pixart-sora-t2i
This is a full rank finetune derived from toilaluan/SoraT2I.
No validation prompt was used during training.
None
Validation settings
- CFG:
7.5
- CFG Rescale:
0.0
- Steps:
30
- Sampler:
euler
- Seed:
42
- Resolution:
1024
Note: The validation settings are not necessarily the same as the training settings.
You can find some example images in the following gallery:
The text encoder was not trained.
You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 0
- Training steps: 1000
- Learning rate: 8e-06
- Effective batch size: 128
- Micro-batch size: 32
- Gradient accumulation steps: 4
- Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Enabled
Datasets
mj-v6
- Repeats: 0
- Total number of images: 134144
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import torch
from diffusers import DiffusionPipeline
model_id = "pixart-sora-t2i"
prompt = "An astronaut is riding a horse through the jungles of Thailand."
negative_prompt = "malformed, disgusting, overexposed, washed-out"
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
negative_prompt='blurry, cropped, ugly',
num_inference_steps=30,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1152,
height=768,
guidance_scale=7.5,
guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")