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| ''' | |
| * Copyright (c) 2023 Salesforce, Inc. | |
| * All rights reserved. | |
| * SPDX-License-Identifier: Apache License 2.0 | |
| * For full license text, see LICENSE.txt file in the repo root or http://www.apache.org/licenses/ | |
| * By Can Qin | |
| * Modified from ControlNet repo: https://github.com/lllyasviel/ControlNet | |
| * Copyright (c) 2023 Lvmin Zhang and Maneesh Agrawala | |
| ''' | |
| import os | |
| import torch | |
| from omegaconf import OmegaConf | |
| import importlib | |
| import numpy as np | |
| from inspect import isfunction | |
| from PIL import Image, ImageDraw, ImageFont | |
| def log_txt_as_img(wh, xc, size=10): | |
| # wh a tuple of (width, height) | |
| # xc a list of captions to plot | |
| b = len(xc) | |
| txts = list() | |
| for bi in range(b): | |
| txt = Image.new("RGB", wh, color="white") | |
| draw = ImageDraw.Draw(txt) | |
| font = ImageFont.truetype('font/DejaVuSans.ttf', size=size) | |
| nc = int(40 * (wh[0] / 256)) | |
| lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) | |
| try: | |
| draw.text((0, 0), lines, fill="black", font=font) | |
| except UnicodeEncodeError: | |
| print("Cant encode string for logging. Skipping.") | |
| txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 | |
| txts.append(txt) | |
| txts = np.stack(txts) | |
| txts = torch.tensor(txts) | |
| return txts | |
| def ismap(x): | |
| if not isinstance(x, torch.Tensor): | |
| return False | |
| return (len(x.shape) == 4) and (x.shape[1] > 3) | |
| def isimage(x): | |
| if not isinstance(x,torch.Tensor): | |
| return False | |
| return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) | |
| def exists(x): | |
| return x is not None | |
| def default(val, d): | |
| if exists(val): | |
| return val | |
| return d() if isfunction(d) else d | |
| def mean_flat(tensor): | |
| """ | |
| https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 | |
| Take the mean over all non-batch dimensions. | |
| """ | |
| return tensor.mean(dim=list(range(1, len(tensor.shape)))) | |
| def count_params(model, verbose=False): | |
| total_params = sum(p.numel() for p in model.parameters()) | |
| if verbose: | |
| print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") | |
| return total_params | |
| def get_state_dict(d): | |
| return d.get('state_dict', d) | |
| def load_state_dict(ckpt_path, location='cpu'): | |
| _, extension = os.path.splitext(ckpt_path) | |
| if extension.lower() == ".safetensors": | |
| import safetensors.torch | |
| state_dict = safetensors.torch.load_file(ckpt_path, device=location) | |
| else: | |
| state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location))) | |
| state_dict = get_state_dict(state_dict) | |
| print(f'Loaded state_dict from [{ckpt_path}]') | |
| return state_dict | |
| def get_obj_from_str(string, reload=False): | |
| module, cls = string.rsplit(".", 1) | |
| if reload: | |
| module_imp = importlib.import_module(module) | |
| importlib.reload(module_imp) | |
| return getattr(importlib.import_module(module, package=None), cls) | |
| def instantiate_from_config(config): | |
| if not "target" in config: | |
| if config == '__is_first_stage__': | |
| return None | |
| elif config == "__is_unconditional__": | |
| return None | |
| raise KeyError("Expected key `target` to instantiate.") | |
| return get_obj_from_str(config["target"])(**config.get("params", dict())) | |
| def create_model(config_path): | |
| config = OmegaConf.load(config_path) | |
| model = instantiate_from_config(config.model).cpu() | |
| print(f'Loaded model config from [{config_path}]') | |
| return model |