L2P-open-source-final/inference.py

import torch
from diffsynth.pipelines.z_image_L2P import ZImagePipeline, ModelConfig

main_model_path = "/path/model-1k-merge.safetensors"

text_encoder_paths = [
    "/path/Z-Image-Turbo/text_encoder/model-00001-of-00003.safetensors",
    "/path/Z-Image-Turbo/text_encoder/model-00002-of-00003.safetensors",
    "/path/Z-Image-Turbo/text_encoder/model-00003-of-00003.safetensors",
]

tokenizer_path = "/path/Z-Image-Turbo/tokenizer"

pipe = ZImagePipeline.from_pretrained(
    torch_dtype=torch.bfloat16,
    device="cuda",
    model_configs=[
        ModelConfig(path=[main_model_path]),
        ModelConfig(path=text_encoder_paths),
    ],
    tokenizer_config=ModelConfig(path=tokenizer_path),
)

prompt = "an origami pig on fire in the middle of a dark room with a pentagram on the floor"

image = pipe(
    prompt=prompt,
    seed=42,
    rand_device="cuda",
    num_inference_steps=30,
    cfg_scale=2.0,
    height=1024,
    width=1024,
)

image.save("example.png")