Datasets:
License:
File size: 3,441 Bytes
0aca6cb |
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 |
---
license: other
base_model: "stabilityai/stable-diffusion-3.5-medium"
tags:
- sd3
- sd3-diffusers
- text-to-image
- diffusers
- simpletuner
- not-for-all-audiences
- lora
- template:sd-lora
- standard
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'Baky tuxedo cat, resting on top of a table, looking to the camera, paws tucked'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
---
# sd35-bucky_v7
This is a standard PEFT LoRA derived from [stabilityai/stable-diffusion-3.5-medium](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium).
The main validation prompt used during training was:
```
Baky tuxedo cat, resting on top of a table, looking to the camera, paws tucked
```
## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.0`
- Steps: `10`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `4201`
- Resolution: `1024`
- Skip-layer guidance:
Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
You can find some example images in the following gallery:
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 5
- Training steps: 640
- Learning rate: 0.0002
- Learning rate schedule: polynomial
- Warmup steps: 64
- Max grad norm: 2.0
- Effective batch size: 2
- Micro-batch size: 2
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow-matching (extra parameters=['shift=3'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 0.0%
- LoRA Rank: 768
- LoRA Alpha: 768.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
## Datasets
### bucky-annotated-2
- Repeats: 1
- Total number of images: 120
- Total number of aspect buckets: 2
- Resolution: 1.0 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = 'stabilityai/stable-diffusion-3.5-medium'
adapter_id = 'jwnt4/sd35-bucky_v7'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "Baky tuxedo cat, resting on top of a table, looking to the camera, paws tucked"
negative_prompt = 'blurry, cropped, ugly'
## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time.
#from optimum.quanto import quantize, freeze, qint8
#quantize(pipeline.transformer, weights=qint8)
#freeze(pipeline.transformer)
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=10,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(4201),
width=1024,
height=1024,
guidance_scale=7.5,
).images[0]
image.save("output.png", format="PNG")
```
|