Reddy V3

🚀 reddy-v3

This project is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev, which is mainly used for text - to - image generation tasks, offering high - quality image generation capabilities.

🚀 Quick Start

Installation

The installation mainly involves setting up the necessary Python environment and installing relevant libraries. You need to ensure that Python and PyTorch are installed, and then install the diffusers library.

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Unmapped2895/reddy-v3'
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=3.5,
).images[0]

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

✨ Features

• Text - to - Image Generation: Capable of generating high - quality images based on text prompts.
• LoRA Adaptation: Utilizes the LoRA technique for efficient model adaptation.
• Multiple Image Styles: Can generate various image styles, such as photography, cyberpunk, etc.

📦 Installation

The installation mainly involves setting up the Python environment and installing relevant libraries. You need to ensure that Python and PyTorch are installed, and then install the diffusers library.

💻 Usage Examples

Basic Usage

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'Unmapped2895/reddy-v3'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
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"

pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
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")

Advanced Usage

# Advanced usage can adjust more parameters, such as changing the learning rate, batch size, etc.
# For example, you can change the guidance_scale parameter to adjust the influence of the text prompt on the generated image.
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=5.0,  # Increase the guidance_scale
).images[0]

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

📚 Documentation

Validation settings

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

Training settings

Datasets

reddy - v2 - 512

reddy - v2 - 1024

🔧 Technical Details

The model is a PEFT LoRA model based on the black - forest - labs/FLUX.1 - dev base model. During training, a series of parameter settings are used, such as learning rate, batch size, etc., to optimize the model. The prediction type uses flow - matching, which is a new generation method that can generate high - quality images. The text encoder is not trained, and the text encoder of the base model can be reused for inference.

📄 License

The license type is other. For detailed license information, please refer to the relevant documentation of the base model.