README.md

---
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 an Albert Bierstadt oil painting, A vast mountain lake reflects snow-capped peaks under a dramatic sky with golden light breaking through storm clouds. In the foreground, a small deer drinks from the crystal-clear water while pine trees frame the scene.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
- text: 'In the style of an Albert Bierstadt oil painting, A majestic waterfall cascades down rocky cliffs surrounded by towering pine trees. Morning mist rises from the valley below while early sunlight catches the spray of the falls, creating a rainbow effect.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_2_0.png
- text: 'In the style of an Albert Bierstadt oil painting, A sweeping valley vista with a winding river catching the last rays of sunset. Massive clouds turn pink and gold above distant mountains while two figures on horseback appear tiny against the epic landscape.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_3_0.png
- text: 'In the style of an Albert Bierstadt oil painting, A lone Range Rover appears dwarfed by towering granite cliffs and ancient sequoias. Golden afternoon light streams through the trees while a waterfall tumbles in the distance. Mist partially shrouds the vehicle, making it seem like a mystical chariot.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_4_0.png
- text: 'In the style of an Albert Bierstadt oil painting, A pristine mountain spring bubbles up through rocks, its crystal waters mixing with the distinctive red of natural cola springs. Native wildlife gather to drink while steam rises in the crisp mountain air. A massive peak looms in the background, partially hidden by dramatic clouds.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_5_0.png
- text: 'In the style of an Albert Bierstadt oil painting, A giant golden hamster stands majestically atop a rocky outcrop overlooking a vast valley, scaled to the size of a buffalo. Morning light catches its fur while smaller hamsters graze peacefully in the meadow below. Storm clouds gather dramatically above snow-capped peaks in the distance.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_6_0.png
- text: 'In the style of an Albert Bierstadt oil painting, A line of electric vehicles winds through a mountain pass, dwarfed by massive cliffs and ancient trees. Late afternoon light creates dramatic shadows while eagles soar overhead. A campsite with solar panels glints in the distance like a modern wagon train.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_7_0.png
- text: 'In the style of an Albert Bierstadt oil painting, Half Dome towers over Yosemite Valley at sunset, while advanced flying vehicles appear as tiny specks against the epic landscape. A crystalline research station nestled among the trees catches the golden light, while traditional hikers on the trail below emphasize the timeless scale of nature.'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_8_0.png
---

# AlbertBierstadt-Flux-LoKr

This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/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: `1024x1280`
- Skip-layer guidance: 

Note: The validation settings are not necessarily the same as the [training settings](#training-settings).

You can find some example images in the following gallery:


<Gallery />

The text encoder **was not** trained.
You may reuse the base model text encoder for inference.


## Training settings

- Training epochs: 0
- Training steps: 800
- Learning rate: 0.0004
  - Learning rate schedule: polynomial
  - Warmup steps: 200
- Max grad norm: 2.0
- 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=['shift=3', '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%

- SageAttention: Enabled inference
### LyCORIS Config:
```json
{
    "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

### ab-512
- Repeats: 12
- Total number of images: 74
- Total number of aspect buckets: 4
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ab-768
- Repeats: 8
- Total number of images: 74
- Total number of aspect buckets: 9
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ab-1024
- Repeats: 5
- Total number of images: 74
- Total number of aspect buckets: 7
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ab-1536
- Repeats: 2
- Total number of images: 73
- Total number of aspect buckets: 5
- Resolution: 2.359296 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### ab-crops-512
- Repeats: 8
- Total number of images: 74
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No
### ab-1024-crop
- Repeats: 6
- Total number of images: 74
- Total number of aspect buckets: 1
- Resolution: 1.048576 megapixels
- Cropped: True
- Crop style: random
- Crop aspect: square
- Used for regularisation data: No


## Inference


```python
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/AlbertBierstadt-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=1024,
    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.