metadata
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: vextharr
widget:
- text: >-
A cinematic close-up of a vextharr, a bioluminescent deep-sea creature
with translucent skin, drifting through a neon-colored coral reef.
output:
url: sample_0.png
- text: >-
An ornate, gold-plated vextharr statue standing guard in the center of a
futuristic cyberpunk plaza under a pouring rain.
output:
url: sample_1.png
- text: >-
A whimsical illustration of a tiny vextharr wearing a top hat, sipping tea
inside a cozy hollowed-out mushroom forest.
output:
url: sample_2.png
Krea 2 LoRA — karmatized/vextharr
- Prompt
- A cinematic close-up of a vextharr, a bioluminescent deep-sea creature with translucent skin, drifting through a neon-colored coral reef.
- Prompt
- An ornate, gold-plated vextharr statue standing guard in the center of a futuristic cyberpunk plaza under a pouring rain.
- Prompt
- A whimsical illustration of a tiny vextharr wearing a top hat, sipping tea inside a cozy hollowed-out mushroom forest.
A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps).
Trigger
Use the token vextharr to invoke the concept.
Samples
"A cinematic close-up of a vextharr, a bioluminescent deep-sea creature with translucent skin, drifting through a neon-colored coral reef."
"An ornate, gold-plated vextharr statue standing guard in the center of a futuristic cyberpunk plaza under a pouring rain."
"A whimsical illustration of a tiny vextharr wearing a top hat, sipping tea inside a cozy hollowed-out mushroom forest."
Use it with diffusers
import torch
from diffusers import Krea2Pipeline
pipe = Krea2Pipeline.from_pretrained("krea/Krea-2-Turbo", torch_dtype=torch.bfloat16).to("cuda")
pipe.load_lora_weights("karmatized/vextharr")
image = pipe("A cinematic close-up of a vextharr, a bioluminescent deep-sea creature with translucent skin, drifting through a neon-colored coral reef.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")