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Zero
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import concurrent.futures
import random
import gradio as gr
import requests
import io, base64, json
import spaces
import torch
from PIL import Image
from openai import OpenAI
from .models import IMAGE_GENERATION_MODELS, IMAGE_EDITION_MODELS, load_pipeline
from .fetch_museum_results import draw_from_imagen_museum, draw2_from_imagen_museum
from serve.upload import get_random_mscoco_prompt
class ModelManager:
def __init__(self):
self.model_ig_list = IMAGE_GENERATION_MODELS
self.model_ie_list = IMAGE_EDITION_MODELS
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)
if 'Stable-cascade' not in model_name:
result = pipe(prompt=prompt).images[0]
else:
prior, decoder = pipe
prior.enable_model_cpu_offload()
prior_output = prior(
prompt=prompt,
height=512,
width=512,
negative_prompt='',
guidance_scale=4.0,
num_images_per_prompt=1,
num_inference_steps=20
)
decoder.enable_model_cpu_offload()
result = decoder(
image_embeddings=prior_output.image_embeddings.to(torch.float16),
prompt=prompt,
negative_prompt='',
guidance_scale=0.0,
output_type="pil",
num_inference_steps=10
).images[0]
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, model_C, model_D):
if model_A == "" and model_B == "" and model_C == "" and model_D == "":
# not_run = [11, 12, 13, 14, 15, 16, 17, 18, 19]
# filtered_models = [model for i, model in enumerate(self.model_ig_list) if i not in not_run]
# model_names = random.sample([model for model in filtered_models], 4)
# model_names = random.sample([model for model in self.model_ig_list], 4)
from .matchmaker import matchmaker
model_ids = matchmaker(num_players=len(self.model_ig_list))
print(model_ids)
model_names = [self.model_ig_list[i] for i in model_ids]
print(model_names)
else:
model_names = [model_A, model_B, model_C, model_D]
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("huggingface")
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], results[2], results[3], \
model_names[0], model_names[1], model_names[2], model_names[3]
def generate_image_ig_museum_parallel_anony(self, model_A, model_B, model_C, model_D):
if model_A == "" and model_B == "" and model_C == "" and model_D == "":
# model_names = random.sample([model for model in self.model_ig_list], 4)
from .matchmaker import matchmaker
model_ids = matchmaker(num_players=len(self.model_ig_list))
print(model_ids)
model_names = [self.model_ig_list[i] for i in model_ids]
print(model_names)
else:
model_names = [model_A, model_B, model_C, model_D]
prompt = get_random_mscoco_prompt()
print(prompt)
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.generate_image_ig, prompt, model) if model.startswith("huggingface")
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], results[2], results[3], \
model_names[0], model_names[1], model_names[2], model_names[3], prompt
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):
if model_A == "" and model_B == "":
model_names = random.sample([model for model in self.model_ie_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):
if model_A == "" and model_B == "":
model_names = random.sample([model for model in self.model_ie_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]
raise NotImplementedError |