---
license: openrail++
base_model: "terminusresearch/pixart-900m-1024-ft-v0.6"
tags:
  - pixart_sigma
  - pixart_sigma-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: 'A photo-realistic image of a cat'
  parameters:
    negative_prompt: 'ugly, cropped, blurry, low-quality, mediocre average'
  output:
    url: ./assets/image_0_0.png
---

# pixart-controlnet-lora-test

This is a ControlNet PEFT LoRA derived from [terminusresearch/pixart-900m-1024-ft-v0.6](https://huggingface.co/terminusresearch/pixart-900m-1024-ft-v0.6).

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


## Validation settings
- CFG: `4.0`
- CFG Rescale: `0.0`
- Steps: `16`
- 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: 224
- Training steps: 450
- Learning rate: 0.0001
  - Learning rate schedule: constant
  - Warmup steps: 500
- Max grad value: 0.01
- Effective batch size: 3
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 3
- Gradient checkpointing: False
- Prediction type: epsilon (extra parameters=['training_scheduler_timestep_spacing=trailing', 'inference_scheduler_timestep_spacing=trailing', 'controlnet_enabled'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Base model precision: `no_change`
- Caption dropout probability: 0.0%


- LoRA Rank: 64
- LoRA Alpha: 64.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### antelope-data-1024
- Repeats: 0
- Total number of images: ~6
- 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 PixArtSigmaPipeline, PixArtSigmaControlNetPipeline
# if you're not in the SimpleTuner environment, this import will fail.
from helpers.models.pixart.controlnet import PixArtSigmaControlNetAdapterModel

# Load base model
base_model_id = "terminusresearch/pixart-900m-1024-ft-v0.6"
controlnet_id = "bghira/pixart-controlnet-lora-test"

# Load ControlNet adapter
controlnet = PixArtSigmaControlNetAdapterModel.from_pretrained(
    f"{controlnet_id}/controlnet"
)

# Create pipeline
pipeline = PixArtSigmaControlNetPipeline.from_pretrained(
    base_model_id,
    controlnet=controlnet,
    torch_dtype=torch.bfloat16
)
pipeline.to('cuda' if torch.cuda.is_available() else 'cpu')

# Load your control image
from PIL import Image
control_image = Image.open("path/to/control/image.png")

# Generate
prompt = "A photo-realistic image of a cat"
image = pipeline(
    prompt=prompt,
    image=control_image,
    num_inference_steps=16,
    guidance_scale=4.0,
    generator=torch.Generator(device='cuda').manual_seed(42),
    controlnet_conditioning_scale=1.0,
).images[0]

image.save("output.png")