File size: 3,706 Bytes
1a3f484 87e2b17 1a3f484 87e2b17 1a3f484 87e2b17 1a3f484 87e2b17 1a3f484 f2d836f 92434e0 1a3f484 92434e0 1a3f484 92434e0 1a3f484 f2d836f 1a3f484 43c0221 92434e0 1a3f484 f2d836f 22b25e3 1a3f484 f2d836f 1a3f484 92434e0 1a3f484 92434e0 1a3f484 f2d836f 92434e0 1a3f484 87e2b17 1a3f484 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 |
---
license: creativeml-openrail-m
base_model: "terminusresearch/pixart-900m-1024-ft-v0.6"
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- simpletuner
- full
inference: true
---
# pixart-900m-1024-ft-v0.7-stage2
This is a full rank finetune derived from [terminusresearch/pixart-900m-1024-ft-v0.6](https://huggingface.co/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](#training-settings).
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 9
- Training steps: 17500
- 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
### celebrities
- Repeats: 4
- Total number of images: 208
- Total number of aspect buckets: 7
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### movieposters
- Repeats: 25
- Total number of images: 192
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### normalnudes
- Repeats: 5
- Total number of images: 992
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### moviecollection
- Repeats: 0
- Total number of images: 1728
- Total number of aspect buckets: 16
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### experimental
- Repeats: 0
- Total number of images: 2816
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### ethnic
- Repeats: 0
- Total number of images: 1808
- Total number of aspect buckets: 3
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
### gay
- Repeats: 0
- Total number of images: 768
- Total number of aspect buckets: 6
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
### 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
### nsfw-1024
- Repeats: 0
- Total number of images: 2224
- Total number of aspect buckets: 2
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: random
## Inference
```python
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")
```
|