Reddy V2

🚀 reddy-v2

Reddy-v2 is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev, which can be used for text - to - image and image - to - image tasks.

🚀 Quick Start

Reddy-v2 is a standard PEFT LoRA based on the black-forest-labs/FLUX.1-dev model. Here are some key details about it.

The main validation prompt used during training was:

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

✨ Features

• Based on a strong base model: Derived from black-forest-labs/FLUX.1-dev.
• Multiple usage scenarios: Suitable for text - to - image and image - to - image tasks.

📦 Installation

This section does not have specific installation steps in the original document, so it is skipped.

💻 Usage Examples

Basic Usage

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Unmapped2895/reddy-v2'
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 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=832,
    height=1216,
    guidance_scale=2.0,
).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: