Update app.py

app.py

@@ -4,7 +4,6 @@ import subprocess
 import shlex
 import spaces
 import torch
-import numpy as numpy
 access_token = os.getenv("HUGGINGFACE_TOKEN")
 subprocess.run(
     shlex.split(
@@ -97,15 +96,15 @@ isomer_color_weights = torch.from_numpy(np.array([1, 0.5, 1, 0.5])).float().to(d
 
 # model initialization and loading
 # flux
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device_0)
-good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=access_token).to(device_0)
-# flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=access_token).to(device=device_0, dtype=torch.bfloat16)
-flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, vae=taef1, token=access_token).to(device_0)
-flux_lora_ckpt_path = hf_hub_download(repo_id="LTT/xxx-ckpt", filename="rgb_normal_large.safetensors", repo_type="model")
-flux_pipe.load_lora_weights(flux_lora_ckpt_path)
-# flux_pipe.to(device=device_0, dtype=torch.bfloat16)
-torch.cuda.empty_cache()
-flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(flux_pipe)
+# taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device_0)
+# good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=access_token).to(device_0)
+# # flux_pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=access_token).to(device=device_0, dtype=torch.bfloat16)
+# flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, vae=taef1, token=access_token).to(device_0)
+# flux_lora_ckpt_path = hf_hub_download(repo_id="LTT/xxx-ckpt", filename="rgb_normal_large.safetensors", repo_type="model")
+# flux_pipe.load_lora_weights(flux_lora_ckpt_path)
+# # flux_pipe.to(device=device_0, dtype=torch.bfloat16)
+# torch.cuda.empty_cache()
+# flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(flux_pipe)
 
 
 # lrm
@@ -308,7 +307,8 @@ def reconstruct_3d_model(images, prompt):
 @spaces.GPU
 def gradio_pipeline(prompt, seed):
     # 生成多视图图像
-    rgb_normal_grid = generate_multi_view_images(prompt, seed)
+    # rgb_normal_grid = generate_multi_view_images(prompt, seed)
+    rgb_normal_grid = np.load("rgb_normal_grid.npy")
     image_preview = Image.fromarray((rgb_normal_grid * 255).astype(np.uint8))
 
     # 3d reconstruction