Text-to-Image
Diffusers
flux
flux-diffusers
simpletuner
Not-For-All-Audiences
lora
template:sd-lora
standard
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
- standard
inference: true
widget:
- text: unconditional (blank prompt)
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_0_0.png
- text: >-
a breathtaking anime-style portrait of Philippe, capturing his essence
with vibrant colors and expressive features
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_1_0.png
- text: >-
a high-quality, detailed photograph of Philippe as a sous-chef, immersed
in the art of culinary creation
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_2_0.png
- text: >-
a lifelike and intimate portrait of Philippe, showcasing his unique
personality and charm
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_3_0.png
- text: >-
a cinematic, visually stunning photo of Philippe, emphasizing his dramatic
and captivating presence
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_4_0.png
- text: >-
an elegant and timeless portrait of Philippe, exuding grace and
sophistication
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_5_0.png
- text: >-
a dynamic and adventurous photo of Philippe, captured in an exciting,
action-filled moment
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_6_0.png
- text: >-
a mysterious and enigmatic portrait of Philippe, shrouded in shadows and
intrigue
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_7_0.png
- text: >-
a vintage-style portrait of Philippe, evoking the charm and nostalgia of a
bygone era
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_8_0.png
- text: >-
an artistic and abstract representation of Philippe, blending creativity
with visual storytelling
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_9_0.png
- text: >-
a futuristic and cutting-edge portrayal of Philippe, set against a
backdrop of advanced technology
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_10_0.png
- text: A picture of Philippe
parameters:
negative_prompt: blurry, cropped, ugly
output:
url: ./assets/image_11_0.png
simpletuner-lora
This is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev.
The main validation prompt used during training was:
A picture of Philippe
Validation settings
- CFG:
3.0
- CFG Rescale:
0.0
- Steps:
20
- Sampler:
None
- Seed:
42
- Resolution:
1024x1024
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: 636
- Training steps: 7000
- Learning rate: 0.0001
- Effective batch size: 1
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: No
- Xformers: Not used
- LoRA Rank: 16
- LoRA Alpha: None
- LoRA Dropout: 0.1
- LoRA initialisation style: default
Datasets
dreambooth-subject
- Repeats: 0
- Total number of images: 11
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
Inference
import torch
from diffusers import DiffusionPipeline
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'PhilSad/simpletuner-lora'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)
prompt = "A picture of Philippe"
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=20,
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=3.0,
).images[0]
image.save("output.png", format="PNG")