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import logging
import math
import os
from io import BytesIO
from tqdm.contrib.concurrent import thread_map
import numpy as np
import requests
import torch
from PIL import Image
from typing import Union, Tuple, List
def fetch_video(ele):
raise ValueError(
"video input is not supported by Ming Image inference; "
"the public API accepts image and text input only"
)
logger = logging.getLogger(__name__)
IMAGE_FACTOR = 28
MIN_PIXELS = 4 * 28 * 28
MAX_PIXELS = 1024 * 28 * 28
MAX_RATIO = 200
VideoInput = Union[
List["Image.Image"],
"np.ndarray",
"torch.Tensor",
List["np.ndarray"],
List["torch.Tensor"],
List[List["Image.Image"]],
List[List["np.ndarray"]],
List[List["torch.Tensor"]],
]
def round_by_factor(number: int, factor: int) -> int:
"""Returns the closest integer to 'number' that is divisible by 'factor'."""
return round(number / factor) * factor
def ceil_by_factor(number: int, factor: int) -> int:
"""Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
return math.ceil(number / factor) * factor
def floor_by_factor(number: int, factor: int) -> int:
"""Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
return math.floor(number / factor) * factor
def is_image(image_file):
if isinstance(image_file, str) and (image_file.startswith("base64,") or image_file.lower().endswith(
('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff'))):
return True
elif isinstance(image_file, Image.Image):
return True
else:
return False
def is_video(video_file):
if isinstance(video_file, str) and video_file.lower().endswith(
('.mp4', '.mkv', '.avi', '.wmv', '.iso', ".webm")):
return True
else:
return False
def is_audio(audio_file):
if isinstance(audio_file, str) and audio_file.lower().endswith(
(".wav", ".mp3", ".aac", ".flac", ".alac", ".m4a", ".ogg", ".wma", ".aiff", ".amr", ".au")):
return True
else:
return False
def smart_resize(
height: int, width: int, factor: int = IMAGE_FACTOR, min_pixels: int = MIN_PIXELS, max_pixels: int = MAX_PIXELS
) -> tuple[int, int]:
"""
Rescales the image so that the following conditions are met:
1. Both dimensions (height and width) are divisible by 'factor'.
2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
3. The aspect ratio of the image is maintained as closely as possible.
"""
if max(height, width) / min(height, width) > MAX_RATIO:
raise ValueError(
f"absolute aspect ratio must be smaller than {MAX_RATIO}, got {max(height, width) / min(height, width)}"
)
h_bar = max(factor, round_by_factor(height, factor))
w_bar = max(factor, round_by_factor(width, factor))
if h_bar * w_bar > max_pixels:
beta = math.sqrt((height * width) / max_pixels)
h_bar = floor_by_factor(height / beta, factor)
w_bar = floor_by_factor(width / beta, factor)
elif h_bar * w_bar < min_pixels:
beta = math.sqrt(min_pixels / (height * width))
h_bar = ceil_by_factor(height * beta, factor)
w_bar = ceil_by_factor(width * beta, factor)
return h_bar, w_bar
def fetch_image(ele: dict[str, str | Image.Image], size_factor: int = IMAGE_FACTOR) -> Image.Image:
if "image" in ele:
image = ele["image"]
else:
image = ele["image_url"]
image_obj = None
if isinstance(image, Image.Image):
image_obj = image
elif image.startswith("http://") or image.startswith("https://"):
image_obj = Image.open(requests.get(image, stream=True).raw)
elif image.startswith("file://"):
image_obj = Image.open(image[7:])
elif image.startswith("data:image"):
if "base64," in image:
_, base64_data = image.split("base64,", 1)
data = base64.b64decode(base64_data)
image_obj = Image.open(BytesIO(data))
else:
image_obj = Image.open(image)
if image_obj is None:
raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
image = image_obj.convert("RGB")
