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---
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") |