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# img_gen.py
#img_gen_modal.py
# img_gen.py
# 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
CACHE_DIR = "/model_cache"
# 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
f"git+https://github.com/huggingface/transformers.git"
)
.env(
{
"HF_HUB_ENABLE_HF_TRANSFER": "1", "HF_HOME": "HF_HOME", "HF_HUB_CACHE": CACHE_DIR
}
)
)
# 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(gpu="t4", volumes={"/data": flux_model_vol},
secrets=[modal.Secret.from_name("huggingface-token")],
# gpu="a100-80gb"
)
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"
# 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)
# DOWNLOADING FROM HERE KEEPS THE /MODELS/ DIRECTORY
# WITH A SCRIPT IT GOES AWAY
# def download_flux():
# from huggingface_hub import snapshot_download
# import transformers
# repo_id = "black-forest-labs/FLUX.1-schnell"
# local_dir = "/data/models/FLUX.1-schnell"
# # **FASTEST METHOD:** Use max_workers for parallel download
# snapshot_download(
# repo_id,
# local_dir=local_dir,
# revision="main",
# #ignore_patterns=["*.pt", "*.bin"], # Skip large model weights
# max_workers=8 # Higher concurrency for parallel chunk downloads
# )
# transformers.utils.move_cache()
# print(f"FLUX model downloaded to {local_dir}")
# download_flux()
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)
# First check if model exists in the volume
local_path = "data/" + model_name
print(f"Loading model from local path: {local_path}")
# Debug: Check if the directory exists and list its contents
for item in os.listdir(local_path):
print(f" - {item}")
print("Initializing PIPE")
# Initialize the pipeline
#cache_dir = "/cache_"
pipe = FluxPipeline.from_pretrained(local_path, torch_dtype=torch.bfloat16,local_files_only=True,
#cache_dir=cache_dir
)
pipe = pipe.to("cuda")
except Exception as e:
print(f"Detailed error: {str(e)}")
return None, f"ERROR: Failed to initialize PIPE. Details: {e}"
try:
print("Sending img gen to pipe")
image = pipe(
prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
# seed=seed
).images[0]
image.save("image.png")
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:
# # The pipeline typically returns images in a specific format
# # Usually it's image.images[0] for the first generated image
# image_output = image.images[0] # Get the actual PIL Image from the output
# image_output.save(output_filename) # Save using PIL's save method
# except Exception as e:
# return None, f"ERROR: Failed to save image. Details: {e}"
# print(f"Image output type: {type(image)}")
# print(f"Image output attributes: {dir(image)}") |