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 Jujutsu Kaisen. Gojo Satoru holding a sign that says 'I LOVE
PROMPTS!', he is standing full body on a beach at sunset. He is wearing
his signature black blindfold and a sleek black outfit. The setting sun
casts a dynamic shadow on his face.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: >-
A scene from Jujutsu Kaisen. Gojo Satoru jumping out of a propeller
airplane, sky diving. He looks excited, his hair is blowing in the wind,
and his blindfold is still on. The sky is clear and blue, there are birds
pictured in the distance.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: >-
A scene from Jujutsu Kaisen. Gojo Satoru 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 smiling confidently.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: >-
A scene from Jujutsu Kaisen. Gojo Satoru is wearing a suit in an office
shaking the hand of a business woman. 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 accomplishment.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: >-
A scene from Jujutsu Kaisen. Gojo Satoru 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.
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
gojo-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: 56
- Micro-batch size: 56
- 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
gojo-512
- Repeats: 2
- Total number of images: 291
- 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/gojo-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")