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---
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
---
# 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).
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 19
- Training steps: 40
- 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")
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