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#img_gen_modal.py
import modal
import random
from datetime import datetime
import random
import io
from config.config import prompts, models # Indirect import
import os
import torch
from huggingface_hub import login
from transformers import AutoTokenizer
# 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"
}
)
)
# 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
#flux_model_vol = modal.Volume.from_name("flux-model-vol", create_if_missing=True) # Reference your volume
@app.function(
secrets=[modal.Secret.from_name("huggingface-token")],
#volumes={"/data": flux_model_vol},
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):
import torch
from diffusers import StableDiffusionPipeline
# 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()}")
# Format the prompt
prompt = prompt.format(team_color=team_color.lower(), enemy_color=enemy_color)
# Print the formatted prompt for debugging
print("\nFormatted Prompt:")
print(prompt)
# Append custom prompt if provided
if custom_prompt and len(custom_prompt.strip()) > 0:
prompt += " " + custom_prompt.strip()
# Randomize seed if needed
if seed == -1:
seed = random.randint(0, 1000000)
# Initialize the pipeline
pipe = StableDiffusionPipeline.from_pretrained(
model_name,
#torch_dtype=torch.float16,
use_safetensors=True,
#variant="fp16"
)
pipe.to("cpu")
# Generate the image
try:
image = pipe(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=torch.Generator("cpu").manual_seed(seed)
).images[0]
except Exception as e:
return None, f"ERROR: Failed to generate image. Details: {e}"
# 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!" |