AI/sdnext
1
0
Fork 0
mirror of https://github.com/vladmandic/sdnext.git synced 2026-01-29 05:02:09 +03:00
Code Activity
Files
460d8094429f06f75f973c7853d94de7b0a19d45
sdnext/modules/sd_models.py
Disty0 6e68dff381 Fix AuraFlow quant
2025-08-29 18:13:53 +03:00

1207 lines
63 KiB
Python
Raw Blame History

import sys
import time
import copy
import inspect
import logging
import os.path
from enum import Enum
import diffusers
import diffusers.loaders.single_file_utils
import torch
import huggingface_hub as hf
from installer import log
from modules import timer, paths, shared, shared_items, modelloader, devices, script_callbacks, sd_vae, sd_unet, errors, sd_models_compile, sd_hijack_accelerate, sd_detect, model_quant, sd_hijack_te
from modules.memstats import memory_stats
from modules.modeldata import model_data
from modules.sd_checkpoint import CheckpointInfo, select_checkpoint, list_models, checkpoints_list, checkpoint_titles, get_closet_checkpoint_match, model_hash, update_model_hashes, setup_model, write_metadata, read_metadata_from_safetensors # pylint: disable=unused-import
from modules.sd_offload import disable_offload, set_diffuser_offload, apply_balanced_offload, set_accelerate # pylint: disable=unused-import
from modules.sd_models_utils import NoWatermark, get_signature, get_call, path_to_repo, patch_diffuser_config, convert_to_faketensors, read_state_dict, get_state_dict_from_checkpoint, apply_function_to_model # pylint: disable=unused-import
model_dir = "Stable-diffusion"
model_path = os.path.abspath(os.path.join(paths.models_path, model_dir))
sd_metadata_file = os.path.join(paths.data_path, "metadata.json")
sd_metadata = None
sd_metadata_pending = 0
sd_metadata_timer = 0
debug_move = log.trace if os.environ.get('SD_MOVE_DEBUG', None) is not None else lambda *args, **kwargs: None
debug_load = os.environ.get('SD_LOAD_DEBUG', None)
debug_process = log.trace if os.environ.get('SD_PROCESS_DEBUG', None) is not None else lambda *args, **kwargs: None
diffusers_version = int(diffusers.__version__.split('.')[1])
checkpoint_tiles = checkpoint_titles # legacy compatibility
allow_post_quant = None
pipe_switch_task_exclude = [
'AnimateDiffPipeline', 'AnimateDiffSDXLPipeline',
'FluxControlPipeline',
'FluxFillPipeline',
'InstantIRPipeline',
'LTXConditionPipeline',
'OmniGenPipeline',
'OmniGen2Pipeline',
'PhotoMakerStableDiffusionXLPipeline',
'PixelSmithXLPipeline',
'StableDiffusion3ControlNetPipeline',
'StableDiffusionAdapterPipeline',
'StableDiffusionAdapterPipeline', 'StableDiffusionXLAdapterPipeline',
'StableDiffusionControlNetXSPipeline', 'StableDiffusionXLControlNetXSPipeline',
'StableDiffusionReferencePipeline',
'StableDiffusionXLInstantIDPipeline',
]
i2i_pipes = [
'LEditsPPPipelineStableDiffusion',
'LEditsPPPipelineStableDiffusionXL',
'OmniGenPipeline',
'OmniGen2Pipeline',
'StableDiffusionAdapterPipeline', 'StableDiffusionXLAdapterPipeline',
'StableDiffusionControlNetXSPipeline', 'StableDiffusionXLControlNetXSPipeline',
]
def set_huggingface_options():
if shared.opts.diffusers_to_gpu: # and model_type.startswith('Stable Diffusion'):
sd_hijack_accelerate.hijack_accelerate()
else:
sd_hijack_accelerate.restore_accelerate()
def set_vae_options(sd_model, vae=None, op:str='model', quiet:bool=False):
ops = {}
if hasattr(sd_model, "vae"):
if vae is not None:
sd_model.vae = vae
ops['name'] = f"{sd_vae.loaded_vae_file}"
if shared.opts.diffusers_vae_upcast != 'default':
sd_model.vae.config.force_upcast = True if shared.opts.diffusers_vae_upcast == 'true' else False
ops['upcast'] = sd_model.vae.config.force_upcast
if shared.opts.no_half_vae and op not in {'decode', 'encode'}:
devices.dtype_vae = torch.float32
sd_model.vae.to(devices.dtype_vae)
ops['no-half'] = True
if hasattr(sd_model, "enable_vae_slicing") and hasattr(sd_model, "disable_vae_slicing"):
ops['slicing'] = shared.opts.diffusers_vae_slicing
if shared.opts.diffusers_vae_slicing:
