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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) | |