How to use from the
Use from the
Diffusers library
pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("krea/Krea-2-Raw", torch_dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("aimalias/j4n4")

prompt = "A J4N4 woman dressed in iridescent bioluminescent armor, standing amidst a neon-lit cyberpunk rainforest with floating holographic fish."
image = pipe(prompt).images[0]

Krea 2 LoRA โ€” aimalias/j4n4

Prompt
A J4N4 woman dressed in iridescent bioluminescent armor, standing amidst a neon-lit cyberpunk rainforest with floating holographic fish.
Prompt
A J4N4 woman in a flowing linen dress reading an ancient leather tome in a sun-drenched Mediterranean library overlooking a turquoise sea.
Prompt
A J4N4 woman as a gritty wasteland scavenger, wearing rusted metal plating and goggles, standing before a massive scrap-metal fortress under a blood-red sky.

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 J4N4 woman to invoke the concept.

Samples

sample

"A J4N4 woman dressed in iridescent bioluminescent armor, standing amidst a neon-lit cyberpunk rainforest with floating holographic fish."

sample

"A J4N4 woman in a flowing linen dress reading an ancient leather tome in a sun-drenched Mediterranean library overlooking a turquoise sea."

sample

"A J4N4 woman as a gritty wasteland scavenger, wearing rusted metal plating and goggles, standing before a massive scrap-metal fortress under a blood-red sky."

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("aimalias/j4n4")
image = pipe("A J4N4 woman dressed in iridescent bioluminescent armor, standing amidst a neon-lit cyberpunk rainforest with floating holographic fish.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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