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Mary-Cassatt-Oil-FullAndCrops-Phase-2-ResumeFast-NormalSettings-Flux-LoKr

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Not-For-All-Audiences
lora
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lycoris
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metadata
license: other
base_model: black-forest-labs/FLUX.1-dev
tags:
  - flux
  - flux-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - not-for-all-audiences
  - lora
  - template:sd-lora
  - lycoris
inference: true
widget:
  - text: unconditional (blank prompt)
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_0_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A woman in a purple patterned
      garment holds an unclothed baby who rests against her shoulder, their arm
      draped over it. The background is rendered in soft, muted green tones with
      visible oil strokes.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A child stands wearing a red
      velvet outfit with white lace cuffs and a large straw hat with a black
      ribbon band. Their light-colored hair shows beneath the hat, and their
      arms are crossed at the waist against a plain, muted background.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_2_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A woman with dark hair wears a
      blue dress with patterned details, sitting against a green background. A
      child with curly blonde hair in white leans close as they examine
      something in the woman's hands.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_3_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A woman in soft colors holds her
      baby close.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_4_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A child in a blue hat stands by a
      window.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_5_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, Two figures lean together, loosely
      rendered.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_6_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A mother in casual clothes checks
      her phone while her baby sleeps against her shoulder, the screen's glow
      creating soft highlights.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_7_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A woman shows her grandmother how
      to use a tablet, both faces illuminated by its light against a dark
      interior.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_8_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A bearded hipster holds his baby
      while building a chair.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_9_0.png
  - text: >-
      In the style of a c4ss4tt oil painting, A mother hamster grooms her baby
      hamster in a sunny window.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_10_0.png

Mary-Cassatt-Oil-FullAndCrops-Phase-2-ResumeFast-NormalSettings-Flux-LoKr

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: 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:

Prompt
unconditional (blank prompt)
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, A woman in a purple patterned garment holds an unclothed baby who rests against her shoulder, their arm draped over it. The background is rendered in soft, muted green tones with visible oil strokes.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, A child stands wearing a red velvet outfit with white lace cuffs and a large straw hat with a black ribbon band. Their light-colored hair shows beneath the hat, and their arms are crossed at the waist against a plain, muted background.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, A woman with dark hair wears a blue dress with patterned details, sitting against a green background. A child with curly blonde hair in white leans close as they examine something in the woman's hands.
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 child in a blue hat stands by a window.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, Two figures lean together, loosely rendered.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, A mother in casual clothes checks her phone while her baby sleeps against her shoulder, the screen's glow creating soft highlights.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, A woman shows her grandmother how to use a tablet, both faces illuminated by its light against a dark interior.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, A bearded hipster holds his baby while building a chair.
Negative Prompt
blurry, cropped, ugly
Prompt
In the style of a c4ss4tt oil painting, A mother hamster grooms her baby hamster in a sunny window.
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: 4
  • Training steps: 10000
  • Learning rate: 0.0001
  • Max grad norm: 0.1
  • Effective batch size: 3
    • Micro-batch size: 3
    • Gradient accumulation steps: 1
    • Number of GPUs: 1
  • Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_value=1.0'])
  • Rescaled betas zero SNR: False
  • Optimizer: adamw_bf16
  • Precision: Pure BF16
  • Quantised: Yes: int8-quanto
  • Xformers: Not used
  • LyCORIS Config:
{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 16,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 16
            },
            "FeedForward": {
                "factor": 8
            }
        }
    }
}

Datasets

cassatt-oil-512

  • Repeats: 22
  • Total number of images: 49
  • Total number of aspect buckets: 7
  • Resolution: 0.262144 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

cassatt-oil-768

  • Repeats: 22
  • Total number of images: 49
  • Total number of aspect buckets: 8
  • Resolution: 0.589824 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

cassatt-oil-1024

  • Repeats: 10
  • Total number of images: 49
  • Total number of aspect buckets: 10
  • Resolution: 1.048576 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

cassatt-oil-1536

  • Repeats: 4
  • Total number of images: 49
  • Total number of aspect buckets: 6
  • 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: 74
  • Total number of aspect buckets: 1
  • 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: 74
  • Total number of aspect buckets: 14
  • 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: 74
  • Total number of aspect buckets: 19
  • Resolution: 1.048576 megapixels
  • Cropped: False
  • Crop style: None
  • Crop aspect: None
  • Used for regularisation data: No

cassatt-crops-1536

  • Repeats: 2
  • Total number of images: 73
  • Total number of aspect buckets: 24
  • 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-FullAndCrops-Phase-2-ResumeFast-NormalSettings-Flux-LoKr'
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(1641421826),
    width=1024,
    height=1024,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")