Training epochs: 24
Training steps: 150
Learning rate: 0.0001
- Learning rate schedule: constant
- Warmup steps: 500
Max grad value: 0.01
Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
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")

ControlNetLoRA/pixart

pixart-controlnet-lora-test

Validation settings

Training settings

Datasets

antelope-data-1024

Inference

ControlNetLoRA/
pixart