pixart-900m-1024-ft
This is a full rank finetune derived from ptx0/pixart-900m-1024-ft-large.
The main validation prompt used during training was:
ethnographic photography of teddy bear at a picnic, ears tucked behind a cozy hoodie looking darkly off to the stormy picnic skies
Validation settings
- CFG:
4.5 - CFG Rescale:
0.0 - Steps:
25 - Sampler:
None - Seed:
42 - Resolutions:
1024x1024,1344x768,916x1152
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: 7
- Training steps: 100000
- Learning rate: 1e-06
- Effective batch size: 192
- Micro-batch size: 24
- Gradient accumulation steps: 1
- Number of GPUs: 8
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Not used
Datasets
photo-concept-bucket
- Repeats: 0
- Total number of images: ~567552
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'pixart-900m-1024-ft'
prompt = 'ethnographic photography of teddy bear at a picnic, ears tucked behind a cozy hoodie looking darkly off to the stormy picnic skies'
negative_prompt = 'blurry, cropped, ugly'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
prompt = "ethnographic photography of teddy bear at a picnic, ears tucked behind a cozy hoodie looking darkly off to the stormy picnic skies"
negative_prompt = "blurry, cropped, ugly"
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=25,
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=4.5,
guidance_rescale=0.0,
).images[0]
image.save("output.png", format="PNG")