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