mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-18 22:08:13 +00:00
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
from diffsynth.pipelines.z_image import (
|
||
ZImagePipeline, ModelConfig,
|
||
ZImageUnit_Image2LoRAEncode, ZImageUnit_Image2LoRADecode
|
||
)
|
||
from modelscope import snapshot_download
|
||
from safetensors.torch import save_file
|
||
import torch
|
||
from PIL import Image
|
||
|
||
# Use `vram_config` to enable LoRA hot-loading
|
||
vram_config = {
|
||
"offload_dtype": torch.bfloat16,
|
||
"offload_device": "cuda",
|
||
"onload_dtype": torch.bfloat16,
|
||
"onload_device": "cuda",
|
||
"preparing_dtype": torch.bfloat16,
|
||
"preparing_device": "cuda",
|
||
"computation_dtype": torch.bfloat16,
|
||
"computation_device": "cuda",
|
||
}
|
||
|
||
# Load models
|
||
pipe = ZImagePipeline.from_pretrained(
|
||
torch_dtype=torch.bfloat16,
|
||
device="cuda",
|
||
model_configs=[
|
||
ModelConfig(model_id="Tongyi-MAI/Z-Image", origin_file_pattern="transformer/*.safetensors", **vram_config),
|
||
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="text_encoder/*.safetensors"),
|
||
ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="vae/diffusion_pytorch_model.safetensors"),
|
||
ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="SigLIP2-G384/model.safetensors"),
|
||
ModelConfig(model_id="DiffSynth-Studio/General-Image-Encoders", origin_file_pattern="DINOv3-7B/model.safetensors"),
|
||
ModelConfig(model_id="DiffSynth-Studio/Z-Image-i2L", origin_file_pattern="model.safetensors"),
|
||
],
|
||
tokenizer_config=ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
|
||
)
|
||
|
||
# Load images
|
||
snapshot_download(
|
||
model_id="DiffSynth-Studio/Z-Image-i2L",
|
||
allow_file_pattern="assets/style/*",
|
||
local_dir="data/Z-Image-i2L_style_input"
|
||
)
|
||
images = [Image.open(f"data/Z-Image-i2L_style_input/assets/style/1/{i}.jpg") for i in range(4)]
|
||
|
||
# Image to LoRA
|
||
with torch.no_grad():
|
||
embs = ZImageUnit_Image2LoRAEncode().process(pipe, image2lora_images=images)
|
||
lora = ZImageUnit_Image2LoRADecode().process(pipe, **embs)["lora"]
|
||
save_file(lora, "lora.safetensors")
|
||
|
||
# Generate images
|
||
prompt = "a cat"
|
||
negative_prompt = "泛黄，发绿，模糊，低分辨率，低质量图像，扭曲的肢体，诡异的外观，丑陋，AI感，噪点，网格感，JPEG压缩条纹，异常的肢体，水印，乱码，意义不明的字符"
|
||
image = pipe(
|
||
prompt=prompt,
|
||
negative_prompt=negative_prompt,
|
||
seed=0, cfg_scale=4, num_inference_steps=50,
|
||
positive_only_lora=lora,
|
||
sigma_shift=8
|
||
)
|
||
image.save("image.jpg")
|