[Docs] Update Imagen Video paper link in schedulers (#12724)

docs: Update Imagen Video paper link in scheduler docstrings.

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.

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.
     """

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.
     """

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

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.

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.