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360 Degree [Flux & Kontext]

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Updated: Jun 30, 2025

stylehdrpanoramavr360°360 degree

Verified:

SafeTensor

Type

LoRA

Stats

976

0

Reviews

Published

Sep 6, 2024

Base Model

Flux.1 S

Training

Steps: 4,000
Epochs: 10

Usage Tips

Strength: 1

Trigger Words

360HDR
seamless

Hash

AutoV2
10444A5062
Happy Mushroom

denrakeiw

Trained with OSTRIS AI-Toolkit @ runpod.io

Flux Kontext:

Comfyui Workflow:


Transform any input image into immersive 360-degree panoramic views with this specialized LoRA model.

Model Description

This Kontext LoRA has been specifically trained to convert regular input images into full 360-degree panoramic representations. The model leverages advanced training techniques to understand spatial relationships and generate contextually appropriate wraparound imagery.

Training Details

  • Dataset: Custom before/after image pairs showcasing the transformation from standard images to 360-degree panoramas

  • Training Framework: Ostris AI Toolkit

  • Model Type: Kontext LoRA for Flux

  • Purpose: Image-to-360° panorama conversion

Usage Notes

  • Base Quality: The raw output without upscaling may appear less refined

  • Recommended Enhancement: Use tiled diffusion upscaling for significantly improved results

  • Workflow: A complete ComfyUI workflow for creating seamless 360-degree images will be uploaded soon

Applications

Perfect for creating immersive content, virtual environments, VR experiences, and panoramic artwork from standard input images.

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Flux Version:

The LoRA can create 360-degree panoramic images, and there is a high probability that the textures at the edges will be seamless.
However, as is often the case with AI-generated images, it doesn't always work perfectly.
But it works ;)

Here’s a tip for creating the images:

Choose a 2:1 format, such as 1536x768 pixels, for the first sampler. Then, use the Ultimate Upscaler to do a 2x or 4x upscaling. This should help maintain quality while generating 360-degree panoramic images.

The 'SCHNELL' version is trained with Rank 64 and SCHNELL as a Base Model, making it a bit more resource-efficient. 'Dev' offers the best image quality with Rank 128, but if speed is the priority, 'SCHNELL' is ideal.