growwithdaisy/dptyqxbssprshps_flat_20241127_110411

This is a LyCORIS adapter derived from FLUX.1-dev.

The main validation prompt used during training was:

a photo of a daisy

Validation settings

• CFG: 3.5
• CFG Rescale: 0.0
• Steps: 20
• Sampler: FlowMatchEulerDiscreteScheduler
• Seed: 69
• 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

bssprshps car interior objects, black background, vertically positioned, antenna

Negative Prompt

blurry, cropped, ugly

Prompt

bssprshps car interior objects, car dashboard cover, gray, car interior background, on top of the dashboard, side view

Negative Prompt

blurry, cropped, ugly

Prompt

bssprshps car interior objects, windshield phone mount, on top of a silky cloth, side view, silky cloth background

Negative Prompt

blurry, cropped, ugly

Prompt

bssprshps car interior objects, windshield phone mount, car interior background, sticking on the windshield, isometric view

Negative Prompt

blurry, cropped, ugly

Prompt

a photo of a daisy

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: 102
• Training steps: 10000
• Learning rate: 0.0001
  • Learning rate schedule: constant
  • Warmup steps: 0
• Max grad norm: 2.0
• Effective batch size: 8
  • Micro-batch size: 2
  • Gradient accumulation steps: 1
  • Number of GPUs: 4
• Gradient checkpointing: True
• Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_mode=constant', 'flux_guidance_value=1.0', 'flow_matching_loss=compatible'])
• Optimizer: optimi-stableadamwweight_decay=1e-3
• Trainable parameter precision: Pure BF16
• Caption dropout probability: 5.0%

LyCORIS Config:

{
    "algo": "lokr",
    "multiplier": 1,
    "linear_dim": 1000000,
    "linear_alpha": 1,
    "factor": 12,
    "init_lokr_norm": 0.001,
    "apply_preset": {
        "target_module": [
            "FluxTransformerBlock",
            "FluxSingleTransformerBlock"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 12
            },
            "FeedForward": {
                "factor": 6
            }
        }
    }
}

Datasets

dptyqxbssprshps_flat-512

• Repeats: 0
• Total number of images: ~216
• Total number of aspect buckets: 9
• Resolution: 0.262144 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

dptyqxbssprshps_flat-768

• Repeats: 0
• Total number of images: ~180
• Total number of aspect buckets: 10
• Resolution: 0.589824 megapixels
• Cropped: False
• Crop style: None
• Crop aspect: None
• Used for regularisation data: No

dptyqxbssprshps_flat-1024

• Repeats: 1
• Total number of images: ~152
• Total number of aspect buckets: 11
• Resolution: 1.048576 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 = 'FLUX.1-dev'
adapter_repo_id = 'playerzer0x/growwithdaisy/dptyqxbssprshps_flat_20241127_110411'
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 = "a photo of a daisy"


## Optional: quantise the model to save on vram.
## Note: The model was not quantised during training, so it is not necessary to quantise it 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(69),
    width=1024,
    height=1024,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")