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450 lines
15 KiB
Python
450 lines
15 KiB
Python
import logging
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import os
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import re
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import sys
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import urllib.parse
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from functools import wraps
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import requests
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import torch
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from huggingface_hub import (
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HfFolder,
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hf_hub_download as _hf_hub_download,
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try_to_load_from_cache,
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)
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from omegaconf import OmegaConf
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from safetensors.torch import load_file
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from imaginairy import config as iconfig
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from imaginairy.config import MODEL_SHORT_NAMES
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from imaginairy.modules import attention
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from imaginairy.paths import PKG_ROOT
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from imaginairy.utils import get_device, instantiate_from_config
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from imaginairy.utils.model_cache import memory_managed_model
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logger = logging.getLogger(__name__)
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MOST_RECENTLY_LOADED_MODEL = None
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class HuggingFaceAuthorizationError(RuntimeError):
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pass
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def load_tensors(tensorfile, map_location=None):
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if tensorfile == "empty":
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# used for testing
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return {}
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if tensorfile.endswith((".ckpt", ".pth")):
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return torch.load(tensorfile, map_location=map_location)
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if tensorfile.endswith(".safetensors"):
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return load_file(tensorfile, device=map_location)
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return load_file(tensorfile, device=map_location)
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# raise ValueError(f"Unknown tensorfile type: {tensorfile}")
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def load_state_dict(weights_location, half_mode=False, device=None):
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if device is None:
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device = get_device()
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if weights_location.startswith("http"):
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ckpt_path = get_cached_url_path(weights_location, category="weights")
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else:
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ckpt_path = weights_location
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logger.info(f"Loading model {ckpt_path} onto {get_device()} backend...")
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state_dict = None
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# weights_cache_key = (ckpt_path, half_mode)
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# if weights_cache_key in GLOBAL_WEIGHTS_CACHE:
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# return GLOBAL_WEIGHTS_CACHE.get(weights_cache_key)
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try:
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state_dict = load_tensors(ckpt_path, map_location="cpu")
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except FileNotFoundError as e:
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if e.errno == 2:
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logger.error(
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f'Error: "{ckpt_path}" not a valid path to model weights.\nPreconfigured models you can use: {MODEL_SHORT_NAMES}.'
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)
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sys.exit(1)
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raise
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except RuntimeError as e:
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err_str = str(e)
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if (
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"PytorchStreamReader failed reading zip archive" in err_str
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and weights_location.startswith("http")
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):
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logger.warning("Corrupt checkpoint. deleting and re-downloading...")
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os.remove(ckpt_path)
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ckpt_path = get_cached_url_path(weights_location, category="weights")
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state_dict = load_tensors(ckpt_path, map_location="cpu")
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if state_dict is None:
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raise
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state_dict = state_dict.get("state_dict", state_dict)
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if half_mode:
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state_dict = {k: v.half() for k, v in state_dict.items()}
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# change device
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state_dict = {k: v.to(device) for k, v in state_dict.items()}
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# GLOBAL_WEIGHTS_CACHE.set(weights_cache_key, state_dict)
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return state_dict
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def load_model_from_config(config, weights_location, half_mode=False):
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model = instantiate_from_config(config.model)
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base_model_dict = load_state_dict(weights_location, half_mode=half_mode)
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model.init_from_state_dict(base_model_dict)
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if half_mode:
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model = model.half()
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model.to(get_device())
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model.eval()
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return model
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def load_model_from_config_old(
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config, weights_location, control_weights_locations=None, half_mode=False
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):
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model = instantiate_from_config(config.model)
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print("instantiated")
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base_model_dict = load_state_dict(weights_location, half_mode=half_mode)
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model.init_from_state_dict(base_model_dict)
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control_weights_locations = control_weights_locations or []
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controlnets = []
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for control_weights_location in control_weights_locations:
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controlnet_state_dict = load_state_dict(
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control_weights_location, half_mode=half_mode
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)
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controlnet_state_dict = {
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k.replace("control_model.", ""): v for k, v in controlnet_state_dict.items()
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}
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controlnet = instantiate_from_config(model.control_stage_config)
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controlnet.load_state_dict(controlnet_state_dict)
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controlnet.to(get_device())
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controlnets.append(controlnet)
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model.set_control_models(controlnets)
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if half_mode:
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model = model.half()
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print("halved")
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model.to(get_device())
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print("moved to device")
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model.eval()
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print("set to eval mode")
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return model
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def add_controlnet(base_state_dict, controlnet_state_dict):
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"""Merges a base sd15 model with a controlnet model."""
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for key in controlnet_state_dict:
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base_state_dict[key] = controlnet_state_dict[key]
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return base_state_dict
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def get_diffusion_model(
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weights_location=iconfig.DEFAULT_MODEL,
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config_path="configs/stable-diffusion-v1.yaml",
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control_weights_locations=None,
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half_mode=None,
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for_inpainting=False,
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for_training=False,
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):
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"""
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Load a diffusion model.
