipe.__class__ = diffusers.pipelines.auto_pipeline._get_task_class(diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING, cls) # pylint: disable=protected-access
|
|
new_pipe = pipe
|
|
else:
|
|
log.error(f'Pipeline class set failed: type={new_pipe_type} pipeline={cls}')
|
|
return pipe
|
|
except Exception as e: # pylint: disable=unused-variable
|
|
log.warning(f'Pipeline class set failed: type={new_pipe_type} pipeline={cls} {e}')
|
|
if debug_load:
|
|
errors.display(e, 'Pipeline switch')
|
|
has_errors = True
|
|
return pipe
|
|
|
|
if new_pipe is None:
|
|
return pipe
|
|
|
|
restore_pipe_components(new_pipe, components_backup)
|
|
components_backup = None # free memory
|
|
|
|
new_pipe.is_sdxl = getattr(pipe, 'is_sdxl', False) # a1111 compatibility item
|
|
new_pipe.is_sd2 = getattr(pipe, 'is_sd2', False)
|
|
new_pipe.is_sd1 = getattr(pipe, 'is_sd1', True)
|
|
if hasattr(new_pipe, 'watermark'):
|
|
new_pipe.watermark = NoWatermark()
|
|
add_noise_pred_to_diffusers_callback(new_pipe)
|
|
|
|
if hasattr(new_pipe, 'pipe'): # also handle nested pipelines
|
|
new_pipe.pipe = set_diffuser_pipe(new_pipe.pipe, new_pipe_type)
|
|
add_noise_pred_to_diffusers_callback(new_pipe.pipe)
|
|
|
|
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
|
|
log.debug(f"Pipeline class change: source={cls} target={new_pipe.__class__.__name__} fn={fn}") # pylint: disable=protected-access
|
|
|
|
if shared.opts.diffusers_offload_mode == 'none' and hasattr(pipe, 'device'):
|
|
move_model(new_pipe, pipe.device)
|
|
else:
|
|
set_diffuser_offload(new_pipe, op='model')
|
|
|
|
pipe = new_pipe
|
|
return pipe
|
|
|
|
|
|
def add_noise_pred_to_diffusers_callback(pipe):
|
|
if not hasattr(pipe, "_callback_tensor_inputs"):
|
|
return pipe
|
|
if pipe.__class__.__name__.startswith("Anima"):
|
|
return pipe
|
|
if pipe.__class__.__name__.startswith("ErnieImage"):
|
|
return pipe
|
|
if pipe.__class__.__name__.startswith("StableCascade") and ("predicted_image_embedding" not in pipe._callback_tensor_inputs): # pylint: disable=protected-access
|
|
pipe.prior_pipe._callback_tensor_inputs.append("predicted_image_embedding") # pylint: disable=protected-access
|
|
elif "noise_pred" not in pipe._callback_tensor_inputs: # pylint: disable=protected-access
|
|
if pipe.__class__.__name__.startswith("StableDiffusion"):
|
|
pipe._callback_tensor_inputs.append("noise_pred") # pylint: disable=protected-access
|
|
elif hasattr(pipe, "scheduler") and "flow" in pipe.scheduler.__class__.__name__.lower():
|
|
pipe._callback_tensor_inputs.append("noise_pred") # pylint: disable=protected-access
|
|
elif hasattr(pipe, "scheduler") and hasattr(pipe.scheduler, "config") and (getattr(pipe.scheduler.config, "prediction_type", "none") == "flow_prediction"):
|
|
pipe._callback_tensor_inputs.append("noise_pred") # pylint: disable=protected-access
|
|
elif hasattr(pipe, "default_scheduler") and ("flow" in pipe.default_scheduler.__class__.__name__.lower()):
|
|
pipe._callback_tensor_inputs.append("noise_pred") # pylint: disable=protected-access
|
|
elif hasattr(pipe, "default_scheduler") and hasattr(pipe.default_scheduler, "config") and (getattr(pipe.default_scheduler.config, "prediction_type", "none") == "flow_prediction"):
|
|
pipe._callback_tensor_inputs.append("noise_pred") # pylint: disable=protected-access
|
|
