add app.py

app.py

@@ -98,120 +98,6 @@ def loadSDModel(unet_pretraining_type, cuda_device):
     return pipe
 
 
-# def load_all_pipelines():
-
-#     """
-#     Loads all the Stable Diffusion Pipelines for each PEFT Type for efficient caching (Design Choice 2)
-
-#     Parameters:
-#         None
-
-#     Returns:
-#         sd_pipeline_full (StableDiffusionPipeline): The Stable Diffusion Pipeline for Full Fine-Tuning
-#         sd_pipeline_norm (StableDiffusionPipeline): The Stable Diffusion Pipeline for Norm Fine-Tuning
-#         sd_pipeline_bias (StableDiffusionPipeline): The Stable Diffusion Pipeline for Bias Fine-Tuning
-#         sd_pipeline_attention (StableDiffusionPipeline): The Stable Diffusion Pipeline for Attention Fine-Tuning
-#         sd_pipeline_NBA (StableDiffusionPipeline): The Stable Diffusion Pipeline for NBA Fine-Tuning
-#         sd_pipeline_difffit (StableDiffusionPipeline): The Stable Diffusion Pipeline for Difffit Fine-Tuning
-#     """
-
-#     # Dictionary containing the path to the best trained models for each PEFT type
-#     MODEL_PATH_DICT = {
-#         "full": "full_diffusion_pytorch_model.safetensors",
-#         "norm": "norm_diffusion_pytorch_model.safetensors",
-#         "bias": "bias_diffusion_pytorch_model.safetensors",
-#         "attention": "attention_diffusion_pytorch_model.safetensors",
-#         "norm_bias_attention": "norm_bias_attention_diffusion_pytorch_model.safetensors",
-#         "difffit": "difffit_diffusion_pytorch_model.safetensors",
-#     }
-
-#     device = "0"
-#     cuda_device = f"cuda:{device}" if torch.cuda.is_available() else "cpu"
-
-#     # Full FT
-#     unet_pretraining_type = "full"
-#     print("Loading Pipeline for Full Fine-Tuning")
-#     sd_pipeline_full = loadSDModel(
-#         unet_pretraining_type=unet_pretraining_type,
-#         exp_path=MODEL_PATH_DICT[unet_pretraining_type],
-#         cuda_device=cuda_device,
-#     )
-
-#     # Norm
-#     unet_pretraining_type = "norm"
-#     print("Loading Pipeline for Norm Fine-Tuning")
-#     sd_pipeline_norm = loadSDModel(
-#         unet_pretraining_type=unet_pretraining_type,
-#         exp_path=MODEL_PATH_DICT[unet_pretraining_type],
-#         cuda_device=cuda_device,
-#     )
-
-#     # bias
-#     unet_pretraining_type = "bias"
-#     print("Loading Pipeline for Bias Fine-Tuning")
-#     sd_pipeline_bias = loadSDModel(
-#         unet_pretraining_type=unet_pretraining_type,
-#         exp_path=MODEL_PATH_DICT[unet_pretraining_type],
-#         cuda_device=cuda_device,
-#     )
-
-#     # attention
-#     unet_pretraining_type = "attention"
-#     print("Loading Pipeline for Attention Fine-Tuning")
-#     sd_pipeline_attention = loadSDModel(
-#         unet_pretraining_type=unet_pretraining_type,
-#         exp_path=MODEL_PATH_DICT[unet_pretraining_type],
-#         cuda_device=cuda_device,
-#     )
-
-#     # NBA
-#     unet_pretraining_type = "norm_bias_attention"
-#     print("Loading Pipeline for NBA Fine-Tuning")
-#     sd_pipeline_NBA = loadSDModel(
-#         unet_pretraining_type=unet_pretraining_type,
-#         exp_path=MODEL_PATH_DICT[unet_pretraining_type],
-#         cuda_device=cuda_device,
-#     )
-
-#     # difffit
-#     unet_pretraining_type = "difffit"
-#     print("Loading Pipeline for Difffit Fine-Tuning")
-#     sd_pipeline_difffit = loadSDModel(
-#         unet_pretraining_type=unet_pretraining_type,
-#         exp_path=MODEL_PATH_DICT[unet_pretraining_type],
-#         cuda_device=cuda_device,
-#     )
-
-#     return (
-#         sd_pipeline_full,
-#         sd_pipeline_norm,
-#         sd_pipeline_bias,
-#         sd_pipeline_attention,
-#         sd_pipeline_NBA,
-#         sd_pipeline_difffit,
-#     )
-
-
-# LOAD ALL PIPELINES FIRST AND CACHE THEM
-# (
-#     sd_pipeline_full,
-#     sd_pipeline_norm,
-#     sd_pipeline_bias,
-#     sd_pipeline_attention,
-#     sd_pipeline_NBA,
-#     sd_pipeline_difffit,
-# ) = load_all_pipelines()
-
-# PIPELINE_DICT = {
-#     "full": sd_pipeline_full,
-#     "norm": sd_pipeline_norm,
-#     "bias": sd_pipeline_bias,
-#     "attention": sd_pipeline_attention,
-#     "norm_bias_attention": sd_pipeline_NBA,
-#     "difffit": sd_pipeline_difffit,
-# }
-
-
 def predict(
     unet_pretraining_type,
     input_text,