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Weights location may also be shortcut name, e.g. "SD-1.5"
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"""
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try:
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return _get_diffusion_model(
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weights_location,
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config_path,
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half_mode,
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for_inpainting,
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control_weights_locations=control_weights_locations,
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for_training=for_training,
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)
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except HuggingFaceAuthorizationError as e:
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if for_inpainting:
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logger.warning(
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f"Failed to load inpainting model. Attempting to fall-back to standard model. {e!s}"
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)
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return _get_diffusion_model(
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iconfig.DEFAULT_MODEL,
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config_path,
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half_mode,
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for_inpainting=False,
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for_training=for_training,
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control_weights_locations=control_weights_locations,
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)
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raise
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def _get_diffusion_model(
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weights_location=iconfig.DEFAULT_MODEL,
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config_path="configs/stable-diffusion-v1.yaml",
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half_mode=None,
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for_inpainting=False,
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for_training=False,
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control_weights_locations=None,
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):
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"""
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Load a diffusion model.
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Weights location may also be shortcut name, e.g. "SD-1.5"
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"""
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global MOST_RECENTLY_LOADED_MODEL
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(
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model_config,
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weights_location,
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config_path,
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control_weights_locations,
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) = resolve_model_paths(
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weights_path=weights_location,
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config_path=config_path,
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control_weights_paths=control_weights_locations,
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for_inpainting=for_inpainting,
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for_training=for_training,
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)
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# some models need the attention calculated in float32
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if model_config is not None:
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attention.ATTENTION_PRECISION_OVERRIDE = model_config.forced_attn_precision
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else:
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attention.ATTENTION_PRECISION_OVERRIDE = "default"
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diffusion_model = _load_diffusion_model(
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config_path=config_path,
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weights_location=weights_location,
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half_mode=half_mode,
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for_training=for_training,
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)
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MOST_RECENTLY_LOADED_MODEL = diffusion_model
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if control_weights_locations:
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controlnets = []
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for control_weights_location in control_weights_locations:
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controlnets.append(load_controlnet(control_weights_location, half_mode))
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diffusion_model.set_control_models(controlnets)
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return diffusion_model
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@memory_managed_model("stable-diffusion", memory_usage_mb=1951)
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def _load_diffusion_model(config_path, weights_location, half_mode, for_training):
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model_config = OmegaConf.load(f"{PKG_ROOT}/{config_path}")
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if for_training:
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model_config.use_ema = True
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# model_config.use_scheduler = True
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# only run half-mode on cuda. run it by default
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half_mode = half_mode is None and get_device() == "cuda"
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model = load_model_from_config(
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config=model_config,
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weights_location=weights_location,
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half_mode=half_mode,
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)
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return model
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@memory_managed_model("controlnet")
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def load_controlnet(control_weights_location, half_mode):
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controlnet_state_dict = load_state_dict(
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control_weights_location, half_mode=half_mode
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)
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controlnet_state_dict = {
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k.replace("control_model.", ""): v for k, v in controlnet_state_dict.items()
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}
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control_stage_config = OmegaConf.load(f"{PKG_ROOT}/configs/control-net-v15.yaml")[
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"model"
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]["params"]["control_stage_config"]
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controlnet = instantiate_from_config(control_stage_config)
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controlnet.load_state_dict(controlnet_state_dict)
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controlnet.to(get_device())
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return controlnet
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def resolve_model_paths(
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weights_path=iconfig.DEFAULT_MODEL,
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config_path=None,
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control_weights_paths=None,
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for_inpainting=False,
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for_training=False,
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):
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"""Resolve weight and config path if they happen to be shortcuts."""
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model_metadata_w = iconfig.MODEL_CONFIG_SHORTCUTS.get(weights_path, None)
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model_metadata_c = iconfig.MODEL_CONFIG_SHORTCUTS.get(config_path, None)
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control_weights_paths = control_weights_paths or []
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control_net_metadatas = [
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iconfig.CONTROLNET_CONFIG_SHORTCUTS.get(control_weights_path, None)
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for control_weights_path in control_weights_paths
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]
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if not control_net_metadatas and for_inpainting:
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model_metadata_w = iconfig.MODEL_CONFIG_SHORTCUTS.get(
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f"{weights_path}-inpaint", model_metadata_w
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)
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model_metadata_c = iconfig.MODEL_CONFIG_SHORTCUTS.get(
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f"{config_path}-inpaint", model_metadata_c
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)
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if model_metadata_w:
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if config_path is None:
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config_path = model_metadata_w.config_path
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if for_training:
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weights_path = model_metadata_w.weights_url_full
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if weights_path is None:
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msg = "No full training weights configured for this model. Edit the code or subimt a github issue."
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raise ValueError(msg)
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else:
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weights_path = model_metadata_w.weights_url
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if model_metadata_c:
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config_path = model_metadata_c.config_path
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if config_path is None:
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config_path = iconfig.MODEL_CONFIG_SHORTCUTS[iconfig.DEFAULT_MODEL].config_path
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if control_net_metadatas:
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if "stable-diffusion-v1" not in config_path:
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msg = "Control net is only supported for stable diffusion v1. Please use a different model."
