File size: 3,354 Bytes
b7843ec 623134d b7843ec a05352f b7843ec |
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 |
---
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-vpred-zsnr
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:
```
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: `7.5`
- CFG Rescale: `0.7`
- Steps: `25`
- Sampler: `None`
- Seed: `42`
- Resolutions: `1024x1024,1344x768,916x1152`
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: 0
- Training steps: 4000
- 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
### ideogram
- Repeats: 15
- Total number of images: ~36096
- 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: ~390912
- 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: ~233664
- 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: ~415680
- 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: ~1121664
- Total number of aspect buckets: 1
- Resolution: 1.0 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = 'pixart-900m-1024-vpred-zsnr'
pipeline = DiffusionPipeline.from_pretrained(model_id)
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.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=7.5,
guidance_rescale=0.7,
).images[0]
image.save("output.png", format="PNG")
```
|