app-old.py

import torch
import gradio as gr
from diffusers import (
    StableDiffusionPipeline,
    StableDiffusionInstructPix2PixPipeline,
    StableVideoDiffusionPipeline,
    WanPipeline,
)
from diffusers.utils import export_to_video, load_image

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

def make_pipe(cls, model_id, **kwargs):
    pipe = cls.from_pretrained(model_id, torch_dtype=dtype, **kwargs)
    pipe.enable_model_cpu_offload()
    return pipe

TXT2IMG_PIPE = None
IMG2IMG_PIPE = None
TXT2VID_PIPE = None
IMG2VID_PIPE = None

def generate_image_from_text(prompt):
    global TXT2IMG_PIPE
    if TXT2IMG_PIPE is None:
        TXT2IMG_PIPE = make_pipe(
            StableDiffusionPipeline, 
            "stabilityai/stable-diffusion-2-1-base"
        ).to(device)
    return TXT2IMG_PIPE(prompt, num_inference_steps=20).images[0]

def generate_image_from_image_and_prompt(image, prompt):
    global IMG2IMG_PIPE
    if IMG2IMG_PIPE is None:
        IMG2IMG_PIPE = make_pipe(
            StableDiffusionInstructPix2PixPipeline,
            "timbrooks/instruct-pix2pix"
        ).to(device)
    out = IMG2IMG_PIPE(prompt=prompt, image=image, num_inference_steps=8)
    return out.images[0]

def generate_video_from_text(prompt):
    global TXT2VID_PIPE
    if TXT2VID_PIPE is None:
        TXT2VID_PIPE = make_pipe(
            WanPipeline,
            "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
        ).to(device)
    frames = TXT2VID_PIPE(prompt=prompt, num_frames=12).frames[0]
    return export_to_video(frames, "/tmp/wan_video.mp4", fps=8)

def generate_video_from_image(image):
    global IMG2VID_PIPE
    if IMG2VID_PIPE is None:
        IMG2VID_PIPE = make_pipe(
            StableVideoDiffusionPipeline,
            "stabilityai/stable-video-diffusion-img2vid-xt",
            variant="fp16" if dtype == torch.float16 else None
        ).to(device)
    image = load_image(image).resize((512, 288))
    frames = IMG2VID_PIPE(image, num_inference_steps=16).frames[0]
    return export_to_video(frames, "/tmp/svd_video.mp4", fps=8)

with gr.Blocks() as demo:
    gr.Markdown("## 🧠 Lightweight Any-to-Any AI Playground")

    with gr.Tab("Text → Image"):
        text_input = gr.Textbox(label="Prompt")
        image_output = gr.Image(label="Generated Image")
        generate_button = gr.Button("Generate")
        generate_button.click(generate_image_from_text, inputs=text_input, outputs=image_output)

    with gr.Tab("Image → Image"):
        input_image = gr.Image(label="Input Image")
        prompt_input = gr.Textbox(label="Edit Prompt")
        output_image = gr.Image(label="Edited Image")
        edit_button = gr.Button("Generate")
        edit_button.click(generate_image_from_image_and_prompt, inputs=[input_image, prompt_input], outputs=output_image)

    with gr.Tab("Text → Video"):
        video_prompt = gr.Textbox(label="Prompt")
        video_output = gr.Video(label="Generated Video")
        video_button = gr.Button("Generate")
        video_button.click(generate_video_from_text, inputs=video_prompt, outputs=video_output)

    with gr.Tab("Image → Video"):
        anim_image = gr.Image(label="Input Image")
        anim_video_output = gr.Video(label="Animated Video")
        anim_button = gr.Button("Animate")
        anim_button.click(generate_video_from_image, inputs=anim_image, outputs=anim_video_output)

demo.launch()