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/ PixArt-alphabash scripts/train.sh src/train_gsvae.py configs/gsvae.yaml gsvae_gobj265k_sd opt_type=gsvae --gradient_accumulation_steps 4# SDXL (fp16-fixed) / PixArt-Sigmabash scripts/train.sh src/train_gsvae.py configs/gsvae.yaml gsvae_gobj265k_sdxl_fp16 opt_type=gsvae_sdxl_fp16 --gradient_accumulation_steps 4# SD3 / SD3.5bash scripts/train.sh src/train_gsvae.py configs/gsvae.yaml gsvae_gobj265k_sd3 opt_type=gsvae_sd35m --gradient_accumulation_steps 42.2 Tiny GSVAE DecoderFor efficient performing rendering loss in the DiffSplat training stage, we train tiny decoders (use pretrained tiny AEs at here) with much smaller sizes than the original decoders.Note that:Tiny GSVAE decoders are only used in DiffSplat rendering loss, not for the final inference.opt.freeze_encoder=true: encoder part of the pretrained GSVAE is fixed.opt.use_tiny_decoder=true: use tiny decoder in this stage.--load_pretrained_model: load pretrained GSVAE models in the previous stage.Set environment variables in scripts/train.sh first, then:# Tiny SD1.5 / SD2.1 / PixArt-alpha decoderbash scripts/train.sh src/train_gsvae.py configs/gsvae.yaml gsvae_gobj265k_sd opt_type=gsvae train.batch_size_per_gpu=8 opt.freeze_encoder=true opt.use_tiny_decoder=true --load_pretrained_model gsvae_gobj265k_sd# Tiny SDXL (fp16-fixed) / PixArt-Sigma decoderbash scripts/train.sh src/train_gsvae.py configs/gsvae.yaml gsvae_gobj265k_sdxl_fp16 opt_type=gsvae_sdxl_fp16 train.batch_size_per_gpu=8 opt.freeze_encoder=true opt.use_tiny_decoder=true --load_pretrained_model gsvae_gobj265k_sdxl_fp16# Tiny SD3 / SD3.5 decoderbash scripts/train.sh src/train_gsvae.py configs/gsvae.yaml gsvae_gobj265k_sd3 opt_type=gsvae_sd35m train.batch_size_per_gpu=8 opt.freeze_encoder=true opt.use_tiny_decoder=true --load_pretrained_model gsvae_gobj265k_sd3Please refer to train_gsvae.py and options are specified in configs/gsvae.yaml and options.py (opt_dict["gsvae"], opt_dict["gsvae_sdxl_fp16"] and opt_dict["gsvae_sd35m"]).3. DiffSplat3.0 Text Embedding PrecomputationText embeddings for captions are precomputed by extensions/encode_prompt_embeds.py:python3 extensions/encode_prompt_embeds.py [MODEL_NAME] [--batch_size 128] [--dataset_name gobj83k]# `MODEL_NAME`: choose from "sd15", "sd21", "sdxl", "paa", "pas", "sd3m", "sd35m", "sd35l"Captions will download automatically in extensions/assets and text embeddings are stored in /tmp/{DATASET_NAME}_{MODEL_NAME}_prompt_embeds by default.3.1 DiffSplat (w/o rendering loss)Note that:opt.view_concat_condition=true opt.input_concat_binary_mask=true: specified for image-conditioned generation.opt.prediction_type=v_prediction: specified for image-conditioned generation. We use v_prediction for better image-conditioned performance.----val_guidance_scales 1 2 3 (default: 1 3 7.5): smaller CFG scales for image conditioning.Set environment variables in scripts/train.sh first, then:# SD1.5 (text-cond)bash scripts/train.sh src/train_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15 --gradient_accumulation_steps 2 --use_ema# SD1.5 (image-cond)bash scripts/train.sh src/train_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15 --gradient_accumulation_steps 2 --use_ema ----val_guidance_scales 1 2 3 opt.view_concat_condition=true opt.input_concat_binary_mask=true opt.prediction_type=v_prediction# PixArt-Sigma (text-cond)bash scripts/train.sh src/train_gsdiff_pas.py configs/gsdiff_pas.yaml gsdiff_gobj83k_pas_fp16 --gradient_accumulation_steps 2 --use_ema# PixArt-Sigma (image-cond)bash scripts/train.sh src/train_gsdiff_pas.py configs/gsdiff_pas.yaml gsdiff_gobj83k_pas_fp16 --gradient_accumulation_steps 2 --use_ema ----val_guidance_scales 1 2 3 opt.view_concat_condition=true opt.input_concat_binary_mask=true opt.prediction_type=v_prediction# SD3.5m (text-cond)bash scripts/train.sh src/train_gsdiff_sd3.py configs/gsdiff_sd35m_80g.yaml gsdiff_gobj83k_sd35m --gradient_accumulation_steps 8 --use_ema# SD3.5m (image-cond)bash scripts/train.sh src/train_gsdiff_sd3.py configs/gsdiff_sd35m_80g.yaml gsdiff_gobj83k_sd35m --gradient_accumulation_steps 8 --use_ema ----val_guidance_scales 1 2 3 opt.view_concat_condition=true opt.input_concat_binary_mask=true opt.prediction_type=v_prediction3.2 DiffSplat (w/ rendering loss)Note that:opt.rendering_loss_prob=1 (default 0) means use rendering loss in the training stage all the time.opt.snr_gamma_rendering=1: we use SNR (signal-noise ratio) weighted rendering loss (weight gamma=1) for PixArt-Sigma models for more robust training. Feel free to tune this weight for other models.opt.use_tiny_decoder=true: use tiny decoder for efficient decoding/rendering in this stage.--load_pretrained_model is used for loading pretrained DiffSplat models in the previous stage.Set environment variables in scripts/train.sh first, then:# SD1.5 (text-cond)bash scripts/train.sh src/train_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15__render --gradient_accumulation_steps 2 --use_ema opt.rendering_loss_prob=1 opt.use_tiny_decoder=true --load_pretrained_model gsdiff_gobj83k_sd15# SD1.5 (image-cond)bash scripts/train.sh src/train_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15_image__render --gradient_accumulation_steps 2 --use_ema ----val_guidance_scales 1 2 3 opt.view_concat_condition=true opt.input_concat_binary_mask=true opt.prediction_type=v_prediction opt.rendering_loss_prob=1 opt.use_tiny_decoder=true --load_pretrained_model gsdiff_gobj83k_sd15# PixArt-Sigma (text-cond)bash scripts/train.sh src/train_gsdiff_pas.py configs/gsdiff_pas.yaml gsdiff_gobj83k_pas_fp16__render --gradient_accumulation_steps 2 --use_ema opt.rendering_loss_prob=1 opt.snr_gamma_rendering=1 opt.use_tiny_decoder=true --load_pretrained_model gsdiff_gobj83k_pas_fp16# PixArt-Sigma (image-cond)bash scripts/train.sh src/train_gsdiff_pas.py configs/gsdiff_pas.yaml gsdiff_gobj83k_pas_fp16_image__render --gradient_accumulation_steps 2 --use_ema ----val_guidance_scales 1 2 3 opt.view_concat_condition=true opt.input_concat_binary_mask=true opt.prediction_type=v_prediction opt.rendering_loss_prob=1 opt.snr_gamma_rendering=1 opt.use_tiny_decoder=true --load_pretrained_model gsdiff_gobj83k_pas_fp16# SD3.5m (text-cond)bash scripts/train.sh src/train_gsdiff_sd3.py configs/gsdiff_sd35m_80g.yaml gsdiff_gobj83k_sd35m__render --gradient_accumulation_steps 8 --use_ema opt.rendering_loss_prob=1 opt.use_tiny_decoder=true --load_pretrained_model gsdiff_gobj83k_sd35m# SD3.5m (image-cond)bash scripts/train.sh src/train_gsdiff_sd3.py configs/gsdiff_sd35m_80g.yaml gsdiff_gobj83k_sd35m_image__render --gradient_accumulation_steps 8 --use_ema ----val_guidance_scales 1 2 3 opt.view_concat_condition=true opt.input_concat_binary_mask=true opt.prediction_type=v_prediction opt.rendering_loss_prob=1 opt.use_tiny_decoder=true --load_pretrained_model gsdiff_gobj83k_sd35mPlease refer to train_gsdiff_{sd, sdxl, paa, pas,

"canny", "elevest"# `--image_cond`: add this flag for downloading image-conditioned modelsFor example, to download the text-cond SD1.5-based DiffSplat:python3 download_ckpt.py --model_type sd15To download the image-cond PixArt-Sigma-based DiffSplat:python3 download_ckpt.py --model_type pas --image_cond1. Text-conditioned 3D Object GenerationNote that:Model differences may not be significant for simple text prompts. We recommend using DiffSplat (SD1.5) for better efficiency, DiffSplat (SD3.5m) for better performance, and DiffSplat (PixArt-Sigma) for a better trade-off.By default, export HF_HOME=~/.cache/huggingface, export TORCH_HOME=~/.cache/torch. You can change these paths in scripts/infer.sh. SD3-related models require HuggingFace token for downloading, which is expected to be stored in HF_HOME.Outputs will be stored in ./out//inference.Prompt is specified by --prompt (e.g., a_toy_robot). Please seperate words by _ and it will be replaced by space in the code automatically.If "gif" is in --output_video_type, the output will be a .gif file. Otherwise, it will be a .mp4 file. If "fancy" is in --output_video_type, the output video will be in a fancy style that 3DGS scales gradually increase while rotating.