Simpletuner Finetuned Sd3

🚀 simpletuner-finetuned-sd3

This is a full - rank finetuned model derived from sd3/unknown-model, specialized in text - to - image and image - to - image tasks.

🚀 Quick Start

This model is a full rank finetune derived from sd3/unknown-model.

The main validation prompt used during training was:

A photo-realistic pathology image

✨ Features

• Multiple Tasks: Supports text - to - image and image - to - image tasks.
• Customizable Settings: Allows users to adjust various parameters for validation and training.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

import torch
from diffusers import DiffusionPipeline

model_id = 'Minh-Ha/simpletuner-finetuned-sd3'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16

prompt = "A photo-realistic pathology image"
negative_prompt = 'blurry, cropped, ugly'

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=1024,
    height=1024,
    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: