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app.py

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+import gradio as gr
+import torch
+from diffusers import AutoPipelineForText2Image
+import time
+
+USE_TORCH_COMPILE = False
+
+dtype = torch.float16
+device = torch.device("cuda:0")
+
+pipeline = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", variant="fp16", torch_dtype=torch.float16)
+pipeline.to("cuda")
+
+
+if USE_TORCH_COMPILE:
+    pipeline.unet = torch.compile(pipeline.unet, mode="reduce-overhead", fullgraph=True)
+    
+def generate(prompt_len: int, num_images_per_prompt: int = 1):
+    prompt = prompt_len * "a"
+    num_inference_steps = 40
+    start_time = time.time()
+    pipeline(prompt, num_images_per_prompt=num_images_per_prompt, num_inference_steps=num_inference_steps).images
+    end_time = time.time()
+
+
+    print(f"For {num_inference_steps} steps", end_time - start_time)
+    print("Avg per step", (end_time - start_time) / num_inference_steps)
+
+
+with gr.Blocks(css="style.css") as demo:
+    batch_size = gr.Slider(
+        label="Batch size",
+        minimum=0,
+        maximum=8,
+        step=1,
+        value=0,
+    )
+    prompt_len = gr.Slider(
+        label="Prompt len",
+        minimum=1,
+        maximum=77,
+        step=20,
+        value=1,
+    )
+    btn = gr.Button("Benchmark!").style(
+        margin=False,
+        rounded=(False, True, True, False),
+        full_width=False,
+    )
+
+    btn.click(fn=generate, inputs=[batch_size, prompt_len])