add model ckpt

README.md

@@ -40,109 +40,25 @@ We present a flexible end-to-end feed-forward framework, named the *LucidFusion*
 
 ## 🔧 Training Instructions
 
-Our code is now released! 
+Our inference code is now released! 
 
-### Install
-```
-conda create -n LucidFusion python=3.9.19
-conda activate LucidFusion
-
-# For example, we use torch 2.3.1 + cuda 11.8, and tested with latest torch (2.4.1) which works with the latest xformers (0.0.28).
-pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118
-
-# Xformers is required! please refer to https://github.com/facebookresearch/xformers for details.
-# [linux only] cuda 11.8 version
-pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu118
+Please refer to our [repo](https://github.com/EnVision-Research/LucidFusion/tree/master) for more details.
 
-# For 3D Gaussian Splatting, we use LGM modified version, details please refer to https://github.com/3DTopia/LGM
-git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization
-pip install ./diff-gaussian-rasterization
-
-# Other dependencies
-pip install -r requirements.txt
-```
 
 ### Pretrained Weights
 
-Our pre-trained weights will be released soon, please check back!
-
 Our current model loads pre-trained diffusion model for config. We use stable-diffusion-2-1-base, to download it, simply run
 ```
 python pretrained/download.py
 ```
 You can omit this step if you already have stable-diffusion-2-1-base, and simply update "model_key" with your local SD-2-1 path for scripts in scripts/ folder.
 
-## 🔥 Inference
-A shell script is provided with example files.
-To run, you first need to set up the pre-trained weights as follows:
-
-```
-cd LucidFusion
-mkdir output/demo
-
-# Download the pretrained weights and name it as best.ckpt
-
-# Place the pretrained weights in LucidFusion/output/demo/best.ckpt
-```
-We have also provided some preprocessed examples.
-
-For GSO files, the example objects are "alarm", "chicken", "hat", "lunch_bag", "mario", and "shoe1".
-
-To run GSO demo:
-```
-# You can adjust "DEMO" field inside the gso_demo.sh to load other examples.
-
-bash scripts/gso_demo.sh
-```
-
-To run the images demo, masks are obtained using preprocess.py. The example objects are "nutella_new", "monkey_chair", "dog_chair".
-
-```
-bash scripts/demo.sh
-```
-
-To run the diffusion demo as a single-image-to-multi-view setup, we use the pixel diffusion trained in the CRM, as described in the paper. You can also use other multi-view diffusion models to generate multi-view outputs from a single image.
-
-For dependencies issue, please check https://github.com/thu-ml/CRM
-
-We also provide LGM's imagegen diffusion, simply set --crm=false in diffusion_demo.sh. You can change the --seed with different seed option.
-
-```
-bash script/diffusion_demo.sh
-```
-
-
-You can also try your own example! To do that:
-
-1. Obtain images and place them in the examples folder:
-```
-LucidFusion
-├── examples/
-|   ├── "your obj name"/
-|   |   ├── "image_01.png"
-|   |   ├── "image_02.png"
-|   |   ├── ...
-```
-2. Run preprocess.py to extract the recentered image and its mask:
-```
-# Run the following will create two folders (images, masks) in "your-obj-name" folder.
-# You can check to see if the extract mask is corrected.
-python preprocess.py examples/you-obj-name --outdir examples/your-obj-name
-```
-
-3. Modify demo.sh to set DEMO=“examples/your-obj-name”, then run the script:
-```
-bash scripts/demo.sh
-```
-
-## 🤗 Gradio Demo
-
-We are currently building an online demo of LucidFusion with Gradio. It is still under development, and will coming out soon!
+Our pre-trained weights is released!
 
 ## 🚧 Todo
 
 - [x] Release the inference codes
-- [ ] Release our weights
+- [x] Release our weights
 - [ ] Release the Gardio Demo
 - [ ] Release the Stage 1 and 2 training codes
 

best.ckpt

@@ -0,0 +1,3 @@
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+oid sha256:7f20c0eba48b1130311b3a82373966a78b75c363b6be77922bca3c6576a4e700
+size 11793105544