mirror of
https://github.com/vladmandic/automatic
synced 2026-08-25 22:20:46 +02:00
CalamitousFelicitousness
c9e1398c71
First natively modular model: the pipeline is driven directly through ModularPipeline, with components fetched per workflow (fl2va covers text and first/last-frame conditioning). - per-generation overrides snap the canvas to /32, align frames to the 17n+5 grid and duration window, and keep the bespoke scheduler pair - group offload for modular pipelines applied per component in sd_offload; re-application is a guarded no-op - audio checkbox pops the audio decode block so decode and muxing are skipped - frames=1 renders a single still image: the duration floor lifts per instance and sub-floor latents pad at the vae decoder - progress and interrupt handling via a transformer forward pre-hook - vae scale factor override, tuple-safe patch size
263 lines
11 KiB
Python
263 lines
11 KiB
Python
from __future__ import annotations
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import os
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import glob
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from typing import TYPE_CHECKING, cast
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import torch
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from modules import shared, errors, paths, devices, sd_models, sd_detect
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from modules.logger import log
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if TYPE_CHECKING:
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from diffusers import DiffusionPipeline
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from modules.sd_checkpoint import CheckpointInfo
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vae_ignore_keys = {"model_ema.decay", "model_ema.num_updates"}
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vae_dict: dict[str, str] = {}
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base_vae = None # Unused
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loaded_vae_file: str | None = None
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checkpoint_info: CheckpointInfo | None = None
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vae_path = os.path.abspath(os.path.join(paths.models_path, 'VAE'))
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debug = os.environ.get('SD_VAE_DEBUG', None) is not None
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unspecified = object()
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vae_scale_override = {
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'WanPipeline': 16,
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'ChronoEditPipeline': 16,
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'AutoencoderKLWan': 16,
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'AutoencoderKLMiniMaxH3': 16,
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}
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def get_vae_scale_factor(model: DiffusionPipeline | None = None):
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if not shared.sd_loaded:
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vae_scale_factor = 8
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return vae_scale_factor
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patch_size = 1
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if model is None:
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model = shared.sd_model
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if model is None:
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vae_scale_factor = 8
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elif model.__class__.__name__ in vae_scale_override:
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vae_scale_factor = vae_scale_override[model.__class__.__name__]
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elif hasattr(model, 'vae') and model.vae.__class__.__name__ in vae_scale_override:
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vae_scale_factor = vae_scale_override[model.vae.__class__.__name__]
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elif hasattr(model, 'vae_scale_factor_spatial'):
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vae_scale_factor = model.vae_scale_factor_spatial
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elif hasattr(model, 'vae_scale_factor'):
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vae_scale_factor = model.vae_scale_factor
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elif hasattr(model, 'pipe') and hasattr(model.pipe, 'vae_scale_factor'):
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vae_scale_factor = model.pipe.vae_scale_factor
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elif hasattr(model, 'config') and hasattr(model.config, 'vae_scale_factor'):
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vae_scale_factor = model.config.vae_scale_factor
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else:
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# log.warning(f'VAE: cls={model.__class__.__name__ if model else "None"} scale=unknown')
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vae_scale_factor = 8
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if model is not None and hasattr(model, 'patch_size'):
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patch_size = model.patch_size
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if isinstance(patch_size, (tuple, list)): # 3d patch sizes are (t, h, w); spatial term is last
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patch_size = patch_size[-1]
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if debug:
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log.trace(f'VAE: cls={model.__class__.__name__ if model else "None"} scale={vae_scale_factor} patch={patch_size}')
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return vae_scale_factor * patch_size
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def load_vae_dict(filename: str):
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vae_ckpt = sd_models.read_state_dict(filename, what='vae')
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vae_dict_1 = {k: v for k, v in vae_ckpt.items() if k[0:4] != "loss" and k not in vae_ignore_keys}
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return vae_dict_1
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def get_filename(filepath: str):
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if filepath.endswith(".json"):
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return os.path.basename(os.path.dirname(filepath))
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else:
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return os.path.basename(filepath)
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def refresh_vae_list():
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global vae_path # pylint: disable=global-statement
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vae_path = shared.opts.vae_dir
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vae_dict.clear()
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vae_paths = []
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if sd_models.model_path is not None and os.path.isdir(sd_models.model_path):
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vae_paths += [os.path.join(sd_models.model_path, 'VAE', '**/*.vae.safetensors')]
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if shared.opts.ckpt_dir is not None and os.path.isdir(shared.opts.ckpt_dir):
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vae_paths += [os.path.join(shared.opts.ckpt_dir, '**/*.vae.safetensors')]