return pipe
|
|
|
|
|
|
def get_native(pipe: diffusers.DiffusionPipeline):
|
|
if hasattr(pipe, "vae") and hasattr(pipe.vae, "config") and hasattr(pipe.vae.config, "sample_size"):
|
|
size = pipe.vae.config.sample_size # Stable Diffusion
|
|
elif hasattr(pipe, "movq") and hasattr(pipe.movq.config, "sample_size"):
|
|
size = pipe.movq.config.sample_size # Kandinsky
|
|
elif hasattr(pipe, "unet") and hasattr(pipe.unet.config, "sample_size"):
|
|
size = pipe.unet.config.sample_size
|
|
else:
|
|
size = 0
|
|
return size
|
|
|
|
|
|
def reload_text_encoder(initial=False):
|
|
global loaded_te # pylint: disable=global-statement
|
|
te = shared.opts.sd_text_encoder
|
|
if initial and (te is None or te == 'Default'):
|
|
loaded_te = te
|
|
return # dont unload
|
|
if not initial and te == loaded_te:
|
|
return # selection unchanged since it was loaded
|
|
if shared.sd_model is None:
|
|
loaded_te = te
|
|
return
|
|
signature = get_signature(shared.sd_model)
|
|
t5 = [k for k, v in signature.items() if 'T5EncoderModel' in str(v)]
|
|
if hasattr(shared.sd_model, 'text_encoder') and te is not None and 'vit' in te.lower():
|
|
from modules.model_te import set_clip
|
|
set_clip(pipe=shared.sd_model)
|
|
elif len(t5) > 0:
|
|
from modules.model_te import set_t5
|
|
log.debug(f'Load module: type=t5 path="{te}" module="{t5[0]}"')
|
|
set_t5(pipe=shared.sd_model, module=t5[0], t5=te, cache_dir=shared.opts.hfcache_dir)
|
|
elif hasattr(shared.sd_model, 'text_encoder_3'):
|
|
from modules.model_te import set_t5
|
|
log.debug(f'Load module: type=t5 path="{te}" module="text_encoder_3"')
|
|
set_t5(pipe=shared.sd_model, module='text_encoder_3', t5=te, cache_dir=shared.opts.hfcache_dir)
|
|
elif not initial:
|
|
# generic text encoder with no in-place swap path (e.g. Qwen3-VL): reload the model so the
|
|
# newly selected encoder is read at load time. loaded_te is set first so the reload's own
|
|
# initial=True call is a no-op rather than recursing.
|
|
log.info(f'Load module: type=te name="{te}" reloading model to apply')
|
|
loaded_te = te
|
|
reload_model_weights(force=True)
|
|
return
|
|
loaded_te = te
|
|
clear_caches(full=True)
|
|
apply_balanced_offload(shared.sd_model)
|
|
|
|
|
|
def reload_model_weights(sd_model=None, info: CheckpointInfo | None = None, op='model', force=False, revision=None):
|
|
global loaded_te # pylint: disable=global-statement
|
|
checkpoint_info = info or select_checkpoint(op=op) # are we selecting model or dictionary
|
|
if checkpoint_info is None:
|
|
unload_model_weights(op=op)
|
|
return None
|
|
jobid = shared.state.begin('Load model')
|
|
if sd_model is None:
|
|
sd_model = model_data.sd_model if op == 'model' or op == 'dict' else model_data.sd_refiner
|
|
loaded_ckpt: CheckpointInfo | None = getattr(sd_model, 'sd_checkpoint_info', None) if sd_model is not None else None
|
|
changed_checkpoint = loaded_ckpt is None or checkpoint_info is None or loaded_ckpt.filename != checkpoint_info.filename
|
|
reset_unet = shared.opts.sd_unet not in (None, 'Default', 'None')
|
|
reset_unet_secondary = shared.opts.sd_unet_secondary not in (None, 'Default', 'None')
|
|
reset_te = shared.opts.sd_text_encoder not in (None, 'Default', 'None')
|
|
if op == 'model' and sd_model is not None and changed_checkpoint and (reset_unet or reset_unet_secondary or reset_te):
