Simpletuner Sdxl Lora Test

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訓練設置

• 訓練輪數：1
• 訓練步數：390
• 學習率：3e - 07
  • 學習率調度：恆定
  • 熱身步數：100
• 最大梯度值：2.0
• 有效批量大小：3
  • 微批量大小：1
  • 梯度累積步數：1
  • GPU 數量：3
• 梯度檢查點：啟用
• 預測類型：epsilon（額外參數=['training_scheduler_timestep_spacing=trailing', 'inference_scheduler_timestep_spacing=trailing']）
• 優化器：bnb - lion8bit
• 可訓練參數精度：純 BF16
• 基礎模型精度：no_change
• 字幕丟棄概率：0.1%

LyCORIS 配置

{
    "bypass_mode": true,
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 12,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 12
            },
            "FeedForward": {
                "factor": 6
            }
        }
    }
}

數據集

signs - discovery

• 重複次數：0
• 圖像總數：~423
• 縱橫比桶總數：5
• 分辨率：1.048576 兆像素
• 裁剪：否
• 裁剪樣式：無
• 裁剪縱橫比：無
• 用於正則化數據：否

signs - discovery - 512

• 重複次數：0
• 圖像總數：~420
• 縱橫比桶總數：4
• 分辨率：0.262144 兆像素
• 裁剪：否
• 裁剪樣式：無
• 裁剪縱橫比：無
• 用於正則化數據：否

💻 使用示例

基礎用法

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-xl-base-1.0'
adapter_repo_id = 'bghira/simpletuner-sdxl-lora-test'
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.unet)
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.unet, weights=qint8)
#freeze(pipeline.unet)
    
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=4.2,
    guidance_rescale=0.0,
).images[0]

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

📄 許可證

本項目採用 creativeml-openrail-m 許可證。