metadata
license: creativeml-openrail-m
base_model: ptx0/pixart-900m-1024-ft-v0.7-stage1
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- simpletuner
- full
inference: true
pixart-900m-1024-ft-v0.7-stage1
This is a full rank finetune derived from ptx0/pixart-900m-1024-ft-v0.7-stage1.
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.
The text encoder was not trained.
You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 0
- Training steps: 3000
- Learning rate: 1e-06
- Effective batch size: 64
- Micro-batch size: 8
- Gradient accumulation steps: 1
- Number of GPUs: 8
- Prediction type: epsilon
- Rescaled betas zero SNR: False
- Optimizer: AdamW, stochastic bf16
- Precision: Pure BF16
- Xformers: Enabled
Datasets
sports
- Repeats: 0
- Total number of images: ~768
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
mj-60
- Repeats: 0
- Total number of images: ~185712
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
id-75k
- Repeats: 0
- Total number of images: ~44760
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
celebrities
- Repeats: 0
- Total number of images: ~1088
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
normalnudes
- Repeats: 0
- Total number of images: ~1024
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
guys
- Repeats: 0
- Total number of images: ~320
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
signs
- Repeats: 0
- Total number of images: ~320
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
dalle3
- Repeats: 0
- Total number of images: ~1120912
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
sfwbooru
- Repeats: 0
- Total number of images: ~403208
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
moviecollection
- Repeats: 0
- Total number of images: ~1792
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
bookcovers
- Repeats: 0
- Total number of images: ~704
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
nijijourney
- Repeats: 0
- Total number of images: ~512
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
experimental
- Repeats: 0
- Total number of images: ~3008
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
ethnic
- Repeats: 0
- Total number of images: ~3072
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
gay
- Repeats: 0
- Total number of images: ~1088
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
architecture
- Repeats: 0
- Total number of images: ~4352
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
shutterstock
- Repeats: 0
- Total number of images: ~29400
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
midjourney-v6-520k-raw
- Repeats: 0
- Total number of images: ~399320
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
nijijourney-v6-520k-raw
- Repeats: 0
- Total number of images: ~423456
- 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: ~7232
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
nsfw-1024
- Repeats: 0
- Total number of images: ~14768
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
anatomy
- Repeats: 5
- Total number of images: ~16384
- 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: ~96144
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
yoga
- Repeats: 0
- Total number of images: ~3584
- 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: ~33088
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
text-1mp
- Repeats: 5
- Total number of images: ~21648
- 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-v0.7-stage1'
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")