README.md

---
license: creativeml-openrail-m
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
tags:
  - sdxl
  - sdxl-diffusers
  - text-to-image
  - image-to-image
  - diffusers
  - simpletuner
  - not-for-all-audiences
  - lora
  - controlnet
  - template:sd-lora
  - standard
pipeline_tag: text-to-image
inference: true
widget:
- text: 'unconditional (blank prompt)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_0_0.png
- text: 'A photo-realistic image of a cat'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
- text: 'prompt not found (2)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_2_0.png
- text: 'prompt not found (3)'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_3_0.png
---

# simpletuner-controlnet-sdxl-lora-test

This is a ControlNet PEFT LoHa derived from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).

The main validation prompt used during training was:
```
A photo-realistic image of a cat
```


## 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](#training-settings).

You can find some example images in the following gallery:


<Gallery />

The text encoder **was not** trained.
You may reuse the base model text encoder for inference.


## Training settings

- Training epochs: 49
- Training steps: 400
- Learning rate: 0.0001
  - Learning rate schedule: constant
  - Warmup steps: 0
- Max grad value: 2.0
- Effective batch size: 3
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 3
- Gradient checkpointing: True
- Prediction type: epsilon (extra parameters=['training_scheduler_timestep_spacing=trailing', 'inference_scheduler_timestep_spacing=trailing'])
- Optimizer: bnb-lion8bit
- Trainable parameter precision: Pure BF16
- Base model precision: `no_change`
- Caption dropout probability: 0.1%


- LoRA Rank: 128
- LoRA Alpha: 128.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### antelope-data
- Repeats: 0
- Total number of images: ~24
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No


## Inference


```python
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")

```