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: 3,906 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | #
# Python Imaging Library
# $Id$
#
# stuff to read (and render) GIMP gradient files
#
# History:
# 97-08-23 fl Created
#
# Copyright (c) Secret Labs AB 1997.
# Copyright (c) Fredrik Lundh 1997.
#
# See the README file for information on usage and redistribution.
#
"""
Stuff to translate curve segments to palette values (derived from
the corresponding code in GIMP, written by Federico Mena Quintero.
See the GIMP distribution for more information.)
"""
from __future__ import annotations
from math import log, pi, sin, sqrt
from typing import IO, Callable
from ._binary import o8
EPSILON = 1e-10
"""""" # Enable auto-doc for data member
def linear(middle: float, pos: float) -> float:
if pos <= middle:
if middle < EPSILON:
return 0.0
else:
return 0.5 * pos / middle
else:
pos = pos - middle
middle = 1.0 - middle
if middle < EPSILON:
return 1.0
else:
return 0.5 + 0.5 * pos / middle
def curved(middle: float, pos: float) -> float:
return pos ** (log(0.5) / log(max(middle, EPSILON)))
def sine(middle: float, pos: float) -> float:
return (sin((-pi / 2.0) + pi * linear(middle, pos)) + 1.0) / 2.0
def sphere_increasing(middle: float, pos: float) -> float:
return sqrt(1.0 - (linear(middle, pos) - 1.0) ** 2)
def sphere_decreasing(middle: float, pos: float) -> float:
return 1.0 - sqrt(1.0 - linear(middle, pos) ** 2)
SEGMENTS = [linear, curved, sine, sphere_increasing, sphere_decreasing]
"""""" # Enable auto-doc for data member
class GradientFile:
gradient: (
list[
tuple[
float,
float,
float,
list[float],
list[float],
Callable[[float, float], float],
]
]
| None
) = None
def getpalette(self, entries: int = 256) -> tuple[bytes, str]:
assert self.gradient is not None
palette = []
ix = 0
x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
for i in range(entries):
x = i / (entries - 1)
while x1 < x:
ix += 1
x0, x1, xm, rgb0, rgb1, segment = self.gradient[ix]
w = x1 - x0
if w < EPSILON:
scale = segment(0.5, 0.5)
else:
scale = segment((xm - x0) / w, (x - x0) / w)
# expand to RGBA
r = o8(int(255 * ((rgb1[0] - rgb0[0]) * scale + rgb0[0]) + 0.5))
g = o8(int(255 * ((rgb1[1] - rgb0[1]) * scale + rgb0[1]) + 0.5))
b = o8(int(255 * ((rgb1[2] - rgb0[2]) * scale + rgb0[2]) + 0.5))
a = o8(int(255 * ((rgb1[3] - rgb0[3]) * scale + rgb0[3]) + 0.5))
# add to palette
palette.append(r + g + b + a)
return b"".join(palette), "RGBA"
class GimpGradientFile(GradientFile):
"""File handler for GIMP's gradient format."""
def __init__(self, fp: IO[bytes]) -> None:
if not fp.readline().startswith(b"GIMP Gradient"):
msg = "not a GIMP gradient file"
raise SyntaxError(msg)
line = fp.readline()
# GIMP 1.2 gradient files don't contain a name, but GIMP 1.3 files do
if line.startswith(b"Name: "):
line = fp.readline().strip()
count = int(line)
self.gradient = []
for i in range(count):
s = fp.readline().split()
w = [float(x) for x in s[:11]]
x0, x1 = w[0], w[2]
xm = w[1]
rgb0 = w[3:7]
rgb1 = w[7:11]
segment = SEGMENTS[int(s[11])]
cspace = int(s[12])
if cspace != 0:
msg = "cannot handle HSV colour space"
raise OSError(msg)
self.gradient.append((x0, x1, xm, rgb0, rgb1, segment))
|