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| #img_gen_modal.py | |
| import modal | |
| # import random | |
| # from datetime import datetime | |
| # import random | |
| # import io | |
| # Define the Modal image | |
| print("Building custom image...") | |
| image = ( | |
| modal.Image.debian_slim(python_version="3.11") # Base image | |
| .pip_install( | |
| "numpy", | |
| "pandas", | |
| "diffusers", | |
| "transformers", | |
| "torch", | |
| "accelerate", | |
| "gradio", | |
| "safetensors", | |
| "pillow", | |
| ) # Install Python packages | |
| .run_commands("echo 'Image build complete!'") # Run a shell command | |
| ) | |
| # Create a Modal app | |
| app = modal.App("ctb-ai-img-gen-modal", image=image) | |
| volume = modal.Volume.from_name("flux-model-vol") # Reference your volume | |
| def generate(prompt_alias, team_color, model_alias, custom_prompt, height=360, width=640, num_inference_steps=20, guidance_scale=2.0, seed=-1): | |
| # Debug: Print a message when the function starts | |
| print("Starting main function inside the container...") | |
| # Import libraries and print their versions | |
| import numpy as np | |
| import pandas as pd | |
| import torch | |
| import diffusers | |
| import transformers | |
| import gradio as gr | |
| from PIL import Image as PILImage | |
| print("Hello from Modal!") | |
| print("NumPy version:", np.__version__) | |
| print("Pandas version:", pd.__version__) | |
| print("PyTorch version:", torch.__version__) | |
| print("Diffusers version:", diffusers.__version__) # Corrected: Use the library's __version__ | |
| print("Transformers version:", transformers.__version__) # Corrected: Use the library's __version__ | |
| print("Gradio version:", gr.__version__) | |
| print("Pillow version:", PILImage.__version__) | |
| try: | |
| # Generate the image | |
| image_path, message = generate_image(prompt_alias, team_color, model_alias, custom_prompt, height, width, num_inference_steps, guidance_scale, seed) | |
| return image_path, message | |
| except Exception as e: | |
| return None, f"An error occurred: {e}" | |
| 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("cuda").manual_seed(seed) | |
| ).images[0] | |
| # Convert PIL image to bytes | |
| img_byte_arr = io.BytesIO() | |
| image.save(img_byte_arr, format='PNG') | |
| img_byte_arr = img_byte_arr.getvalue() | |
| 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 img_byte_arr, "Image generated successfully!" | |
| except Exception as e: | |
| return None, f"ERROR: Failed to generate image. Details: {e}" | |
| return output_filename, "Image generated successfully!" |