pixart-900m-1024-ft-v0.7-stage2
This is a full rank finetune derived from terminusresearch/pixart-900m-1024-ft-v0.6.
The main validation prompt used during training was:
a cute anime character named toast, holding a sign that reads SOON
Validation settings
- CFG:
4.0
- CFG Rescale:
0.7
- Steps:
30
- Sampler:
None
- Seed:
420420420
- Resolution:
1024x1024
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: 9
- Training steps: 29500
- Learning rate: 1e-06
- Effective batch size: 16
- Micro-batch size: 16
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Enabled
Datasets
shutterstock
- Repeats: 0
- Total number of images: 21040
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
nijijourney
- Repeats: 0
- Total number of images: 21488
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
bg20k-1024
- Repeats: 0
- Total number of images: 89296
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
photo-aesthetics
- Repeats: 0
- Total number of images: 33120
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
text-1mp
- Repeats: 5
- Total number of images: 13184
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
cinemamix-1mp
- Repeats: 0
- Total number of images: 7376
- Total number of aspect buckets: 5
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'pixart-900m-1024-ft-v0.7-stage2'
pipeline = DiffusionPipeline.from_pretrained(model_id)
prompt = "a cute anime character named toast, holding a sign that reads SOON"
negative_prompt = "blurry, cropped, ugly"
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=4.0,
guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")