## resize
if "resized_height" in ele and "resized_width" in ele:
resized_height, resized_width = smart_resize(
ele["resized_height"],
ele["resized_width"],
factor=size_factor,
)
else:
width, height = image.size
min_pixels = ele.get("min_pixels", MIN_PIXELS)
max_pixels = ele.get("max_pixels", MAX_PIXELS)
resized_height, resized_width = smart_resize(
height,
width,
factor=size_factor,
min_pixels=min_pixels,
max_pixels=max_pixels,
)
image = image.resize((resized_width, resized_height))
return image
def fetch_image_wo_resize(ele: dict[str, str | Image.Image], size_factor: int = IMAGE_FACTOR) -> Image.Image:
if "image" in ele:
image = ele["image"]
else:
image = ele["image_url"]
image_obj = None
if isinstance(image, Image.Image):
image_obj = image
elif image.startswith("http://") or image.startswith("https://"):
image_obj = Image.open(requests.get(image, stream=True).raw)
elif image.startswith("file://"):
image_obj = Image.open(image[7:])
elif image.startswith("data:image"):
if "base64," in image:
_, base64_data = image.split("base64,", 1)
data = base64.b64decode(base64_data)
image_obj = Image.open(BytesIO(data))
else:
image_obj = Image.open(image)
if image_obj is None:
raise ValueError(f"Unrecognized image input, support local path, http url, base64 and PIL.Image, got {image}")
#image = image_obj.convert("RGB")
return image_obj
def fetch_audio(ele: dict[str, str | torch.Tensor], return_tensor="pt") -> Tuple[Union[torch.Tensor, np.ndarray], int]:
import torchaudio
if "audio" in ele:
audio = ele["audio"]
else:
audio = ele["audio_url"]
if isinstance(audio, torch.Tensor):
waveform = audio
sample_rate: int = ele.get("sample_rate", 16000)
elif audio.startswith("http://") or audio.startswith("https://"):
audio_file = BytesIO(requests.get(audio, stream=True).content)
waveform, sample_rate = torchaudio.load(audio_file)
elif audio.startswith("file://"):
waveform, sample_rate = torchaudio.load(audio[7:])
else:
waveform, sample_rate = torchaudio.load(audio)
if return_tensor == "pt":
return waveform, sample_rate
else:
return waveform.numpy(), sample_rate
def extract_vision_info(conversations: list[dict] | list[list[dict]]) -> list[dict]:
vision_infos = []
if isinstance(conversations[0], dict):
conversations = [conversations]
for conversation in conversations:
for message in conversation:
if isinstance(message["content"], list):
for ele in message["content"]:
if (
"image" in ele
or "image_url" in ele
or "video" in ele
or "video_url" in ele
or "audio" in ele
or "audio_url" in ele
or ele["type"] in ["image", "image_url", "video", "video_url", "audio", "audio_url"]
):
vision_infos.append(ele)
return vision_infos
def process_reference_vision_info(
conversations: list[dict] | list[list[dict]],
) -> list[Image.Image] | None:
vision_infos = extract_vision_info(conversations)
## Read images
image_inputs = []
def inner_process_func(vision_info):
if "image" in vision_info or "image_url" in vision_info:
res_list = []
if "image" in vision_info and isinstance(vision_info["image"], (tuple, list)):
for i in range(len(vision_info["image"])):
res_list.append(fetch_image_wo_resize({"type": "image", "image": vision_info["image"][i]}))
elif "image_url" in vision_info and vision_info["image_url"].get("url", None) is not None:
vision_info["image_url"] = vision_info["image_url"].get("url")
res_list.extend([fetch_image_wo_resize(vision_info)])
else:
res_list.extend([fetch_image_wo_resize(vision_info)])
return {'image_inputs':res_list}
else:
return None
vision_infos_reslist = thread_map(inner_process_func, vision_infos, disable=True)
for res in vision_infos_reslist:
if res is None:
raise ValueError("image, image_url, video, video_url, audio or audio_url should in content.")
