sayakpaul HF staff commited on
Commit
632014f
1 Parent(s): 26d8e81

remove variant

Browse files
Files changed (1) hide show
  1. app.py +6 -6
app.py CHANGED
@@ -82,12 +82,12 @@ def load_pipeline(
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  if "ControlNet" in pipeline_to_benchmark:
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  controlnet_ckpt = pipeline_details[2]
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  controlnet = ControlNetModel.from_pretrained(
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- controlnet_ckpt, variant="fp16", torch_dtype=torch.float16
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  ).to(device)
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  elif "Adapters" in pipeline_to_benchmark:
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  adapter_clpt = pipeline_details[2]
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  adapter = T2IAdapter.from_pretrained(
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- adapter_clpt, variant="fp16", torch_dtype=torch.float16
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  ).to(device)
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  # Load pipeline.
@@ -95,9 +95,7 @@ def load_pipeline(
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  "ControlNet" not in pipeline_to_benchmark
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  or "Adapters" not in pipeline_to_benchmark
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  ):
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- pipeline = pipeline_cls.from_pretrained(
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- pipeline_ckpt, variant="fp16", torch_dtype=dtype
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- )
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  elif "ControlNet" in pipeline_to_benchmark:
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  pipeline = pipeline_cls.from_pretrained(pipeline_ckpt, controlnet=controlnet)
@@ -205,7 +203,9 @@ def generate(
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  print(f"For {num_inference_steps} steps", end_time - start_time)
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  print("Avg per step", (end_time - start_time) / num_inference_steps)
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- return f"Avg per step: {((end_time - start_time) / num_inference_steps):.4f} seconds."
 
 
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  with gr.Blocks(css="style.css") as demo:
 
82
  if "ControlNet" in pipeline_to_benchmark:
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  controlnet_ckpt = pipeline_details[2]
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  controlnet = ControlNetModel.from_pretrained(
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+ controlnet_ckpt, torch_dtype=torch.float16
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  ).to(device)
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  elif "Adapters" in pipeline_to_benchmark:
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  adapter_clpt = pipeline_details[2]
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  adapter = T2IAdapter.from_pretrained(
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+ adapter_clpt, torch_dtype=torch.float16
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  ).to(device)
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  # Load pipeline.
 
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  "ControlNet" not in pipeline_to_benchmark
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  or "Adapters" not in pipeline_to_benchmark
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  ):
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+ pipeline = pipeline_cls.from_pretrained(pipeline_ckpt, torch_dtype=dtype)
 
 
99
 
100
  elif "ControlNet" in pipeline_to_benchmark:
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  pipeline = pipeline_cls.from_pretrained(pipeline_ckpt, controlnet=controlnet)
 
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  print(f"For {num_inference_steps} steps", end_time - start_time)
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  print("Avg per step", (end_time - start_time) / num_inference_steps)
205
 
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+ return (
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+ f"Avg per step: {((end_time - start_time) / num_inference_steps):.4f} seconds."
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+ )
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210
 
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  with gr.Blocks(css="style.css") as demo: