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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 | |
| 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!" |