From 7839648341fe4d0b67c5356dba7908c74015ef79 Mon Sep 17 00:00:00 2001
From: David El Malih <david@billiv.fr>
Date: Tue, 25 Nov 2025 23:32:47 +0100
Subject: [PATCH] docs: Update Imagen Video paper link in scheduler docstrings.

---
 .../schedulers/scheduling_cosine_dpmsolver_multistep.py         | 2 +-
 src/diffusers/schedulers/scheduling_ddim_cogvideox.py           | 2 +-
 src/diffusers/schedulers/scheduling_ddim_inverse.py             | 2 +-
 src/diffusers/schedulers/scheduling_ddim_parallel.py            | 2 +-
 src/diffusers/schedulers/scheduling_ddpm.py                     | 2 +-
 src/diffusers/schedulers/scheduling_ddpm_parallel.py            | 2 +-
 src/diffusers/schedulers/scheduling_deis_multistep.py           | 2 +-
 src/diffusers/schedulers/scheduling_dpm_cogvideox.py            | 2 +-
 .../schedulers/scheduling_dpmsolver_multistep_inverse.py        | 2 +-
 src/diffusers/schedulers/scheduling_dpmsolver_sde.py            | 2 +-
 src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py     | 2 +-
 src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py  | 2 +-
 src/diffusers/schedulers/scheduling_edm_euler.py                | 2 +-
 src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py | 2 +-
 src/diffusers/schedulers/scheduling_euler_discrete.py           | 2 +-
 src/diffusers/schedulers/scheduling_euler_discrete_flax.py      | 2 +-
 src/diffusers/schedulers/scheduling_heun_discrete.py            | 2 +-
 .../schedulers/scheduling_k_dpm_2_ancestral_discrete.py         | 2 +-
 src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py         | 2 +-
 src/diffusers/schedulers/scheduling_lcm.py                      | 2 +-
 src/diffusers/schedulers/scheduling_lms_discrete.py             | 2 +-
 src/diffusers/schedulers/scheduling_lms_discrete_flax.py        | 2 +-
 src/diffusers/schedulers/scheduling_pndm_flax.py                | 2 +-
 src/diffusers/schedulers/scheduling_sasolver.py                 | 2 +-
 src/diffusers/schedulers/scheduling_tcd.py                      | 2 +-
 src/diffusers/schedulers/scheduling_unipc_multistep.py          | 2 +-
 26 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py
index 8d50ee6c7ea9..103cca81c6a5 100644
--- a/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py
+++ b/src/diffusers/schedulers/scheduling_cosine_dpmsolver_multistep.py
@@ -53,7 +53,7 @@ class CosineDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `v_prediction`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         solver_type (`str`, defaults to `midpoint`):
             Solver type for the second-order solver; can be `midpoint` or `heun`. The solver type slightly affects the
             sample quality, especially for a small number of steps. It is recommended to use `midpoint` solvers.
diff --git a/src/diffusers/schedulers/scheduling_ddim_cogvideox.py b/src/diffusers/schedulers/scheduling_ddim_cogvideox.py
index acb5a5f3e522..f2683d1304ec 100644
--- a/src/diffusers/schedulers/scheduling_ddim_cogvideox.py
+++ b/src/diffusers/schedulers/scheduling_ddim_cogvideox.py
@@ -157,7 +157,7 @@ class CogVideoXDDIMScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_ddim_inverse.py b/src/diffusers/schedulers/scheduling_ddim_inverse.py
index a7717940e2a1..8ae13ad49d10 100644
--- a/src/diffusers/schedulers/scheduling_ddim_inverse.py
+++ b/src/diffusers/schedulers/scheduling_ddim_inverse.py
@@ -160,7 +160,7 @@ class DDIMInverseScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         timestep_spacing (`str`, defaults to `"leading"`):
             The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
             Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
diff --git a/src/diffusers/schedulers/scheduling_ddim_parallel.py b/src/diffusers/schedulers/scheduling_ddim_parallel.py
index d957ade901b3..10873a082fee 100644
--- a/src/diffusers/schedulers/scheduling_ddim_parallel.py
+++ b/src/diffusers/schedulers/scheduling_ddim_parallel.py
@@ -164,7 +164,7 @@ class DDIMParallelScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, default `epsilon`, optional):
             prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
             process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
+            https://huggingface.co/papers/2210.02303)
         thresholding (`bool`, default `False`):
             whether to use the "dynamic thresholding" method (introduced by Imagen,
             https://huggingface.co/papers/2205.11487). Note that the thresholding method is unsuitable for latent-space
diff --git a/src/diffusers/schedulers/scheduling_ddpm.py b/src/diffusers/schedulers/scheduling_ddpm.py
index 1d0ad49c58cd..ded88b8e1e0a 100644
--- a/src/diffusers/schedulers/scheduling_ddpm.py
+++ b/src/diffusers/schedulers/scheduling_ddpm.py
@@ -154,7 +154,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`"epsilon"`, `"sample"`, or `"v_prediction"`, defaults to `"epsilon"`):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_ddpm_parallel.py b/src/diffusers/schedulers/scheduling_ddpm_parallel.py
index 78011d0e46a1..941fc16be080 100644
--- a/src/diffusers/schedulers/scheduling_ddpm_parallel.py
+++ b/src/diffusers/schedulers/scheduling_ddpm_parallel.py
@@ -160,7 +160,7 @@ class DDPMParallelScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, default `epsilon`, optional):
             prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
             process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
+            https://huggingface.co/papers/2210.02303)
         thresholding (`bool`, default `False`):
             whether to use the "dynamic thresholding" method (introduced by Imagen,
             https://huggingface.co/papers/2205.11487). Note that the thresholding method is unsuitable for latent-space
diff --git a/src/diffusers/schedulers/scheduling_deis_multistep.py b/src/diffusers/schedulers/scheduling_deis_multistep.py
index 45d11c942660..09ce338a9222 100644
--- a/src/diffusers/schedulers/scheduling_deis_multistep.py
+++ b/src/diffusers/schedulers/scheduling_deis_multistep.py
@@ -101,7 +101,7 @@ class DEISMultistepScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_dpm_cogvideox.py b/src/diffusers/schedulers/scheduling_dpm_cogvideox.py
index c5d79b5fe54a..0a9082208cf4 100644
--- a/src/diffusers/schedulers/scheduling_dpm_cogvideox.py
+++ b/src/diffusers/schedulers/scheduling_dpm_cogvideox.py
@@ -158,7 +158,7 @@ class CogVideoXDPMScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py
index 2c5d798be0bf..6696b0375f9f 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py
@@ -101,7 +101,7 @@ class DPMSolverMultistepInverseScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_sde.py b/src/diffusers/schedulers/scheduling_dpmsolver_sde.py
index ef89feb1cad6..81c9e4134f57 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_sde.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_sde.py
@@ -182,7 +182,7 @@ class DPMSolverSDEScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         use_karras_sigmas (`bool`, *optional*, defaults to `False`):
             Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If `True`,
             the sigmas are determined according to a sequence of noise levels {σi}.
diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
index c51171cc9835..55c9fb6e7384 100644
--- a/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
+++ b/src/diffusers/schedulers/scheduling_dpmsolver_singlestep.py
@@ -103,7 +103,7 @@ class DPMSolverSinglestepScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py
index 5b1e84dc3a25..d4e8ca5e8b18 100644
--- a/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py
+++ b/src/diffusers/schedulers/scheduling_edm_dpmsolver_multistep.py
@@ -57,7 +57,7 @@ class EDMDPMSolverMultistepScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_edm_euler.py b/src/diffusers/schedulers/scheduling_edm_euler.py
index 0bf17356a7fa..2ed05d396514 100644
--- a/src/diffusers/schedulers/scheduling_edm_euler.py
+++ b/src/diffusers/schedulers/scheduling_edm_euler.py
@@ -74,7 +74,7 @@ class EDMEulerScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         rho (`float`, *optional*, defaults to 7.0):
             The rho parameter used for calculating the Karras sigma schedule, which is set to 7.0 in the EDM paper [1].
