Mary-Cassatt-Oil-Flux-LoKr-Messier-Phase1-EMA-SS1_5-Log-SNR-Blocks

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

No validation prompt was used during training.

None

Validation settings

CFG: 3.0
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolution: 968x1280
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
In the style of a c4ss4tt oil painting, A child wearing an elaborate blue silk dress with ruffles and white lace trim sits near a window, the fabric catching soft light.

Negative Prompt
blurry, cropped, ugly

Prompt
In the style of a c4ss4tt oil painting, A close portrait of a young child's face with rosy cheeks and delicate features, gentle lighting from a nearby window.

Negative Prompt
blurry, cropped, ugly

Prompt
In the style of a c4ss4tt oil painting, Strong window light falls across a child's face and shoulder, creating bold shadows on their blue dress.

Negative Prompt
blurry, cropped, ugly

Prompt
In the style of a c4ss4tt oil painting, A child in a blue hat stands by a window.

Negative Prompt
blurry, cropped, ugly

Prompt
In the style of a c4ss4tt oil painting, A woman in soft colors holds her baby close.

Negative Prompt
blurry, cropped, ugly

Prompt
In the style of a c4ss4tt oil painting, A woman in a detailed white lace dress reads while seated by a window with gauzy curtains, various textures visible in the furnishings.

Negative Prompt
blurry, cropped, ugly

Prompt
In the style of a c4ss4tt oil painting, A mother in a textured knit sweater checks her phone while her baby sleeps against her shoulder.

Negative Prompt
blurry, cropped, ugly

Prompt
In the style of a c4ss4tt oil painting, A mother cat grooms her kitten by a sunlit window, their fur catching the light.

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: 600
Learning rate: 0.0004
- Learning rate schedule: polynomial
- Warmup steps: 150
Max grad norm: 0.1
Effective batch size: 2
- Micro-batch size: 2
- Gradient accumulation steps: 1
- Number of GPUs: 1
Gradient checkpointing: True
Prediction type: flow-matching (extra parameters=['shift=1.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible'])
Optimizer: adamw_bf16
Trainable parameter precision: Pure BF16
Caption dropout probability: 10.0%

LyCORIS Config:

{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 16,
    "apply_preset": {
        "name_algo_map": {
            "transformer_blocks.0.attn": {
                "factor": 16
            },
            "transformer_blocks.1.attn": {
                "factor": 16
            },
            "transformer_blocks.2.attn": {
                "factor": 16
            },
            "single_transformer_blocks.0.attn": {
                "factor": 16
            },
            "single_transformer_blocks.4.attn": {
                "factor": 16
            },
            "single_transformer_blocks.12.attn": {
                "factor": 16
            },
            "single_transformer_blocks.4.proj_out": {
                "factor": 8
            },
            "single_transformer_blocks.12.proj_out": {
                "factor": 8
            },
            "context_embedder": {
                "factor": 8
            },
            "time_text_embed": {
                "factor": 8
            },
            "proj_out": {
                "factor": 8
            }
        }
    }
}

Datasets

cassatt-512

Repeats: 22
Total number of images: 50
Total number of aspect buckets: 2
Resolution: 0.262144 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

cassatt-768

Repeats: 22
Total number of images: 50
Total number of aspect buckets: 2
Resolution: 0.589824 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

cassatt-1024

Repeats: 10
Total number of images: 50
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

cassatt-1536

Repeats: 10
Total number of images: 49
Total number of aspect buckets: 13
Resolution: 2.359296 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

cassatt-crops-512

Repeats: 11
Total number of images: 25
Total number of aspect buckets: 10
Resolution: 0.262144 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

cassatt-crops-768

Repeats: 11
Total number of images: 25
Total number of aspect buckets: 1
Resolution: 0.589824 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

cassatt-crops-1024

Repeats: 5
Total number of images: 25
Total number of aspect buckets: 17
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

cassatt-crops-1536

Repeats: 5
Total number of images: 24
Total number of aspect buckets: 17
Resolution: 2.359296 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

Inference

import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights


def download_adapter(repo_id: str):
    import os
    from huggingface_hub import hf_hub_download
    adapter_filename = "pytorch_lora_weights.safetensors"
    cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models'))
    cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_")
    path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path)
    path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename)
    os.makedirs(path_to_adapter, exist_ok=True)
    hf_hub_download(
        repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter
    )

    return path_to_adapter_file
    
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_repo_id = 'davidrd123/Mary-Cassatt-Oil-Flux-LoKr-Messier-Phase1-EMA-SS1_5-Log-SNR-Blocks'
adapter_filename = 'pytorch_lora_weights.safetensors'
adapter_file_path = download_adapter(repo_id=adapter_repo_id)
pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer)
wrapper.merge_to()

prompt = "An astronaut is riding a horse through the jungles of Thailand."


## 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=968,
    height=1280,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")

Exponential Moving Average (EMA)

SimpleTuner generates a safetensors variant of the EMA weights and a pt file.

The safetensors file is intended to be used for inference, and the pt file is for continuing finetuning.

The EMA model may provide a more well-rounded result, but typically will feel undertrained compared to the full model as it is a running decayed average of the model weights.

davidrd123
/

Mary-Cassatt-Oil-Flux-LoKr-Messier-Phase1-EMA-SS1_5-Log-SNR-Blocks