Overview
Architecture Lora Chinese Style is a lora training model with sdxl1.0 base model, latent text-to-image diffusion model. The model has been fine-tuned using a learning rate of 1e-5
over 3000 total steps with a batch size of 4 on a curated dataset of superior-quality chinese building style images. This model is derived from Stable Diffusion XL 1.0.
Model Description
- Developed by: FrankChieng
- Model type: Diffusion-based text-to-image generative model
- License: CreativeML Open RAIL++-M License
- Finetuned from model [optional]: Stable Diffusion XL 1.0 base
How to Use:
- Download
Lora model
here, the model is in.safetensors
format. - You need to use include siheyuan prompt in natural language, then you will get realistic result image
- You can use any generic negative prompt or use the following suggested negative prompt to guide the model towards high aesthetic generationse:
low quality, low resolution,watermark, mark, nsfw, lowres, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark
- And, the following should also be prepended to prompts to get high aesthetic results:
masterpiece, best quality
🧨 Diffusers
Make sure to upgrade diffusers to >= 0.18.2:
pip install diffusers --upgrade
In addition make sure to install transformers
, safetensors
, accelerate
as well as the invisible watermark:
pip install invisible_watermark transformers accelerate safetensors
Running the pipeline (if you don't swap the scheduler it will run with the default EulerDiscreteScheduler in this example we are swapping it to EulerAncestralDiscreteScheduler:
pip install -q --upgrade diffusers invisible_watermark transformers accelerate safetensors
pip install huggingface_hub
from huggingface_hub import notebook_login
notebook_login()
import torch
from torch import autocast
from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
base_model_id = "stabilityai/stable-diffusion-xl-base-1.0"
lora_model = "frank-chieng/sdxl_lora_architecture_siheyuan"
pipe = StableDiffusionXLPipeline.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
use_safetensors=True,
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.load_lora_weights(lora_model, weight_name="sdxl_lora_architecture_siheyuan.safetensors")
pipe.to('cuda')
prompt = "siheyuan, chinese modern architecture, perfectly shaded, night lighting, medium closeup, mystical setting, during the day"
negative_prompt = "watermark"
image = pipe(
prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
guidance_scale=7,
target_size=(1024,1024),
original_size=(4096,4096),
num_inference_steps=28
).images[0]
image.save("chinese_siheyuan.png")
Limitation
This model inherit Stable Diffusion XL 1.0 limitation
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