Use HF Papers (#11567)

* Use HF Papers

* Apply style fixes

---------
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

src/diffusers/schedulers/scheduling_sasolver.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# DISCLAIMER: check https://arxiv.org/abs/2309.05019
+# DISCLAIMER: check https://huggingface.co/papers/2309.05019
 # The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
 
 import math
@@ -109,7 +109,7 @@ class SASolverScheduler(SchedulerMixin, ConfigMixin):
             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
             from vanilla diffusion SDE if tau_func is set to `lambda t: 1`. For more details, please check
-            https://arxiv.org/abs/2309.05019
+            https://huggingface.co/papers/2309.05019
         thresholding (`bool`, defaults to `False`):
             Whether to use the "dynamic thresholding" method. This is unsuitable for latent-space diffusion models such
             as Stable Diffusion.
@@ -273,7 +273,7 @@ class SASolverScheduler(SchedulerMixin, ConfigMixin):
         clipped_idx = torch.searchsorted(torch.flip(self.lambda_t, [0]), self.config.lambda_min_clipped)
         last_timestep = ((self.config.num_train_timesteps - clipped_idx).numpy()).item()
 
-        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
+        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
         if self.config.timestep_spacing == "linspace":
             timesteps = (
                 np.linspace(0, last_timestep - 1, num_inference_steps + 1).round()[::-1][:-1].copy().astype(np.int64)
@@ -348,7 +348,7 @@ class SASolverScheduler(SchedulerMixin, ConfigMixin):
         pixels from saturation at each step. We find that dynamic thresholding results in significantly better
         photorealism as well as better image-text alignment, especially when using very large guidance weights."
 
-        https://arxiv.org/abs/2205.11487
+        https://huggingface.co/papers/2205.11487
         """
         dtype = sample.dtype
         batch_size, channels, *remaining_dims = sample.shape

src/diffusers/schedulers/scheduling_sde_ve_flax.py

@@ -61,7 +61,7 @@ class FlaxScoreSdeVeScheduler(FlaxSchedulerMixin, ConfigMixin):
     """
     The variance exploding stochastic differential equation (SDE) scheduler.
 
-    For more information, see the original paper: https://arxiv.org/abs/2011.13456
+    For more information, see the original paper: https://huggingface.co/papers/2011.13456
 
     [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
     function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.

src/diffusers/schedulers/scheduling_tcd.py

@@ -96,7 +96,7 @@ def betas_for_alpha_bar(
 # Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
 def rescale_zero_terminal_snr(betas: torch.Tensor) -> torch.Tensor:
     """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
+    Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
 
 
     Args:
@@ -334,7 +334,7 @@ class TCDScheduler(SchedulerMixin, ConfigMixin):
         pixels from saturation at each step. We find that dynamic thresholding results in significantly better
         photorealism as well as better image-text alignment, especially when using very large guidance weights."
 
-        https://arxiv.org/abs/2205.11487
+        https://huggingface.co/papers/2205.11487
         """
         dtype = sample.dtype
         batch_size, channels, *remaining_dims = sample.shape

src/diffusers/schedulers/scheduling_unclip.py

@@ -191,7 +191,7 @@ class UnCLIPScheduler(SchedulerMixin, ConfigMixin):
         else:
             beta = 1 - alpha_prod_t / alpha_prod_t_prev
 
-        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
+        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://huggingface.co/papers/2006.11239)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         variance = beta_prod_t_prev / beta_prod_t * beta
@@ -266,7 +266,7 @@ class UnCLIPScheduler(SchedulerMixin, ConfigMixin):
             alpha = 1 - beta
 
         # 2. compute predicted original sample from predicted noise also called
-        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
+        # "predicted x_0" of formula (15) from https://huggingface.co/papers/2006.11239
         if self.config.prediction_type == "epsilon":
             pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
         elif self.config.prediction_type == "sample":
@@ -284,12 +284,12 @@ class UnCLIPScheduler(SchedulerMixin, ConfigMixin):
             )
 
