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#img_gen_modal.py
import modal
import random
import io
from config.config import prompts, models # Indirect import
import os
import gradio as gr
#MOVED FROM IMAGE IMPORT LIST
import torch
import sentencepiece
import torch
from huggingface_hub import login
from transformers import AutoTokenizer
import random
from datetime import datetime
#import xformers
import gradio as gr
import numpy as np
#import spaces
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
CACHE_DIR = "/model_cache"
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Define the Modal image
image = (
modal.Image.from_registry("nvidia/cuda:12.2.0-devel-ubuntu22.04", add_python="3.9").pip_install_from_requirements("requirements.txt")
#modal.Image.debian_slim(python_version="3.9") # Base image
# .apt_install(
# "git",
# )
# .pip_install(
# "diffusers",
# "transformers",
# "xformers",
# "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("live-preview-test", image=image)
with image.imports():
import os
flux_model_vol = modal.Volume.from_name("flux-model-vol", create_if_missing=True) # Reference your volume
# GPU FUNCTION
@app.function(volumes={"/data": flux_model_vol},
secrets=[modal.Secret.from_name("huggingface-token")],
gpu="L40S",
timeout = 300
)
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
taef1 = AutoencoderTiny.from_pretrained("/data/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained("/data/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained("/data/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
output_type="pil",
good_vae=good_vae,
):
yield img, seed
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