import torch
from diffusers import DiffusionPipeline
import gradio as gr
import numpy as np
import openai
import os
import spaces
import base64
# Setup logging
# logging.basicConfig(level=logging.DEBUG)
# logger = logging.getLogger(__name__)
# Retrieve the OpenAI API key from the environment
API_KEY = os.getenv('OPEN_AI_API_KEYS')
DESCRIPTION = '''
'''
# load both base and refiner
base = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to("cuda:0")
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0",
text_encoder_2=base.text_encoder_2,
vae=base.vae,
torch_dtype=torch.float16,
use_safetensor=True,
variant="fp16").to("cuda:0")
chat_mode = {}
def encode_image(image_path):
chat_mode["the_mode"] = "diffusing"
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def generation(message, history):
"""
Generates a response based on the input message and optionally an image.
"""
global chat_mode
image_path = None
if "files" in message and message["files"]:
if type(message["files"][-1]) == dict:
image_path = message["files"][-1]["path"]
else:
image_path = message["files"][-1]
else:
for hist in history:
if type(hist[0]) == tuple:
image_path = hist[0][0]
input_prompt = message if isinstance(message, str) else message.get("text", "")
if image_path is None:
chat_mode["mode"] = "text"
client = openai.OpenAI(api_key=API_KEY)
stream = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "system", "content": "You are a helpful assistant called 'chimera'."},
{"role": "user", "content": input_prompt}],
stream=True,
)
return stream
else:
chat_mode["mode"] = "image"
base64_image = encode_image(image_path=image_path)
client = openai.OpenAI(api_key=API_KEY)
stream = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "system", "content": "You are a helpful assistant called 'chimera'."},
{"role": "user", "content": [
{"type": "text", "text": input_prompt},
{"type": "image_url", "image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}}
]}],
stream=True,
)
return stream
# function to take input and generate text tokena
@spaces.GPU(duration=120)
def diffusing(prompt: str,
n_steps: int,
denoising: float):
"""
Takes input, passes it into the pipeline,
get the top 5 scores, and ouput those scores into images
"""
# Generate image based on text
image_base = base(
prompt=prompt,
num_inference_steps=n_steps,
denoising_end=denoising,
output_type="latent"
).images
image = refiner(
prompt=prompt,
num_inference_steps=n_steps,
denoising_start=denoising,
image=image_base
).images[0]
return image
def check_cuda_availability():
if torch.cuda.is_available():
return f"GPU: {torch.cuda.get_device_name(0)}"
else:
return "No CUDA device found."
# Image created from diffusing
image_created = {}
@spaces.GPU(duration=120)
def bot_comms(message, history):
"""
Handles communication between Gradio and the models.
"""
# ensures message is a dictionary
if not isinstance(message, dict):
message = {"text": message}
if message["text"] == "check cuda":
yield check_cuda_availability()
return
buffer = ""
gpt_outputs = []
stream = generation(message, history)
for chunk in stream:
if chunk.choices[0].delta.content is not None:
text = chunk.choices[0].delta.content
if text:
gpt_outputs.append(text)
buffer += text
yield "".join(gpt_outputs)
chat_input = gr.MultimodalTextbox(interactive=True, file_types=["images"], placeholder="Enter your question or upload an image.", show_label=False)
with gr.Blocks(fill_height=True) as demo:
with gr.Row():
# Diffusing
with gr.Column():
gr.Markdown(DESCRIPTION)
image_prompt = gr.Textbox(label="Image Prompt")
output_image = gr.Image(label="Generated Image")
generate_image_button = gr.Button("Generate Image")
# generate_image_button.click(fn=diffusing, inputs=image_prompt, outputs=output_image)
with gr.Accordion(label="⚙️ Parameters", open=False):
steps_slider = gr.Slider(
minimum=20,
maximum=100,
step=1,
value=40,
label="Number of Inference Steps"
)
denoising_slider = gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.8,
label="High Noise Fraction"
)
generate_image_button.click(
fn=diffusing,
inputs=[image_prompt, steps_slider, denoising_slider],
outputs=output_image
)
with gr.Column():
# GPT-3.5
gr.Markdown('''
Chimera Text Generation
This contains a Generative LLM from Open AI called GPT-3.5-Turbo and Vision.
For Instructions on how to use the models view this
''')
chat = gr.ChatInterface(fn=bot_comms,
multimodal=True,
textbox=chat_input)
demo.launch()