Reinitialize repository without history

app.py

@@ -1,8 +1,15 @@
 import gradio as gr
+import os
 
 import torch
 from PIL import Image
+from diffusers import (
+    AutoencoderKL,
+    DiffusionPipeline,
+    # UNet2DConditionModel,
+)
 
+from transformers import CLIPTextModel, CLIPTokenizer
 from depthmaster import DepthMasterPipeline
 from depthmaster.modules.unet_2d_condition import UNet2DConditionModel
 
@@ -21,9 +28,15 @@ torch_dtype = torch.float32
 
 # pipe = DepthMasterPipeline.from_pretrained('eval', torch_dtype=torch_dtype)
 # unet = UNet2DConditionModel.from_pretrained(os.path.join('eval', f'unet'))
-pipe = DepthMasterPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-unet = UNet2DConditionModel.from_pretrained(model_repo_id, subfolder="unet", torch_dtype=torch_dtype)
-pipe.unet = unet
+# pipe = DepthMasterPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
+# unet = UNet2DConditionModel.from_pretrained(model_repo_id, subfolder="unet", torch_dtype=torch_dtype)
+# pipe.unet = unet
+vae = AutoencoderKL.from_pretrained(model_repo_id, subfolder="vae", torch_dtype=torch_dtype, allow_pickle=False)
+unet = UNet2DConditionModel.from_pretrained(model_repo_id, subfolder="unet", torch_dtype=torch_dtype, allow_pickle=False)
+text_encoder = CLIPTextModel.from_pretrained(model_repo_id, subfolder="text_encoder", torch_dtype=torch_dtype)
+tokenizer = CLIPTokenizer.from_pretrained(model_repo_id, subfolder="tokenizer", torch_dtype=torch_dtype)
+pipe = DepthMasterPipeline(vae=vae, unet=unet, text_encoder=text_encoder, tokenizer=tokenizer)
+
 
 try:
     pipe.enable_xformers_memory_efficient_attention()