elif 'image_inputs' in res:
image_inputs.extend(res['image_inputs'])
if len(image_inputs) > 1: # multi-image input keeps only the first image as the VAE reference
image_inputs = [image_inputs[0]]
if len(image_inputs) == 0:
image_inputs = None
return image_inputs
def process_vision_info(
conversations: list[dict] | list[list[dict]],
) -> tuple[list[Image.Image] | None, list[torch.Tensor | list[Image.Image]] | None, list[
torch.Tensor | list[np.ndarray]] | None]:
vision_infos = extract_vision_info(conversations)
## Read images, videos or audios
image_inputs = []
video_inputs = []
audio_inputs = []
def inner_process_func(vision_info):
if "image" in vision_info or "image_url" in vision_info:
res_list = []
if "image" in vision_info and isinstance(vision_info["image"], (tuple, list)):
for i in range(len(vision_info["image"])):
res_list.append(fetch_image({"type": "image", "image": vision_info["image"][i]}))
elif "image_url" in vision_info and vision_info["image_url"].get("url", None) is not None:
vision_info["image_url"] = vision_info["image_url"].get("url")
res_list.extend([fetch_image(vision_info)])
else:
res_list.extend([fetch_image(vision_info)])
return {'image_inputs':res_list}
elif "video" in vision_info or "video_url" in vision_info:
if "video_url" in vision_info and vision_info["video_url"].get("url", None) is not None:
data_value = vision_info["video_url"].get("url")
elif "video" in vision_info and not os.path.isdir(vision_info['video']):
data_value = vision_info['video']
else:
data_value = [os.path.join(vision_info['video'], frame) for frame in sorted(os.listdir(vision_info['video']))]
vision_info['video']=data_value
return {"video_inputs": [fetch_video(vision_info)]}
elif "audio" in vision_info or "audio_url" in vision_info:
if "audio" in vision_info and isinstance(vision_info["audio"], (tuple, list)):
return {"audio_inputs":[fetch_audio(info) for info in vision_info["audio"]]}
elif "audio_url" in vision_info and vision_info["audio_url"].get("url", None) is not None:
vision_info["audio_url"] = vision_info["audio_url"].get("url")
return {"audio_inputs":[fetch_audio(vision_info)]}
else:
return {"audio_inputs":[fetch_audio(vision_info)]}
else:
return None
vision_infos_reslist = thread_map(inner_process_func, vision_infos, disable=True)
for res in vision_infos_reslist:
if res is None:
raise ValueError("image, image_url, video, video_url, audio or audio_url should in content.")
elif 'image_inputs' in res:
image_inputs.extend(res['image_inputs'])
elif 'video_inputs' in res:
video_inputs.extend(res['video_inputs'])
elif 'audio_inputs' in res:
audio_inputs.extend(res['audio_inputs'])
if len(image_inputs) == 0:
image_inputs = None
if len(video_inputs) == 0:
video_inputs = None
if len(audio_inputs) == 0:
audio_inputs = None
return image_inputs, video_inputs, audio_inputs
def get_closest_ratio(height: float, width: float, aspect_ratios: dict):
aspect_ratio = height / width
closest_ratio = min(aspect_ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio))
return aspect_ratios[closest_ratio], float(closest_ratio)
def process_ratio(ori_h, ori_w, highres=512):
ASPECT_RATIO_512 = {
"0.25": [256, 1024], "0.26": [256, 992], "0.27": [256, 960], "0.28": [256, 928],
"0.32": [288, 896], "0.33": [288, 864], "0.35": [288, 832], "0.4": [320, 800],
"0.42": [320, 768], "0.48": [352, 736], "0.5": [352, 704], "0.52": [352, 672],
"0.5455": [384, 704], "0.57": [384, 672], "0.6": [384, 640], "0.65": [416, 640],
"0.68": [416, 608], "0.72": [416, 576], "0.78": [448, 576],
