Simpletuner Lora

文本编码器未进行训练，推理时可复用基础模型的文本编码器。

训练设置

• 训练轮数: 1
• 训练步数: 10000
• 学习率: 0.0001
  • 学习率调度: 多项式
  • 热身步数: 100
• 最大梯度值: 2.0
• 有效批量大小: 2
  • 微批量大小: 1
  • 梯度累积步数: 1
  • GPU 数量: 2
• 梯度检查点: 启用
• 预测类型: 流匹配 (额外参数=['shift=3'])
• 优化器: adamw_bf16
• 可训练参数精度: 纯 BF16
• 基础模型精度: no_change
• 字幕丢弃概率: 10.0%

LyCORIS 配置

{
    "algo": "lokr",
    "multiplier": 1.0,
    "full_matrix": true,
    "linear_alpha": 1,
    "factor": 16,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 16
            },
            "FeedForward": {
                "factor": 8
            }
        }
    }
}

数据集

pseudo-camera-10k-sd3

• 重复次数: 0
• 图像总数: ~14102
• 纵横比桶总数: 1
• 分辨率: 1.048576 兆像素
• 裁剪: 是
• 裁剪样式: 居中
• 裁剪纵横比: 方形
• 用于正则化数据: 否

💻 使用示例

基础用法

import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights


def download_adapter(repo_id: str):
    import os
    from huggingface_hub import hf_hub_download
    adapter_filename = "pytorch_lora_weights.safetensors"
    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
    os.makedirs(path_to_adapter, exist_ok=True)
    hf_hub_download(
        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
    )

    return path_to_adapter_file
    
model_id = 'stabilityai/stable-diffusion-3.5-large'
adapter_repo_id = 'ShanZard/simpletuner-lora'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()

prompt = "A photo-realistic image of a cat"
negative_prompt = 'blurry, cropped, ugly'

## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
    
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
model_output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=20,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]

model_output.save("output.png", format="PNG")

高级用法

在基础用法的基础上，你可以根据需要调整以下参数：

• prompt 和 negative_prompt：修改生成图像的提示词和负面提示词。
• num_inference_steps：调整推理步数，影响图像生成的质量和速度。
• guidance_scale：调整引导比例，控制生成图像与提示词的匹配程度。

# 示例：修改提示词和推理步数
prompt = "A beautiful landscape with a lake and mountains"
negative_prompt = 'low quality, blurry'
num_inference_steps = 30

model_output = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=num_inference_steps,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]

model_output.save("output_advanced.png", format="PNG")

📄 许可证

本项目使用其他许可证。具体请参考相关文件。