File size: 3,801 Bytes
9014145 e3fd757 9014145 2d5ed98 9014145 575e0df 3bc6efd 9014145 3bc6efd 9014145 14e652a 9014145 |
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 |
---
license: creativeml-openrail-m
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
tags:
- sdxl
- sdxl-diffusers
- text-to-image
- image-to-image
- diffusers
- simpletuner
- not-for-all-audiences
- lora
- controlnet
- template:sd-lora
- standard
pipeline_tag: text-to-image
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'A photo-realistic image of a cat'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
- text: 'prompt not found (2)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_2_0.png
- text: 'prompt not found (3)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_3_0.png
---
# simpletuner-controlnet-sdxl-lora-test
This is a ControlNet PEFT LoHa derived from [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0).
The main validation prompt used during training was:
```
A photo-realistic image of a cat
```
## Validation settings
- CFG: `4.2`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `ddim`
- Seed: `42`
- Resolution: `1024x1024`
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: 49
- Training steps: 400
- Learning rate: 0.0001
- Learning rate schedule: constant
- Warmup steps: 0
- Max grad value: 2.0
- Effective batch size: 3
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 3
- Gradient checkpointing: True
- Prediction type: epsilon (extra parameters=['training_scheduler_timestep_spacing=trailing', 'inference_scheduler_timestep_spacing=trailing'])
- Optimizer: bnb-lion8bit
- Trainable parameter precision: Pure BF16
- Base model precision: `no_change`
- Caption dropout probability: 0.1%
- LoRA Rank: 128
- LoRA Alpha: 128.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
## Datasets
### antelope-data
- Repeats: 0
- Total number of images: ~24
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
## Inference
```python
import torch
from diffusers import DiffusionPipeline
model_id = 'stabilityai/stable-diffusion-xl-base-1.0'
adapter_id = 'bghira/simpletuner-controlnet-sdxl-lora-test'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
prompt = "A photo-realistic image of a cat"
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.unet, weights=qint8)
#freeze(pipeline.unet)
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
model_output = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
width=1024,
height=1024,
guidance_scale=4.2,
guidance_rescale=0.0,
).images[0]
model_output.save("output.png", format="PNG")
```
|