metadata
base_model: krea/Krea-2-Raw
tags:
- text-to-image
- diffusers
- lora
- krea2
- template:sd-lora
license: apache-2.0
instance_prompt: dark fantasy ink style
widget:
- text: >-
A colossal skeletal dragon coiled around a crumbling gothic spire under a
blood-red moon, dark fantasy ink style
output:
url: sample_0.png
- text: >-
A hooded plague doctor walking through a fog-drenched cobblestone alleyway
lit by a single flickering lantern, dark fantasy ink style
output:
url: sample_1.png
- text: >-
An ornate, decaying throne room overgrown with black thorny vines and
shimmering ghostly embers, dark fantasy ink style
output:
url: sample_2.png
Krea 2 LoRA — AllwaysFurious/classicdnd
- Prompt
- A colossal skeletal dragon coiled around a crumbling gothic spire under a blood-red moon, dark fantasy ink style
- Prompt
- A hooded plague doctor walking through a fog-drenched cobblestone alleyway lit by a single flickering lantern, dark fantasy ink style
- Prompt
- An ornate, decaying throne room overgrown with black thorny vines and shimmering ghostly embers, dark fantasy ink style
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 phrase dark fantasy ink style to invoke the concept.
Samples
"A colossal skeletal dragon coiled around a crumbling gothic spire under a blood-red moon, dark fantasy ink style"
"A hooded plague doctor walking through a fog-drenched cobblestone alleyway lit by a single flickering lantern, dark fantasy ink style"
"An ornate, decaying throne room overgrown with black thorny vines and shimmering ghostly embers, dark fantasy ink style"
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("AllwaysFurious/classicdnd")
image = pipe("A colossal skeletal dragon coiled around a crumbling gothic spire under a blood-red moon, dark fantasy ink style", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")