Rangerover Phase1 2e 4 Constant Wcrops

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

This project is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev. It focuses on text - to - image generation, offering various examples of generating Range Rover images.

🚀 Quick Start

The text encoder of this model was not trained. You may reuse the base model text encoder for inference.

✨ Features

• Tags: This model is associated with tags such as flux, flux - diffusers, text - to - image, image - to - image, diffusers, simpletuner, lora, etc., indicating its capabilities and application scenarios.
• Pipeline Tag: It is primarily used for text - to - image tasks.
• Inference: Inference is supported, and you can use the provided widget examples to generate images.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

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")

📚 Documentation

Validation settings

Note: The validation settings are not necessarily the same as the training settings.

You can find some example images in the following gallery: