metadata
license: other
base_model: black-forest-labs/FLUX.1-dev
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- not-for-all-audiences
- 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 this scene from the animated series "Helluva Boss," Loona, the
wolf-like receptionist of the Immediate Murder Professionals (I.M.P), is
depicted leaning against a wall outside the office. She is casually
engrossed in her phone, displaying her typical aloof and detached
demeanor. Loona's appearance includes her usual whitish fur, light grey
hair, black-tipped ears, and red eyes, complemented by her punk-inspired
attire featuring a black choker with spikes, a dark grey top, fingerless
wrist-length black gloves, and black shorts.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: >-
Loona shrugs with an exasperated expression, her red eyes wide and
frustrated, as she seemingly questions or challenges something said in the
I.M.P office. Still from Helluva boss. Loona's appearance includes her
usual whitish fur, light grey hair, black-tipped ears, and red eyes,
complemented by her punk-inspired attire featuring a black choker with
spikes, a dark grey top, fingerless wrist-length black gloves, and black
shorts.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: >-
A scene from the animated series "Helluva Boss," set in the office. Loona,
the wolf-like receptionist with white fur, black-tipped ears, and red
eyes, is seated on a couch, facing towards the viewer. Loona's appearance
is complemented by her punk-inspired attire featuring a black choker with
spikes, a dark grey top, fingerless wrist-length black gloves, and black
shorts. She holds a piece of paper that says,"Welcome to Losercity,
jerks". In the background, the office has a striped wall pattern and
visible damage on the ceiling, indicating a chaotic or rough environment.
On the right side of the image, two imp characters appear to be engaged in
conversation.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: >-
Loona from Helluva Boss is dressed in an oversized taco costume, looking
visibly irritated and embarrassed. Her red eyes convey her annoyance as
she crosses her arms and glares to the side. Loona's appearance includes
her usual whitish fur, light grey hair, black-tipped ears, and red eyes
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: Loona is standing next to Blitzo (Helluva boss)
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
- text: >-
In this "Helluva Boss" scene, Loona, the wolf-like receptionist, stands in
an elevator with a tense and irritated expression, her teeth bared in a
snarl. Blitzø, the red demon with distinctive black and white horns, leans
close and makes an adorable look, as if asking for a favor. The ornate
elevator setting hints at a tense moment, possibly involving a challenging
mission or conflict within the I.M.P team.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_6_0.png
- text: >-
a 2D simple drawing of a madeleine cake, with a green cloud drawn next to
it
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_7_0.png
- text: >-
a 3D captivating YouTube thumbnail depicting of a full detailed,it's on a
party real people like, on front there is a giant pulling a nose of a
black African real like lady down to size of elephant nose,be creative and
unique
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_8_0.png
- text: >-
Whiskers the cat. Whiskers becomes a mentor to other animals.Impressed by
Whiskers' intelligence, other animals in the neighborhood seek his
guidance. Whiskers sets up a virtual learning platform using AI
technology, where animals can ask questions, receive personalized lessons,
and acquire knowledge in various subjects. Whiskers becomes a mentor,
helping others unlock their potential.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_9_0.png
- text: >-
As the stock market fluctuates, the investor remains calm and collected at
their desk, surrounded by charts and graphs. Their tailored suit and
polished briefcase are a symbol of their expertise and experience in the
world of finance.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_10_0.png
- text: loona from helluva boss is eating a donut
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_11_0.png
flux-training-losercity-next-lycoris8
This is a LyCORIS adapter derived from black-forest-labs/FLUX.1-dev.
The main validation prompt used during training was:
loona from helluva boss is eating a donut
Validation settings
- CFG:
3.5
- CFG Rescale:
0.0
- Steps:
15
- Sampler:
None
- Seed:
42
- Resolution:
1024
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: 0
- Training steps: 2700
- Learning rate: 4e-05
- Effective batch size: 16
- Micro-batch size: 1
- Gradient accumulation steps: 16
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: fp8-quanto
- Xformers: Not used
- LyCORIS Config:
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 1000000,
"linear_alpha": 1,
"factor": 10,
"full_matrix": true,
"apply_preset": {
"target_module": [
"FluxTransformerBlock",
"FluxSingleTransformerBlock"
],
"name_algo_map": {
"transformer_blocks.[0-7]*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.[8-15]*": {
"algo": "lokr",
"factor": 6,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"transformer_blocks.[16-18]*": {
"algo": "lokr",
"factor": 12,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"single_transformer_blocks.[0-15]*": {
"algo": "lokr",
"factor": 8,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"single_transformer_blocks.[16-23]*": {
"algo": "lokr",
"factor": 6,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
},
"single_transformer_blocks.[24-37]*": {
"algo": "lokr",
"factor": 4,
"linear_dim": 1000000,
"linear_alpha": 1,
"full_matrix": true
}
},
"use_fnmatch": true
}
}
Datasets
default_dataset_arb
- Repeats: 9999
- Total number of images: 41
- Total number of aspect buckets: 1
- Resolution: 1.33 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
default_dataset_arb2
- Repeats: 9999
- Total number of images: 2565
- Total number of aspect buckets: 1
- Resolution: 1.33 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
default_dataset_arb3
- Repeats: 9999
- Total number of images: 3220
- Total number of aspect buckets: 14
- Resolution: 1.33 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
default_dataset
- Repeats: 9999
- Total number of images: 42
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_512
- Repeats: 9999
- Total number of images: 42
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_640
- Repeats: 9999
- Total number of images: 42
- Total number of aspect buckets: 1
- Resolution: 0.4096 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_768
- Repeats: 9999
- Total number of images: 42
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_896
- Repeats: 9999
- Total number of images: 42
- Total number of aspect buckets: 1
- Resolution: 0.802816 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_uncaptioned
- Repeats: 9999
- Total number of images: 2565
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_uncaptioned_512
- Repeats: 9999
- Total number of images: 2565
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_art
- Repeats: 9999
- Total number of images: 2482
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_art_512
- Repeats: 9999
- Total number of images: 3193
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: center
- Crop aspect: square
default_dataset_art_640
- Repeats: 9999
- Total number of images: 3115
- Total number of aspect buckets: 1
- Resolution: 0.4096 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
default_dataset_art_768
- Repeats: 9999
- Total number of images: 2989
- Total number of aspect buckets: 1
- Resolution: 0.589824 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
default_dataset_art_896
- Repeats: 9999
- Total number of images: 2787
- Total number of aspect buckets: 1
- Resolution: 0.802816 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
Inference
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "loona from helluva boss is eating a donut"
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
num_inference_steps=15,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1024,
height=1024,
guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")