Flux Lora Training

🚀 flux-lora-training

这是一个基于 black-forest-labs/FLUX.1-dev 的标准PEFT LoRA模型。它可用于文本到图像、图像到图像等多种任务，为图像生成提供了强大的支持。

✨ 主要特性

• 基于 black-forest-labs/FLUX.1-dev 基础模型派生而来。
• 支持文本到图像、图像到图像等多种图像生成任务。
• 提供了详细的训练和验证设置。

📦 安装指南

文档未提及具体安装步骤，故跳过该章节。

💻 使用示例

基础用法

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")

📚 详细文档

验证设置

• CFG：3.5
• CFG Rescale：0.0
• 步数：20
• 采样器：FlowMatchEulerDiscreteScheduler
• 种子：42
• 分辨率：1024x1024
• 跳过层引导：

注意：验证设置不一定与训练设置相同。

你可以在以下图库中找到一些示例图像：