|
|
# compare detected model type, not pipeline class: custom-loader arches (e.g. Krea2) load as a
|
|
# concrete class but detect as generic DiffusionPipeline, so a class compare would falsely reset
|
|
# across same-arch checkpoints (Base vs Turbo). detect both sides so the comparison is symmetric.
|
|
try:
|
|
_, new_type = sd_detect.detect_pipeline(checkpoint_info, op)
|
|
_, old_type = sd_detect.detect_pipeline(loaded_ckpt, op) if loaded_ckpt is not None else (None, None)
|
|
except Exception:
|
|
new_type = old_type = None
|
|
if new_type is not None and old_type is not None and new_type != old_type: # architecture changed: custom components no longer fit
|
|
if reset_unet:
|
|
log.info(f'Load model: type="{old_type}" changed="{new_type}" unet="{shared.opts.sd_unet}" set to default')
|
|
shared.opts.data["sd_unet"] = 'Default'
|
|
sd_unet.loaded_unet = None
|
|
if reset_unet_secondary:
|
|
log.info(f'Load model: type="{old_type}" changed="{new_type}" unet_secondary="{shared.opts.sd_unet_secondary}" set to default')
|
|
shared.opts.data["sd_unet_secondary"] = 'Default'
|
|
sd_unet.loaded_unet_secondary = None
|
|
if reset_te:
|
|
log.info(f'Load model: type="{old_type}" changed="{new_type}" te="{shared.opts.sd_text_encoder}" set to default')
|
|
shared.opts.data["sd_text_encoder"] = 'Default'
|
|
loaded_te = None
|
|
if sd_model is None: # previous model load failed
|
|
current_checkpoint_info = None
|
|
else:
|
|
current_checkpoint_info = getattr(sd_model, 'sd_checkpoint_info', None)
|
|
if current_checkpoint_info is not None and checkpoint_info is not None and current_checkpoint_info.filename == checkpoint_info.filename and not force:
|
|
shared.state.end(jobid)
|
|
return None
|
|
else:
|
|
move_model(sd_model, devices.cpu)
|
|
unload_model_weights(op=op)
|
|
sd_model = None
|
|
timer.load = timer.Timer()
|
|
# TODO model load: implement model in-memory caching
|
|
timer.load.record("config")
|
|
if sd_model is None or force:
|
|
sd_model = None
|
|
load_diffuser(checkpoint_info, op=op, revision=revision)
|
|
shared.state.end(jobid)
|
|
if op == 'model':
|
|
shared.opts.data["sd_model_checkpoint"] = checkpoint_info.title
|
|
return model_data.sd_model
|
|
else:
|
|
shared.opts.data["sd_model_refiner"] = checkpoint_info.title
|
|
return model_data.sd_refiner
|
|
shared.state.end(jobid)
|
|
return None # should not be here
|
|
|
|
|
|
def clear_caches(full: bool = False):
|
|
from modules import prompt_parser_diffusers, memstats, sd_offload
|
|
from modules.lora import lora_common, lora_load
|
|
prompt_parser_diffusers.cache.clear()
|
|
memstats.reset_stats()
|
|
lora_common.loaded_networks.clear()
|
|
lora_common.previously_loaded_networks.clear()
|
|
lora_load.lora_cache.clear()
|
|
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
|
|
log.debug(f'Cache clear: full={full} fn={fn}')
|
|
if full:
|
|
sd_offload.offload_hook_instance = None
|
|
|
|
|
|
def unload_model_weights(op='model'):
|
|
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
|
|
if model_data.sd_model or model_data.sd_refiner:
|
|
clear_caches(full=True)
|
|
devices.torch_reset()
|
|
if shared.compiled_model_state is not None:
|
|
shared.compiled_model_state.compiled_cache.clear()
|
|
shared.compiled_model_state.req_cache.clear()
|
|
shared.compiled_model_state.partitioned_modules.clear()
|
|
if (op == 'model' or op == 'dict') and model_data.sd_model:
|
|
log.debug(f'Current {op}: {memory_stats()}')
|
|
if not ('Model' in shared.opts.cuda_compile and (shared.opts.cuda_compile_backend == "openvino_fx" or shared.opts.cuda_compile_backend == "openvino")):
|
|
disable_offload(model_data.sd_model)
|
|
move_model(model_data.sd_model, 'meta')
|
|
model_data.sd_model = None
|
|
from sdnq.common import reset_compile_caches
|
|
reset_compile_caches() # dead compiled-dequant graphs and their lifetime recompile counters otherwise accumulate across switches
|
|
devices.torch_gc(force=True, reason='unload')
|
|
log.debug(f'Unload {op}: {memory_stats()} fn={fn}')
|
|
elif (op == 'refiner') and model_data.sd_refiner:
|
|
log.debug(f'Current {op}: {memory_stats()}')
