Create app.py

app.py

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+import gradio as gr
+from diffusers import StableDiffusionPipeline, DiffusionPipeline
+import torch
+from PIL import Image
+
+# Load Text ➤ Image model
+text2img = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
+).to("cuda")
+
+# Load Image ➤ Video model
+img2vid = DiffusionPipeline.from_pretrained(
+    "damo-vilab/image-to-video", torch_dtype=torch.float16, variant="fp16"
+).to("cuda")
+
+def generate_image(prompt):
+    image = text2img(prompt).images[0]
+    return image
+
+def generate_video(image):
+    video_frames = img2vid(image).frames
+    return video_frames  # Gradio will auto-render as video
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🎨 Text ➤ Image ➤ Video Generator")
+
+    with gr.Row():
+        prompt = gr.Textbox(label="📝 Enter Prompt")
+        img_output = gr.Image(label="🖼️ Generated Image")
+        btn_img = gr.Button("Generate Image")
+
+    with gr.Row():
+        img_input = gr.Image(label="📥 Drop Image Here")
+        vid_output = gr.Video(label="🎬 Generated Video")
+        btn_vid = gr.Button("Generate Video")
+
+    btn_img.click(fn=generate_image, inputs=prompt, outputs=img_output)
+    btn_vid.click(fn=generate_video, inputs=img_input, outputs=vid_output)
+
+demo.launch()