README.md

metadata

tags:
  - text-to-image
  - lora
  - diffusers
  - template:diffusion-lora
widget:
  - output:
      url: images/ComfyUI_04526_.png
    text: >-
      A blue Cleanbot wearing an orange backpack and vacuuming the kitchen
      floor. The Cleanbot has happy eyes. The background shows a modern kitchen
      with stainless steel stovetop and white cabinets.
  - output:
      url: images/ComfyUI_04542_.png
    text: >-
      A blue Cleanbot with happy eyes looking at a neverending line of houses in
      the suburbs that stretches off to the horizon.
  - output:
      url: images/ComfyUI_04564_.png
    text: >-
      A purple Cleanbot standing in front of an ornate palace, holding a broom
      and ready to clean the intricate architecture.
  - output:
      url: images/ComfyUI_04579_.png
    text: >-
      A sepia photograph of an old-fashioned cast iron Cleanbot with steampunk
      fittings and a wooden backpack, holding a straw broom, standing outside of
      a prairie homestead. 
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Cleanbot
license: cc-by-nc-sa-4.0

Cleanbot

Prompt

A blue Cleanbot wearing an orange backpack and vacuuming the kitchen floor. The Cleanbot has happy eyes. The background shows a modern kitchen with stainless steel stovetop and white cabinets.

Prompt

A blue Cleanbot with happy eyes looking at a neverending line of houses in the suburbs that stretches off to the horizon.

Prompt

A purple Cleanbot standing in front of an ornate palace, holding a broom and ready to clean the intricate architecture.

Prompt

A sepia photograph of an old-fashioned cast iron Cleanbot with steampunk fittings and a wooden backpack, holding a straw broom, standing outside of a prairie homestead.

Trigger words

You should use Cleanbot to trigger the image generation.

You can use happy eyes for black and white eyes, or glowing [$COLOR] eyes for glowing eyes with an optional color (defaults to blue).

Download model

Download them in the Files & versions tab.

Training parameters

export PARAMS_D_COEF=1.5
export PARAMS_LEARNING_RATE=1.0
export PARAMS_LEARNING_SCHEDULER=constant_with_warmup
export PARAMS_MAX_STEPS=1500
export PARAMS_NETWORK_ALPHA=16
export PARAMS_NETWORK_DIM=8
export PARAMS_NOISE_OFFSET=0.1
export PARAMS_NUM_CYCLES=1
export PARAMS_OPTIMIZER_ARGS='decouple=True weight_decay=0.1 betas=0.9,0.999 use_bias_correction=False safeguard_warmup=False'
export PARAMS_OPTIMIZER_TYPE=prodigy
export PARAMS_SAVE_EVERY_N_STEPS=100
export PARAMS_SAMPLE_EVERY_N_STEPS=5000
export PARAMS_SPLIT_QKV=False
export PARAMS_TIMESTAMP_SAMPLING=shift
export PARAMS_WEIGHT_DECAY=0.1

{
        "datasets": [{
                "resolution": 1024,
                "enable_bucket": true,
                "min_bucket_reso": 512,
                "max_bucket_reso": 1024,
                "bucket_reso_steps": 128,
                "bucket_no_upscale": true,
                "batch_size": 3,
                "subsets": [{
                        "image_dir": "/mnt/training/datasets/friendly-robot-v01/refluxed",
                        "caption_extension": ".txt",
                        "num_repeats": 10
                }]
        }]
}

Training dataset

1. Generated a detailed description of the Cleanbot concept
2. Generated 8 images showing Cleanbots doing various tasks
3. Enhanced images using reflux process
4. Trained with 10 repeats