---
license: other
base_model: "stabilityai/stable-diffusion-3.5-large"
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: 'k4s4, linechart with 1 lines. Line 1: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate. Overall Description: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
- text: 'k4s4, linechart with 3 lines. Line 1: ylabel increases at a constant rate Line 2: ylabel increases at a roughly constant rate Line 3: ylabel increases at a roughly constant rate Overall Description: Lines 2 and 3 share an intersection point at xlabel 1.75 and ylabel 0.75'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_2_0.png
- text: 'k4s4, linechart with 4 lines. Line 1: ylabel decreases roughly at a constant rate and then abruptly drops and decreases at a decreasing rate Line 2: ylabel decreases roughly at a constant rate and then abruptly drops and decreases at a decreasing rate and lastly plateaus to 0 Line 3: ylabel decreases roughly at a constant rate and then abruptly drops and decreases at a decreasing rate and lastly plateaus to 0 Line 4: ylabel first plateaus at 100, then decreases at a decreasing rate Overall Description: All lines converge towards a value of 0 on ylabel'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_3_0.png
- text: 'k4s4, linechart with 1 lines. Line 1: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate. Overall Description: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_4_0.png
---

# simpletuner-lora

This is a standard PEFT LoRA derived from [stabilityai/stable-diffusion-3.5-large](https://huggingface.co/stabilityai/stable-diffusion-3.5-large).


The main validation prompt used during training was:
```
k4s4, linechart with 1 lines. Line 1: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate. Overall Description: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate.
```


## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.0`
- Steps: `28`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `1024x1024`
- 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: 36
- Training steps: 6500
- Learning rate: 0.0001
  - Learning rate schedule: polynomial
  - Warmup steps: 400
- Max grad norm: 2.0
- Effective batch size: 16
  - Micro-batch size: 16
  - 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: 10.0%


- LoRA Rank: 768
- LoRA Alpha: 768.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### linechart
- Repeats: 0
- Total number of images: 2822
- 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-3.5-large'
adapter_id = 'aryamankeyora/simpletuner-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "k4s4, linechart with 1 lines. Line 1: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate. Overall Description: Line is noisy and trends decreasing at a decreasing rate then decreasing at a constant rate."
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=28,
    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=7.5,
).images[0]
image.save("output.png", format="PNG")
```