mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
86 lines
2.4 KiB
Python
86 lines
2.4 KiB
Python
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import importlib
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import torch
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import numpy as np
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from inspect import isfunction
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from PIL import Image, ImageDraw, ImageFont
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def log_txt_as_img(wh, xc, size=10):
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# wh a tuple of (width, height)
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# xc a list of captions to plot
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b = len(xc)
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txts = list()
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for bi in range(b):
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txt = Image.new("RGB", wh, color="white")
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draw = ImageDraw.Draw(txt)
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font = ImageFont.truetype('data/DejaVuSans.ttf', size=size)
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nc = int(40 * (wh[0] / 256))
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lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
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try:
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draw.text((0, 0), lines, fill="black", font=font)
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except UnicodeEncodeError:
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print("Cant encode string for logging. Skipping.")
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txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
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txts.append(txt)
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txts = np.stack(txts)
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txts = torch.tensor(txts)
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return txts
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def ismap(x):
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if not isinstance(x, torch.Tensor):
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return False
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return (len(x.shape) == 4) and (x.shape[1] > 3)
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def isimage(x):
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if not isinstance(x,torch.Tensor):
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return False
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return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
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def exists(x):
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return x is not None
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def default(val, d):
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if exists(val):
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return val
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return d() if isfunction(d) else d
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def mean_flat(tensor):
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"""
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https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
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Take the mean over all non-batch dimensions.
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"""
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return tensor.mean(dim=list(range(1, len(tensor.shape))))
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def count_params(model, verbose=False):
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total_params = sum(p.numel() for p in model.parameters())
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if verbose:
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print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
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return total_params
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def instantiate_from_config(config):
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if not "target" in config:
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if config == '__is_first_stage__':
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return None
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elif config == "__is_unconditional__":
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return None
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raise KeyError("Expected key `target` to instantiate.")
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return get_obj_from_str(config["target"])(**config.get("params", dict()))
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def get_obj_from_str(string, reload=False):
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module, cls = string.rsplit(".", 1)
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if reload:
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module_imp = importlib.import_module(module)
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importlib.reload(module_imp)
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return getattr(importlib.import_module(module, package=None), cls)
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