icon-generator / README.md
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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
  - standard
inference: true
widget:
  - text: unconditional (blank prompt)
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_0_0.png
  - text: Minimalist icon, alert circle
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png
  - text: Minimalist icon, mood smile
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_2_0.png
  - text: Minimalist icon, brand facebook
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_3_0.png
  - text: Minimalist icon, badge hd
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_4_0.png
  - text: Minimalist icon, coin off
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_5_0.png
  - text: Minimalist icon, arrow up
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_6_0.png

icon-generator

This is a standard PEFT LoRA derived from black-forest-labs/FLUX.1-dev.

The main validation prompt used during training was:

Minimalist icon, arrow up

Validation settings

  • CFG: 3.0
  • 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:

Prompt
unconditional (blank prompt)
Negative Prompt
blurry, cropped, ugly
Prompt
Minimalist icon, alert circle
Negative Prompt
blurry, cropped, ugly
Prompt
Minimalist icon, mood smile
Negative Prompt
blurry, cropped, ugly
Prompt
Minimalist icon, brand facebook
Negative Prompt
blurry, cropped, ugly
Prompt
Minimalist icon, badge hd
Negative Prompt
blurry, cropped, ugly
Prompt
Minimalist icon, coin off
Negative Prompt
blurry, cropped, ugly
Prompt
Minimalist icon, arrow up
Negative Prompt
blurry, cropped, ugly

The text encoder was not trained. You may reuse the base model text encoder for inference.

Training settings

  • Training epochs: 0

  • Training steps: 1000

  • Learning rate: 8e-05

    • Learning rate schedule: polynomial
    • Warmup steps: 100
  • Max grad norm: 1.0

  • Effective batch size: 1

    • Micro-batch size: 1
    • 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: 5.0%

  • LoRA Rank: 16

  • LoRA Alpha: None

  • LoRA Dropout: 0.1

  • LoRA initialisation style: default

Datasets

tabler-icons-1024

  • Repeats: 10
  • Total number of images: 4739
  • Total number of aspect buckets: 1
  • Resolution: 1.048576 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 = 'noahyoungs/icon-generator'
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
pipeline.load_lora_weights(adapter_id)

prompt = "Minimalist icon, arrow up"


## 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=3.0,
).images[0]
image.save("output.png", format="PNG")