mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
ef68a419f1
- user can select which weight files to download using huggingface cache - user must log in to huggingface, generate an access token, and accept license terms the very first time this is run. After that, everything works automatically. - added placeholder for docs for installing models - also got rid of unused config files. hopefully they weren't needed for textual inversion, but I don't think so.
358 lines
13 KiB
Python
358 lines
13 KiB
Python
'''
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Manage a cache of Stable Diffusion model files for fast switching.
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They are moved between GPU and CPU as necessary. If CPU memory falls
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below a preset minimum, the least recently used model will be
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cleared and loaded from disk when next needed.
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'''
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import torch
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import os
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import io
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import time
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import gc
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import hashlib
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import psutil
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import transformers
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import traceback
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import os
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from sys import getrefcount
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from omegaconf import OmegaConf
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from omegaconf.errors import ConfigAttributeError
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from ldm.util import instantiate_from_config
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GIGS=2**30
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AVG_MODEL_SIZE=2.1*GIGS
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DEFAULT_MIN_AVAIL=2*GIGS
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class ModelCache(object):
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def __init__(self, config:OmegaConf, device_type:str, precision:str, min_avail_mem=DEFAULT_MIN_AVAIL):
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'''
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Initialize with the path to the models.yaml config file,
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the torch device type, and precision. The optional
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min_avail_mem argument specifies how much unused system
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(CPU) memory to preserve. The cache of models in RAM will
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grow until this value is approached. Default is 2G.
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'''
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# prevent nasty-looking CLIP log message
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transformers.logging.set_verbosity_error()
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self.config = config
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self.precision = precision
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self.device = torch.device(device_type)
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self.min_avail_mem = min_avail_mem
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self.models = {}
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self.stack = [] # this is an LRU FIFO
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self.current_model = None
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def get_model(self, model_name:str):
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'''
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Given a model named identified in models.yaml, return
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the model object. If in RAM will load into GPU VRAM.
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If on disk, will load from there.
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'''
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if model_name not in self.config:
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print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
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return None
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if self.current_model != model_name:
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self.unload_model(self.current_model)
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if model_name in self.models:
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requested_model = self.models[model_name]['model']
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print(f'>> Retrieving model {model_name} from system RAM cache')
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self.models[model_name]['model'] = self._model_from_cpu(requested_model)
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width = self.models[model_name]['width']
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height = self.models[model_name]['height']
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hash = self.models[model_name]['hash']
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else:
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self._check_memory()
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try:
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requested_model, width, height, hash = self._load_model(model_name)
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self.models[model_name] = {}
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self.models[model_name]['model'] = requested_model
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self.models[model_name]['width'] = width
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self.models[model_name]['height'] = height
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self.models[model_name]['hash'] = hash
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except Exception as e:
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print(f'** model {model_name} could not be loaded: {str(e)}')
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print(traceback.format_exc())
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print(f'** restoring {self.current_model}')
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self.get_model(self.current_model)
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return None
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self.current_model = model_name
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self._push_newest_model(model_name)
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return {
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'model':requested_model,
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'width':width,
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'height':height,
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'hash': hash
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}
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def default_model(self) -> str:
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'''
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Returns the name of the default model, or None
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if none is defined.
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'''
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for model_name in self.config:
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if self.config[model_name].get('default',False):
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return model_name
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return None
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def set_default_model(self,model_name:str):
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'''
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Set the default model. The change will not take
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effect until you call model_cache.commit()
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'''
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assert model_name in self.models,f"unknown model '{model_name}'"
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for model in self.models:
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self.models[model].pop('default',None)
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self.models[model_name]['default'] = True
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def list_models(self) -> dict:
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'''
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Return a dict of models in the format:
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{ model_name1: {'status': ('active'|'cached'|'not loaded'),
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'description': description,
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},
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model_name2: { etc }
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'''
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result = {}
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for name in self.config:
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try:
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description = self.config[name].description
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except ConfigAttributeError:
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description = '<no description>'
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if self.current_model == name:
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status = 'active'
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elif name in self.models:
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status = 'cached'
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else:
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status = 'not loaded'
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result[name]={}
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result[name]['status']=status
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result[name]['description']=description
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return result
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def print_models(self):
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'''
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Print a table of models, their descriptions, and load status
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'''
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models = self.list_models()
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for name in models:
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line = f'{name:25s} {models[name]["status"]:>10s} {models[name]["description"]}'
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if models[name]['status'] == 'active':
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print(f'\033[1m{line}\033[0m')
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else:
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print(line)
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def del_model(self, model_name:str) ->bool:
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'''
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Delete the named model.
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'''
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omega = self.config
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del omega[model_name]
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if model_name in self.stack:
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self.stack.remove(model_name)
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return True
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def add_model(self, model_name:str, model_attributes:dict, clobber=False) ->True:
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'''
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Update the named model with a dictionary of attributes. Will fail with an
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assertion error if the name already exists. Pass clobber=True to overwrite.
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On a successful update, the config will be changed in memory and the
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method will return True. Will fail with an assertion error if provided
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attributes are incorrect or the model name is missing.
