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import argparse
import binascii
import glob
import openai
import os
import os.path
import numpy as np
import matplotlib.pyplot as plt
import random
import sys
import tempfile
import time
import torch
from PIL import Image
from IPython.display import Audio
from diffusers import StableDiffusionPipeline
from diffusers import DiffusionPipeline
from transformers import pipeline
from transformers import ViTFeatureExtractor, ViTForImageClassification
from audiodiffusion import AudioDiffusion
import requests
notes = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]
def fake_gan():
images = [
(random.choice(
[
"https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
"https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
"https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
"https://images.unsplash.com/photo-1546456073-92b9f0a8d413?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
"https://images.unsplash.com/photo-1601412436009-d964bd02edbc?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=464&q=80",
]
), f"label {i}" if i != 0 else "label" * 50)
for i in range(3)
]
return images
def imageClassifier(inputImage):
#fn=artist_lib.imageClassifier,
#url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
#image = Image.open(requests.get(url, stream=True).raw)
image = inputImage
feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224')
model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
#print("Predicted class:", model.config.id2label[predicted_class_idx])
return "Predicted class:", model.config.id2label[predicted_class_idx]
def audioGenerator(inputText):
device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)
output = pipe()
from IPython.display import display
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
print("sample rate is ", pipe.mel.get_sample_rate())
#print(Audio(output.audios[0]))
sr=int(pipe.mel.get_sample_rate())
audio=Audio(output.audios[0])
#return int(pipe.mel.get_sample_rate()), Audio(output.audios[0])
return sr, audio
def generate_spectrogram_audio_and_loop(model_id):
audio_diffusion = AudioDiffusion(model_id=model_id)
image, (sample_rate,
audio) = audio_diffusion.generate_spectrogram_and_audio()
loop = AudioDiffusion.loop_it(audio, sample_rate)
if loop is None:
loop = audio
return image, (sample_rate, audio), (sample_rate, loop)
def generate_tone(note, octave, duration):
sr = 48000
a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)
frequency = a4_freq * 2 ** (tones_from_a4 / 12)
duration = int(duration)
audio = np.linspace(0, duration, duration * sr)
audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)
return sr, audio
def draw(inp, this_model, force_new):
drawing = inp
if this_model == "stable-diffusion-2":
this_model_addr = "stabilityai/stable-diffusion-2"
images_dir = 'images2/'
elif this_model == "stable-diffusion-2-1":
this_model_addr = "stabilityai/stable-diffusion-2-1"
images_dir = 'images2-1/'
elif this_model == "stable-diffusion-v1-5":
this_model_addr = "runwayml/stable-diffusion-v1-5"
images_dir = 'images/'
else:
raise gr.Error("Unknown Model!")
mkdir_if_not_exist(images_dir)
drawing_filename = images_dir + drawing.replace(' ', '_') + '.png'
if os.path.exists(drawing_filename):
if force_new:
new_drawing_filename = images_dir + drawing.replace(' ', '_') + '.' + str(time.time()) + '.png'
os.replace(drawing_filename, new_drawing_filename)
else:
print("found drawing ", drawing_filename)
return Image.open(drawing_filename)
print("generating drawing '", drawing, "'", drawing_filename)
pipe = StableDiffusionPipeline.from_pretrained(this_model_addr, torch_dtype=torch.float16)
pipe.enable_attention_slicing()
pipe = pipe.to("cuda")
image = pipe(drawing).images[0]
image.seek(0)
image.save(drawing_filename)
return image
def write_blog(inp, this_model, min_length, max_length, force_new):
blog_post_name = inp
if this_model == "gpt-neo-1.3B":
this_model_addr = "EleutherAI/gpt-neo-1.3B"
text_dir = 'text1.3/'
elif this_model == "gpt-neo-2.7B":
this_model_addr = "EleutherAI/gpt-neo-2.7B"
text_dir = 'text2.7/'
else:
raise gr.Error("Unknown Model!")
mkdir_if_not_exist(text_dir)
target_filename = text_dir + blog_post_name.replace(' ', '_') + '.txt'
if os.path.exists(target_filename):
if force_new:
new_target_filename = text_dir + blog_post_name.replace(' ', '_') + '.' + str(time.time()) + '.txt'
os.replace(target_filename, new_target_filename)
else:
print("found drawing ", target_filename)
with open(target_filename, 'r') as file:
return file.read()
print("generating blog '", blog_post_name, "'", target_filename)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#generator = pipeline('text-generation', model='EleutherAI/gpt-neo-2.7B', device=device, torch_dtype=torch.float16)
#generator = pipeline('text-generation', model=this_model_addr, torch_dtype=torch.float16)
#generator = pipeline('text-generation', model=this_model_addr)
generator = pipeline('text-generation', model=this_model_addr, device=device, torch_dtype=torch.float16)
# AttributeError: 'TextGenerationPipeline' object has no attribute 'enable_attention_slicing'
#generator.enable_attention_slicing()
res = generator(blog_post_name, min_length=min_length, max_length=max_length, do_sample=True, temperature=0.7)
blog_post_text = res[0]['generated_text']
with open(target_filename, 'w') as file:
file.write(blog_post_text)
return blog_post_text
def nameMyPet(inp):
animal = inp
response = openai.Completion.create(
model="text-davinci-003",
prompt=generate_prompt(animal),
temperature=0.6,
)
return response.choices[0].text
def mkdir_if_not_exist(path):
if os.path.exists(path):
return 0
else:
os.mkdir(path)
def generate_prompt(animal):
return """Suggest three names for an animal that is a superhero.
Animal: Cat
Names: Captain Sharpclaw, Agent Fluffball, The Incredible Feline
Animal: Dog
Names: Ruff the Protector, Wonder Canine, Sir Barks-a-Lot
Animal: {}
Names:""".format(
animal.capitalize()
)
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