"0.82": [448, 544], "0.88": [480, 544], "0.94": [480, 512],
"1.0": [512, 512], "1.07": [512, 480], "1.13": [544, 480], "1.21": [544, 448],
"1.29": [576, 448], "1.38": [576, 416],
"1.46": [608, 416], "1.5385": [640, 416], "1.67": [640, 384], "1.75": [672, 384],
"1.8333": [704, 384], "2.0": [704, 352], "2.09": [736, 352], "2.4": [768, 320],
"2.5": [800, 320], "2.89": [832, 288], "3.0": [864, 288], "3.11": [896, 288],
"3.62": [928, 256], "3.75": [960, 256], "3.88": [992, 256], "4.0": [1024, 256],
}
ASPECT_RATIO_1024 = {
"0.25": [512, 2048], "0.26": [512, 1984], "0.27": [512, 1920], "0.28": [512, 1856],
"0.32": [576, 1792], "0.33": [576, 1728], "0.35": [576, 1664], "0.4": [640, 1600],
"0.42": [640, 1536], "0.48": [704, 1472], "0.5": [704, 1408], "0.52": [704, 1344],
"0.5581": [768, 1376], "0.5625": [720, 1280], "0.5647": [768, 1360], "0.57": [768, 1344],
"0.6": [768, 1280], "0.622": [816, 1312], "0.625": [800, 1280], "0.65": [832, 1280],
"0.6582": [832, 1264], "0.6667": [832, 1248], "0.6709": [848, 1264], "0.68": [832, 1216],
"0.7013": [864, 1232], "0.72": [832, 1152], "0.7467": [896, 1200], "0.75": [864, 1152],
"0.7568": [896, 1184], "0.78": [896, 1152], "0.8": [896, 1120], "0.8056": [928, 1152],
"0.82": [896, 1088], "0.88": [960, 1088], "0.94": [960, 1024], "0.9846": [1024, 1040],
"1.0": [1024, 1024], "1.07": [1024, 960], "1.13": [1088, 960], "1.21": [1088, 896],
"1.2414": [1152, 928], "1.25": [1120, 896], "1.2807": [1168, 912], "1.29": [1152, 896],
"1.3333": [1152, 864], "1.3393": [1200, 896], "1.38": [1152, 832], "1.46": [1216, 832],
"1.4906": [1264, 848], "1.5": [1248, 832], "1.6": [1280, 800], "1.67": [1280, 768],
"1.75": [1344, 768], "1.7708": [1360, 768], "1.7778": [1280, 720], "2.0": [1408, 704],
"2.09": [1472, 704], "2.4": [1536, 640], "2.5": [1600, 640], "2.89": [1664, 576],
"3.0": [1728, 576], "3.11": [1792, 576], "3.62": [1856, 512], "3.75": [1920, 512],
"3.88": [1984, 512], "4.0": [2048, 512],
}
ASPECT_RATIO_672 = {
"0.28": [352, 1280], "0.32": [384, 1184], "0.38": [416, 1088], "0.44": [448, 1024],
"0.52": [480, 928], "0.5636": [496, 880], "0.57": [512, 896], "0.65": [544, 832],
"0.6667": [544, 816], "0.75": [576, 768], "0.8": [576, 720], "0.83": [608, 736],
"0.91": [640, 704], "1.00": [672, 672], "1.10": [704, 640], "1.21": [736, 608],
"1.25": [720, 576], "1.33": [768, 576], "1.39": [800, 576], "1.5": [816, 544],
"1.53": [832, 544], "1.69": [864, 512], "1.75": [896, 512], "1.7742": [880, 496],
"1.93": [928, 480], "2.00": [960, 480],
"2.21": [992, 448], "2.29": [1024, 448], "2.54": [1056, 416], "2.62": [1088, 416],
"2.69": [1120, 416], "3.00": [1152, 384], "3.08": [1184, 384], "3.17": [1216, 384],
"3.55": [1248, 352], "3.64": [1280, 352],
}
ASPECT_RATIO_2048 = {
"0.25": [1024, 4096], "0.26": [1024, 3968], "0.27": [1024, 3840], "0.28": [1024, 3712],
"0.32": [1152, 3584], "0.33": [1152, 3456], "0.35": [1152, 3328], "0.4": [1280, 3200],
"0.42": [1280, 3072], "0.48": [1408, 2944], "0.5": [1408, 2816], "0.52": [1408, 2688],
"0.5625": [1440, 2560], "0.57": [1536, 2688], "0.6": [1536, 2560], "0.6667": [1664, 2496],
"0.68": [1664, 2432], "0.72": [1664, 2304], "0.75": [1824, 2432], "0.78": [1792, 2304],
"0.7917": [1824, 2304], "0.8": [1792, 2240], "0.82": [1792, 2176], "0.88": [1920, 2176],
"0.94": [1920, 2048],
"1.0": [2048, 2048], "1.07": [2048, 1920], "1.13": [2176, 1920], "1.21": [2176, 1792],
"1.25": [2240, 1792], "1.2632": [2304, 1824], "1.29": [2304, 1792],
"1.3333": [2432, 1824], "1.38": [2304, 1664],
"1.46": [2432, 1664], "1.5": [2496, 1664], "1.67": [2560, 1536], "1.75": [2688, 1536],
