A figurine of a character with green hair, wearing a white shirt, a black vest, and a gray cap, sitting with one hand on their knee and the other hand making a peace sign. The character is wearing a blue pendant and has a gold bracelet. In the background, there are green plants and a tree branch.

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: 0
Training steps: 158000
Learning rate: 5e-06
Effective batch size: 5
- Micro-batch size: 1
- Gradient accumulation steps: 1
- Number of GPUs: 5
Prediction type: flow-matching
Rescaled betas zero SNR: False
Optimizer: adamw_bf16
Precision: Pure BF16
Quantised: Yes: int8-quanto
Xformers: Not used
LyCORIS Config:

json

{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 1000000,
    "linear_alpha": 1,
    "factor": 2,
    "full_matrix": true,
    "apply_preset": {
        "name_algo_map": {
            "transformer_blocks.[0-7]*": {
                "algo": "lokr",
                "factor": 4,
                "linear_dim": 1000000,
                "linear_alpha": 1,
                "full_matrix": true
            },
            "transformer_blocks.[8-15]*": {
                "algo": "lokr",
                "factor": 5,
                "linear_dim": 1000000,
                "linear_alpha": 1,
                "full_matrix": true
            },
            "transformer_blocks.[16-18]*": {
                "algo": "lokr",
                "factor": 10,
                "linear_dim": 1000000,
                "linear_alpha": 1,
                "full_matrix": true
            },
            "single_transformer_blocks.[0-15]*": {
                "algo": "lokr",
                "factor": 8,
                "linear_dim": 1000000,
                "linear_alpha": 1,
                "full_matrix": true
            },
            "single_transformer_blocks.[16-23]*": {
                "algo": "lokr",
                "factor": 5,
                "linear_dim": 1000000,
                "linear_alpha": 1,
                "full_matrix": true
            },
            "single_transformer_blocks.[24-37]*": {
                "algo": "lokr",
                "factor": 4,
                "linear_dim": 1000000,
                "linear_alpha": 1,
                "use_scalar": true,
                "full_matrix": true
            }
        },
        "use_fnmatch": true
    }
}

Datasets

default_dataset_arb

Repeats: 0
Total number of images: ~39950
Total number of aspect buckets: 6
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest

default_dataset_arb2

Repeats: 0
Total number of images: ~40005
Total number of aspect buckets: 6
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest

default_dataset_arb3

Repeats: 0
Total number of images: ~64080
Total number of aspect buckets: 6
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest

default_dataset_arb4

Repeats: 0
Total number of images: ~70340
Total number of aspect buckets: 6
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest

default_dataset_arb5

Repeats: 0
Total number of images: ~31790
Total number of aspect buckets: 6
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest

default_dataset_arb6

Repeats: 0
Total number of images: ~22035
Total number of aspect buckets: 5
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest

default_dataset_arb7

Repeats: 0
Total number of images: ~39390
Total number of aspect buckets: 6
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: closest

default_dataset

Repeats: 0
Total number of images: ~121070
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square

default_dataset2

Repeats: 0
Total number of images: ~123100
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square

default_dataset3

Repeats: 0
Total number of images: ~64355
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square

default_dataset4

Repeats: 0
Total number of images: ~69970
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square

default_dataset5

Repeats: 0
Total number of images: ~32300
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square

default_dataset6

Repeats: 0
Total number of images: ~22190
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square

default_dataset7

Repeats: 0
Total number of images: ~39560
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square

Inference

python

import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights

model_id = 'black-forest-labs/FLUX.1-schnell'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()

prompt = "A figurine of a character with green hair, wearing a white shirt, a black vest, and a gray cap, sitting with one hand on their knee and the other hand making a peace sign. The character is wearing a blue pendant and has a gold bracelet. In the background, there are green plants and a tree branch."

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")

jimmycarter/flux-training

flux-training