Rangerover Phase1 2e 4 Constant Wcrops

🚀 rangerover-phase1-2e-4-constant-wcrops

这是一个基于 black-forest-labs/FLUX.1-dev 的标准PEFT LoRA模型。该模型可用于文本到图像的转换，能生成高质量的图像。

🚀 快速开始

本项目是一个基于特定基础模型的标准PEFT LoRA模型。在使用模型时，需要了解验证设置和训练设置的相关参数，同时可以参考推理部分的代码示例进行图像生成。

📦 安装指南

文档未提及具体安装步骤，可参考基础模型 black-forest-labs/FLUX.1-dev 的安装说明。

💻 使用示例

基础用法

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'mipat12/rangerover-phase1-2e-4-constant-wcrops'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "An astronaut is riding a horse through the jungles of Thailand."


## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same 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=1344,
    height=768,
    guidance_scale=3.0,
).images[0]

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

高级用法

文档未提及高级用法相关代码，可根据基础用法进行拓展，例如调整更多的参数以获得不同风格的图像。

📚 详细文档

验证设置

• CFG: 3.0
• CFG Rescale: 0.0
• Steps: 20
• Sampler: FlowMatchEulerDiscreteScheduler
• Seed: 42
• Resolution: 1344x768
• Skip-layer guidance:

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：