README.md

---
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: 'gh logo, on four cans, black background'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_1_0.png
- text: 'gh logo, green background, on a bottle and a can'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_2_0.png
- text: 'gh logo, on a bottle and 4 cans, white background'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_3_0.png
- text: 'gh logo, on a bottle and two cans, red orange background'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_4_0.png
- text: 'gh cans, yellow background, brown red can, served with other drinks'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_5_0.png
- text: 'gh cans, white background'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_6_0.png
- text: 'gh cans, in a bucket of ice, white background'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_7_0.png
- text: 'gh cans, being held, out of focus background'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_8_0.png
- text: 'dct desert rally racing background, driving a Ducati bike, out of focus shrubbery'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_9_0.png
- text: 'dct desert rally racing background, driving a Ducati bike, splashing water'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_10_0.png
- text: 'dct desert rally racing background, driving a Ducati bike, rider in a white motocross outfit, dust cloud'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_11_0.png
- text: 'dct desert rally racing background, driving a Ducati bike, view of a lake with shrubs and trees in the background, other riders and spectators'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_12_0.png
- text: 'anytylrjy woman, wearing a neckless and an opaque brown dress standing on the red carpet, hair done up, front view'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_13_0.png
- text: 'anytylrjy woman, putting on lipstick in front of a mirror, wearing a flowing white dress or bathrobe, looking in multiple mirrors, makeup on table'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_14_0.png
- text: 'anytylrjy woman, holding a makeup kit, staring directly at camera, brown background, wearing a ring and eggshell dress'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_15_0.png
- text: 'anytylrjy woman, wearing an elegant emerald green dress, wearing earrings and a necklace, white background'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_16_0.png
- text: 'mrtnprr style'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_17_0.png
- text: 'mrtnprr style'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_18_0.png
- text: 'mrtnprr style'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_19_0.png
- text: 'mrtnprr style'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_20_0.png
- text: 'a photo of a daisy'
  parameters:
    negative_prompt: 'blurry, cropped, ugly'
  output:
    url: ./assets/image_21_0.png
---

# growwithdaisy/ghxdct_style_focus_20241113_125207

This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co./black-forest-labs/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: `None`
- Seed: `69`
- Resolution: `1024x1024`

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: 44
- Training steps: 4000
- Learning rate: 5e-06
- Max grad norm: 2.0
- Effective batch size: 8
  - Micro-batch size: 1
  - Gradient accumulation steps: 1
  - Number of GPUs: 8
- Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_value=1.0'])
- Rescaled betas zero SNR: False
- Optimizer: optimi-stableadamwweight_decay=1e-3
- Precision: Pure BF16
- Quantised: No
- Xformers: Not used
- LyCORIS Config:
```json
{
    "algo": "lokr",
    "multiplier": 1,
    "linear_dim": 1000000,
    "linear_alpha": 1,
    "factor": 16,
    "init_lokr_norm": 0.001,
    "apply_preset": {
        "target_module": [
            "FluxTransformerBlock",
            "FluxSingleTransformerBlock"
        ],
        "module_algo_map": {
            "Attention": {
                "factor": 16
            },
            "FeedForward": {
                "factor": 8
            }
        }
    }
}
```

## Datasets

### gh_logo-512
- Repeats: 0
- Total number of images: ~32
- Total number of aspect buckets: 4
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### gh_cans-512
- Repeats: 0
- Total number of images: ~56
- Total number of aspect buckets: 6
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### gh_cans-768
- Repeats: 0
- Total number of images: ~40
- Total number of aspect buckets: 5
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### gh_cans-1024
- Repeats: 0
- Total number of images: ~16
- Total number of aspect buckets: 2
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### dct_desert_rally_racing_background-512
- Repeats: 0
- Total number of images: ~56
- Total number of aspect buckets: 5
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### dct_desert_rally_racing_background-768
- Repeats: 0
- Total number of images: ~56
- Total number of aspect buckets: 5
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### dct_desert_rally_racing_background-1024
- Repeats: 0
- Total number of images: ~48
- Total number of aspect buckets: 3
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### anytylrjy_woman-512
- Repeats: 0
- Total number of images: ~64
- Total number of aspect buckets: 7
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### anytylrjy_woman-768
- Repeats: 0
- Total number of images: ~64
- Total number of aspect buckets: 7
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### anytylrjy_woman-1024
- Repeats: 0
- Total number of images: ~72
- Total number of aspect buckets: 8
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### mrtnprr_style-512
- Repeats: 1
- Total number of images: ~48
- Total number of aspect buckets: 6
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### mrtnprr_style-768
- Repeats: 1
- Total number of images: ~40
- Total number of aspect buckets: 5
- Resolution: 0.589824 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- Used for regularisation data: No
### mrtnprr_style-1024
- Repeats: 1
- Total number of images: ~16
- Total number of aspect buckets: 2
- Resolution: 1.048576 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
- 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 = 'playerzer0x/growwithdaisy/ghxdct_style_focus_20241113_125207'
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(1641421826),
    width=1024,
    height=1024,
    guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")
```