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raise ValueError(msg)
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control_weights_paths = [cnm.weights_url for cnm in control_net_metadatas]
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config_path = control_net_metadatas[0].config_path
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model_metadata = model_metadata_w or model_metadata_c
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logger.debug(f"Loading model weights from: {weights_path}")
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logger.debug(f"Loading model config from: {config_path}")
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return model_metadata, weights_path, config_path, control_weights_paths
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def get_model_default_image_size(weights_location):
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model_config = iconfig.MODEL_CONFIG_SHORTCUTS.get(weights_location, None)
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if model_config:
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return model_config.default_image_size
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return 512
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def get_current_diffusion_model():
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return MOST_RECENTLY_LOADED_MODEL
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def get_cache_dir():
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xdg_cache_home = os.getenv("XDG_CACHE_HOME", None)
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if xdg_cache_home is None:
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user_home = os.getenv("HOME", None)
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if user_home:
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xdg_cache_home = os.path.join(user_home, ".cache")
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if xdg_cache_home is not None:
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return os.path.join(xdg_cache_home, "imaginairy")
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return os.path.join(os.path.dirname(__file__), ".cached-aimg")
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def get_cached_url_path(url, category=None):
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"""
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Gets the contents of a url, but caches the response indefinitely.
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While we attempt to use the cached_path from huggingface transformers, we fall back
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to our own implementation if the url does not provide an etag header, which `cached_path`
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requires. We also skip the `head` call that `cached_path` makes on every call if the file
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is already cached.
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"""
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try:
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if url.startswith("https://huggingface.co"):
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return huggingface_cached_path(url)
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except (OSError, ValueError):
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pass
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filename = url.split("/")[-1]
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dest = get_cache_dir()
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if category:
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dest = os.path.join(dest, category)
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os.makedirs(dest, exist_ok=True)
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# Replace possibly illegal destination path characters
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safe_filename = re.sub('[*<>:"|?]', "_", filename)
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dest_path = os.path.join(dest, safe_filename)
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if os.path.exists(dest_path):
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return dest_path
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# check if it's saved at previous path and rename it
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old_dest_path = os.path.join(dest, filename)
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if os.path.exists(old_dest_path):
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os.rename(old_dest_path, dest_path)
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return dest_path
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r = requests.get(url)
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with open(dest_path, "wb") as f:
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f.write(r.content)
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return dest_path
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def check_huggingface_url_authorized(url):
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if not url.startswith("https://huggingface.co/"):
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return None
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token = HfFolder.get_token()
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headers = {}
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if token is not None:
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headers["authorization"] = f"Bearer {token}"
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response = requests.head(url, allow_redirects=True, headers=headers, timeout=5)
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if response.status_code == 401:
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msg = "Unauthorized access to HuggingFace model. This model requires a huggingface token. Please login to HuggingFace or set HUGGING_FACE_HUB_TOKEN to your User Access Token. See https://huggingface.co/docs/huggingface_hub/quick-start#login for more information"
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raise HuggingFaceAuthorizationError(msg)
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return None
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@wraps(_hf_hub_download)
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def hf_hub_download(*args, **kwargs):
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"""
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backwards compatible wrapper for huggingface's hf_hub_download.
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they changed the argument name from `use_auth_token` to `token`
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"""
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try:
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return _hf_hub_download(*args, **kwargs)
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except TypeError as e:
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if "unexpected keyword argument 'token'" in str(e):
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kwargs["use_auth_token"] = kwargs.pop("token")
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return _hf_hub_download(*args, **kwargs)
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raise
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def huggingface_cached_path(url):
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# bypass all the HEAD calls done by the default `cached_path`
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repo, commit_hash, filepath = extract_huggingface_repo_commit_file_from_url(url)
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dest_path = try_to_load_from_cache(
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repo_id=repo, revision=commit_hash, filename=filepath
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)
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if not dest_path:
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check_huggingface_url_authorized(url)
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token = HfFolder.get_token()
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logger.info(f"Downloading {url} from huggingface")
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dest_path = hf_hub_download(
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repo_id=repo, revision=commit_hash, filename=filepath, token=token
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)
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# make a refs folder so caching works
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# work-around for
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# https://github.com/huggingface/huggingface_hub/pull/1306
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# https://github.com/brycedrennan/imaginAIry/issues/171
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refs_url = dest_path[: dest_path.index("/snapshots/")] + "/refs/"
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os.makedirs(refs_url, exist_ok=True)
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return dest_path
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def extract_huggingface_repo_commit_file_from_url(url):
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parsed_url = urllib.parse.urlparse(url)
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path_components = parsed_url.path.strip("/").split("/")
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repo = "/".join(path_components[0:2])
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assert path_components[2] == "resolve"
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commit_hash = path_components[3]
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filepath = "/".join(path_components[4:])
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return repo, commit_hash, filepath
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