mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
381 lines
12 KiB
Python
381 lines
12 KiB
Python
import importlib
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import math
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import multiprocessing as mp
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import os
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import re
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import io
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import base64
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from collections import abc
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from inspect import isfunction
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from pathlib import Path
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from queue import Queue
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from threading import Thread
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import numpy as np
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import requests
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import torch
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from PIL import Image, ImageDraw, ImageFont
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from tqdm import tqdm
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import invokeai.backend.util.logging as logger
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from .devices import torch_dtype
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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.load_default()
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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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logger.warning("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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logger.debug(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, **kwargs):
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if "target" not 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()), **kwargs)
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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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def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
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# create dummy dataset instance
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# run prefetching
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if idx_to_fn:
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res = func(data, worker_id=idx)
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else:
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res = func(data)
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Q.put([idx, res])
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Q.put("Done")
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def parallel_data_prefetch(
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func: callable,
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data,
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n_proc,
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target_data_type="ndarray",
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cpu_intensive=True,
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use_worker_id=False,
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):
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# if target_data_type not in ["ndarray", "list"]:
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# raise ValueError(
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# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
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# )
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if isinstance(data, np.ndarray) and target_data_type == "list":
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raise ValueError("list expected but function got ndarray.")
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elif isinstance(data, abc.Iterable):
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if isinstance(data, dict):
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logger.warning(
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'"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
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)
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data = list(data.values())
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if target_data_type == "ndarray":
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data = np.asarray(data)
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else:
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data = list(data)
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else:
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raise TypeError(
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f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
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)
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if cpu_intensive:
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Q = mp.Queue(1000)
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proc = mp.Process
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else:
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Q = Queue(1000)
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proc = Thread
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# spawn processes
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if target_data_type == "ndarray":
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arguments = [[func, Q, part, i, use_worker_id] for i, part in enumerate(np.array_split(data, n_proc))]
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else:
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step = int(len(data) / n_proc + 1) if len(data) % n_proc != 0 else int(len(data) / n_proc)
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arguments = [
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[func, Q, part, i, use_worker_id]
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for i, part in enumerate([data[i : i + step] for i in range(0, len(data), step)])
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]
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processes = []
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for i in range(n_proc):
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p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
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processes += [p]
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# start processes
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logger.info("Start prefetching...")
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import time
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start = time.time()
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gather_res = [[] for _ in range(n_proc)]
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try:
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for p in processes:
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p.start()
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k = 0
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while k < n_proc:
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# get result
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res = Q.get()
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if res == "Done":
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k += 1
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else:
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gather_res[res[0]] = res[1]
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except Exception as e:
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logger.error("Exception: ", e)
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for p in processes:
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p.terminate()
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raise e
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finally:
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for p in processes:
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p.join()
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logger.info(f"Prefetching complete. [{time.time() - start} sec.]")
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if target_data_type == "ndarray":
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if not isinstance(gather_res[0], np.ndarray):
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return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
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# order outputs
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return np.concatenate(gather_res, axis=0)
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elif target_data_type == "list":
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out = []
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for r in gather_res:
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out.extend(r)
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return out
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else:
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return gather_res
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def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
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delta = (res[0] / shape[0], res[1] / shape[1])
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d = (shape[0] // res[0], shape[1] // res[1])
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grid = (
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torch.stack(
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torch.meshgrid(
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torch.arange(0, res[0], delta[0]),
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torch.arange(0, res[1], delta[1]),
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indexing="ij",
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),
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dim=-1,
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).to(device)
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% 1
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)
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rand_val = torch.rand(res[0] + 1, res[1] + 1)
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angles = 2 * math.pi * rand_val
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gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1).to(device)
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tile_grads = (
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lambda slice1, slice2: gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]]
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.repeat_interleave(d[0], 0)
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.repeat_interleave(d[1], 1)
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)
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def dot(grad, shift):
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return (
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torch.stack(
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(
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grid[: shape[0], : shape[1], 0] + shift[0],
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grid[: shape[0], : shape[1], 1] + shift[1],
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),
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dim=-1,
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)
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* grad[: shape[0], : shape[1]]
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).sum(dim=-1)
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n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]).to(device)
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n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]).to(device)
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n01 = dot(tile_grads([0, -1], [1, None]), [0, -1]).to(device)
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n11 = dot(tile_grads([1, None], [1, None]), [-1, -1]).to(device)
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t = fade(grid[: shape[0], : shape[1]])
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noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(
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device
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)
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return noise.to(dtype=torch_dtype(device))
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def ask_user(question: str, answers: list):
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from itertools import chain, repeat
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user_prompt = f"\n>> {question} {answers}: "
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invalid_answer_msg = "Invalid answer. Please try again."
