Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import DiffusionPipeline, AutoencoderKL
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    vae=vae, torch_dtype=torch.float16, variant="fp16",
+    use_safetensors=True
+)
+
+# This is where you load your trained weights
+pipe.load_lora_weights("victor/outicon")
+
+pipe.to("cuda")
+
+def infer (prompt):
+    image = pipe(prompt=prompt, num_inference_steps=50).images[0]
+    return image
+
+css = """
+#col-container {max-width: 780px; margin-left: auto; margin-right: auto;}
+"""
+with gr.Blocks() as demo:
+    with gr.Column(elem_id="col-container"):
+        prompt_in = gr.Textbox(label="Prompt")
+        submit_btn = gr.Button("Submit")
+        image_out = gr.Image(label="Image output")
+
+    submit_btn.click(
+        fn = infer,
+        inputs = [prompt_in],
+        outputs = [image_out]
+    )
+
+demo.queue().launch()