sd_model.enable_vae_slicing()
else:
sd_model.disable_vae_slicing()
if hasattr(sd_model, "enable_vae_tiling") and hasattr(sd_model, "disable_vae_tiling"):
ops['tiling'] = shared.opts.diffusers_vae_tiling
if shared.opts.diffusers_vae_tiling:
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):
if getattr(sd_model.vae, "tile_sample_min_size_backup", None) is None:
sd_model.vae.tile_sample_min_size_backup = sd_model.vae.tile_sample_min_size
sd_model.vae.tile_latent_min_size_backup = sd_model.vae.tile_latent_min_size
sd_model.vae.tile_overlap_factor_backup = sd_model.vae.tile_overlap_factor
if shared.opts.diffusers_vae_tile_size > 0:
sd_model.vae.tile_sample_min_size = int(shared.opts.diffusers_vae_tile_size)
sd_model.vae.tile_latent_min_size = int(shared.opts.diffusers_vae_tile_size / (2 ** (len(sd_model.vae.config.block_out_channels) - 1)))
else:
sd_model.vae.tile_sample_min_size = getattr(sd_model.vae, "tile_sample_min_size_backup", sd_model.vae.tile_sample_min_size)
sd_model.vae.tile_latent_min_size = getattr(sd_model.vae, "tile_latent_min_size_backup", sd_model.vae.tile_latent_min_size)
if shared.opts.diffusers_vae_tile_overlap != 0.25:
sd_model.vae.tile_overlap_factor = float(shared.opts.diffusers_vae_tile_overlap)
else:
sd_model.vae.tile_overlap_factor = getattr(sd_model.vae, "tile_overlap_factor_backup", sd_model.vae.tile_overlap_factor)
ops['tile'] = sd_model.vae.tile_sample_min_size
ops['overlap'] = sd_model.vae.tile_overlap_factor
sd_model.enable_vae_tiling()
else:
sd_model.disable_vae_tiling()
if hasattr(sd_model, "vqvae"):
ops['upcast'] = True
sd_model.vqvae.to(torch.float32) # vqvae is producing nans in fp16
if not quiet and len(ops) > 0:
shared.log.quiet(quiet, f'Setting {op}: component=vae {ops}')
def set_diffuser_options(sd_model, vae=None, op:str='model', offload:bool=True, quiet:bool=False):
if sd_model is None:
shared.log.warning(f'{op} is not loaded')
return
if hasattr(sd_model, "watermark"):
sd_model.watermark = NoWatermark()
if not (hasattr(sd_model, "has_accelerate") and sd_model.has_accelerate):
sd_model.has_accelerate = False
clear_caches()
set_vae_options(sd_model, vae, op, quiet)
set_diffusers_attention(sd_model, quiet)
if shared.opts.diffusers_fuse_projections and hasattr(sd_model, 'fuse_qkv_projections'):
try:
sd_model.fuse_qkv_projections()
shared.log.quiet(quiet, f'Setting {op}: fused-qkv=True')
except Exception as e:
shared.log.error(f'Setting {op}: fused-qkv=True {e}')
if shared.opts.diffusers_fuse_projections and hasattr(sd_model, 'transformer') and hasattr(sd_model.transformer, 'fuse_qkv_projections'):
try:
sd_model.transformer.fuse_qkv_projections()
shared.log.quiet(quiet, f'Setting {op}: fused-qkv=True')
except Exception as e:
shared.log.error(f'Setting {op}: fused-qkv=True {e}')
if shared.opts.diffusers_eval:
shared.log.debug(f'Setting {op}: eval=True')
def eval_model(model, op=None, sd_model=None): # pylint: disable=unused-argument
if hasattr(model, "requires_grad_"):
model.requires_grad_(False)
model.eval()
return model
sd_model = apply_function_to_model(sd_model, eval_model, ["Model", "VAE", "TE"], op="eval")
if shared.opts.opt_channelslast and hasattr(sd_model, 'unet'):
shared.log.quiet(quiet, f'Setting {op}: channels-last=True')
sd_model.unet.to(memory_format=torch.channels_last)
if offload:
set_diffuser_offload(sd_model, op, quiet)
def move_model(model, device=None, force=False):
if model is None or device is None:
return
if hasattr(model, 'pipe'):
move_model(model.pipe, device, force)
fn = f'{sys._getframe(2).f_code.co_name}:{sys._getframe(1).f_code.co_name}' # pylint: disable=protected-access
if getattr(model, 'vae', None) is not None and get_diffusers_task(model) != DiffusersTaskType.TEXT_2_IMAGE:
if device == devices.device and model.vae.device.type != "meta": # force vae back to gpu if not in txt2img mode
model.vae.to(device)
if hasattr(model.vae, '_hf_hook'):
