metadata

license: creativeml-openrail-m
base_model: black-forest-labs/FLUX.1-dev
tags:
  - stable-diffusion
  - stable-diffusion-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - lora
  - template:sd-lora
inference: true
widget:
  - text: unconditional (blank prompt)
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_0_0.png
  - text: a cute pokemon in a city scene
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png

flux-training-pocket-creature-nocrop

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

The main validation prompt used during training was:

a cute pokemon in a city scene

Validation settings

CFG: 3.5
CFG Rescale: 0.0
Steps: 15
Sampler: None
Seed: 42
Resolution: 512

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
a cute pokemon in a city scene

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: 17
Training steps: 2600
Learning rate: 0.0001
Effective batch size: 6
- Micro-batch size: 6
- Gradient accumulation steps: 1
- Number of GPUs: 1
Prediction type: flow-matching
Rescaled betas zero SNR: False
Optimizer: AdamW, stochastic bf16
Precision: Pure BF16
Xformers: Enabled
LoRA Rank: 32
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

flux-pocket-creature-nocrop

Repeats: 0
Total number of images: 900
Total number of aspect buckets: 1
Resolution: 512 px
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 = 'toxicwind/flux-training-pocket-creature-nocrop'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "a cute pokemon in a city scene"


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=15,
    generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
    width=512,
    height=512,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")