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", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("aimalias/tessa-krea-simple")

prompt = "A T3F0W woman wearing ornate gold armor, standing atop a floating crystal island amidst a nebula of violet and teal cosmic dust."
image = pipe(prompt).images[0]

Krea 2 LoRA โ€” aimalias/tessa-krea-simple

Prompt
A T3F0W woman wearing ornate gold armor, standing atop a floating crystal island amidst a nebula of violet and teal cosmic dust.
Prompt
A candid street photography shot of a T3F0W woman in a yellow raincoat, walking through a neon-lit Tokyo alleyway during a heavy rainstorm.
Prompt
A T3F0W woman dressed in linen robes, reading an ancient scroll in a sun-drenched Mediterranean library filled with overgrown ivy and white marble pillars.

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 T3F0W woman to invoke the concept.

Samples

sample

"A T3F0W woman wearing ornate gold armor, standing atop a floating crystal island amidst a nebula of violet and teal cosmic dust."

sample

"A candid street photography shot of a T3F0W woman in a yellow raincoat, walking through a neon-lit Tokyo alleyway during a heavy rainstorm."

sample

"A T3F0W woman dressed in linen robes, reading an ancient scroll in a sun-drenched Mediterranean library filled with overgrown ivy and white marble pillars."

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/tessa-krea-simple")
image = pipe("A T3F0W woman wearing ornate gold armor, standing atop a floating crystal island amidst a nebula of violet and teal cosmic dust.", num_inference_steps=8, guidance_scale=0.0).images[0]
image.save("output.png")
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