debug_move(f'Model move: to={device} class={model.vae.__class__} fn={fn}') # pylint: disable=protected-access
model.vae._hf_hook.execution_device = device # pylint: disable=protected-access
if hasattr(model, "components"): # accelerate patch
for name, m in model.components.items():
if not hasattr(m, "_hf_hook"): # not accelerate hook
break
if not isinstance(m, torch.nn.Module) or name in model._exclude_from_cpu_offload: # pylint: disable=protected-access
continue
for module in m.modules():
if (hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None): # pylint: disable=protected-access
try:
module._hf_hook.execution_device = device # pylint: disable=protected-access
except Exception as e:
if os.environ.get('SD_MOVE_DEBUG', None):
shared.log.error(f'Model move execution device: device={device} {e}')
if getattr(model, 'has_accelerate', False) and not force:
return
if hasattr(model, "device") and devices.normalize_device(model.device) == devices.normalize_device(device) and not force:
return
try:
t0 = time.time()
try:
if hasattr(model, 'to'):
model.to(device)
if hasattr(model, "prior_pipe"):
model.prior_pipe.to(device)
except Exception as e0:
if 'Cannot copy out of meta tensor' in str(e0) or 'must be Tensor, not NoneType' in str(e0):
if hasattr(model, "components"):
for _name, component in model.components.items():
if hasattr(component, 'modules'):
for module in component.modules():
try:
if hasattr(module, 'to'):
module.to(device)
except Exception as e2:
if 'Cannot copy out of meta tensor' in str(e2):
if os.environ.get('SD_MOVE_DEBUG', None):
shared.log.warning(f'Model move meta: module={module.__class__}')
module.to_empty(device=device)
elif 'enable_sequential_cpu_offload' in str(e0):
pass # ignore model move if sequential offload is enabled
elif 'Params4bit' in str(e0) or 'Params8bit' in str(e0):
pass # ignore model move if quantization is enabled
elif 'already been set to the correct devices' in str(e0):
pass # ignore errors on pre-quant models
elif 'Casting a quantized model to' in str(e0):
pass # ignore errors on quantized models
else:
raise e0
t1 = time.time()
except Exception as e1:
t1 = time.time()
shared.log.error(f'Model move: device={device} {e1}')
if 'move' not in timer.process.records:
timer.process.records['move'] = 0
timer.process.records['move'] += t1 - t0
if os.environ.get('SD_MOVE_DEBUG', None) is not None or (t1-t0) > 2:
shared.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
devices.torch_gc()
def move_base(model, device):
if hasattr(model, 'transformer'):
key = 'transformer'
elif hasattr(model, 'unet'):
key = 'unet'
else:
shared.log.warning(f'Model move: model={model.__class__} device={device} key=unknown')
return None
shared.log.debug(f'Model move: module={key} device={device}')
model = getattr(model, key)
R = model.device
move_model(model, device)
return R
def load_diffuser_initial(diffusers_load_config, op='model'):
sd_model = None
checkpoint_info = None
ckpt_basename = os.path.basename(shared.cmd_opts.ckpt)
model_name = modelloader.find_diffuser(ckpt_basename)
if model_name is not None:
shared.log.info(f'Load model {op}: path="{model_name}"')
model_file = modelloader.download_diffusers_model(hub_id=model_name, variant=diffusers_load_config.get('variant', None))
try:
shared.log.debug(f'Load {op}: config={diffusers_load_config}')
sd_model = diffusers.DiffusionPipeline.from_pretrained(model_file, **diffusers_load_config)
except Exception as e:
shared.log.error(f'Failed loading model: {model_file} {e}')
errors.display(e, f'Load {op}: path="{model_file}"')
return None, None
list_models() # rescan for downloaded model
checkpoint_info = CheckpointInfo(model_name)
return sd_model, checkpoint_info
def load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op='model'):
sd_model = None
global allow_post_quant # pylint: disable=global-statement
unload_model_weights(op=op)
shared.sd_model = None
try:
