Sdxl

🚀 simpletuner-controlnet-sdxl-lora-test

This project presents a ControlNet PEFT LoHa derived from stabilityai/stable-diffusion-xl-base-1.0, which can generate photo - realistic images, such as images of cats.

🚀 Quick Start

This is a ControlNet PEFT LoHa derived from stabilityai/stable-diffusion-xl-base-1.0. The main validation prompt used during training was:

A photo-realistic image of a cat

✨ Features

• The text encoder was not trained. You may reuse the base model text encoder for inference.
• It can generate photo - realistic images according to the given prompts.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

Basic Usage

import torch
from diffusers import DiffusionPipeline

model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
adapter_id = 'bghira/simpletuner-controlnet-sdxl-lora-test'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "A photo-realistic image of a cat"
negative_prompt = 'blurry, cropped, ugly'

## 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.unet, weights=qint8)
#freeze(pipeline.unet)
    
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=4.2,
    guidance_rescale=0.0,
).images[0]

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

📚 Documentation

Validation settings

• CFG: 4.2
• CFG Rescale: 0.0
• Steps: 20
• Sampler: ddim
• Seed: 42
• Resolution: 1024x1024

Note: The validation settings are not necessarily the same as the training settings. You can find some example images in the following gallery: