Update app.py

app.py

@@ -1,3 +1,53 @@
+import torch
+import spaces
 import gradio as gr
 
-gr.load("models/Tramac/style-portrait-v1-5").launch()
+from diffusers import StableDiffusionPipeline
+
+
+repo = "Tramac/style-portrait-v1-5"
+# Ensure model and scheduler are initialized in GPU-enabled function
+if torch.cuda.is_available():
+    pipeline = StableDiffusionPipeline.from_pretrained(
+        repo,
+        torch_dtype=torch.float16,
+        variant="fp16"
+    ).to("cuda")
+
+@spaces.GPU(enable_queue=True)
+def generate_image(prompt, neg_prompt):
+    results = pipe(
+        prompt=prompt,
+        negative_prompt=neg_prompt,
+        height=832,
+        width=512,
+        num_inference_steps=20,
+    )
+    return results.images[0]
+
+
+
+with gr.Blocks() as demo:
+    gr.HTML("<h1><center>Style Portrait</center></h1>")
+    gr.HTML("<p><center>text-to-image generation</center></p><p><center><a href='https://huggingface.co/Tramac/style-portrait-v1-5'></a></center></p>")
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label='Enter your prompt', scale=8)
+            neg_prompt = gr.Textbox(label='Enter your negative prompt', scale=8)
+            submit = gr.Button(scale=1, variant='primary')
+
+        with gr.Column():
+            img = gr.Image(label='Style-Portrait Generated Image')
+
+    prompt.submit(
+        fn=generate_image,
+        inputs=[prompt, neg_prompt],
+        outputs=img,
+    )
+    submit.click(
+        fn=generate_image,
+        inputs=[prompt, neg_prompt],
+        outputs=img,
+    )
+
+demo.queue().launch(share=True)