World Initializer Image To Panorama
GenEx World Initializer is panorama generation pipeline built on top of the FluxFillPipeline.
Source du modèle
- Type
- Modèle
- Taille du fichier
- 160 MB–9,30 GB
- Modèle de base
- black-forest-labs/FLUX.1-Fill-dev
- Version
- vRevision bdf9a02
- Mis à jour
- 6 oct. 2026
- Téléchargements
- 56
- Source
- Hugging Face
Description de la source
GenEx-World-Initializer 🧭🌍
GenEx World Initializer is panorama generation pipeline built on top of the FluxFillPipeline.
It transforms a single view image into a 360° panoramic image using vision-conditioned inpainting.
- 🖼️ Input: One image (any size, will be center-cropped to square)
- 🧠 Prompt: Optional text to guide panoramic generation
- 🎯 Output: 2048 × 1024 equirectangular image
- 🧩 Mask: Uses a fixed panoramic mask
📦 Usage
from diffusers import DiffusionPipeline
from PIL import Image
import torch
pipe = DiffusionPipeline.from_pretrained(
"genex-world/World-Initializer-image-to-panorama",
custom_pipeline="genex_world_initializer_pipeline",
torch_dtype=torch.bfloat16,
trust_remote_code=True
).to("cuda")
# Load your image (any resolution)
image = Image.open("example_input.jpg")
# Run inference
front_view, output = pipe(image=image)
output.images[0]
🏁 Mask
The following mask is used to train the inpainting diffuser and used to inference automatically.
🔧 Requirements
diffusers>=0.33.1
transformers
numpy
pillow
sentencepiece
✨ BibTex
@misc{lu2025genexgeneratingexplorableworld,
title={GenEx: Generating an Explorable World},
author={Taiming Lu and Tianmin Shu and Junfei Xiao and Luoxin Ye and Jiahao Wang and Cheng Peng and Chen Wei and Daniel Khashabi and Rama Chellappa and Alan Yuille and Jieneng Chen},
year={2025},
eprint={2412.09624},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.09624},
}
Exigences
Modèles
1vae/diffusion_pytorch_model.safetensors
Fichier non vérifiéVAE · Hugging Face · genex-world/World-Initializer-image-to-panorama · Z-Image Turbo VAE
Sources
1 source
Sources
1 sourceVérifié 6 oct.
Artefacts du modèle
7 artefacts
Artefacts du modèle
7 artefactstext_encoder_2/model-00001-of-00002.safetensors
safetensors · 4,65 GB · SHA-256 ec87bffd1923…893e · Hugging Face
Téléchargertext_encoder_2/model-00002-of-00002.safetensors
safetensors · 4,22 GB · SHA-256 a5640855b301…da00 · Hugging Face
Téléchargertext_encoder/model.safetensors
safetensors · 235 MB · SHA-256 893d67a23f46…d082 · Hugging Face
Téléchargertransformer/diffusion_pytorch_model-00001-of-00003.safetensors
safetensors · 9,30 GB · SHA-256 cfaf8fec448f…da0b · Hugging Face
Téléchargertransformer/diffusion_pytorch_model-00002-of-00003.safetensors
safetensors · 9,27 GB · SHA-256 35a81003fdc8…58c2 · Hugging Face
Téléchargertransformer/diffusion_pytorch_model-00003-of-00003.safetensors
safetensors · 3,60 GB · SHA-256 c6f7c82ed67d…a5c4 · Hugging Face
Téléchargervae/diffusion_pytorch_model.safetensors
safetensors · 160 MB · SHA-256 f5b59a268515…40a3 · Hugging Face
TéléchargerExtraits de sources
3 extraits
Extraits de sources
3 extraits--- library_name: diffusers pipeline_tag: image-to-image inference: parameters: guidance_scale: 3.5 widget: - src: example_input.jpg text: GenEx Panoramic World Initialization example_title: Panoramic generation from image crop datasets: - TaiMingLu/GenEx-DB-Panorama-World base_model: - black-forest-labs/FLUX.1-Fill-dev license: cc-by-4.0 --- # GenEx-World-Initializer 🧭🌍 **GenEx World Initializer** is panorama generation pipeline built on top of the [FluxFillPipeline](https://huggingface.co/black-forest-labs/FLUX.1-Fill-dev). It transforms a **single view image** into a **360° panoramic image** using vision-conditioned inpainting. - 🖼️ Input: One image (any size, will be center-cropped to square) - 🧠 Prompt: Optional text to guide panoramic generation - 🎯 Output: 2048 × 1024 equirectangular image - 🧩 Mask: Uses a fixed panoramic mask  ## 📦 Usage ```python from diffusers import DiffusionPipeline from PIL import Image import torch pipe = DiffusionPipeline.from_pretrained( "genex-world/World-Initializer-image-to-panorama", custom_pipeline="genex_world_initializer_pipeline", torch_dtype=torch.bfloat16, trust_remote_code=True ).to("cuda") # Load your image (any resolution) image = Image.open("example_input.jpg") # Run inference front_view, output = pipe(image=image) output.images[0] ``` ## 🏁 Mask The following mask is used to train the inpainting diffuser and used to inference automatically.  ## 🔧 Requirements ```txt diffusers>=0.33.1 transformers numpy pillow sentencepiece ``` ## ✨ BibTex ``` @misc{lu2025genexgeneratingexplorableworld, title={GenEx: Generating an Explorable World}, author={Taiming Lu and Tianmin Shu and Junfei Xiao and Luoxin Ye and Jiahao Wang and Cheng Peng and Chen Wei and Daniel Khashabi and Rama Chellappa and Alan Yuille and Jieneng Chen}, year={2025}, eprint={2412.09624}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.09624}, } ```
Source context: 56 downloads · 8 likes · Pipeline image-to-image · Library diffusers · Repo genex-world/World-Initializer-image-to-panorama
Source context: 77 downloads · 8 likes · Pipeline image-to-image · Library diffusers · Repo genex-world/World-Initializer-image-to-panorama