|
|
disable_offload(model_data.sd_refiner)
|
|
move_model(model_data.sd_refiner, 'meta')
|
|
model_data.sd_refiner = None
|
|
devices.torch_gc(force=True, reason='unload')
|
|
log.debug(f'Unload {op}: {memory_stats()} fn={fn}')
|
|
|
|
|
|
def hf_auth_check(checkpoint_info: CheckpointInfo | str, force:bool=False):
|
|
if shared.opts.offline_mode:
|
|
log.info('Offline mode: skipping auth check')
|
|
return False
|
|
login = None
|
|
if not force:
|
|
try:
|
|
fn = checkpoint_info.path if isinstance(checkpoint_info, CheckpointInfo) else checkpoint_info
|
|
if (fn.endswith('.safetensors') and os.path.isfile(fn)): # skip check for single-file safetensors models
|
|
return True
|
|
if os.path.exists(fn) and os.path.isdir(fn) and any(os.path.isfile(os.path.join(fn, f)) for f in ('model_index.json', 'modular_model_index.json')): # skip check for local diffusers folders
|
|
return True
|
|
except Exception:
|
|
pass
|
|
repo_id = path_to_repo(checkpoint_info) # already handles str or CheckpointInfo
|
|
if repo_id is None or '/' not in repo_id:
|
|
# log.warning(f'Auth: repo="{repo_id}" invalid repo id')
|
|
return False
|
|
auth_ok = False
|
|
try:
|
|
login = modelloader.hf_login()
|
|
token = os.environ.get('HF_TOKEN', None)
|
|
hf.auth_check(repo_id, write=False, token=token)
|
|
auth_ok = True
|
|
except Exception as e:
|
|
log.error(f'Auth: repo="{repo_id}" login={login} auth={auth_ok} {e}')
|
|
return auth_ok
|
|
|
|
|
|
def save_model(name: str, path: str | None = None, shard: str = "5GB", overwrite = False):
|
|
if (name is None) or len(name.strip()) == 0:
|
|
log.error('Save model: invalid model name')
|
|
return 'Invalid model name'
|
|
if not shared.sd_loaded:
|
|
log.error('Save model: model not loaded')
|
|
return 'Model not loaded'
|
|
from sdnq import save_sdnq_model
|
|
if path is None:
|
|
path = shared.opts.diffusers_dir
|
|
model_name = os.path.join(path.strip(), name.strip())
|
|
if os.path.exists(model_name) and not overwrite:
|
|
log.error(f'Save model: path="{model_name}" exists')
|
|
return f'Path exists: {model_name}'
|
|
if not shard.strip():
|
|
shard = "5GB" # Guard against empty input
|
|
try:
|
|
torch.cuda.synchronize()
|
|
except Exception:
|
|
pass
|
|
jobid = shared.state.begin('Save model')
|
|
try:
|
|
t0 = time.time()
|
|
log.info(f'Save model: path="{model_name}" cls={shared.sd_model.__class__.__name__} start')
|
|
if hasattr(shared.sd_model, '_component_specs'): # modular pipeline: the saved index must reference the destination folder, not the source repos; save_sdnq_model lives in the sdnq submodule and does not pass this flag
|
|
import functools
|
|
shared.sd_model.save_pretrained = functools.partial(shared.sd_model.save_pretrained, overwrite_modular_index=True)
|
|
save_sdnq_model(
|
|
model=shared.sd_model,
|
|
model_path=model_name,
|
|
max_shard_size=shard,
|
|
is_pipeline=True,
|
|
)
|
|
t1 = time.time()
|
|
log.info(f'Save model: path="{model_name}" cls={shared.sd_model.__class__.__name__} time={t1 - t0:.2f}')
|
|
return f'Saved: {model_name}'
|
|
except Exception as e:
|
|
log.error(f'Save model: path="{model_name}" {e}')
|
|
errors.display(e, 'Save model')
|
|
return f'Error: {e}'
|
|
finally:
|
|
if 'save_pretrained' in vars(shared.sd_model):
|
|
del shared.sd_model.save_pretrained # drop the instance shadow, restoring the class method
|
|
shared.state.end(jobid)
|
|
|
|
|
|
def list_hfcache():
|
|
checkpoints = []
|
|
for f in os.scandir(shared.opts.hfcache_dir):
|
|
if not os.path.isdir(f) or not f.name.startswith('models--'):
|
|
continue
|
|
checkpoint = CheckpointInfo(filename=f.path, name=path_to_repo(f.name), model_type='hfcache')
|
|
checkpoints.append(checkpoint)
|
|
return checkpoints
|
|
|
|
|
|
def warn_group_offload(min_vram: int = 0):
|
|
vram = gpu_stats()
|
|
vram = round(vram['total'] if "total" in vram else 0)
|
|
if (0 < vram < min_vram) and (shared.opts.diffusers_offload_mode in ['none', 'balanced', 'model']):
|
|
log.warning(f'Load model: vram={vram} min={min_vram} offload={shared.opts.diffusers_offload_mode} recommended=group reason="insufficient vram"')
|