digit_upscaled/checkpoint-2000/README.md

---
license: other
base_model: "sd3/unknown-model"
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: 'A simplistic, hand-drawn illustration of an elephant. the elephant is depicted in a walking pose, with its trunk raised slightly. the drawing is done in black ink on a white background. the elephant''s posture and the positioning of its legs suggest movement. the style is minimalistic, with clean lines and a lack of intricate details. the lighting appears to be coming from the top left, casting a shadow on the right side of the elephant.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
---

# simpletuner-lora

This is a standard PEFT LoRA derived from [sd3/unknown-model](https://huggingface.co/sd3/unknown-model).


The main validation prompt used during training was:
```
A simplistic, hand-drawn illustration of an elephant. the elephant is depicted in a walking pose, with its trunk raised slightly. the drawing is done in black ink on a white background. the elephant's posture and the positioning of its legs suggest movement. the style is minimalistic, with clean lines and a lack of intricate details. the lighting appears to be coming from the top left, casting a shadow on the right side of the elephant.
```


## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.0`
- Steps: `35`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- Resolution: `512x512`
- 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: 1
- Training steps: 2000
- Learning rate: 0.0001
  - Learning rate schedule: cosine
  - Warmup steps: 100
- Max grad norm: 2.0
- Effective batch size: 16
  - Micro-batch size: 4
  - Gradient accumulation steps: 4
  - 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: 128
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### pacs
- Repeats: 0
- Total number of images: 24000
- Total number of aspect buckets: 1
- 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 = '/ephemeral/shashmi/llava_lets_go/chimaa_finetuner/stable-diffusion-3.5-medium'
adapter_id = 'Sarim-Hash/simpletuner-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "A simplistic, hand-drawn illustration of an elephant. the elephant is depicted in a walking pose, with its trunk raised slightly. the drawing is done in black ink on a white background. the elephant's posture and the positioning of its legs suggest movement. the style is minimalistic, with clean lines and a lack of intricate details. the lighting appears to be coming from the top left, casting a shadow on the right side of the elephant."
negative_prompt = 'blurry, cropped, ugly'

## Optional: quantise the model to save on vram.
## Note: The model was quantised during training, and so it is recommended to do the same 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=35,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(42),
    width=512,
    height=512,
    guidance_scale=7.5,
).images[0]
image.save("output.png", format="PNG")
```