"1.7778": [2560, 1440], "2.0": [2816, 1408], "2.09": [2944, 1408], "2.4": [3072, 1280],
"2.5": [3200, 1280], "2.89": [3328, 1152], "3.0": [3456, 1152], "3.11": [3584, 1152],
"3.62": [3712, 1024], "3.75": [3840, 1024], "3.88": [3968, 1024], "4.0": [4096, 1024],
"0.2941": [1120, 3808], "0.3043": [1120, 3680], "0.3679": [1248, 3392],
"0.3846": [1280, 3328], "0.433": [1344, 3104], "0.4574": [1376, 3008],
"0.4681": [1408, 3008], "0.5465": [1504, 2752], "0.6296": [1632, 2592],
"0.6582": [1664, 2528], "0.7432": [1760, 2368], "0.8551": [1888, 2208],
"0.9692": [2016, 2080], "1.0317": [2080, 2016], "1.1695": [2208, 1888],
"1.3455": [2368, 1760], "1.5192": [2528, 1664], "1.5882": [2592, 1632],
"1.8298": [2752, 1504], "1.913": [2816, 1472], "2.1364": [3008, 1408],
"2.186": [3008, 1376], "2.3095": [3104, 1344], "2.6": [3328, 1280],
"2.7179": [3392, 1248], "3.2857": [3680, 1120], "3.4": [3808, 1120],
}
aspect_ratio_dict = {
512 : ASPECT_RATIO_512,
672 : ASPECT_RATIO_672,
1024 : ASPECT_RATIO_1024,
2048 : ASPECT_RATIO_2048,
}
if highres is None or highres is False:
highres = 512
elif highres is True:
highres = 1024
aspect_ratio = aspect_ratio_dict[min([i for i in aspect_ratio_dict], key=lambda x: abs(x - highres))]
closest_size, _ = get_closest_ratio(ori_h, ori_w, aspect_ratios=aspect_ratio)
closest_size = list(map(lambda x: int(x), closest_size))
if closest_size[0] / ori_h > closest_size[1] / ori_w:
resize_size = closest_size[0], int(ori_w * closest_size[0] / ori_h)
else:
resize_size = int(ori_h * closest_size[1] / ori_w), closest_size[1]
return closest_size, resize_size
def find_first_index_of_consecutive_ones(lst):
"""
Given a list of 0s and 1s, return the index of the first 1 of each
consecutive run of 1s.
Args:
lst (list): list of 0s and 1s
Returns:
list: indices of the first 1 of each consecutive run of 1s
"""
result = []
i = 0
n = len(lst)
while i < n:
if lst[i] == 1:
# find the start of a consecutive run of 1s
result.append(i)
# skip the remainder of the consecutive run of 1s
while i < n and lst[i] == 1:
i += 1
else:
i += 1
return result
def merge_consecutive_ones(lst, n):
"""
Given a list of 0s and 1s, merge every n consecutive 1s of each run
(length >= 1) into a single 1. Each run must have a length divisible by n.
The relative order of 0s and 1s is preserved.
Args:
lst: list of 0s and 1s
n: positive integer merge unit size
Returns:
list: the merged list
"""
assert isinstance(lst, list), "input must be a list"
assert isinstance(n, int) and n > 0, "n must be a positive integer"
# iterate over the list, extract runs of 1s, and verify each run length is divisible by n
i = 0
while i < len(lst):
if lst[i] == 1:
count = 0
start = i
# count the run of consecutive 1s
while i < len(lst) and lst[i] == 1:
count += 1
i += 1
# every run of 1s must be divisible by n
assert count % n == 0, f"run of 1s at index {start} has length {count}, not divisible by n={n}"
else:
i += 1
# build the new list by merging groups
result = []
i = 0
while i < len(lst):
if lst[i] == 0:
result.append(0)
i += 1
else:
# process a run of 1s
count = 0
while i < len(lst) and lst[i] == 1:
count += 1
i += 1
# merge every n 1s into a single 1
result.extend([1] * (count // n))
return result
def get_default_image_gen_hw(image_gen_highres, image_gen_aspect_ratio):
if image_gen_aspect_ratio is None:
image_gen_aspect_ratio = 1.0
closest_size, _ = process_ratio(ori_h=512, ori_w=int(512.0 * image_gen_aspect_ratio), highres=image_gen_highres)
h, w = closest_size
return h, w
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