README.md

---
license: other
base_model: stabilityai/stable-diffusion-3.5-large
tags:
- sd3
- sd3-diffusers
- text-to-image
- diffusers
- simpletuner
- lora
- template:sd-lora
- standard
inference: true
widget:
- text: unconditional (blank prompt)
  parameters:
    negative_prompt: ''''
  output:
    url: ./assets/image_0_0.png
- text: >-
    This image shows a y3o golden retriever happily standing on a sunlit
    Caribbean beach, its mouth slightly open, revealing a playful pink tongue.
    The dog's thick, lustrous golden coat, characteristic of the breed, shines
    warmly under the tropical sun, while its gentle, dark eyes reflect a joyful
    and friendly demeanor. Behind the retriever, crystal-clear turquoise waters
    gently lap against pristine white sand, and lush palm trees sway softly in
    the ocean breeze, creating an idyllic Caribbean setting that perfectly
    complements the dog's cheerful presence.
  parameters:
    negative_prompt: ''''
  output:
    url: ./assets/image_1_0.png
---

# subject-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:



```
This image shows a y3o golden retriever happily standing on a sunlit Caribbean beach, its mouth slightly open, revealing a playful pink tongue. The dog's thick, lustrous golden coat, characteristic of the breed, shines warmly under the tropical sun, while its gentle, dark eyes reflect a joyful and friendly demeanor. Behind the retriever, crystal-clear turquoise waters gently lap against pristine white sand, and lush palm trees sway softly in the ocean breeze, creating an idyllic Caribbean setting that perfectly complements the dog's cheerful presence.
```

## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.0`
- Steps: `30`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024`

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: 300
- Learning rate: 0.000505
- Max grad norm: 0.01
- Effective batch size: 12
  - Micro-batch size: 12
  - Gradient accumulation steps: 1
  - Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: No
- Xformers: Not used
- LoRA Rank: 768
- LoRA Alpha: 768.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### subject
- Repeats: 5
- Total number of images: 6
- 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 = 'stabilityai/stable-diffusion-3.5-large'
adapter_id = 'AngelZeur/subject-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "This image shows a y3o golden retriever happily standing on a sunlit Caribbean beach, its mouth slightly open, revealing a playful pink tongue. The dog's thick, lustrous golden coat, characteristic of the breed, shines warmly under the tropical sun, while its gentle, dark eyes reflect a joyful and friendly demeanor. Behind the retriever, crystal-clear turquoise waters gently lap against pristine white sand, and lush palm trees sway softly in the ocean breeze, creating an idyllic Caribbean setting that perfectly complements the dog's cheerful presence."
negative_prompt = ''
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    num_inference_steps=30,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=1024,
    height=1024,
    guidance_scale=7.5,
).images[0]
image.save("output.png", format="PNG")
```