if model_type in ['Stable Cascade']: # forced pipeline
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']: # forced pipeline
pipeline = diffusers.utils.get_class_from_dynamic_module('instaflow_one_step', module_file='pipeline.py')
shared_items.pipelines['InstaFlow'] = pipeline
sd_model = pipeline.from_pretrained(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
allow_post_quant = True
elif model_type in ['SegMoE']: # forced pipeline
from pipelines.segmoe.segmoe_model import SegMoEPipeline
sd_model = SegMoEPipeline(checkpoint_info.path, cache_dir=shared.opts.diffusers_dir, **diffusers_load_config)
sd_model = sd_model.pipe # segmoe pipe does its stuff in __init__ and __call__ is the original pipeline
allow_post_quant = True
shared_items.pipelines['SegMoE'] = SegMoEPipeline
elif model_type in ['PixArt Sigma']: # forced pipeline
from pipelines.model_pixart import load_pixart
sd_model = load_pixart(checkpoint_info, diffusers_load_config)
allow_post_quant = False
elif model_type in ['Sana']: # forced pipeline
from pipelines.model_sana import load_sana
sd_model = load_sana(checkpoint_info, diffusers_load_config)
allow_post_quant = False
elif model_type in ['Lumina-Next']: # forced pipeline
from pipelines.model_lumina import load_lumina
sd_model = load_lumina(checkpoint_info, diffusers_load_config)
allow_post_quant = True
elif model_type in ['Kolors']: # forced pipeline
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']: # forced pipeline
from pipelines.model_auraflow import load_auraflow
sd_model = load_auraflow(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 ['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 ['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 ['Lumina 2']:
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 ['CogView 3']: # forced pipeline
from pipelines.model_cogview import load_cogview3
sd_model = load_cogview3(checkpoint_info, diffusers_load_config)
allow_post_quant = False
elif model_type in ['CogView 4']: # forced pipeline
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']: # forced pipeline
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']: # forced pipeline
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']: # forced pipeline
from pipelines.model_omnigen import load_omnigen
sd_model = load_omnigen(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 ['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 ['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 ['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 ['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 ['Kandinsky 2.1']:
from pipelines.model_kandinsky import load_kandinsky21
sd_model = load_kandinsky21(checkpoint_info, diffusers_load_config)
allow_post_quant = True
elif model_type in ['Kandinsky 2.2']:
from pipelines.model_kandinsky import load_kandinsky22
sd_model = load_kandinsky22(checkpoint_info, diffusers_load_config)
allow_post_quant = True
elif model_type in ['Kandinsky 3.0']:
from pipelines.model_kandinsky import load_kandinsky3
sd_model = load_kandinsky3(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 ['hdm']:
from pipelines.model_hdm import load_hdm
sd_model = load_hdm(checkpoint_info, diffusers_load_config)
allow_post_quant = False
except Exception as e:
shared.log.error(f'Load {op}: path="{checkpoint_info.path}" {e}')
if debug_load:
errors.display(e, 'Load')
return None
return sd_model
def load_diffuser_folder(model_type, pipeline, checkpoint_info, diffusers_load_config, op='model'):
sd_model = None
files = shared.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'
if (model_type is not None) and (pipeline is not None) and ('ONNX' in model_type): # forced pipeline
try:
sd_model = pipeline.from_pretrained(checkpoint_info.path)
except Exception as e:
shared.log.error(f'Load {op}: type=ONNX path="{checkpoint_info.path}" {e}')
if debug_load:
errors.display(e, 'Load')
return None
else:
