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import gradio as gr
import cv2
import torch
import numpy as np
from diffusers import StableDiffusionPipeline
from PIL import Image
# Load the Real-Time Latent Consistency Model (LCM)
device = "cuda" if torch.cuda.is_available() else "cpu"
lcm_pipe = StableDiffusionPipeline.from_pretrained("latent-consistency/lcm-lora-sdv1-5").to(device)
def process_frame(image, prompt="A futuristic landscape", negative_prompt="not blurry"):
"""Modify the input image using the real-time latent consistency model (LCM)."""
image = image.resize((512, 512))
result = lcm_pipe(prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=4, guidance_scale=7.5).images[0]
return np.array(result)
def video_stream(prompt, negative_prompt):
"""Captures video feed from webcam and sends it to LCM in real time."""
cap = cv2.VideoCapture(0)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
frame = process_frame(image, prompt, negative_prompt)
yield frame
cap.release()
# Create Gradio App
with gr.Blocks() as demo:
gr.Markdown("## 🎨 Real-Time AI-Enhanced Webcam using Latent Consistency Model (LCM)")
with gr.Row():
webcam_feed = gr.Camera(streaming=True, label="Live Webcam")
processed_image = gr.Image(label="AI-Enhanced Webcam Feed")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload Image for Processing")
canvas_output = gr.Image(interactive=True, label="Canvas - Processed Image Output")
prompt_input = gr.Textbox(label="Real-Time LCM Prompt", value="A futuristic landscape")
negative_prompt_input = gr.Textbox(label="Negative Prompt", value="")
start_button = gr.Button("Start Real-Time AI Enhancement")
process_button = gr.Button("Process Uploaded Image")
start_button.click(fn=video_stream, inputs=[prompt_input, negative_prompt_input], outputs=[processed_image, canvas_output])
process_button.click(fn=process_frame, inputs=[image_input, prompt_input, negative_prompt_input], outputs=[canvas_output])
demo.launch(share=True)
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