CALAMITY: Horror Themed Text-to-Image Model v1.0
CALAMITY is a specialized horror-themed text-to-image model built upon the Prometheus base model, which itself is a full rank finetune of the Playground v2.5 architecture. This checkpoint aims to generate haunting, surreal, and unsettling images with a distinctive aesthetic that sets it apart from mainstream models.
Key Features
Unique focus on dark, creepy, and horror themed visuals Trained on a curated dataset of horror imagery to capture genre-specific style Seamless integration with the Prometheus base model's enhanced accessibility features Advanced custom CLIP integration for improved text-to-image alignment Optimized for generating high-resolution, evocative horror artwork
Recommended Settings
Clip Skip: 2 CFG Scale: 7 Steps: 30-60 Sampler: DPM++ 2M SDE Scheduler: Karras Resolution: 1024x1024
Style Trigger word:"Sythentic Anime"
Use Cases
Generating evocative, nightmarish artwork for games, films, and books Concept art and visual brainstorming for horror-themed projects Exploring surreal, uncanny aesthetics and compositions Pushing creative boundaries in the horror genre through AI-augmented workflows
CALAMITY opens up new avenues for horror-themed image generation, providing creatives with a powerful tool for conjuring up unsettling and imaginative visuals. Experiment with different prompt combinations and settings to delve into a rich spectrum of macabre imagery.
Use it with 🧨 diffusers
import torch
from diffusers import (
StableDiffusionXLPipeline,
KDPM2AncestralDiscreteScheduler,
AutoencoderKL
)
# Load VAE component
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16
)
# Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
"dataautogpt3/CALAMITY",
vae=vae,
torch_dtype=torch.float16
)
pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')
# Define prompts and generate image
prompt = "Sythentic Anime"
negative_prompt = ""
image = pipe(
prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
guidance_scale=7,
num_inference_steps=50,
clip_skip=2
).images[0]
image.save("generated_image.png")