err1, err2, err3 = 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')):
shared.log.debug(f'Load {op}: type=pickle')
diffusers_load_config['use_safetensors'] = False
if debug_load:
shared.log.debug(f'Load {op}: args={diffusers_load_config}')
try: # 1 - autopipeline, best choice but not all pipelines are available
try:
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):
shared.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):
shared.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')
# shared.log.error(f'AutoPipeline: {e}')
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")
# shared.log.error(f'DiffusionPipeline: {e}')
try: # 3 - try basic pipeline just in case
if err2 is not None:
sd_model = diffusers.StableDiffusionPipeline.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 # ignore last error
shared.log.error(f"StableDiffusionPipeline: {e}")
if debug_load:
errors.display(e, "Load StableDiffusionPipeline")
if err3 is not None:
shared.log.error(f'Load {op}: {checkpoint_info.path} auto={err1} diffusion={err2}')
return None
return sd_model
def load_diffuser_file(model_type, pipeline, checkpoint_info, diffusers_load_config, op='model'):
sd_model = None
diffusers_load_config["extract_ema"] = shared.opts.diffusers_extract_ema
if pipeline is None:
shared.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:
shared.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['use_safetensors'] = True
diffusers_load_config['cache_dir'] = shared.opts.hfcache_dir # use hfcache instead of diffusers dir as this is for config only in case of single-file
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, **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:
shared.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 debug_load:
# shared.log.debug(f'Model args: {diffusers_load_config}')
if sd_model is not None:
diffusers_load_config.pop('vae', None)
diffusers_load_config.pop('safety_checker', None)
diffusers_load_config.pop('requires_safety_checker', None)
diffusers_load_config.pop('config_files', None)
diffusers_load_config.pop('local_files_only', None)
shared.log.debug(f'Setting {op}: pipeline={sd_model.__class__.__name__} config={diffusers_load_config}') # pylint: disable=protected-access
except Exception as e:
shared.log.error(f'Load {op}: file="{checkpoint_info.path}" pipeline={shared.opts.diffusers_pipeline}/{sd_model.__class__.__name__} config={diffusers_load_config} {e}')
if 'Weights for this component appear to be missing in the checkpoint' in str(e):
shared.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 set_overrides(sd_model, checkpoint_info):
checkpoint_info_name = checkpoint_info.name.lower()
if 'bigaspv25' in checkpoint_info_name or 'nyaflow' in checkpoint_info_name:
scheduler_config = sd_model.scheduler.config
scheduler_config['prediction_type'] = 'flow_prediction'
scheduler_config['use_flow_sigmas'] = True
scheduler_config['beta_schedule'] = 'linear'
sd_model.scheduler = diffusers.UniPCMultistepScheduler.from_config(scheduler_config)
shared.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['rescale_betas_zero_snr'] = True
sd_model.scheduler = diffusers.EulerDiscreteScheduler.from_config(scheduler_config)
shared.log.info(f'Setting override: model="{checkpoint_info.name}" component=scheduler prediction="v-prediction" rescale=True')
elif checkpoint_info.path.lower().endswith('.safetensors'):
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'
if 'ztsnr' in keys:
scheduler_config['rescale_betas_zero_snr'] = True
sd_model.scheduler = diffusers.EulerDiscreteScheduler.from_config(scheduler_config)
shared.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:
shared.log.debug(f'Setting override from keys failed: {e}')
def set_defaults(sd_model, checkpoint_info):
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} {percentage:3.0f}% {n_fmt}/{total_fmt} {elapsed} {remaining}', ncols=80, colour='#327fba')