--seed is used for random seed setting. --gpu_id is used for specifying the GPU device.Use --half_precision for BF16 half-precision inference. It will reduce the memory usage but may slightly affect the quality.# DiffSplat (SD1.5)bash scripts/infer.sh src/infer_gsdiff_sd.py configs/gsdiff_sd15.yaml gsdiff_gobj83k_sd15__render \--prompt a_toy_robot --output_video_type gif \--gpu_id 0 --seed 0 [--half_precision]# DiffSplat (PixArt-Sigma)bash scripts/infer.sh src/infer_gsdiff_pas.py configs/gsdiff_pas.yaml gsdiff_gobj83k_pas_fp16__render \--prompt a_toy_robot --output_video_type gif \--gpu_id 0 --seed 0 [--half_precision]# DiffSplat (SD3.5m)bash scripts/infer.sh src/infer_gsdiff_sd3.py configs/gsdiff_sd35m_80g.yaml gsdiff_gobj83k_sd35m__render \--prompt a_toy_robot --output_video_type gif \--gpu_id 0 --seed 0 [--half_precision]You will get:DiffSplat (SD1.5)DiffSplat (PixArt-Sigma)DiffSplat (SD3.5m)More Advanced Arguments:--prompt_file: instead of using --prompt, --prompt_file will read prompts from a .txt file line by line.Diffusion configurations:--scheduler_type: choose from ddim, dpmsolver++, sde-dpmsolver++, etc.--num_inference_timesteps: the number of diffusion steps.--guidance_scale: classifier-free guidance (CFG) scale; 1.0 means no CFG.--eta: specified for DDIM scheduler; the weight of noise for added noise in diffusion steps.Instant3D tricks:--init_std, --init_noise_strength, --init_bg: initial noise settings, cf. Instant3D Sec. 3.1; NOT used by default, as we found it's not that helpful in our case.Others:--elevation: elevation for viewing and rendering; not necessary for text-conditioned generation; set to 10 by default (from xz-plane (0) to +y axis (90)).--negative_prompt: empty prompt ("") by default; used with CFG for better visual quality (e.g., more vibrant colors), but we found it causes lower metric values (such as ImageReward).--save_ply: save the generated 3DGS as a .ply file; used with --opacity_threshold_ply to filter out low-opacity splats for a much smaller .ply file size.--eval_text_cond: evaluate text-conditioned generation automatically....Please refer to infer_gsdiff_sd.py, infer_gsdiff_pas.py, and infer_gsdiff_sd3.py for more argument details.2. Image-conditioned 3D Object GenerationNote that:Most of the arguments are the same as text-conditioned generation. Our method support text and image as conditions simultaneously.Elevation is necessary for image-conditioned generation. You can specify the elevation angle by --elevation for viewing and rendering (from xz-plane (0) to +y axis (90)) or estimate it from the input image by --use_elevest (download the pretrained ElevEst model by python3 download_ckpt.py --model_type elevest) first. But we found that the estimated elevation is not always accurate, so it's better to set it manually.Text prompt is optional for image-conditioned generation. If you want to use text prompt, you can specify it

