Simpletuner Lora Screenprinting

🚀 simpletuner-lora-screenprinting

This is a standard PEFT LoRA that solves the problem of generating screen - printing - suitable images based on the stable - diffusion model, providing a practical solution for text - to - image tasks.

🚀 Quick Start

This is a standard PEFT LoRA derived from stabilityai/stable-diffusion-3.5-large.

The main validation prompt used during training was:

an image apt for screenprinting process.

✨ Features

• Text - to - Image Generation: Capable of generating images suitable for the screenprinting process from text prompts.
• Customizable Settings: Allows users to adjust various parameters during validation and inference, such as CFG, steps, sampler, etc.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

Basic Usage

import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-3.5-large'
adapter_id = 'DovahChikiin72/simpletuner-lora-screenprinting'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "an image apt for screenprinting process."
negative_prompt = 'blurry, cropped, ugly'

## 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,
    negative_prompt=negative_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=512,
    height=512,
    guidance_scale=5.0,
).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: