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Functions2,606 in github.com/NVlabs/Sana

↓ 1 callersFunctionmain
()
diffusion/post_training/stat_tracking.py:145
↓ 1 callersFunctionmain
()
diffusion/model/nets/fastlinear/develop_triton_litemla.py:115
↓ 1 callersFunctionmain
()
diffusion/model/nets/fastlinear/develop_triton_ffn.py:107
↓ 1 callersFunctionmain
()
train_video_scripts/train_longsana.py:12
↓ 1 callersFunctionmain
(cfg: SanaVideoConfig)
train_video_scripts/train_video_ivjoint.py:739
↓ 1 callersFunctionmain
(cfg: SanaVideoConfig)
train_video_scripts/train_video_ivjoint_chunk.py:854
↓ 1 callersFunctionmain
(cfg: SanaConfig)
train_scripts/train.py:621
↓ 1 callersFunctionmain
(cfg: SanaConfig)
train_scripts/train_scm_ladd.py:956
↓ 1 callersFunctionmain
(args)
train_scripts/train_dreambooth_lora_sana.py:820
↓ 1 callersFunctionmain
()
sana/cli/run.py:33
↓ 1 callersFunctionmain
()
sana/cli/upload2hf.py:50
↓ 1 callersFunctionmain
()
inference_video_scripts/wm/inference_sana_wm_streaming.py:334
↓ 1 callersFunctionmain
()
inference_video_scripts/wm/inference_sana_wm.py:2128
↓ 1 callersFunctionmake_plural
(name: str)
tools/metrics/geneval/prompts/create_prompts.py:29
↓ 1 callersMethodmarginal_log_mean_coeff
Compute log(alpha_t) of a given continuous-time label t in [0, T].
diffusion/model/dpm_solver.py:222
↓ 1 callersFunctionmodel_fn
The noise predicition model function that is used for DPM-Solver.
diffusion/model/dpm_solver.py:439
↓ 1 callersFunctionmodel_fn
The noise predicition model function that is used for DPM-Solver.
diffusion/model/sa_solver.py:335
↓ 1 callersFunctionmodel_video_camctrl_init_config
(config: SanaVideoCamCtrlConfig, latent_size: int = 32)
diffusion/utils/camctrl_config.py:145
↓ 1 callersFunctionmodel_wrapper
Create a wrapper function for the noise prediction model. DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discr
diffusion/model/dpm_solver.py:266
↓ 1 callersFunctionmodel_wrapper
Thanks to DPM-Solver for their code base
diffusion/model/sa_solver.py:211
↓ 1 callersFunctionmultistep_dpm_solver_second_order_update
( model_output_list: List[torch.Tensor], sigmas, step_index, sample: torch.Tensor = None, )
diffusion/post_training/diffusers_patch/solver.py:318
↓ 1 callersMethodmultistep_dpm_solver_second_update
Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initial value at t
diffusion/model/dpm_solver.py:1246
↓ 1 callersMethodmultistep_dpm_solver_third_update
Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initial value at t
diffusion/model/dpm_solver.py:1307
↓ 1 callersMethodnames
(self)
diffusion/data/wids/wids_tar.py:83
↓ 1 callersFunctionndarr_image
( tensor: Union[torch.Tensor, List[torch.Tensor]], **kwargs, )
scripts/interface.py:82
↓ 1 callersFunctionnew_forward
(bn, mean_est, var_est)
diffusion/model/norms.py:100
↓ 1 callersFunctionnew_forward
(bn, mean_est, var_est)
diffusion/model/nets/fastlinear/modules/nn/norm.py:101
↓ 1 callersFunctionnew_forward
(bn, mean_est, var_est)
diffusion/model/dc_ae/efficientvit/models/nn/norm.py:135
↓ 1 callersFunctionnext_header
(offset, header)
diffusion/data/wids/wids_mmtar.py:53
↓ 1 callersFunctionnms
(x, t, s)
tools/controlnet/annotator/util.py:42
↓ 1 callersFunctionnorm_ip
(img, low, high)
app/app_sana_sprint.py:107
↓ 1 callersFunctionnorm_ip
(img, low, high)
app/app_sana_controlnet_hed.py:86
↓ 1 callersFunctionnorm_ip
(img, low, high)
app/app_sana.py:117
↓ 1 callersFunctionnormalized_guidance
( pred_cond: torch.Tensor, pred_uncond: torch.Tensor, guidance_scale: float, momentum_buffer:
diffusion/guiders/adaptive_projected_guidance.py:106
↓ 1 callersMethodnumerical_clip_alpha
For some beta schedules such as cosine schedule, the log-SNR has numerical isssues. We clip the log-SNR near t=T within -5.1 to ensur
diffusion/model/dpm_solver.py:149
↓ 1 callersFunctionnvila_compare
( prompt='a cyberpunk cat with a neon sign that says "Sana"', files=[f"output/sana_test_prompt/0.png",
tools/inference_scaling/nvila_sana_pick.py:24
↓ 1 callersMethodp_sample
Sample x_{t-1} from the model at the given timestep. :param model: the model to sample from. :param x: the current tensor at
diffusion/model/gaussian_diffusion.py:421
↓ 1 callersMethodp_sample_loop_progressive
Generate samples from the model and yield intermediate samples from each timestep of diffusion. Arguments are the same as p_s
diffusion/model/gaussian_diffusion.py:507
↓ 1 callersFunctionparse_args
()
tools/convert_sana_wm_refiner_to_diffusers.py:54
↓ 1 callersFunctionparse_args
()
tools/metrics/dpg_bench/compute_dpg_bench.py:25
↓ 1 callersFunctionparse_args
()
tools/metrics/pytorch-fid/compute_fid.py:239
↓ 1 callersFunctionparse_args
()
tools/metrics/geneval/generation/diffusers_generate.py:20
↓ 1 callersFunctionparse_args
()
tools/metrics/geneval/evaluation/evaluate_images.py:369
↓ 1 callersFunctionparse_args
()
tools/metrics/clip-score/clip_score.py:324
↓ 1 callersFunctionparse_args
()
tools/metrics/image_reward/compute_image_reward.py:11
↓ 1 callersFunctionparse_args
(input_args=None)
train_scripts/train_dreambooth_lora_sana.py:179
↓ 1 callersFunctionparse_args
()
scripts/inference_geneval.py:339
↓ 1 callersFunctionparse_args
()
scripts/inference_sana_sprint_geneval.py:318
↓ 1 callersFunctionparse_args
()
scripts/inference_geneval_diffusers.py:120
↓ 1 callersFunctionpatchify
(x, patch_size)
diffusion/model/wan2_2/vae.py:258
↓ 1 callersFunctionpil_image
( tensor, **kwargs, )
scripts/inference_geneval.py:87
↓ 1 callersFunctionpil_image
( tensor, **kwargs, )
scripts/inference_sana_sprint_geneval.py:86
↓ 1 callersFunctionpos_interpolate
(pos, seq_len)
diffusion/model/wan/clip.py:24
↓ 1 callersFunctionprepare_dpg_data
(args)
tools/metrics/dpg_bench/compute_dpg_bench.py:85
↓ 1 callersMethodprepare_flexattention
( self, cfg_size, num_head, head_dim, dtype, device, c
diffusion/model/nets/sana_multi_scale_video_camctrl.py:1226
↓ 1 callersMethodprepend_context
Prepend previous chunk's latent tail as left context. Args: z: Current latent chunk [B, C, T, H, W] prepend_prev_late
diffusion/model/ltx2/causal_vae.py:142
↓ 1 callersMethodprint_layer_info
Print layer information, including parameter size and whether 8bit quantization is used Args: param_shape (tuple): parameter shap
diffusion/utils/optimizer.py:501
↓ 1 callersFunctionprocess_data
Processes a directory containing PNG files, generates corresponding JSON files, and packages all files into a TAR file. It also counts the nu
tools/convert_scripts/convert_ImgDataset_to_WebDatasetMS_format.py:13
↓ 1 callersFunctionprocess_directory
(path)
tools/convert_scripts/convert_py_to_yaml.py:19
↓ 1 callersMethodprocess_mask
Process mask with optional dilation and blur Args: mask: PIL Image mask blur_factor: int, blur radius (0 = n
app/app_sana_inpaint.py:147
↓ 1 callersFunctionprocess_timesteps
( weighting_scheme: str | None, train_sampling_steps: int, size: Tuple, device: th.device,
diffusion/model/respace.py:320
↓ 1 callersFunctionproj_divide_bwd
Input: grad_y: (B, N, H*C) proj_weight: (H*C, H*C) vk_q: (B, N, H, C+1) Output: grad_vk_q: (B, N, H, C+1)
diffusion/model/nets/fastlinear/modules/triton_lite_mla_kernels/proj_divide_bwd.py:252
↓ 1 callersFunctionproject_ucm_points
Project 3D points in camera frame to UCM image plane.
diffusion/model/nets/sana_camctrl_blocks.py:235
↓ 1 callersFunctionproject_ucm_points_fov
Project 3D points in camera frame to UCM image plane using FoV-based intrinsics.
diffusion/model/nets/sana_camctrl_blocks.py:261
↓ 1 callersMethodq_mean_variance
Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diff
diffusion/model/gaussian_diffusion.py:243
↓ 1 callersFunctionrandomize_seed_fn
(seed: int, randomize_seed: bool)
app/app_sana_sprint.py:208
↓ 1 callersFunctionrandomize_seed_fn
(seed: int, randomize_seed: bool)
app/app_sana_4bit.py:166
↓ 1 callersFunctionrandomize_seed_fn
(seed: int, randomize_seed: bool)
app/app_sana.py:226
↓ 1 callersFunctionrandomize_seed_fn
(seed: int, randomize_seed: bool)
scripts/interface.py:101
↓ 1 callersFunctionread_yaml_config
(file, base_dir=None)
diffusion/utils/misc.py:46
↓ 1 callersFunctionreduce_ratio
(h, w)
tools/controlnet/inference_controlnet.py:108
↓ 1 callersFunctionreduce_ratio
(h, w)
app/sana_controlnet_pipeline.py:63
↓ 1 callersMethodrefine_latents
Run the LTX-2 refiner and return refined VAE latents. When ``block_size`` is ``None`` (default), uses the legacy single-shot path tha
diffusion/refiner/diffusers_ltx2_refiner.py:284
↓ 1 callersMethodregister_progress_bar
Register a progress bar callback function Args: progress_fn: Callback function that takes current step and total steps a
diffusion/model/dpm_solver.py:757
↓ 1 callersMethodregister_progress_bar
(self, progress_fn=None)
app/sana_controlnet_pipeline.py:183
↓ 1 callersMethodregister_progress_bar
(self, progress_fn=None)
app/sana_pipeline_inpaint.py:182
↓ 1 callersFunctionrelative_position
Give position of A relative to B, factoring in object dimensions
tools/metrics/geneval/evaluation/evaluate_images.py:127
↓ 1 callersMethodrelu_quadratic_att
(self, qkv: torch.Tensor)
diffusion/model/dc_ae/efficientvit/models/nn/ops.py:796
↓ 1 callersMethodrender_panel
Return an RGBA overlay of size ``(width, height)``.
diffusion/utils/action_overlay.py:264
↓ 1 callersMethodreparameterize
(self, mu, log_var)
diffusion/model/wan/vae.py:523
↓ 1 callersFunctionrescale_noise_cfg
r""" Rescales `noise_cfg` tensor based on `guidance_rescale` to improve image quality and fix overexposure. Based on Section 3.4 from [Common
diffusion/guiders/adaptive_projected_guidance.py:149
↓ 1 callersFunctionrescale_zero_terminal_snr
Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) Args: betas (`torch.FloatTensor`
diffusion/scheduler/lcm_scheduler.py:88
↓ 1 callersMethodreset_saved_frames
(self)
diffusion/utils/misc.py:281
↓ 1 callersMethodreset_stats
(self)
diffusion/data/wids/wids.py:405
↓ 1 callersFunctionresize
( x: torch.Tensor, size: Optional[Any] = None, scale_factor: Optional[list[float]] = None, mod
diffusion/model/dc_ae/efficientvit/models/utils/network.py:57
↓ 1 callersFunctionresize_crop_to_fill
(clip, target_size)
diffusion/data/transforms.py:54
↓ 1 callersFunctionresize_crop_to_fill_image
(pil_image, image_size)
diffusion/data/transforms.py:74
↓ 1 callersFunctionresize_image
(input_image, resolution)
tools/controlnet/annotator/util.py:29
↓ 1 callersFunctionresize_scale
(clip, target_size, interpolation_mode)
diffusion/data/transforms.py:131
↓ 1 callersFunctionresolve_checkpoint
(path_or_uri: str)
tools/convert_sana_wm_refiner_to_diffusers.py:92
↓ 1 callersFunctionresolve_dsdesc
Resolve a dataset description. This rebases the shards as necessary and loads any remote references. Dataset descriptions are JSON files. Th
diffusion/data/wids/wids_specs.py:126
↓ 1 callersFunctionresolve_ffmpeg_exe
Return an ffmpeg executable path, falling back to imageio-ffmpeg.
inference_video_scripts/wm/streaming_mp4_writer.py:37
↓ 1 callersFunctionrot_x
(angle_rad: float)
inference_video_scripts/wm/camera_control.py:71
↓ 1 callersFunctionrot_y
(angle_rad: float)
inference_video_scripts/wm/camera_control.py:76
↓ 1 callersFunctionround_to_multiple
(idx)
diffusion/model/utils.py:202
↓ 1 callersFunctionrun_cuda_cam
CUDA cam path: reads q/k/v [B,H,D,N] directly, writes fp32 [B,H,D,N] directly (no packing, no transpose). Phase B via Triton (skip_z). reuse_
diffusion/model/ops/fused_gdn_chunkwise_cuda.py:923
↓ 1 callersFunctionrun_inference
(num_imgs=1)
app/app_sana_sprint.py:134
↓ 1 callersFunctionrun_streaming_inference
Drive the three-stage chunked pipeline end-to-end. Args: stage1_chunk_iter: Iterator (typically from ``SelfForcingFlowEulerCa
inference_video_scripts/wm/streaming_pipeline.py:121
↓ 1 callersFunctionsafe_step
(x, step=2)
tools/controlnet/annotator/util.py:77
↓ 1 callersFunctionsafetensor_keys
(path: Path)
tools/convert_sana_wm_refiner_to_diffusers.py:107
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