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# img_gen.py
import random
import io
from config.config import prompts, models # Indirect import
import os
import gradio as gr
import diffusers
import os
import torch
import sentencepiece
import torch
from huggingface_hub import login
from transformers import AutoTokenizer
import random
from datetime import datetime
# 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,
progress=gr.Progress(track_tqdm=True) # Add progress parameter
):
print("Hello from ctb_modal!")
# progress(0, desc="Starting...") # Initial progress
# yield "Initializing image generation..." # Yield the initial message
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()
# progress(0.2, desc="Preprocessing input...")
# yield "Preprocessing inputs..." # Yield the preprocessing message
# 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"{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 /:")
print(os.listdir("/"))
# CHECK FOR TORCH USING CUDA
print("CHECK FOR TORCH USING CUDA")
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print("inside if")
print(f"CUDA device count: {torch.cuda.device_count()}")
print(f"Current device: {torch.cuda.current_device()}")
print(f"Device name: {torch.cuda.get_device_name(torch.cuda.current_device())}")
# progress(0.5, desc="Running the model...")
# yield "Running the model..." # Yield the model running message
# INITIALIZING CPU PIPE
print("-----INITIALIZING PIPE-----")
pipe = FluxPipeline.from_pretrained(
local_path,
torch_dtype=torch.bfloat16,
#torch_dtype=torch.float16,
#torch_dtype=torch.float32,
local_files_only=True
)
if torch.cuda.is_available():
print("CUDA available")
print("using gpu")
pipe = pipe.to("cuda")
pipe_message = "CUDA"
else:
print("CUDA not available")
print("using cpu")
#pipe = pipe.to("cpu")
pipe_message = "CPU"
#pipe.enable_model_cpu_offload() # Use official recommended method
print(f"-----{pipe_message} PIPE INITIALIZED-----")
print(f"Using device: {pipe.device}")
except Exception as e:
print(f"Detailed error: {str(e)}")
return None, f"ERROR: Failed to initialize PIPE2. Details: {e}"
try:
print("-----SENDING IMG GEN TO PIPE-----")
print("-----HOLD ON-----")
# progress(0.8, desc="Postprocessing the output...")
# yield "Postprocessing the output..." # Yield the postprocessing message
image = pipe(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
max_sequence_length=512
# seed=seed
).images[0]
print("-----RENDER DONE!-----")
print(image)
except Exception as e:
return f"ERROR: Failed to initialize InferenceClient. Details: {e}"
try:
print("-----IMAGE GENERATED SUCCESSFULLY!-----")
print("-----CALL THE BANNERS!-----")
print("-----SAVING TO DISK-----")
# 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"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! Call the banners!"
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