🚀 stylloha
This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev, which can be used for text - to - image and image - to - image tasks.
Model Information
Property |
Details |
Model Type |
LyCORIS adapter |
Base Model |
black-forest-labs/FLUX.1-dev |
Tags |
flux, flux-diffusers, text-to-image, image-to-image, diffusers, simpletuner, not-for-all-audiences, lora, template:sd-lora, lycoris |
Pipeline Tag |
text-to-image |
Inference |
true |
Validation Images
You can find some example images in the following gallery:
The main validation prompt used during training was:
A photo-realistic image of a cat
🚀 Quick Start
Validation settings
- CFG:
3.0
- CFG Rescale:
0.0
- Steps:
20
- Sampler:
FlowMatchEulerDiscreteScheduler
- Seed:
42
- Resolution:
1024x1024
- Skip-layer guidance:
Note: The validation settings are not necessarily the same as the training settings.
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 3
- Training steps: 6000
- Learning rate: 0.0001
- Learning rate schedule: polynomial
- Warmup steps: 100
- Max grad value: 2.0
- Effective batch size: 3
- Micro-batch size: 3
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Gradient checkpointing: True
- Prediction type: flow_matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Base model precision:
no_change
- Caption dropout probability: 0.05%
LyCORIS Config:
{
"algo": "loha",
"multiplier": 1.0,
"linear_dim": 32,
"linear_alpha": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
Datasets
styl-256
- Repeats: 10
- Total number of images: 43
- Total number of aspect buckets: 2
- Resolution: 0.065536 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
styl-crop-256
- Repeats: 10
- Total number of images: 43
- Total number of aspect buckets: 1
- Resolution: 0.065536 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
styl-512
- Repeats: 10
- Total number of images: 43
- Total number of aspect buckets: 2
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
styl-crop-512
- Repeats: 10
- Total number of images: 43
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
styl-768
- Repeats: 10
- Total number of images: 43
- Total number of aspect buckets: 2
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
styl-crop-768
- Repeats: 10
- Total number of images: 43
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
- Used for regularisation data: No
styl-1024
- Repeats: 10
- Total number of images: 16
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
💻 Usage Examples
Basic Usage
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
def download_adapter(repo_id: str):
import os
from huggingface_hub import hf_hub_download
adapter_filename = "pytorch_lora_weights.safetensors"
cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
os.makedirs(path_to_adapter, exist_ok=True)
hf_hub_download(
repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
)
return path_to_adapter_file
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_repo_id = 'quzo/stylloha'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()
prompt = "A photo-realistic image of a cat"
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
model_output = pipeline(
prompt=prompt,
num_inference_steps=20,
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=3.0,
).images[0]
model_output.save("output.png", format="PNG")
Advanced Usage
The above code already contains some optional operations for advanced usage, such as model quantization to save VRAM. You can uncomment the relevant code to use these advanced features.
📄 License
This project is under the [other] license.