Spaces:
Running
on
Zero
Running
on
Zero
File size: 11,678 Bytes
e368cec 619dcd0 e368cec cfd968b b34109c e368cec 944dd2b 868a596 9bf990d e368cec bd0bce1 e368cec bd0bce1 e368cec 619dcd0 2af380b e368cec bd0bce1 2af380b e368cec 5777088 94bd22c f7a6c5f e368cec 868a596 e368cec 868a596 e368cec 94bd22c a071819 e368cec 5777088 f7a6c5f 868a596 f7a6c5f 868a596 f7a6c5f e368cec 94bd22c a071819 e368cec bd0bce1 f7a6c5f 3bb2685 e368cec 2af380b e368cec 5777088 f7a6c5f e368cec a071819 e368cec 5777088 f7a6c5f e368cec 868a596 e368cec 868a596 e368cec a071819 944dd2b f7a6c5f 868a596 f7a6c5f 868a596 f7a6c5f 83e2394 944dd2b 94bd22c f7a6c5f b34109c f7a6c5f 944dd2b 868a596 944dd2b 868a596 944dd2b 94bd22c a071819 944dd2b f7a6c5f 868a596 f7a6c5f 868a596 9bf990d f7a6c5f b34109c f7a6c5f 944dd2b 94bd22c a071819 f7a6c5f b34109c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 |
import concurrent.futures
import random
import gradio as gr
import requests
import io, base64, json
import spaces
from PIL import Image
from .models import IMAGE_GENERATION_MODELS, IMAGE_EDITION_MODELS, VIDEO_GENERATION_MODELS, MUSEUM_UNSUPPORTED_MODELS, DESIRED_APPEAR_MODEL, load_pipeline
from .fetch_museum_results import draw_from_imagen_museum, draw2_from_imagen_museum, draw_from_videogen_museum, draw2_from_videogen_museum
class ModelManager:
def __init__(self):
self.model_ig_list = IMAGE_GENERATION_MODELS
self.model_ie_list = IMAGE_EDITION_MODELS
self.model_vg_list = VIDEO_GENERATION_MODELS
self.excluding_model_list = MUSEUM_UNSUPPORTED_MODELS
self.desired_model_list = DESIRED_APPEAR_MODEL
self.loaded_models = {}
def load_model_pipe(self, model_name):
if not model_name in self.loaded_models:
pipe = load_pipeline(model_name)
self.loaded_models[model_name] = pipe
else:
pipe = self.loaded_models[model_name]
return pipe
@spaces.GPU(duration=120)
def generate_image_ig(self, prompt, model_name):
pipe = self.load_model_pipe(model_name)
result = pipe(prompt=prompt)
return result
def generate_image_ig_api(self, prompt, model_name):
pipe = self.load_model_pipe(model_name)
result = pipe(prompt=prompt)
return result
def generate_image_ig_museum(self, model_name):
model_name = model_name.split('_')[1]
result_list = draw_from_imagen_museum("t2i", model_name)
image_link = result_list[0]
prompt = result_list[1]
return image_link, prompt
def generate_image_ig_parallel_anony(self, prompt, model_A, model_B):
# Using list comprehension to get the difference between two lists
picking_list = [item for item in self.model_ig_list if item not in self.excluding_model_list]
if model_A == "" and model_B == "":
model_names = random.sample([model for model in picking_list], 2)
else:
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("imagenhub")
else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
results = [future.result() for future in futures]
return results[0], results[1], model_names[0], model_names[1]
def generate_image_ig_museum_parallel_anony(self, model_A, model_B):
# Using list comprehension to get the difference between two lists
picking_list = [item for item in self.model_ig_list if item not in self.excluding_model_list]
if model_A == "" and model_B == "":
model_names = random.sample([model for model in picking_list], 2)
else:
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
model_1 = model_names[0].split('_')[1]
model_2 = model_names[1].split('_')[1]
result_list = draw2_from_imagen_museum("t2i", model_1, model_2)
image_links = result_list[0]
prompt_list = result_list[1]
return image_links[0], image_links[1], model_names[0], model_names[1], prompt_list[0]
def generate_image_ig_parallel(self, prompt, model_A, model_B):
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("imagenhub")
else executor.submit(self.generate_image_ig_api, prompt, model) for model in model_names]
results = [future.result() for future in futures]
return results[0], results[1]
def generate_image_ig_museum_parallel(self, model_A, model_B):
with concurrent.futures.ThreadPoolExecutor() as executor:
model_1 = model_A.split('_')[1]
model_2 = model_B.split('_')[1]
result_list = draw2_from_imagen_museum("t2i", model_1, model_2)
image_links = result_list[0]
prompt_list = result_list[1]
return image_links[0], image_links[1], prompt_list[0]
@spaces.GPU(duration=200)
def generate_image_ie(self, textbox_source, textbox_target, textbox_instruct, source_image, model_name):
pipe = self.load_model_pipe(model_name)
result = pipe(src_image = source_image, src_prompt = textbox_source, target_prompt = textbox_target, instruct_prompt = textbox_instruct)
return result
def generate_image_ie_museum(self, model_name):
model_name = model_name.split('_')[1]
result_list = draw_from_imagen_museum("tie", model_name)
image_links = result_list[0]
prompt_list = result_list[1]
# image_links = [src, model]
# prompt_list = [source_caption, target_caption, instruction]
return image_links[0], image_links[1], prompt_list[0], prompt_list[1], prompt_list[2]
def generate_image_ie_parallel(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B):
