README.md

---
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, [speech-bubble-2] [people-2] [panel-1] [background-undefined] [camera-medium-shot] The scene depicts two characters in a heated exchange, with one character appearing visibly distressed or angry. They are engaged in a conversation, as indicated by the speech bubbles. The background is not clearly defined, suggesting an interior space, possibly a room with limited visibility of details. The shot captures both characters from a medium distance, emphasizing their expressions and the intensity of the moment.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
---

# hwasan-yc-tag-1024-lora-500

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, [speech-bubble-2] [people-2] [panel-1] [background-undefined] [camera-medium-shot] The scene depicts two characters in a heated exchange, with one character appearing visibly distressed or angry. They are engaged in a conversation, as indicated by the speech bubbles. The background is not clearly defined, suggesting an interior space, possibly a room with limited visibility of details. The shot captures both characters from a medium distance, emphasizing their expressions and the intensity of the moment.
```


## Validation settings
- CFG: `7.5`
- CFG Rescale: `0.0`
- Steps: `30`
- Sampler: `FlowMatchEulerDiscreteScheduler`
- Seed: `42`
- 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: 8
- Training steps: 800
- Learning rate: 0.0001
  - Learning rate schedule: cosine
  - Warmup steps: 2400
- Max grad norm: 2.0
- Effective batch size: 6
  - Micro-batch size: 6
  - 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: 500
- LoRA Alpha: 500.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
    

## Datasets

### webtoon-storyboard
- Repeats: 2
- Total number of images: 191
- 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 = 'gunchoi/hwasan-yc-tag-1024-lora-500'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "k4s4, [speech-bubble-2] [people-2] [panel-1] [background-undefined] [camera-medium-shot] The scene depicts two characters in a heated exchange, with one character appearing visibly distressed or angry. They are engaged in a conversation, as indicated by the speech bubbles. The background is not clearly defined, suggesting an interior space, possibly a room with limited visibility of details. The shot captures both characters from a medium distance, emphasizing their expressions and the intensity of the moment."
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=30,
    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")
```