Upload src/pipeline.py with huggingface_hub

src/pipeline.py

@@ -9,7 +9,8 @@ from huggingface_hub.constants import HF_HUB_CACHE
 from pipelines.models import TextToImageRequest
 from torch import Generator
 from torchao.quantization import quantize_, int8_weight_only
-from transformers import T5EncoderModel, CLIPTextModel
+from transformers import T5EncoderModel, CLIPTextModel, logging 
+logging.set_verbosity_error()
 
 
 Pipeline: TypeAlias = FluxPipeline
@@ -53,6 +54,7 @@ def load_pipeline() -> Pipeline:
     #    quantize_(pipeline.vae, int8_weight_only())
     pipeline.vae = torch.compile(pipeline.vae)
     pipeline.to("cuda")
+    pipeline.set_progress_bar_config(disable=True)
     
     for _ in range(2):
         pipeline("cat", num_inference_steps=4)