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
- standard
inference: true
widget:
- text: unconditional (blank prompt)
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_0_0.png
- text: >-
A scene from My Hero Academia. Katsuki Bakugo holding a sign that says 'I
LOVE PROMPTS!', he is standing full body on a beach at sunset. He is
wearing his black and orange hero costume with grenade-like gauntlets on
his arms. The setting sun casts a dynamic shadow on his determined
expression.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: >-
A scene from My Hero Academia. Katsuki Bakugo jumping out of a propeller
airplane, sky diving. He looks intense and exhilarated, his spiky blonde
hair blowing in the wind. The sky is clear and blue, with birds flying in
the distance.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: >-
A scene from My Hero Academia. Katsuki Bakugo spinning a basketball on his
finger on a basketball court. He is wearing a Lakers jersey with the #12
on it. The basketball hoop and crowd are in the background cheering him.
He is smirking confidently.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: >-
A scene from My Hero Academia. Katsuki Bakugo is wearing a suit in an
office shaking the hand of a businesswoman. The woman has purple hair and
is wearing professional attire. There is a Google logo in the background.
It is during daytime, and the overall sentiment is one of fiery
determination and success.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: >-
A scene from My Hero Academia. Katsuki Bakugo is fighting a large brown
grizzly bear, deep in a forest. The bear is tall and standing on two legs,
roaring. The bear is also wearing a crown because it is the king of all
bears. Around them are tall trees and other animals watching as Bakugo
prepares to unleash an explosion.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
bakugo-standard-lora-1
This is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev.
No validation prompt was used during training.
None
Validation settings
- CFG:
3.5
- 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: 166
Training steps: 3000
Learning rate: 0.0001
- Learning rate schedule: constant
- Warmup steps: 100
Max grad norm: 2.0
Effective batch size: 48
- Micro-batch size: 48
- 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', 'flux_lora_target=all'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Caption dropout probability: 0.0%
LoRA Rank: 128
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default
Datasets
bakugo-512
- Repeats: 2
- Total number of images: 279
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'adipanda/bakugo-standard-lora-1'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)
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=3.5,
).images[0]
image.save("output.png", format="PNG")