Create main.py

main.py

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+import torch
+import gradio as gr
+from PIL import Image
+from diffusers import StableDiffusionPipeline
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+caption_image = pipeline("image-to-text",
+                model="Salesforce/blip-image-captioning-large", device=device)
+
+
+def image_generation(prompt):
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    pipeline = StableDiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-3-medium",
+        torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+    )
+    #pipeline.to(device)
+    pipeline.enable_model_cpu_offload()
+    
+    image = pipeline(
+        prompt=prompt + " 8K, Ultra HD",
+        negative_prompt="blurred, ugly, watermark, low resolution, blurry, nude",
+        num_inference_steps=40,
+        height=1024,
+        width=1024,
+        guidance_scale=9.0
+    ).images[0]
+
+    return image
+
+def caption_my_image(pil_image):
+    semantics = caption_image(images=pil_image)[0]['generated_text']
+    images = image_generation(semantics)
+    return images
+
+demo = gr.Interface(fn=caption_my_image,
+                    inputs=[gr.Image(label="Select Image",type="pil")],
+                    outputs=[gr.Image(label="New Image genrated using SD3",type="pil")],
+                    title="PicTalker | ImageNarrator | SnapSpeech | SpeakScene",
+                    description="🌟 Transform Ordinary Photos into Extraordinary Art!")
+demo.launch()