inference.py

import os
import sys
sys.path.append("./")


import torch
from torchvision import transforms
from src.transformer import Transformer2DModel
from src.pipeline import Pipeline
from src.scheduler import Scheduler
from transformers import (
    CLIPTextModelWithProjection,
    CLIPTokenizer,
)
from diffusers import VQModel

device = 'cuda'

model_path = "MeissonFlow/Meissonic"
model = Transformer2DModel.from_pretrained(model_path,subfolder="transformer",)
vq_model = VQModel.from_pretrained(model_path, subfolder="vqvae", )
text_encoder = CLIPTextModelWithProjection.from_pretrained(model_path,subfolder="text_encoder",)
tokenizer = CLIPTokenizer.from_pretrained(model_path,subfolder="tokenizer",)
scheduler = Scheduler.from_pretrained(model_path,subfolder="scheduler",)
pipe=Pipeline(vq_model, tokenizer=tokenizer,text_encoder=text_encoder,transformer=model,scheduler=scheduler)

pipe = pipe.to(device)

steps = 64
CFG = 9
resolution = 1024 
negative_prompts = "worst quality, low quality, low res, blurry, distortion, watermark, logo, signature, text, jpeg artifacts, signature, sketch, duplicate, ugly, identifying mark"

prompts = [
    "Two actors are posing for a pictur with one wearing a black and white face paint.",
    "A large body of water with a rock in the middle and mountains in the background.",
    "A white and blue coffee mug with a picture of a man on it.",
    "A statue of a man with a crown on his head.",
    "A man in a yellow wet suit is holding a big black dog in the water.",
    "A white table with a vase of flowers and a cup of coffee on top of it.",
    "A woman stands on a dock in the fog.",
    "A woman is standing next to a picture of another woman."
]

image = pipe(prompt=prompts[0],negative_prompt=negative_prompts,height=resolution,width=resolution,guidance_scale=CFG,num_inference_steps=steps).images[0]

output_dir = "./output"
os.makedirs(output_dir, exist_ok=True)
image.save(output_dir, f"{prompt[:10]}_{resolution}_{steps}_{CFG}.png")