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [
executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image,
model) for model in model_names]
results = [future.result() for future in futures]
return results[0], results[1]
def generate_image_ie_museum_parallel(self, model_A, model_B):
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
model_1 = model_names[0].split('_')[1]
model_2 = model_names[1].split('_')[1]
result_list = draw2_from_imagen_museum("tie", model_1, model_2)
image_links = result_list[0]
prompt_list = result_list[1]
# image_links = [src, model_A, model_B]
# prompt_list = [source_caption, target_caption, instruction]
return image_links[0], image_links[1], image_links[2], prompt_list[0], prompt_list[1], prompt_list[2]
def generate_image_ie_parallel_anony(self, textbox_source, textbox_target, textbox_instruct, source_image, model_A, model_B):
# Using list comprehension to get the difference between two lists
picking_list = [item for item in self.model_ie_list if item not in self.excluding_model_list]
if model_A == "" and model_B == "":
model_names = random.sample([model for model in picking_list], 2)
else:
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.generate_image_ie, textbox_source, textbox_target, textbox_instruct, source_image, model) for model in model_names]
results = [future.result() for future in futures]
return results[0], results[1], model_names[0], model_names[1]
def generate_image_ie_museum_parallel_anony(self, model_A, model_B):
# Using list comprehension to get the difference between two lists
picking_list = [item for item in self.model_ie_list if item not in self.excluding_model_list]
if model_A == "" and model_B == "":
model_names = random.sample([model for model in picking_list], 2)
else:
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
model_1 = model_names[0].split('_')[1]
model_2 = model_names[1].split('_')[1]
result_list = draw2_from_imagen_museum("tie", model_1, model_2)
image_links = result_list[0]
prompt_list = result_list[1]
# image_links = [src, model_A, model_B]
# prompt_list = [source_caption, target_caption, instruction]
return image_links[0], image_links[1], image_links[2], prompt_list[0], prompt_list[1], prompt_list[2], model_names[0], model_names[1]
@spaces.GPU(duration=150)
def generate_video_vg(self, prompt, model_name):
pipe = self.load_model_pipe(model_name)
result = pipe(prompt=prompt)
return result
def generate_video_vg_api(self, prompt, model_name):
pipe = self.load_model_pipe(model_name)
result = pipe(prompt=prompt)
return result
def generate_video_vg_museum(self, model_name):
model_name = model_name.split('_')[1]
result_list = draw_from_videogen_museum("t2v", model_name)
video_link = result_list[0]
prompt = result_list[1]
return video_link, prompt
def generate_video_vg_parallel_anony(self, prompt, model_A, model_B):
# Using list comprehension to get the difference between two lists
picking_list = [item for item in self.model_vg_list if item not in self.excluding_model_list]
if model_A == "" and model_B == "":
model_names = random.sample([model for model in picking_list], 2)
else:
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.generate_video_vg, prompt, model) if model.startswith("videogenhub")
else executor.submit(self.generate_video_vg_api, prompt, model) for model in model_names]
results = [future.result() for future in futures]
return results[0], results[1], model_names[0], model_names[1]
def generate_video_vg_museum_parallel_anony(self, model_A, model_B):
# Using list comprehension to get the difference between two lists
picking_list = [item for item in self.model_vg_list if item not in self.excluding_model_list]
if model_A == "" and model_B == "":
model_names = random.sample([model for model in picking_list], 2)
#override the random selection
model_names[random.choice([0, 1])] = random.choice(self.desired_model_list)
else:
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
model_1 = model_names[0].split('_')[1]
model_2 = model_names[1].split('_')[1]
result_list = draw2_from_videogen_museum("t2v", model_1, model_2)
video_links = result_list[0]
prompt_list = result_list[1]
return video_links[0], video_links[1], model_names[0], model_names[1], prompt_list[0]
def generate_video_vg_parallel(self, prompt, model_A, model_B):
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.generate_video_vg, prompt, model) if model.startswith("videogenhub")
else executor.submit(self.generate_video_vg_api, prompt, model) for model in model_names]
results = [future.result() for future in futures]
return results[0], results[1]
def generate_video_vg_museum_parallel(self, model_A, model_B):
model_names = [model_A, model_B]
with concurrent.futures.ThreadPoolExecutor() as executor:
model_1 = model_A.split('_')[1]
model_2 = model_B.split('_')[1]
result_list = draw2_from_videogen_museum("t2v", model_1, model_2)
video_links = result_list[0]
prompt_list = result_list[1]
return video_links[0], video_links[1], prompt_list[0] |