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: ''''
    output:
      url: ./assets/image_0_0.png
  - text: >-
      african waitress holding beers in both hands, looking straight into
      camera, wearing black sandals, in a bar , dressed in traditional Bavarian
      or Alpine attire, which consists of a white lace blouse under a dark green
      dirndl dress. The dress is cinched at the waist with a belt that has an
      ornate metal clasp, half sleeves dress
    parameters:
      negative_prompt: ''''
    output:
      url: ./assets/image_1_0.png

dirndlletz_v7

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

The main validation prompt used during training was:

african waitress holding beers in both hands, looking straight into camera, wearing black sandals, in a bar , dressed in traditional Bavarian or Alpine attire, which consists of a white lace blouse under a dark green dirndl dress. The dress is cinched at the waist with a belt that has an ornate metal clasp, half sleeves dress

Validation settings

CFG: 3.5
CFG Rescale: 0.0
Steps: 15
Sampler: None
Seed: 42
Resolution: 1024

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
'

Prompt
african waitress holding beers in both hands, looking straight into camera, wearing black sandals, in a bar , dressed in traditional Bavarian or Alpine attire, which consists of a white lace blouse under a dark green dirndl dress. The dress is cinched at the waist with a belt that has an ornate metal clasp, half sleeves dress

Negative Prompt
'

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

Training settings

Training epochs: 999
Training steps: 1000
Learning rate: 0.0001
Effective batch size: 4
- Micro-batch size: 4
- Gradient accumulation steps: 1
- Number of GPUs: 1
Prediction type: flow-matching
Rescaled betas zero SNR: False
Optimizer: AdamW, stochastic bf16
Precision: Pure BF16
Quantised: No
Xformers: Not used
LoRA Rank: 32
LoRA Alpha: None
LoRA Dropout: 0.1
LoRA initialisation style: default

Datasets

dirndlletz_v7

Repeats: 0
Total number of images: 4
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: center
Crop aspect: square

Inference

import torch
from diffusers import DiffusionPipeline

model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'histin116/dirndlletz_v7'
pipeline = DiffusionPipeline.from_pretrained(model_id)
pipeline.load_lora_weights(adapter_id)

prompt = "african waitress holding beers in both hands, looking straight into camera, wearing black sandals, in a bar , dressed in traditional Bavarian or Alpine attire, which consists of a white lace blouse under a dark green dirndl dress. The dress is cinched at the waist with a belt that has an ornate metal clasp, half sleeves dress"


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=1024,
    height=1024,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")