metadata
license: other
base_model: black-forest-labs/FLUX.1-dev
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- lora
- template:sd-lora
- lycoris
inference: true
widget:
- text: unconditional (blank prompt)
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_0_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A warrior princess
in flowing silver armor rides a white horse through falling snow, her long
cape billowing behind her. She holds a glowing crystal staff while three
ravens circle overhead near a stone archway.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A bearded wizard
in a star-patterned robe stands atop a rocky cliff, raising his hands
toward storm clouds while ships with golden sails battle waves below. Sea
creatures with gleaming scales leap from the turbulent waters.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A woman in an
emerald dress with intricate gold embroidery sits beneath a flowering
tree, offering a silver goblet to a deer. In the background, a castle with
twisted spires rises against a sunset sky.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A giant golden
hamster wearing burnished steel armor and a crimson velvet cape sits upon
an ornate throne carved from ancient oak and golden wheat. Mice in blue
and silver livery bow before him, presenting jeweled acorns on silk
cushions while court musicians play tiny silver trumpets.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A mysterious
merchant in an emerald robe and golden mask holds up a glowing Coca-Cola
bottle beneath a canopy of twisted oak branches. Forest creatures in
medieval dress gather around its ruby light, while silver-winged fairies
dance through moonbeams that filter through the leaves.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A Range Rover with
brass-and-silver clockwork wheels and gleaming armor plates crosses an
ancient stone bridge. Four mechanical horses with steam-breathing nostrils
and copper manes pull it through swirling silver mist, while a wizard in a
pinstripe suit raises a crystal staff from the driver's seat.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_6_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A sorcerer in
purple silk robes trimmed with gold stands atop a winding stone staircase,
conducting floating books with a feather quill that trails sparks. Beneath
gothic arches, apprentices in pointed hats ride enchanted carpets between
towering bookshelves of ancient tomes.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_7_0.png
- text: >-
In the style of Frank C. Pape fairy tale illustrations, A grand feast hall
with tapestry-hung walls where animal nobles in velvet and silk dine at a
table of polished oak. At its center, a towering crystal fountain flows
with sparkling Coca-Cola, while rabbit jesters in bells and motley juggle
glowing bottles beneath chandeliers.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_8_0.png
FrankPape-RussianStoryBook-Flux-LoKr-4e-4
This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev.
No validation prompt was used during training.
None
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.
You can find some example images in the following gallery:
The text encoder was not trained. You may reuse the base model text encoder for inference.
Training settings
- Training epochs: 27
- Training steps: 6000
- Learning rate: 1e-05
- Learning rate schedule: constant
- Warmup steps: 200
- Max grad norm: 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', 'flow_matching_loss=compatible'])
- Optimizer: adamw_bf16
- Trainable parameter precision: Pure BF16
- Caption dropout probability: 10.0%
LyCORIS Config:
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
Datasets
fws-512
- Repeats: 10
- Total number of images: 16
- Total number of aspect buckets: 2
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
fws-1024
- Repeats: 6
- Total number of images: 16
- Total number of aspect buckets: 2
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
fws-512-crop
- Repeats: 10
- Total number of images: 16
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
fws-1024-crop
- Repeats: 6
- Total number of images: 16
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
Inference
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 = 'davidrd123/FrankPape-RussianStoryBook-Flux-LoKr-4e-4'
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) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()
prompt = "An astronaut is riding a horse through the jungles of Thailand."
## 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,
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]
image.save("output.png", format="PNG")
Exponential Moving Average (EMA)
SimpleTuner generates a safetensors variant of the EMA weights and a pt file.
The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.
The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.