half_illustration

Introduction
The HALF_ILLUSTRATION model by davisbro generates unique images combining photographic and illustrative elements, suitable for creating avant-garde and dramatic visuals. It is developed using the FLUX.1-dev base model and is accessible via Hugging Face's model hub and Replicate.

Architecture
The model leverages the FLUX.1-dev architecture, designed to produce hybrid images that incorporate both photographic and illustrative styles. It is compatible with the Diffusers library, allowing for efficient text-to-image transformations.

Training
HALF_ILLUSTRATION is trained using the FLUX Dev 1 framework on Replicate, employing specific trigger words like "in the style of TOK" to maintain stylistic consistency. The model's training methodology emphasizes incorporating various illustrative elements to enhance the visuals.

Guide: Running Locally
To run the model locally, follow these steps:

1. Install the Diffusers library: pip install diffusers.
2. Import the necessary modules in Python:

   from diffusers import AutoPipelineForText2Image
   import torch
   

3. Load the model and LoRA weights:

   pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
   pipeline.load_lora_weights('davisbro/half_illustration', weight_name='flux_train_replicate.safetensors')
   

4. Generate an image using a custom prompt:

   image = pipeline('A 50mm bokeh photo of a fashion show in paris, midday sun...').images[0]
   

5. For optimal performance, consider using cloud GPU providers like AWS, Google Cloud Platform, or Azure.

License
The model is distributed under the flux-dev-license. Users must comply with the terms specified in the license document.