def load_diffuser(checkpoint_info=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,
"safety_checker": None, # sd15 specific but we cant know ahead of time
"requires_safety_checker": False, # sd15 specific but we cant know ahead of time
# "use_safetensors": True,
}
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:
shared.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
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
# detect pipeline
pipeline, model_type = sd_detect.detect_pipeline(checkpoint_info.path, 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:
shared.log.error(f'Load {op}: pipeline={shared.opts.diffusers_pipeline} not detected')
return
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_diffusers(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:
sd_model = load_diffuser_force(model_type, checkpoint_info, diffusers_load_config, op)
if sd_model is not None and not sd_model:
shared.log.error(f'Load {op}: type="{model_type}" pipeline="{pipeline}" not loaded')
return
# load from hf folder-style
if sd_model is None:
if os.path.isdir(checkpoint_info.path) or checkpoint_info.type == 'huggingface' or checkpoint_info.type == 'transformer':
sd_model = load_diffuser_folder(model_type, pipeline, checkpoint_info, diffusers_load_config, op)
# load from single-file
if sd_model is None:
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)
if sd_model is None:
shared.log.error(f'Load {op}: name="{checkpoint_info.name if checkpoint_info is not None else None}" not loaded')
return
set_overrides(sd_model, checkpoint_info)
set_defaults(sd_model, checkpoint_info)
if "Kandinsky" in sd_model.__class__.__name__: # need a special case
sd_model.scheduler.name = 'DDIM'
if hasattr(sd_model, "unet") and model_type not in ['Stable Cascade']: # others calls load_diffuser again
sd_unet.load_unet(sd_model)
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:
shared.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)
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)
if op == 'refiner' and shared.opts.diffusers_move_refiner:
shared.log.debug('Moving refiner model to CPU')
move_model(sd_model, devices.cpu)
else:
move_model(sd_model, devices.device)
timer.load.record("move")
if shared.opts.ipex_optimize:
sd_model = sd_models_compile.ipex_optimize(sd_model)
if ('Model' in shared.opts.cuda_compile and shared.opts.cuda_compile_backend != 'none'):
sd_model = sd_models_compile.compile_diffusers(sd_model)
timer.load.record("compile")
except Exception as e:
shared.log.error(f"Load {op}: {e}")
errors.display(e, "Model")
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()
shared.log.info(f"Load {op}: family={shared.sd_model_type} time={timer.load.dct()} native={get_native(sd_model)} memory={memory_stats()}")
class DiffusersTaskType(Enum):
TEXT_2_IMAGE = 1
IMAGE_2_IMAGE = 2
INPAINTING = 3
INSTRUCT = 4
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 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: diffusers.DiffusionPipeline, pipeline: diffusers.DiffusionPipeline = None, force = False, args = {}):
"""
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 isinstance(cls, str):
shared.log.debug(f'Pipeline switch: custom={cls}')
cls = diffusers.utils.get_class_from_dynamic_module(cls, module_file='pipeline.py')
if pipeline is None:
pipeline = shared.sd_model
new_pipe = None
signature = get_signature(cls)
possible = signature.keys()
if not force and isinstance(pipeline, cls) 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:
shared.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(**pipe_dict)
switch_mode = 'auto'
elif 'tokenizer_2' in possible and hasattr(pipeline, 'tokenizer_2'):
new_pipe = cls(
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(
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:
shared.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:
shared.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':
