Text-to-Image
Transformers
PyTorch
English
multi-model-orchestrator
ai
machine-learning
multimodal
image-captioning
orchestration
Instructions to use kunaliitkgp09/multi-model-orchestrator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kunaliitkgp09/multi-model-orchestrator with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kunaliitkgp09/multi-model-orchestrator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 3,577 Bytes
b4740c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 | #
# The Python Imaging Library
# $Id$
#
# base class for raster font file parsers
#
# history:
# 1997-06-05 fl created
# 1997-08-19 fl restrict image width
#
# Copyright (c) 1997-1998 by Secret Labs AB
# Copyright (c) 1997-1998 by Fredrik Lundh
#
# See the README file for information on usage and redistribution.
#
from __future__ import annotations
import os
from typing import BinaryIO
from . import Image, _binary
WIDTH = 800
def puti16(
fp: BinaryIO, values: tuple[int, int, int, int, int, int, int, int, int, int]
) -> None:
"""Write network order (big-endian) 16-bit sequence"""
for v in values:
if v < 0:
v += 65536
fp.write(_binary.o16be(v))
class FontFile:
"""Base class for raster font file handlers."""
bitmap: Image.Image | None = None
def __init__(self) -> None:
self.info: dict[bytes, bytes | int] = {}
self.glyph: list[
tuple[
tuple[int, int],
tuple[int, int, int, int],
tuple[int, int, int, int],
Image.Image,
]
| None
] = [None] * 256
def __getitem__(self, ix: int) -> (
tuple[
tuple[int, int],
tuple[int, int, int, int],
tuple[int, int, int, int],
Image.Image,
]
| None
):
return self.glyph[ix]
def compile(self) -> None:
"""Create metrics and bitmap"""
if self.bitmap:
return
# create bitmap large enough to hold all data
h = w = maxwidth = 0
lines = 1
for glyph in self.glyph:
if glyph:
d, dst, src, im = glyph
h = max(h, src[3] - src[1])
w = w + (src[2] - src[0])
if w > WIDTH:
lines += 1
w = src[2] - src[0]
maxwidth = max(maxwidth, w)
xsize = maxwidth
ysize = lines * h
if xsize == 0 and ysize == 0:
return
self.ysize = h
# paste glyphs into bitmap
self.bitmap = Image.new("1", (xsize, ysize))
self.metrics: list[
tuple[tuple[int, int], tuple[int, int, int, int], tuple[int, int, int, int]]
| None
] = [None] * 256
x = y = 0
for i in range(256):
glyph = self[i]
if glyph:
d, dst, src, im = glyph
xx = src[2] - src[0]
x0, y0 = x, y
x = x + xx
if x > WIDTH:
x, y = 0, y + h
x0, y0 = x, y
x = xx
s = src[0] + x0, src[1] + y0, src[2] + x0, src[3] + y0
self.bitmap.paste(im.crop(src), s)
self.metrics[i] = d, dst, s
def save(self, filename: str) -> None:
"""Save font"""
self.compile()
# font data
if not self.bitmap:
msg = "No bitmap created"
raise ValueError(msg)
self.bitmap.save(os.path.splitext(filename)[0] + ".pbm", "PNG")
# font metrics
with open(os.path.splitext(filename)[0] + ".pil", "wb") as fp:
fp.write(b"PILfont\n")
fp.write(f";;;;;;{self.ysize};\n".encode("ascii")) # HACK!!!
fp.write(b"DATA\n")
for id in range(256):
m = self.metrics[id]
if not m:
puti16(fp, (0,) * 10)
else:
puti16(fp, m[0] + m[1] + m[2])
|