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---
license: other
base_model: "stabilityai/stable-diffusion-3.5-medium"
tags:
- sd3
- sd3-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: 'A photo-realistic image of a cat'
parameters:
negative_prompt: 'ugly, cropped, blurry, low-quality, mediocre average'
output:
url: ./assets/image_0_0.png
---
# sd3-controlnet-lora-test
This is a ControlNet PEFT LoRA derived from [stabilityai/stable-diffusion-3.5-medium](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium).
The main validation prompt used during training was:
```
A photo-realistic image of a cat
```
## Validation settings
- CFG: `4.0`
- CFG Rescale: `0.0`
- Steps: `16`
- 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: 17
- Training steps: 500
- Learning rate: 0.0001
- Learning rate schedule: constant
- Warmup steps: 500
- Max grad value: 2.0
- Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow_matching (extra parameters=['shift=3.0'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Base model precision: `int8-quanto`
- Caption dropout probability: 0.0%
- LoRA Rank: 64
- LoRA Alpha: 64.0
- LoRA Dropout: 0.1
- LoRA initialisation style: default
## Datasets
### antelope-data-256
- Repeats: 0
- Total number of images: 29
- Total number of aspect buckets: 1
- Resolution: 0.065536 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-medium'
adapter_id = 'bghira/sd3-controlnet-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 = 'ugly, cropped, blurry, low-quality, mediocre average'
## 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
model_output = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=16,
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=4.0,
).images[0]
model_output.save("output.png", format="PNG")
```
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