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# img_gen.py
#img_gen_modal.py
# img_gen.py
# img_gen_modal.py
import modal
import random
import io
from config.config import prompts, models # Indirect import
import os
CACHE_DIR = "/model_cache"
# Define the Modal image
image = (
#modal.Image.from_registry("nvidia/cuda:12.2.0-devel-ubuntu22.04", add_python="3.9")
modal.Image.debian_slim(python_version="3.9") # Base image
.apt_install(
"git",
)
.pip_install(
"diffusers",
"transformers",
"torch",
"accelerate",
"gradio>=4.44.1",
"safetensors",
"pillow",
"sentencepiece",
"hf_transfer",
"huggingface_hub[hf_transfer]",
"aria2", # aria2 for ultra-fast parallel downloads
f"git+https://github.com/huggingface/transformers.git"
)
.env(
{
"HF_HUB_ENABLE_HF_TRANSFER": "1", "HF_HOME": "HF_HOME", "HF_HUB_CACHE": CACHE_DIR
}
)
)
# Create a Modal app
app = modal.App("tools-test-dir", image=image)
with image.imports():
import diffusers
import os
import gradio
from datetime import datetime
flux_model_vol = modal.Volume.from_name("flux-model-vol", create_if_missing=True) # Reference your volume
@app.function(volumes={"/data": flux_model_vol},
secrets=[modal.Secret.from_name("huggingface-token")],
#gpu="a100-80gb"
)
def test_dir():
# OS
# Get the current working directory (should be /root)
current_directory = os.getcwd()
print(f"Current working directory: {current_directory}")
# List the contents of the current directory
print("Contents of current modal directory:")
print(os.listdir(current_directory))
# MODAL
print ("MODAL")
print ("MODAL ROOT")
# List contents of the volume
file_entries = flux_model_vol.listdir("/") # Replace "/data" with your volume path
# Extract and print only the paths
paths = [entry.path for entry in file_entries]
print("Paths in volume:")
print(paths)
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