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# img_gen.py
import sys
import os
import random
from huggingface_hub import InferenceClient
from datetime import datetime
from config.config import models, prompts, api_token # Direct import
import modal
# Define the Modal image
image = (
modal.Image.from_registry("nvidia/cuda:12.2.0-devel-ubuntu22.04", add_python="3.9")
.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
)
.env(
{
"HF_HUB_ENABLE_HF_TRANSFER": "1", "HF_HOME": "HF_HOME"
}
)
)
# Create a Modal app
app = modal.App("img-gen-modal", image=image)
with image.imports():
import diffusers
import os
import gradio
import torch
import sentencepiece
import transformers
from huggingface_hub import InferenceClient, login
@app.function(
secrets=[modal.Secret.from_name("huggingface-token")],
gpu="t4",
timeout=600
)
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):
# 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"
# if team.lower() == "red":
# winning_team_text = " The winning army is dressed in red armor and banners."
# elif team.lower() == "blue":
# winning_team_text = " The winning army is dressed in blue armor and banners."
# 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)
# Initialize the InferenceClient
try:
print ("starting inference")
print("token:")
print (api_token)
client = InferenceClient(model_name, token=api_token)
except Exception as e:
return None, f"ERROR: Failed to initialize InferenceClient. Details: {e}"
#Generate the image
try:
image = client.text_to_image(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
seed=seed
)
except Exception as e:
return None, f"ERROR: Failed to generate image. Details: {e}"
#return prompt # For testing purposes, return the formatted prompt
# Save the image with a timestamped filename
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_filename = f"{timestamp}_{model_alias.replace(' ', '_').lower()}_{prompt_alias.replace(' ', '_').lower()}_{team_color.lower()}.png"
try:
image.save(output_filename)
except Exception as e:
return None, f"ERROR: Failed to save image. Details: {e}"
return output_filename, "Image generated successfully!" |