         final_sigmas_type (`str`, defaults to `"zero"`):
diff --git a/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py b/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py
index 8f39507301ce..8f042474afed 100644
--- a/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py
+++ b/src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py
@@ -152,7 +152,7 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         timestep_spacing (`str`, defaults to `"linspace"`):
             The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
             Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
diff --git a/src/diffusers/schedulers/scheduling_euler_discrete.py b/src/diffusers/schedulers/scheduling_euler_discrete.py
index 5ea926c4ca38..a55a76626cec 100644
--- a/src/diffusers/schedulers/scheduling_euler_discrete.py
+++ b/src/diffusers/schedulers/scheduling_euler_discrete.py
@@ -155,7 +155,7 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`Literal["epsilon", "sample", "v_prediction"]`, defaults to `"epsilon"`, *optional*):
             Prediction type of the scheduler function; can be `"epsilon"` (predicts the noise of the diffusion
             process), `"sample"` (directly predicts the noisy sample`) or `"v_prediction"` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         interpolation_type (`Literal["linear", "log_linear"]`, defaults to `"linear"`, *optional*):
             The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be one of
             `"linear"` or `"log_linear"`.
diff --git a/src/diffusers/schedulers/scheduling_euler_discrete_flax.py b/src/diffusers/schedulers/scheduling_euler_discrete_flax.py
index dae01302ac6a..09341c909d2e 100644
--- a/src/diffusers/schedulers/scheduling_euler_discrete_flax.py
+++ b/src/diffusers/schedulers/scheduling_euler_discrete_flax.py
@@ -74,7 +74,7 @@ class FlaxEulerDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin):
         prediction_type (`str`, default `epsilon`, optional):
             prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
             process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
+            https://huggingface.co/papers/2210.02303)
         dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
             the `dtype` used for params and computation.
     """
diff --git a/src/diffusers/schedulers/scheduling_heun_discrete.py b/src/diffusers/schedulers/scheduling_heun_discrete.py
index 930b0344646d..b63da9576f2a 100644
--- a/src/diffusers/schedulers/scheduling_heun_discrete.py
+++ b/src/diffusers/schedulers/scheduling_heun_discrete.py
@@ -115,7 +115,7 @@ class HeunDiscreteScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         clip_sample (`bool`, defaults to `True`):
             Clip the predicted sample for numerical stability.
         clip_sample_range (`float`, defaults to 1.0):
diff --git a/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py b/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
index 595b93c39d4c..da40bed635e1 100644
--- a/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
+++ b/src/diffusers/schedulers/scheduling_k_dpm_2_ancestral_discrete.py
@@ -125,7 +125,7 @@ class KDPM2AncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         timestep_spacing (`str`, defaults to `"linspace"`):
             The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
             Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
diff --git a/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py b/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
index 7db12227229e..6dc08d4d0a86 100644
--- a/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
+++ b/src/diffusers/schedulers/scheduling_k_dpm_2_discrete.py
@@ -124,7 +124,7 @@ class KDPM2DiscreteScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         timestep_spacing (`str`, defaults to `"linspace"`):
             The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
             Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
diff --git a/src/diffusers/schedulers/scheduling_lcm.py b/src/diffusers/schedulers/scheduling_lcm.py
index a7b0644de4f5..0527f3533851 100644
--- a/src/diffusers/schedulers/scheduling_lcm.py
+++ b/src/diffusers/schedulers/scheduling_lcm.py
@@ -170,7 +170,7 @@ class LCMScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_lms_discrete.py b/src/diffusers/schedulers/scheduling_lms_discrete.py
index d0766eed1b66..276af6eeacb7 100644
--- a/src/diffusers/schedulers/scheduling_lms_discrete.py
+++ b/src/diffusers/schedulers/scheduling_lms_discrete.py
@@ -120,7 +120,7 @@ class LMSDiscreteScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`"epsilon"`, `"sample"`, or `"v_prediction"`, defaults to `"epsilon"`):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         timestep_spacing (`"linspace"`, `"leading"`, or `"trailing"`, defaults to `"linspace"`):
             The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and
             Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information.
