Reddy V4

🚀 reddy-v4

This project is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev, aiming to generate high - quality text - to - image results.

🚀 Quick Start

Prerequisites

Make sure you have the necessary libraries installed, such as torch and diffusers.

Inference Steps

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Unmapped2895/reddy-v4'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "Realistic wide shot photo of woman posing in a luxurious satin lingerie set, featuring a plunging bra, delicate thong and a classic garter belt with black stockings. The satin lingerie shimmers softly in the light, and the cut emphasizes both sophistication and a hint of allure. The lingerie is detailed with fine lace edges, highlighting her alluring figure. She elegantly styled hair as if getting ready for a formal event. The photo has a cinematic quality with rays of light and dramatic play of shadow and light"


## 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=832,
    height=1216,
    guidance_scale=3.5,
).images[0]

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

✨ Features

• Derived from black-forest-labs/FLUX.1-dev, leveraging its powerful text - to - image generation capabilities.
• The text encoder was not trained, allowing for the reuse of the base model text encoder for inference.
• Multiple validation and training settings are provided to ensure the quality and flexibility of the model.

📦 Installation

There is no specific installation process provided in the original document. You need to install the required libraries according to the inference code, such as torch and diffusers.

📚 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:

Training settings

Datasets

reddy - v2 - 512

reddy - v2 - 1024

📄 License

The license is other.