shared.log.debug(f'Pipeline switch: from={pipeline.__class__.__name__} to={new_pipe.__class__.__name__} components={components_used} skipped={components_skipped} missing={components_missing}')
else:
shared.log.debug(f'Pipeline switch: from={pipeline.__class__.__name__} to={new_pipe.__class__.__name__} mode={switch_mode}')
return new_pipe
else:
shared.log.error(f'Pipeline switch error: from={pipeline.__class__.__name__} to={cls.__name__} empty pipeline')
except Exception as e:
shared.log.error(f'Pipeline switch error: from={pipeline.__class__.__name__} to={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.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.task_args = getattr(orig_pipe, 'task_args', 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 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),
'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),
}
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.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 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
# 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
new_pipe = None
# 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)
components_backup = backup_pipe_components(pipe)
if new_pipe is None:
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:
shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={cls}')
return pipe
except Exception as e: # pylint: disable=unused-variable
shared.log.warning(f'Pipeline class change failed: type={new_pipe_type} pipeline={cls} {e}')
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:
shared.log.error(f'Pipeline class set failed: type={new_pipe_type} pipeline={cls}')
return pipe
except Exception as e: # pylint: disable=unused-variable
shared.log.warning(f'Pipeline class set failed: type={new_pipe_type} pipeline={cls} {e}')
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
shared.log.debug(f"Pipeline class change: original={cls} target={new_pipe.__class__.__name__} device={pipe.device} fn={fn}") # pylint: disable=protected-access
pipe = new_pipe
return pipe
def set_diffusers_attention(pipe, quiet:bool=False):
import diffusers.models.attention_processor as p
def set_attn(pipe, attention, name:str=None, quiet:bool=False):
if attention is None:
return
# other models uses their own attention processor
if pipe.__class__.__name__.startswith("StableDiffusion") and hasattr(pipe, "unet"):
pipe.unet.set_attn_processor(attention)
elif not quiet:
shared.log.warning(f"Attention: {name if name is not None else attention.__class__.__name__} is not compatible with {pipe.__class__.__name__}")
# if hasattr(pipe, 'pipe'):
# set_diffusers_attention(pipe.pipe)
if 'Control' in pipe.__class__.__name__ or 'Adapter' in pipe.__class__.__name__ or not (pipe.__class__.__name__.startswith("StableDiffusion") and hasattr(pipe, "unet")):
if shared.opts.cross_attention_optimization not in {"Scaled-Dot-Product", "Disabled"}:
shared.log.warning(f"Attention: {shared.opts.cross_attention_optimization} is not compatible with {pipe.__class__.__name__}")
else:
pipe.current_attn_name = shared.opts.cross_attention_optimization
return
shared.log.quiet(quiet, f'Setting model: attention="{shared.opts.cross_attention_optimization}"')
if shared.opts.cross_attention_optimization == "Disabled":
pass # do nothing
elif shared.opts.cross_attention_optimization == "Scaled-Dot-Product": # The default set by Diffusers
set_attn(pipe, p.AttnProcessor2_0(), name="Scaled-Dot-Product", quiet=True)
elif shared.opts.cross_attention_optimization == "xFormers":
if hasattr(pipe, 'enable_xformers_memory_efficient_attention'):
pipe.enable_xformers_memory_efficient_attention()
else:
shared.log.warning(f"Attention: xFormers is not compatible with {pipe.__class__.__name__}")
elif shared.opts.cross_attention_optimization == "Split attention":
if hasattr(pipe, "enable_attention_slicing"):