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pose_question = chain([user_prompt], repeat("\n".join([invalid_answer_msg, user_prompt])))
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user_answers = map(input, pose_question)
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valid_response = next(filter(answers.__contains__, user_answers))
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return valid_response
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# -------------------------------------
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def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path:
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"""
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Download a model file.
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:param url: https, http or ftp URL
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:param dest: A Path object. If path exists and is a directory, then we try to derive the filename
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from the URL's Content-Disposition header and copy the URL contents into
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dest/filename
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:param access_token: Access token to access this resource
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"""
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header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
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open_mode = "wb"
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exist_size = 0
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resp = requests.get(url, header, stream=True)
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content_length = int(resp.headers.get("content-length", 0))
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if dest.is_dir():
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try:
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file_name = re.search('filename="(.+)"', resp.headers.get("Content-Disposition")).group(1)
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except AttributeError:
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file_name = os.path.basename(url)
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dest = dest / file_name
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else:
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dest.parent.mkdir(parents=True, exist_ok=True)
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if dest.exists():
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exist_size = dest.stat().st_size
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header["Range"] = f"bytes={exist_size}-"
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open_mode = "ab"
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resp = requests.get(url, headers=header, stream=True) # new request with range
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if exist_size > content_length:
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logger.warning("corrupt existing file found. re-downloading")
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os.remove(dest)
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exist_size = 0
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if resp.status_code == 416 or (content_length > 0 and exist_size == content_length):
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logger.warning(f"{dest}: complete file found. Skipping.")
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return dest
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elif resp.status_code == 206 or exist_size > 0:
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logger.warning(f"{dest}: partial file found. Resuming...")
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elif resp.status_code != 200:
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logger.error(f"An error occurred during downloading {dest}: {resp.reason}")
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else:
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logger.info(f"{dest}: Downloading...")
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try:
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if content_length < 2000:
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logger.error(f"ERROR DOWNLOADING {url}: {resp.text}")
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return None
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with open(dest, open_mode) as file, tqdm(
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desc=str(dest),
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initial=exist_size,
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total=content_length,
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unit="iB",
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unit_scale=True,
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unit_divisor=1000,
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) as bar:
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for data in resp.iter_content(chunk_size=1024):
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size = file.write(data)
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bar.update(size)
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except Exception as e:
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logger.error(f"An error occurred while downloading {dest}: {str(e)}")
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return None
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return dest
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def url_attachment_name(url: str) -> dict:
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try:
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resp = requests.get(url, stream=True)
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match = re.search('filename="(.+)"', resp.headers.get("Content-Disposition"))
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return match.group(1)
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except Exception:
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return None
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def download_with_progress_bar(url: str, dest: Path) -> bool:
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result = download_with_resume(url, dest, access_token=None)
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return result is not None
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def image_to_dataURL(image: Image.Image, image_format: str = "PNG") -> str:
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"""
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Converts an image into a base64 image dataURL.
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"""
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buffered = io.BytesIO()
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image.save(buffered, format=image_format)
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mime_type = Image.MIME.get(image_format.upper(), "image/" + image_format.lower())
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image_base64 = f"data:{mime_type};base64," + base64.b64encode(buffered.getvalue()).decode("UTF-8")
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return image_base64
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class Chdir(object):
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"""Context manager to chdir to desired directory and change back after context exits:
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Args:
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path (Path): The path to the cwd
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"""
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def __init__(self, path: Path):
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self.path = path
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self.original = Path().absolute()
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def __enter__(self):
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os.chdir(self.path)
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def __exit__(self, *args):
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os.chdir(self.original)
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