metadata
license: mit
base_model: VelvetToroyashi/WahtasticMerge
New Model Name (e.g., ArtFusionXL)
This is a fine-tuned model based on VelvetToroyashi/WahtasticMerge.
Model Description
TIt has been trained on a dataset of approximately 15,000 images sourced primarily from ArtStation, Twitter, and OpenGameArt.
Training Data
The model was trained on a curated dataset of 15,000 images. The primary sources for these images were:
- ArtStation: For high-quality, professional digital art.
- Twitter: For a diverse range of contemporary art styles.
- OpenGameArt: For assets related to game development, including characters and environments.
This diverse dataset aims to provide the model with a broad understanding of various artistic conventions and styles.
How to Use
This model can be used with any standard SDXL-compatible interface or library (e.g., Diffusers, Automatic1111, ComfyUI).
Recommended Settings
For optimal results, we recommend the following inference parameters:
- Sampler: Euler or Euler A
- Scheduler: Normal or Beta
- Steps: 16-24
- CFG Scale: 3-6
- Resolution: 832x1200 (or similar aspect ratios with a total area around 1024x1024)
Example Usage (Python with Diffusers)
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"YOUR_HUGGINGFACE_REPO_ID/YOUR_MODEL_NAME", # Replace with your actual Hugging Face repo ID and model name
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
).to("cuda")
prompt = "a majestic fantasy landscape, vibrant colors, epic, detailed, masterpiece"
negative_prompt = "low quality, bad anatomy, deformed, ugly, distorted"
image = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
num_inference_steps=20,
guidance_scale=5,
height=1200, # Example resolution
width=832
).images
image.save("generated_image.png")