pipe.enable_attention_slicing()
else:
shared.log.warning(f"Attention: Split attention is not compatible with {pipe.__class__.__name__}")
elif shared.opts.cross_attention_optimization == "Batch matrix-matrix":
set_attn(pipe, p.AttnProcessor(), name="Batch matrix-matrix")
elif shared.opts.cross_attention_optimization == "Dynamic Attention BMM":
from modules.sd_hijack_dynamic_atten import DynamicAttnProcessorBMM
set_attn(pipe, DynamicAttnProcessorBMM(), name="Dynamic Attention BMM")
pipe.current_attn_name = shared.opts.cross_attention_optimization
def add_noise_pred_to_diffusers_callback(pipe):
if not hasattr(pipe, "_callback_tensor_inputs"):
return pipe
if pipe.__class__.__name__.startswith("StableDiffusion"):
pipe._callback_tensor_inputs.append("noise_pred") # pylint: disable=protected-access
elif pipe.__class__.__name__.startswith("StableCascade"):
pipe.prior_pipe._callback_tensor_inputs.append("predicted_image_embedding") # 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, "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):
if initial and (shared.opts.sd_text_encoder is None or shared.opts.sd_text_encoder == 'Default'):
return # dont unload
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 'vit' in shared.opts.sd_text_encoder.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
shared.log.debug(f'Load module: type=t5 path="{shared.opts.sd_text_encoder}" module="{t5[0]}"')
set_t5(pipe=shared.sd_model, module=t5[0], t5=shared.opts.sd_text_encoder, cache_dir=shared.opts.diffusers_dir)
elif hasattr(shared.sd_model, 'text_encoder_3'):
from modules.model_te import set_t5
shared.log.debug(f'Load module: type=t5 path="{shared.opts.sd_text_encoder}" module="text_encoder_3"')
set_t5(pipe=shared.sd_model, module='text_encoder_3', t5=shared.opts.sd_text_encoder, cache_dir=shared.opts.diffusers_dir)
clear_caches(full=True)
apply_balanced_offload(shared.sd_model)
def reload_model_weights(sd_model=None, info=None, op='model', force=False, revision=None):
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
orig_state = copy.deepcopy(shared.state)
shared.state.begin('Load')
if sd_model is None:
sd_model = model_data.sd_model if op == 'model' or op == 'dict' else model_data.sd_refiner
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:
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()
shared.state = orig_state
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
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()
if full:
shared.log.debug('Cache clear')
sd_offload.offload_hook_instance = None
def unload_model_weights(op='model'):
clear_caches(full=True)
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:
shared.log.debug(f'Current {op}: {memory_stats()}')
if not ('Model' in shared.opts.cuda_compile and shared.opts.cuda_compile_backend == "openvino_fx"):
disable_offload(model_data.sd_model)
move_model(model_data.sd_model, 'meta')
model_data.sd_model = None
devices.torch_gc(force=True, reason='unload')
shared.log.debug(f'Unload {op}: {memory_stats()} after')
elif (op == 'refiner') and model_data.sd_refiner:
shared.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')
shared.log.debug(f'Unload {op}: {memory_stats()}')
def hf_auth_check(checkpoint_info, force:bool=False):
login = None
if not force:
try:
# skip check for single-file safetensors models
if (checkpoint_info.path.endswith('.safetensors') and os.path.isfile(checkpoint_info.path)):
return True
# skip check for local diffusers folders
if (os.path.exists(checkpoint_info.path) and os.path.isdir(checkpoint_info.path) and os.path.isfile(os.path.join(checkpoint_info.path, 'model_index.json'))):
return True
except Exception:
pass
try:
login = modelloader.hf_login()
repo_id = path_to_repo(checkpoint_info)
return hf.auth_check(repo_id)
except Exception as e:
shared.log.error(f'Load model: repo="{repo_id}" login={login} {e}')
return False
View Git Blame Copy Permalink