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if shared.opts.vae_dir is not None and os.path.isdir(shared.opts.vae_dir):
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vae_paths += [os.path.join(shared.opts.vae_dir, '**/*.safetensors')]
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vae_paths += [
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os.path.join(sd_models.model_path, 'VAE', '**/*.json'),
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os.path.join(shared.opts.vae_dir, '**/*.json'),
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]
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candidates = []
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for path in vae_paths:
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candidates += glob.iglob(path, recursive=True)
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candidates = [os.path.abspath(path) for path in candidates]
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for filepath in candidates:
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name = get_filename(filepath)
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if name == 'VAE':
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continue
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if filepath.endswith(".json"):
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vae_dict[name] = os.path.dirname(filepath)
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else:
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vae_dict[name] = filepath
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log.info(f'Available VAEs: path="{vae_path}" items={len(vae_dict)}')
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return vae_dict
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def find_vae_near_checkpoint(checkpoint_file: str):
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checkpoint_path = os.path.splitext(checkpoint_file)[0]
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for vae_location in [f"{checkpoint_path}.vae.pt", f"{checkpoint_path}.vae.ckpt", f"{checkpoint_path}.vae.safetensors"]:
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if os.path.isfile(vae_location):
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return vae_location
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return None
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def resolve_vae(checkpoint_file: str):
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if shared.opts.sd_vae == 'TAESD':
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return None, None
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if shared.cmd_opts.vae is not None: # 1st
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return cast("str", shared.cmd_opts.vae), 'forced'
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if shared.opts.sd_vae == "Default": # 2nd
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return None, None
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vae_near_checkpoint = find_vae_near_checkpoint(checkpoint_file)
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if vae_near_checkpoint is not None: # 3rd
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return vae_near_checkpoint, 'near-checkpoint'
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if shared.opts.sd_vae == "Automatic": # 4th
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basename = os.path.splitext(os.path.basename(checkpoint_file))[0]
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if vae_dict.get(basename, None) is not None:
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return vae_dict[basename], 'automatic'
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else:
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vae_from_options = vae_dict.get(shared.opts.sd_vae, None) # 5th
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if vae_from_options is not None:
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return vae_from_options, 'settings'
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vae_from_options = vae_dict.get(shared.opts.sd_vae + '.safetensors', None) # 6th
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if vae_from_options is not None:
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return vae_from_options, 'settings'
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log.warning(f"VAE not found: {shared.opts.sd_vae}")
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return None, None
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def apply_vae_config(model_file: str, vae_file: str, sd_model: DiffusionPipeline):
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def get_vae_config():
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config_file = os.path.join(paths.sd_configs_path, os.path.splitext(os.path.basename(model_file))[0] + '_vae.json')
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if config_file is not None and os.path.exists(config_file):
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return shared.readfile(config_file, as_type="dict")
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config_file = os.path.join(paths.sd_configs_path, os.path.splitext(os.path.basename(vae_file))[0] + '.json') if vae_file else None
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if config_file is not None and os.path.exists(config_file):
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return shared.readfile(config_file, as_type="dict")
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config_file = os.path.join(paths.sd_configs_path, shared.sd_model_type, 'vae', 'config.json')
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if config_file is not None and os.path.exists(config_file):
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return shared.readfile(config_file, as_type="dict")
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return {}
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if hasattr(sd_model, 'vae') and hasattr(sd_model.vae, 'config'):
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config = get_vae_config()
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for k, v in config.items():
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if k in sd_model.vae.config and not k.startswith('_'):
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sd_model.vae.config[k] = v
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def load_vae(model_file: str, vae_file: str | None = None, vae_source: str | None = "unknown-source"):
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if vae_file is None:
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return None
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if not os.path.exists(vae_file):
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log.error(f'VAE not found: model{vae_file}')
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return None
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diffusers_load_config = {
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"low_cpu_mem_usage": False,
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"torch_dtype": devices.dtype_vae,
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"use_safetensors": True,
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}
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if shared.opts.diffusers_vae_load_variant == 'default':
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if devices.dtype_vae == torch.float16:
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diffusers_load_config['variant'] = 'fp16'