diff --git a/src/diffusers/schedulers/scheduling_lms_discrete_flax.py b/src/diffusers/schedulers/scheduling_lms_discrete_flax.py
index b8e08ff9e134..3fd4dc8a5d61 100644
--- a/src/diffusers/schedulers/scheduling_lms_discrete_flax.py
+++ b/src/diffusers/schedulers/scheduling_lms_discrete_flax.py
@@ -77,7 +77,7 @@ class FlaxLMSDiscreteScheduler(FlaxSchedulerMixin, ConfigMixin):
         prediction_type (`str`, default `epsilon`, optional):
             prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
             process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
+            https://huggingface.co/papers/2210.02303)
         dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
             the `dtype` used for params and computation.
     """
diff --git a/src/diffusers/schedulers/scheduling_pndm_flax.py b/src/diffusers/schedulers/scheduling_pndm_flax.py
index 12e22005afaf..44bafccd5520 100644
--- a/src/diffusers/schedulers/scheduling_pndm_flax.py
+++ b/src/diffusers/schedulers/scheduling_pndm_flax.py
@@ -103,7 +103,7 @@ class FlaxPNDMScheduler(FlaxSchedulerMixin, ConfigMixin):
         prediction_type (`str`, default `epsilon`, optional):
             prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
             process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
-            https://imagen.research.google/video/paper.pdf)
+            https://huggingface.co/papers/2210.02303)
         dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
             the `dtype` used for params and computation.
     """
diff --git a/src/diffusers/schedulers/scheduling_sasolver.py b/src/diffusers/schedulers/scheduling_sasolver.py
index 9eb37c44aea9..5783e20de69d 100644
--- a/src/diffusers/schedulers/scheduling_sasolver.py
+++ b/src/diffusers/schedulers/scheduling_sasolver.py
@@ -105,7 +105,7 @@ class SASolverScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         tau_func (`Callable`, *optional*):
             Stochasticity during the sampling. Default in init is `lambda t: 1 if t >= 200 and t <= 800 else 0`.
             SA-Solver will sample from vanilla diffusion ODE if tau_func is set to `lambda t: 0`. SA-Solver will sample
diff --git a/src/diffusers/schedulers/scheduling_tcd.py b/src/diffusers/schedulers/scheduling_tcd.py
index 37b41c87f8a2..7b4840ffdb19 100644
--- a/src/diffusers/schedulers/scheduling_tcd.py
+++ b/src/diffusers/schedulers/scheduling_tcd.py
@@ -171,7 +171,7 @@ class TCDScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
diff --git a/src/diffusers/schedulers/scheduling_unipc_multistep.py b/src/diffusers/schedulers/scheduling_unipc_multistep.py
index 606dfeb239be..6800c1220177 100644
--- a/src/diffusers/schedulers/scheduling_unipc_multistep.py
+++ b/src/diffusers/schedulers/scheduling_unipc_multistep.py
@@ -139,7 +139,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         prediction_type (`str`, defaults to `epsilon`, *optional*):
             Prediction type of the scheduler function; can be `epsilon` (predicts the noise of the diffusion process),
             `sample` (directly predicts the noisy sample`) or `v_prediction` (see section 2.4 of [Imagen
-            Video](https://imagen.research.google/video/paper.pdf) paper).
+            Video](https://huggingface.co/papers/2210.02303) paper).
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.