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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 GPU app
app = modal.App("img-gen-modal-gpu", image=image)
with image.imports():
import diffusers
import os
import gradio
import torch
import sentencepiece
import torch
from huggingface_hub import login
from transformers import AutoTokenizer
import random
from datetime import datetime
flux_model_vol = modal.Volume.from_name("flux-model-vol", create_if_missing=True) # Reference your volume
# CPU FUNCTION
@app.function(volumes={"/data": flux_model_vol},
secrets=[modal.Secret.from_name("huggingface-token")],
gpu="L40S"
#memory = 70000
)
# MAIN GENERATE IMAGE FUNCTION
def generate_image(prompt_alias, team_color, model_alias, custom_prompt, height=360, width=640, num_inference_steps=20, guidance_scale=2.0, seed=-1):
#with modal.enable_output():
print("Hello from ctb_modal!")
print("Running debug check...")
# Debug function to check installed packages
def check_dependencies():
packages = [
"diffusers", # For Stable Diffusion
"transformers", # For Hugging Face models
"torch", # PyTorch
"accelerate", # For distributed training/inference
"gradio", # For the Gradio interface (updated to latest version)
"safetensors", # For safe model loading
"pillow", # For image processing
"sentencepiece"
]
for package in packages:
try:
import importlib
module = importlib.import_module(package)
print(f" {package} is installed. Version:")
except ImportError:
print(f" {package} is NOT installed.")
check_dependencies()
# Find the selected prompt and model
try:
prompt = next(p for p in prompts if p["alias"] == prompt_alias)["text"]
model_name = next(m for m in models if m["alias"] == model_alias)["name"]
except StopIteration:
return None, "ERROR: Invalid prompt or model selected."
# Determine the enemy color
enemy_color = "blue" if team_color.lower() == "red" else "red"
# Print the original prompt and dynamic values for debugging
print("Original Prompt:")
print(prompt)
print(f"Enemy Color: {enemy_color}")
print(f"Team Color: {team_color.lower()}")
prompt = prompt.format(team_color=team_color.lower(), enemy_color=enemy_color)
# Print the formatted prompt for debugging
print("\nFormatted Prompt:")
print(prompt)
# Append the custom prompt (if provided)
if custom_prompt and len(custom_prompt.strip()) > 0:
prompt += " " + custom_prompt.strip()
# Randomize the seed if needed
if seed == -1:
seed = random.randint(0, 1000000)
try:
from diffusers import FluxPipeline
print("Initializing HF TOKEN")
hf_token = os.environ["HF_TOKEN"]
print(hf_token)
print("HF TOKEN:")
login(token=hf_token)
print("model_name:")
print(model_name)
# Use absolute path with leading slash
local_path = f"/data/{model_name}" # Changed from "data/" to "/data/"
print(f"Loading model from local path: {local_path}")
# Debug: Check if the directory exists and list its contents
if os.path.exists(local_path):
print("Directory exists. Contents:")
for item in os.listdir(local_path):
print(f" - {item}")
else:
print(f"Directory does not exist: {local_path}")
print("Contents of /data:")
print(os.listdir("/data"))
# INITIALIZING PIPE
print("Initializing PIPE2")
pipe = FluxPipeline.from_pretrained(
local_path,
torch_dtype=torch.bfloat16,
local_files_only=True
)
#pipe.enable_model_cpu_offload() # Use official recommended method
#pipe = pipe.to("cuda")
pipe = pipe.to("cuda")
except Exception as e:
print(f"Detailed error: {str(e)}")
return None, f"ERROR: Failed to initialize PIPE. Details: {e}"
try:
print("Sending img gen to pipe")
image = pipe(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
# seed=seed
).images[0]
print("render done")
print(image)
except Exception as e:
return f"ERROR: Failed to initialize InferenceClient. Details: {e}"
try:
print("SAVING")
# Save the image with a timestamped filename
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_filename = f"/data/images/{timestamp}_{model_alias.replace(' ', '_').lower()}_{prompt_alias.replace(' ', '_').lower()}_{team_color.lower()}.png"
# Save the image using PIL's save method
image.save(output_filename)
print(f"Image saved! File path: {output_filename}")
except Exception as e:
print(f"ERROR: Failed to save image. Details: {e}")
# Return the filename and success message
return image, "Image generated successfully!"
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