LTT commited on
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299b7d2
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1 Parent(s): e569b69

Update app.py

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Files changed (1) hide show
  1. app.py +11 -11
app.py CHANGED
@@ -96,14 +96,14 @@ isomer_color_weights = torch.from_numpy(np.array([1, 0.5, 1, 0.5])).float().to(d
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  # model initialization and loading
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  # flux
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- # taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device_0)
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- # good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=access_token).to(device_0)
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- 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)
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- # flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, vae=taef1, token=access_token).to(device_0)
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- flux_lora_ckpt_path = hf_hub_download(repo_id="LTT/xxx-ckpt", filename="rgb_normal_large.safetensors", repo_type="model", token=access_token)
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- flux_pipe.load_lora_weights(flux_lora_ckpt_path)
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- flux_pipe.to(device=device_0, dtype=torch.bfloat16)
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- torch.cuda.empty_cache()
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  # flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(flux_pipe)
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@@ -306,7 +306,7 @@ def reconstruct_3d_model(images, prompt):
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  @spaces.GPU
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  def gradio_pipeline(prompt, seed):
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  # 生成多视图图像
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- rgb_normal_grid = generate_multi_view_images(prompt, seed)
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  # rgb_normal_grid = np.load("rgb_normal_grid.npy")
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  image_preview = Image.fromarray((rgb_normal_grid[0] * 255).astype(np.uint8))
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@@ -314,8 +314,8 @@ def gradio_pipeline(prompt, seed):
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  # 重建 3D 模型并返回 glb 路径
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- # save_glb_addr = reconstruct_3d_model(rgb_normal_grid, prompt)
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- save_glb_addr = None
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  return image_preview, save_glb_addr
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  # Gradio Blocks 应用
 
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  # model initialization and loading
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  # flux
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+ # # taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16).to(device_0)
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+ # # good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=torch.bfloat16, token=access_token).to(device_0)
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+ # 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)
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+ # # flux_pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, vae=taef1, token=access_token).to(device_0)
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+ # flux_lora_ckpt_path = hf_hub_download(repo_id="LTT/xxx-ckpt", filename="rgb_normal_large.safetensors", repo_type="model", token=access_token)
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+ # flux_pipe.load_lora_weights(flux_lora_ckpt_path)
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+ # flux_pipe.to(device=device_0, dtype=torch.bfloat16)
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+ # torch.cuda.empty_cache()
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  # flux_pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(flux_pipe)
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  @spaces.GPU
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  def gradio_pipeline(prompt, seed):
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  # 生成多视图图像
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+ # rgb_normal_grid = generate_multi_view_images(prompt, seed)
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  # rgb_normal_grid = np.load("rgb_normal_grid.npy")
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  image_preview = Image.fromarray((rgb_normal_grid[0] * 255).astype(np.uint8))
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  # 重建 3D 模型并返回 glb 路径
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+ save_glb_addr = reconstruct_3d_model(rgb_normal_grid, prompt)
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+ # save_glb_addr = None
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  return image_preview, save_glb_addr
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  # Gradio Blocks 应用