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'''
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omega = self.config
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# check that all the required fields are present
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for field in ('description','weights','height','width','config'):
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assert field in model_attributes, f'required field {field} is missing'
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assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
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config = omega[model_name] if model_name in omega else {}
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for field in model_attributes:
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config[field] = model_attributes[field]
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omega[model_name] = config
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if clobber:
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self._invalidate_cached_model(model_name)
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return True
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def _check_memory(self):
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avail_memory = psutil.virtual_memory()[1]
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if AVG_MODEL_SIZE + self.min_avail_mem > avail_memory:
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least_recent_model = self._pop_oldest_model()
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if least_recent_model is not None:
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del self.models[least_recent_model]
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gc.collect()
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def _load_model(self, model_name:str):
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"""Load and initialize the model from configuration variables passed at object creation time"""
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if model_name not in self.config:
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print(f'"{model_name}" is not a known model name. Please check your models.yaml file')
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return None
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mconfig = self.config[model_name]
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config = mconfig.config
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weights = mconfig.weights
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vae = mconfig.get('vae',None)
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width = mconfig.width
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height = mconfig.height
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print(f'>> Loading {model_name} from {weights}')
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# for usage statistics
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if self._has_cuda():
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torch.cuda.reset_peak_memory_stats()
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torch.cuda.empty_cache()
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tic = time.time()
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# this does the work
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c = OmegaConf.load(config)
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with open(weights,'rb') as f:
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weight_bytes = f.read()
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model_hash = self._cached_sha256(weights,weight_bytes)
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pl_sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
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del weight_bytes
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sd = pl_sd['state_dict']
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model = instantiate_from_config(c.model)
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m, u = model.load_state_dict(sd, strict=False)
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if self.precision == 'float16':
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print(' | Using faster float16 precision')
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model.to(torch.float16)
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else:
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print(' | Using more accurate float32 precision')
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# look and load a matching vae file. Code borrowed from AUTOMATIC1111 modules/sd_models.py
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if vae and os.path.exists(vae):
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print(f' | Loading VAE weights from: {vae}')
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vae_ckpt = torch.load(vae, map_location="cpu")
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vae_dict = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss"}
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model.first_stage_model.load_state_dict(vae_dict, strict=False)
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model.to(self.device)
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# model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
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model.cond_stage_model.device = self.device
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model.eval()
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for m in model.modules():
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if isinstance(m, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
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m._orig_padding_mode = m.padding_mode
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# usage statistics
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toc = time.time()
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print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
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if self._has_cuda():
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print(
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'>> Max VRAM used to load the model:',
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'%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
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'\n>> Current VRAM usage:'
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'%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
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)
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return model, width, height, model_hash
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def unload_model(self, model_name:str):
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if model_name not in self.models:
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return
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print(f'>> Caching model {model_name} in system RAM')
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model = self.models[model_name]['model']
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self.models[model_name]['model'] = self._model_to_cpu(model)
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gc.collect()
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if self._has_cuda():
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torch.cuda.empty_cache()
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def commit(self,config_file_path:str):
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'''
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Write current configuration out to the indicated file.
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'''
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yaml_str = OmegaConf.to_yaml(self.config)
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tmpfile = os.path.join(os.path.dirname(config_file_path),'new_config.tmp')
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with open(tmpfile, 'w') as outfile:
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outfile.write(self.preamble())
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outfile.write(yaml_str)
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os.rename(tmpfile,config_file_path)
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def preamble(self):
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'''
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Returns the preamble for the config file.
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'''
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return '''# This file describes the alternative machine learning models
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# available to InvokeAI script.
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#
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# To add a new model, follow the examples below. Each
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# model requires a model config file, a weights file,
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# and the width and height of the images it
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# was trained on.
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'''
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def _invalidate_cached_model(self,model_name:str):
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self.unload_model(model_name)
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if model_name in self.stack:
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self.stack.remove(model_name)
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self.models.pop(model_name,None)
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def _model_to_cpu(self,model):
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if self.device != 'cpu':
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model.cond_stage_model.device = 'cpu'
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model.first_stage_model.to('cpu')
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model.cond_stage_model.to('cpu')
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model.model.to('cpu')
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return model.to('cpu')
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else:
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return model
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def _model_from_cpu(self,model):
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if self.device != 'cpu':
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model.to(self.device)
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model.first_stage_model.to(self.device)
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model.cond_stage_model.to(self.device)
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model.cond_stage_model.device = self.device
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return model
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def _pop_oldest_model(self):
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'''
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Remove the first element of the FIFO, which ought
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to be the least recently accessed model. Do not
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pop the last one, because it is in active use!
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'''
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if len(self.stack) > 1:
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return self.stack.pop(0)
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def _push_newest_model(self,model_name:str):
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'''
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Maintain a simple FIFO. First element is always the
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least recent, and last element is always the most recent.
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'''
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try:
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self.stack.remove(model_name)
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except ValueError:
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pass
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self.stack.append(model_name)
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def _has_cuda(self):
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return self.device.type == 'cuda'
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def _cached_sha256(self,path,data):
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dirname = os.path.dirname(path)
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basename = os.path.basename(path)
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base, _ = os.path.splitext(basename)
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hashpath = os.path.join(dirname,base+'.sha256')
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if os.path.exists(hashpath) and os.path.getmtime(path) <= os.path.getmtime(hashpath):
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with open(hashpath) as f:
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hash = f.read()
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return hash
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print(f'>> Calculating sha256 hash of weights file')
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tic = time.time()
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sha = hashlib.sha256()
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sha.update(data)
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hash = sha.hexdigest()
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toc = time.time()
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print(f'>> sha256 = {hash}','(%4.2fs)' % (toc - tic))
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with open(hashpath,'w') as f:
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f.write(hash)
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return hash
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