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 a Raphael oil painting, Three figures in red and white
religious attire, with one seated at a table holding a magnifying glass,
and two standing figures behind. The table is covered with a red cloth and
holds an open book and a silver bell. Dark background and muted color
palette.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: >-
In the style of a Raphael oil painting, A knight on a white horse is
spearing a dragon lying on the ground. The knight wears armor and a blue
cape, while a woman in a red dress stands in the background beside a rock
formation. The setting includes tall trees and a distant cityscape.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: >-
In the style of a Raphael oil painting, A bearded man wearing a black robe
and cap sits at a table holding papers in one hand. An apple rests on the
table alongside a book with a ring visible on his finger. The background
is plain and neutral.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: >-
In the style of a Raphael oil painting, A seated figure in a blue robe and
red dress holds a book, surrounded by two young, unclothed children in a
natural setting with trees and mountains in the background. One child
holds a bird while the other reaches out towards it. The setting includes
a rock and a grassy landscape.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: >-
In the style of a Raphael oil painting, A scholar-alchemist in flowing
robes stands amid glass vessels and astronomical instruments, while light
streams through a Gothic window. A mechanical armillary sphere sits
prominently on a wooden table, while an assistant in the background tends
to a burning crucible.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
- text: >-
In the style of a Raphael oil painting, Neptune rises from turbulent
waters on the steps of Venice's St. Mark's Basilica, offering a golden
ring to a figure representing the Maritime Republic. Merchants in
Renaissance dress observe from gondolas, while angels hold scrolls of
maritime law above.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_6_0.png
- text: >-
In the style of a Raphael oil painting, Aristotle and Plato walk through a
Renaissance medicinal garden, discussing a dissected flower. Young
apprentices sketch botanical specimens nearby, while in the background,
monks tend to rows of healing herbs beneath a pergola covered in grape
vines.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_7_0.png
- text: >-
In the style of a Raphael oil painting, Angels and scholars share a vast
library with soaring Renaissance architecture, where celestial maps float
in mid-air. Some angels point to globes showing undiscovered continents,
while others transcribe from books bound in supernatural light. A
telescope made of gold and ivory points through an open dome to the stars.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_8_0.png
Flux-Raphael-LoRA-NoCrops-EMA-Fast
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:
1024x1280
- 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: 16
- Training steps: 4000
- Learning rate: 0.001
- Learning rate schedule: polynomial
- Warmup steps: 100
- Max grad norm: 2.0
- Effective batch size: 4
- Micro-batch size: 2
- Gradient accumulation steps: 2
- 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
raphael-512
- Repeats: 11
- Total number of images: 28
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
raphael-768
- Repeats: 11
- Total number of images: 28
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
raphael-1024
- Repeats: 5
- Total number of images: 28
- Total number of aspect buckets: 11
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- 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/Flux-Raphael-LoRA-NoCrops-EMA-Fast'
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=1280,
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.