app.py

from PIL import Image
import gradio as gr
from huggingface_hub import hf_hub_download
from model import Model
from app_edge import create_demo as create_demo_edge
from app_depth import create_demo as create_demo_depth
import os
import torch

import subprocess

# def install_requirements():
#     try:
#         # subprocess.run(['pip', 'install', 'torch==2.1.2+cu118', '--extra-index-url', 'https://download.pytorch.org/whl/cu118'], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#         subprocess.run(['pip', 'show', 'torch'], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#         # result = subprocess.run(['pip', 'install', '-r', 'requirements.txt'], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
#         print("安装成功！")
#         # print("输出:", result.stdout.decode('utf-8'))
#     except subprocess.CalledProcessError as e:
#         print("安装失败！")
#         print("错误:", e.stderr.decode('utf-8'))

# install_requirements()
print("Torch version:", torch.__version__)

# hf_hub_download(repo_id='wondervictor/ControlAR',
#                 filename='canny_MR.safetensors',
#                 local_dir='./checkpoints/')
# hf_hub_download(repo_id='wondervictor/ControlAR',
#                 filename='depth_MR.safetensors',
#                 local_dir='./checkpoints/')
# # hf_hub_download('google/flan-t5-xl', cache_dir='./checkpoints/')
ckpt_folder = './checkpoints'
t5_folder = os.path.join(ckpt_folder, "flan-t5-xl/flan-t5-xl")
# dinov2_folder = os.path.join(ckpt_folder, "dinov2-small")
dinov2_folder = os.path.join(ckpt_folder, "dinov2-base")
hf_hub_download(repo_id="google/flan-t5-xl", filename="config.json", local_dir=t5_folder)
hf_hub_download(repo_id="google/flan-t5-xl", filename="pytorch_model-00001-of-00002.bin", local_dir=t5_folder)
hf_hub_download(repo_id="google/flan-t5-xl", filename="pytorch_model-00002-of-00002.bin", local_dir=t5_folder)
hf_hub_download(repo_id="google/flan-t5-xl", filename="pytorch_model.bin.index.json", local_dir=t5_folder)
hf_hub_download(repo_id="google/flan-t5-xl", filename="special_tokens_map.json", local_dir=t5_folder)
hf_hub_download(repo_id="google/flan-t5-xl", filename="spiece.model", local_dir=t5_folder)
hf_hub_download(repo_id="google/flan-t5-xl", filename="tokenizer_config.json", local_dir=t5_folder)

hf_hub_download(repo_id="lllyasviel/Annotators", filename="dpt_hybrid-midas-501f0c75.pt", local_dir=ckpt_folder)

hf_hub_download(repo_id="wondervictor/ControlAR", filename="edge_base.safetensors", local_dir=ckpt_folder)
hf_hub_download(repo_id="wondervictor/ControlAR", filename="depth_base.safetensors", local_dir=ckpt_folder)

hf_hub_download(repo_id="facebook/dinov2-base", filename="config.json", local_dir=dinov2_folder)
hf_hub_download(repo_id="facebook/dinov2-base", filename="preprocessor_config.json", local_dir=dinov2_folder)
hf_hub_download(repo_id="facebook/dinov2-base", filename="pytorch_model.bin", local_dir=dinov2_folder)


DESCRIPTION = "# [ControlAR: Controllable Image Generation with Autoregressive Models](https://arxiv.org/abs/2410.02705) \n ### The first image in outputs is the condition. The others are the images generated by ControlAR.  \n ### You can run locally by following the instruction on our [Github Repo](https://github.com/hustvl/ControlAR)."
SHOW_DUPLICATE_BUTTON = os.getenv("SHOW_DUPLICATE_BUTTON") == "1"
model = Model()
# device = "cuda"
# model.to(device)
with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(
        value="Duplicate Space for private use",
        elem_id="duplicate-button",
        visible=SHOW_DUPLICATE_BUTTON,
    )
    with gr.Tabs():
        with gr.TabItem("Depth to Image"):
            create_demo_depth(model.process_depth)
        with gr.TabItem("Edge to Image"):
            create_demo_edge(model.process_edge)

if __name__ == "__main__":
    demo.launch(share=False)