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elif shared.opts.diffusers_vae_load_variant == 'fp32':
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pass
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else:
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diffusers_load_config['variant'] = shared.opts.diffusers_vae_load_variant
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if shared.opts.diffusers_vae_upcast != 'default':
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diffusers_load_config['force_upcast'] = True if shared.opts.diffusers_vae_upcast == 'true' else False
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_pipeline, model_type = sd_detect.detect_pipeline(model_file, 'vae')
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vae_config = sd_detect.get_load_config(model_file, model_type, config_type='json')
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if vae_config is not None:
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diffusers_load_config['config'] = os.path.join(vae_config, 'vae')
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vae = None
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try:
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import diffusers
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vae_class = None
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vae_loader = None
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if shared.sd_model is not None and getattr(shared.sd_model, 'vae', None) is not None:
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vae_class = shared.sd_model.vae.__class__
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vae_loader = vae_class.from_single_file if os.path.isfile(vae_file) else vae_class.from_pretrained
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elif os.path.isfile(vae_file):
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size = os.path.getsize(vae_file)
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if size > 1310944880: # 1.3GB
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vae_class = diffusers.ConsistencyDecoderVAE
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vae_loader = vae_class.from_pretrained
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vae_file = 'openai/consistency-decoder'
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elif size < 25000000: # 25MB
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log.error(f'Load module: type=VAE file="{vae_file}" size={size} invalid')
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vae_loader = None
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vae_class = None
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else: # fallback
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vae_class = diffusers.AutoencoderKL
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vae_loader = vae_class.from_single_file
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else:
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if 'consistency-decoder' in vae_file:
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vae_class = diffusers.ConsistencyDecoderVAE
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else: # fallback
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vae_class = diffusers.AutoencoderKL
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vae_loader = vae_class.from_pretrained
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if vae_loader is not None:
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log.info(f'Load module: type=VAE model="{vae_file}" source={vae_source} cls={vae_class.__name__} config={diffusers_load_config}')
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vae = vae_loader(vae_file, **diffusers_load_config)
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vae = vae.to(devices.dtype_vae)
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global loaded_vae_file # pylint: disable=global-statement
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loaded_vae_file = os.path.basename(vae_file)
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if shared.opts.diffusers_offload_mode == 'none':
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sd_models.move_model(vae, devices.device)
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return vae
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except Exception as e:
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log.error(f"Load module: type=VAE model={vae_file} {e}")
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if debug:
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errors.display(e, 'VAE')
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return None
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def reload_vae_weights(sd_model: DiffusionPipeline | None = None, vae_file = unspecified):
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if not sd_model:
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sd_model = shared.sd_model
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if sd_model is None:
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return None
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global checkpoint_info # pylint: disable=global-statement
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checkpoint_info = sd_model.sd_checkpoint_info
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checkpoint_file = checkpoint_info.filename
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if vae_file == unspecified:
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vae_file_path, vae_source = resolve_vae(checkpoint_file)
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else:
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vae_file_path = cast("str | None", vae_file)
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vae_source = "function-argument"
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if vae_file_path is None or vae_file_path == 'None':
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if hasattr(sd_model, 'original_vae'):
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sd_models.set_diffuser_options(sd_model, vae=sd_model.original_vae, op='vae')
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log.info("VAE restored")
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return None
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if loaded_vae_file == vae_file_path:
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return None
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if vae_file_path is not None and hasattr(sd_model, "vae") and getattr(sd_model, "sd_checkpoint_info", None) is not None:
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vae = load_vae(sd_model.sd_checkpoint_info.filename, vae_file_path, vae_source)
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if vae is not None:
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if not hasattr(sd_model, 'original_vae'):
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sd_model.original_vae = sd_model.vae
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sd_models.move_model(sd_model.original_vae, devices.cpu)
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sd_models.set_diffuser_options(sd_model, vae=vae, op='vae')
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apply_vae_config(sd_model.sd_checkpoint_info.filename, vae_file_path, sd_model)
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_models.move_model(sd_model, devices.device)
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return sd_model
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