---
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, X (k.a. 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.
*   **X:** 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, Stable Diffusion WEBUI, ComfyUI.

### Recommended Settings

For optimal results, we recommend the following inference parameters:

*   **Sampler:** Euler or Euler ancestral
*   **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)

```python
from diffusers import AutoPipelineForText2Image
import torch

pipeline = AutoPipelineForText2Image.from_pretrained(
    "Pixel-Dust/Micromerge",
    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,
    width=832
).images

image.save("generated_image.png")