README.md

metadata

license: other
base_model: black-forest-labs/FLUX.1-dev
tags:
  - flux
  - flux-diffusers
  - text-to-image
  - diffusers
  - simpletuner
  - safe-for-work
  - 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 Juan Gris cubist painting, a coffee pot and two cups sit
      on a café table. A folded newspaper and half-eaten croissant rest nearby.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_1_0.png
  - text: >-
      In the style of a Juan Gris cubist painting, a mandolin rests on a wooden
      stand next to sheet music. A glass of wine and pipe lie on the side table.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_2_0.png
  - text: >-
      In the style of a Juan Gris cubist painting, an ink bottle stands next to
      a fountain pen and open journal. Several sealed letters and a wax seal
      rest alongside them.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_3_0.png
  - text: >-
      In the style of a Juan Gris cubist painting, a brass telescope points
      skyward through a domed window. Star charts and astronomical instruments
      lie on a curved desk.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_4_0.png
  - text: >-
      In the style of a Juan Gris cubist painting, a vintage sewing machine sits
      centered on a wooden table. Spools of thread, scissors, and fabric
      patterns are scattered around it.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_5_0.png
  - text: >-
      In the style of a Juan Gris cubist painting, a hamster sits in its glass
      habitat next to its exercise wheel. A water bottle and food dish are
      visible inside.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_6_0.png
  - text: >-
      In the style of a Juan Gris cubist painting, a classic Coca-Cola bottle
      stands on a table next to an empty glass with ice cubes. Condensation
      beads on both surfaces.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_7_0.png
  - text: >-
      In the style of a Juan Gris cubist painting, a Range Rover SUV is parked
      in front of a modern garage. Its distinctive grille and headlights face
      forward.
    parameters:
      negative_prompt: blurry, cropped, ugly
    output:
      url: ./assets/image_8_0.png

JuanGris-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: FlowMatchEulerDiscreteScheduler
• Seed: 42
• Resolution: 896x1280
• 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 Juan Gris cubist painting, a coffee pot and two cups sit on a café table. A folded newspaper and half-eaten croissant rest nearby.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Juan Gris cubist painting, a mandolin rests on a wooden stand next to sheet music. A glass of wine and pipe lie on the side table.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Juan Gris cubist painting, an ink bottle stands next to a fountain pen and open journal. Several sealed letters and a wax seal rest alongside them.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Juan Gris cubist painting, a brass telescope points skyward through a domed window. Star charts and astronomical instruments lie on a curved desk.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Juan Gris cubist painting, a vintage sewing machine sits centered on a wooden table. Spools of thread, scissors, and fabric patterns are scattered around it.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Juan Gris cubist painting, a hamster sits in its glass habitat next to its exercise wheel. A water bottle and food dish are visible inside.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Juan Gris cubist painting, a classic Coca-Cola bottle stands on a table next to an empty glass with ice cubes. Condensation beads on both surfaces.

Negative Prompt

blurry, cropped, ugly

Prompt

In the style of a Juan Gris cubist painting, a Range Rover SUV is parked in front of a modern garage. Its distinctive grille and headlights face forward.

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: 22

• Training steps: 9500

• Learning rate: 0.0001

  • Learning rate schedule: constant
  • Warmup steps: 100

• Max grad norm: 0.1

• Effective batch size: 3

  • Micro-batch size: 3
  • Gradient accumulation steps: 1
  • Number of GPUs: 1

• Gradient checkpointing: True

• Prediction type: flow-matching (extra parameters=['flux_schedule_auto_shift', 'shift=0.0', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flux_beta_schedule_alpha=4.0', 'flux_beta_schedule_beta=2.0', 'flow_matching_loss=compatible'])

• Optimizer: adamw_bf16

• Trainable parameter precision: Pure BF16

• Caption dropout probability: 10.0%

• SageAttention: Enabled inference

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

juangris-512

• Repeats: 10
• Total number of images: 23
• Total number of aspect buckets: 7
• Resolution: 0.262144 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

juangris-1024

• Repeats: 10
• Total number of images: 23
• Total number of aspect buckets: 12
• Resolution: 1.048576 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

juangris-512-crop

• Repeats: 10
• Total number of images: 23
• Total number of aspect buckets: 1
• Resolution: 0.262144 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• Used for regularisation data: No

juangris-1024-crop

• Repeats: 10
• Total number of images: 23
• Total number of aspect buckets: 1
• Resolution: 1.048576 megapixels
• Cropped: True
• Crop style: random
• Crop aspect: square
• 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/JuanGris-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(42),
    width=896,
    height=1280,
    guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")