         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+        # See formula (7) from https://huggingface.co/papers/2006.11239
         pred_original_sample_coeff = (alpha_prod_t_prev ** (0.5) * beta) / beta_prod_t
         current_sample_coeff = alpha ** (0.5) * beta_prod_t_prev / beta_prod_t
 
         # 5. Compute predicted previous sample µ_t
-        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
+        # See formula (7) from https://huggingface.co/papers/2006.11239
         pred_prev_sample = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
 
         # 6. Add noise

src/diffusers/schedulers/scheduling_unipc_multistep.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-# DISCLAIMER: check https://arxiv.org/abs/2302.04867 and https://github.com/wl-zhao/UniPC for more info
+# DISCLAIMER: check https://huggingface.co/papers/2302.04867 and https://github.com/wl-zhao/UniPC for more info
 # The codebase is modified based on https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py
 
 import math
@@ -78,7 +78,7 @@ def betas_for_alpha_bar(
 # Copied from diffusers.schedulers.scheduling_ddim.rescale_zero_terminal_snr
 def rescale_zero_terminal_snr(betas):
     """
-    Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1)
+    Rescales betas to have zero terminal SNR Based on https://huggingface.co/papers/2305.08891 (Algorithm 1)
 
 
     Args:
@@ -308,7 +308,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
             device (`str` or `torch.device`, *optional*):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
         """
-        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
+        # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://huggingface.co/papers/2305.08891
         if self.config.timestep_spacing == "linspace":
             timesteps = (
                 np.linspace(0, self.config.num_train_timesteps - 1, num_inference_steps + 1)
@@ -429,7 +429,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin):
         pixels from saturation at each step. We find that dynamic thresholding results in significantly better
         photorealism as well as better image-text alignment, especially when using very large guidance weights."
 
-        https://arxiv.org/abs/2205.11487
+        https://huggingface.co/papers/2205.11487
         """
         dtype = sample.dtype
         batch_size, channels, *remaining_dims = sample.shape

src/diffusers/training_utils.py

@@ -149,9 +149,9 @@ def compute_dream_and_update_latents(
     dream_detail_preservation: float = 1.0,
 ) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor]]:
     """
-    Implements "DREAM (Diffusion Rectification and Estimation-Adaptive Models)" from http://arxiv.org/abs/2312.00210.
-    DREAM helps align training with sampling to help training be more efficient and accurate at the cost of an extra
-    forward step without gradients.
+    Implements "DREAM (Diffusion Rectification and Estimation-Adaptive Models)" from
+    https://huggingface.co/papers/2312.00210. DREAM helps align training with sampling to help training be more
+    efficient and accurate at the cost of an extra forward step without gradients.
 
     Args:
         `unet`: The state unet to use to make a prediction.
@@ -261,7 +261,7 @@ def compute_density_for_timestep_sampling(
 
     Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
 
-    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    SD3 paper reference: https://huggingface.co/papers/2403.03206v1.
     """
     if weighting_scheme == "logit_normal":
         u = torch.normal(mean=logit_mean, std=logit_std, size=(batch_size,), device=device, generator=generator)
@@ -280,7 +280,7 @@ def compute_loss_weighting_for_sd3(weighting_scheme: str, sigmas=None):
 
     Courtesy: This was contributed by Rafie Walker in https://github.com/huggingface/diffusers/pull/8528.
 
-    SD3 paper reference: https://arxiv.org/abs/2403.03206v1.
+    SD3 paper reference: https://huggingface.co/papers/2403.03206v1.
     """
     if weighting_scheme == "sigma_sqrt":
         weighting = (sigmas**-2.0).float()

src/diffusers/utils/torch_utils.py

@@ -91,7 +91,7 @@ def is_compiled_module(module) -> bool:
 
 
 def fourier_filter(x_in: "torch.Tensor", threshold: int, scale: int) -> "torch.Tensor":
-    """Fourier filter as introduced in FreeU (https://arxiv.org/abs/2309.11497).
+    """Fourier filter as introduced in FreeU (https://huggingface.co/papers/2309.11497).
 
     This version of the method comes from here:
     https://github.com/huggingface/diffusers/pull/5164#issuecomment-1732638706