Upload sayak_lcm_benchmark.py

sayak_lcm_benchmark.py

@@ -0,0 +1,54 @@
+import torch
+import torch.utils.benchmark as benchmark
+import argparse
+from diffusers import DiffusionPipeline, LCMScheduler
+
+PROMPT = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux"
+MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
+LORA_ID = "latent-consistency/lcm-lora-sdxl"
+
+
+def benchmark_fn(f, *args, **kwargs):
+    t0 = benchmark.Timer(
+        stmt="f(*args, **kwargs)", globals={"args": args, "kwargs": kwargs, "f": f}
+    )
+    return t0.blocked_autorange().mean * 1e6
+
+
+def load_pipeline(standard_sdxl=False):
+    pipe = DiffusionPipeline.from_pretrained(MODEL_ID, variant="fp16")
+    if not standard_sdxl:
+        pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+        pipe.load_lora_weights(LORA_ID)
+
+    pipe.to(device="cuda", dtype=torch.float16)
+    pipe.enable_model_cpu_offload()
+    return pipe
+
+
+def call_pipeline(pipe, batch_size, num_inference_steps, guidance_scale):
+    images = pipe(
+        prompt=PROMPT,
+        num_inference_steps=num_inference_steps,
+        num_images_per_prompt=batch_size,
+        guidance_scale=guidance_scale,
+    ).images[0]
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--standard_sdxl", action="store_true")
+    args = parser.parse_args()
+
+    pipeline = load_pipeline(args.standard_sdxl)
+    if args.standard_sdxl:
+        num_inference_steps = 25
+        guidance_scale = 5
+    else:
+        num_inference_steps = 4
+        guidance_scale = 1
+
+    time = benchmark_fn(call_pipeline, pipeline, args.batch_size, num_inference_steps, guidance_scale)
+
+    print(f"Batch size: {args.batch_size} in {time/1e6:.3f} seconds")