Flux Lora Training

🚀 flux-lora-training

這是一個基於 black-forest-labs/FLUX.1-dev 的標準PEFT LoRA模型。它可用於文本到圖像、圖像到圖像等多種任務，為圖像生成提供了強大的支持。

✨ 主要特性

• 基於 black-forest-labs/FLUX.1-dev 基礎模型派生而來。
• 支持文本到圖像、圖像到圖像等多種圖像生成任務。
• 提供了詳細的訓練和驗證設置。

📦 安裝指南

文檔未提及具體安裝步驟，故跳過該章節。

💻 使用示例

基礎用法

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'hok00i3/flux-lora-training'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "(ultra detailed,photo,photograph,best quality,high resolution,4k,8k,photorealistic,Japanese early twenties,(slim) and curvy body,waist,detailed beautiful eyes,super detailed eyes and skins,very beautiful woman:3.0), (Wearing tight short sleeves white (one piece) sailor uniform, blue collar, red neckerchief, dark blue pleated dress, standing in empty classroom,sweet smiling:2.0), long straight hair, standing in empty classroom,"


## 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,
    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=3.5,
).images[0]

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

📚 詳細文檔

驗證設置

• CFG：3.5
• CFG Rescale：0.0
• 步數：20
• 採樣器：FlowMatchEulerDiscreteScheduler
• 種子：42
• 分辨率：1024x1024
• 跳過層引導：

注意：驗證設置不一定與訓練設置相同。

你可以在以下圖庫中找到一些示例圖像：