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[ICLR 2025] DiffSplatThis repository contains the official implementation of the paper: DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation, which is accepted to ICLR 2025.DiffSplat is a generative framework to synthesize 3D Gaussian Splats from text prompts & single-view images in 1~2 seconds. It is fine-tuned directly from a pretrained text-to-image diffusion model.Feel free to contact me ([email protected]) or open an issue if you have any questions or suggestions.📢 News2025-03-06: Training instructions for DiffSplat and ControlNet are provided.2025-02-11: Training instructions for GSRecon and GSVAE are provided.2025-02-02: Inference instructions (text-conditioned & image-conditioned & controlnet) are provided.2025-01-29: The source code and pretrained models are released. Happy 🐍 Chinese New Year 🎆!2025-01-22: DiffSplat is accepted to ICLR 2025.📋 TODO Provide detailed instructions for inference. Provide detailed instructions for GSRecon & GSVAE training. Provide detailed instructions for DiffSplat training. Implement a Gradio demo at HuggingFace🤗 Space.🔧 InstallationYou may need to modify the specific version of torch in settings/setup.sh according to your CUDA version.There are not restrictions on the torch version, feel free to use your preferred one.git clone DiffSplatbash settings/setup.📊 DatasetWe use G-Objaverse with about 265K 3D objects and 10.6M rendered images (265K x 40 views, including RGB, normal and depth maps) for GSRecon and GSVAE training. Its subset with about 83K 3D objects provided by LGM is used for DiffSplat training. Their text descriptions are provided by the latest version of Cap3D (i.e., refined by DiffuRank).We find the filtering is crucial for the generation quality of DiffSplat, and a larger dataset is beneficial for the performance of GSRecon and GSVAE.We store the dataset in an internal HDFS cluster in this project. Thus, the training code can NOT be directly run on your local machine. Please implement your own dataloading logic referring to our provided dataset & dataloader code.🚀 Usage📷 Camera ConventionsThe camera and world coordinate systems in this project are both defined in the OpenGL convention, i.e., X: right, Y: up, Z: backward. The camera is located at (0, 0, 1.4) in the world coordinate system, and the camera looks at the origin (0, 0, 0).Please refer to kiuikit camera doc for visualizations of the camera and world coordinate systems.🤗 Pretrained ModelsAll pretrained models are available at HuggingFace🤗.Model NameFine-tined From#Param.LinkNoteGSReconFrom scratch42Mgsrecon_gobj265k_cnp_even4Feed-forward reconstruct per-pixel 3DGS from 4-view (RGB, normal, coordinate) mapsGSVAE (SD)SD1.5 VAE84Mgsvae_gobj265k_sdGSVAE (SDXL)SDXL fp16 VAE84Mgsvae_gobj265k_sdxl_fp16fp16-fixed SDXL VAE is more robustGSVAE (SD3)SD3 VAE84Mgsvae_gobj265k_sd3DiffSplat (SD1.5)SD1.50.86BText-cond: gsdiff_gobj83k_sd15__render Image-cond: gsdiff_gobj83k_sd15_image__renderBest efficiencyDiffSplat (PixArt-Sigma)PixArt-Sigma0.61BText-cond: gsdiff_gobj83k_pas_fp16__render Image-cond: gsdiff_gobj83k_pas_fp16_image__renderBest Trade-offDiffSplat (SD3.5m)SD3.5 median2.24BText-cond: gsdiff_gobj83k_sd35m__render Image-cond: gsdiff_gobj83k_sd35m_image__renderBest performanceDiffSplat ControlNet (SD1.5)From scratch361MDepth: gsdiff_gobj83k_sd15__render__depth Normal: gsdiff_gobj83k_sd15__render__normal Canny: gsdiff_gobj83k_sd15__render__canny(Optional) ElevEstdinov2_vitb14_reg86 Melevest_gobj265k_b_C25(Optional) Single-view image elevation estimation⚡ Inference0. Download Pretrained ModelsNote that:Pretrained weights will download from HuggingFace and stored in ./out.Other pretrained models (such as CLIP, T5, image VAE, etc.) will be downloaded automatically and stored in your HuggingFace cache directory.If you face problems in visiting HuggingFace Hub, you can try to set the environment variable export HF_ENDPOINT= pretrained weights is NOT really used during inference. Only its rendering function is used for visualization.python3 download_ckpt.py --model_type [MODEL_TYPE] [--image_cond]# `MODEL_TYPE`: choose from "sd15", "pas", "sd35m", "depth", "normal",