flux-test-q1sV25

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

The main validation prompt used during training was:

an oil painting of a q1s woman in a green dress standing on the moutain. an oil painting of, art.

Validation settings

CFG: 3.5
CFG Rescale: 0.0
Steps: 20
Sampler: FlowMatchEulerDiscreteScheduler
Seed: 42
Resolution: 1024x1024
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
a photo of a q1s woman in a suit standing on the streets of Tokyo

Negative Prompt
blurry, cropped, ugly

Prompt
a anime of a q1s woman in school uniform standing on a bridge

Negative Prompt
blurry, cropped, ugly

Prompt
a photo of a q1s woman in leather jacket on the beach

Negative Prompt
blurry, cropped, ugly

Prompt
A stunning portrait of a q1s woman adorned with a luxurious wide-brimmed red hat that elegantly covers part of her face, illustration, vibrant, sophisticated, portrait photography

Negative Prompt
blurry, cropped, ugly

Prompt
A grainy, sepia-toned photograph taken with a Kodak Portra 400 film camera in the late 1990s of a q1s woman with captivating gaze poses confidently. She wears a vintage, black MinMin T-shirt, slightly oversized and slightly worn, with the band's iconic logo prominently displayed. The background is blurred, suggesting a dimly lit, underground music venue or a friend's basement.

Negative Prompt
blurry, cropped, ugly

Prompt
A captivating minimalist masterpiece illustration of a q1s woman exuding elegance and tranquility. The beautiful subject is dressed in a linen dress, featuring pastel beige and light grey hues. The background consists of a harmonious composition of rectangles and squares, subtly outlined in black pencil. This vibrant and conceptual artwork elicits a cinematic and fashionable illustration.

Negative Prompt
blurry, cropped, ugly

Prompt
a digital painting of a q1s woman in a green hoodie and purple skirt standing in front of an office building

Negative Prompt
blurry, cropped, ugly

Prompt
A whimsical miniature 3D alcohol ink portrait of a q1s woman mermaid nestled in a giant seashell. She has a shimmering fish tail adorned with intricate scales in various shades of blue and turquoise. She wears a top made of tiny seashells and pearls. Surrounding her are small, bioluminescent jellyfish, adding a magical glow to the scene. The lighting creates a dreamy, underwater atmosphere that captures the essence of oceanic wonder and enchantment.

Negative Prompt
blurry, cropped, ugly

Prompt
a photo of a q1s woman as a historical marble statue in a grand bathroom

Negative Prompt
blurry, cropped, ugly

Prompt
A captivating watercolor portrayal of a q1s woman. Her eyes are the focal point. The vibrant color palette features bright red, deep green, and pastel beige and white, creating a striking contrast. The fluid, free-flowing brushstrokes imbue the artwork with movement and emotion. The background is an abstract tapestry of color, with splashes of the same hues, adding depth and dimension to the portrait. Painting, illustration.

Negative Prompt
blurry, cropped, ugly

Prompt
an oil painting of a q1s woman in a green dress standing on the moutain. an oil painting of, art.

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: 6
Training steps: 12000
Learning rate: 0.001
- Learning rate schedule: polynomial
- Warmup steps: 600
Max grad norm: 1.0
Effective batch size: 1
- Micro-batch size: 1
- 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', 'flow_matching_loss=compatible'])
Optimizer: adamw_bf16weight_decay=1e-3
Trainable parameter precision: Pure BF16
Caption dropout probability: 20.0%

LyCORIS Config:

{
    "algo": "lokr",
    "multiplier": 1.0,
    "linear_dim": 10000,
    "linear_alpha": 1,
    "factor": 12,
    "apply_preset": {
        "target_module": [
            "Attention",
            "FeedForward"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 12
            },
            "FeedForward": {
                "factor": 6
            }
        }
    }
}

Datasets

q1sV25-1024

Repeats: 30
Total number of images: 13
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

q1sV25-512

Repeats: 30
Total number of images: 17
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: False
Crop style: None
Crop aspect: None
Used for regularisation data: No

gen-and-inpaint-q1s-1024-reg-crop

Repeats: 0
Total number of images: 530
Total number of aspect buckets: 1
Resolution: 1.048576 megapixels
Cropped: True
Crop style: random
Crop aspect: square
Used for regularisation data: Yes

gen-and-inpaint-q1s-512-reg-crop

Repeats: 0
Total number of images: 530
Total number of aspect buckets: 1
Resolution: 0.262144 megapixels
Cropped: True
Crop style: random
Crop aspect: square
Used for regularisation data: Yes

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 = 'DragonQuix/flux-test-q1sV25'
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 oil painting of a q1s woman in a green dress standing on the moutain. an oil painting of, art."


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

DragonQuix
/

flux-test-q1sV25