Merge branch 'main' into grad-tts

src/diffusers/schedulers/scheduling_ddpm.py

@@ -44,7 +44,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             clip_predicted_image=clip_predicted_image,
         )
         self.timesteps = int(timesteps)
-        self.timestep_values = timestep_values # save the fixed timestep values for BDDM
+        self.timestep_values = timestep_values  # save the fixed timestep values for BDDM
         self.clip_image = clip_predicted_image
         self.variance_type = variance_type
 

src/diffusers/schedulers/scheduling_pndm.py

@@ -84,7 +84,9 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
 
         inference_step_times = list(range(0, self.timesteps, self.timesteps // num_inference_steps))
 
-        warmup_time_steps = np.array(inference_step_times[-self.pndm_order:]).repeat(2) + np.tile(np.array([0, self.timesteps // num_inference_steps // 2]), self.pndm_order)
+        warmup_time_steps = np.array(inference_step_times[-self.pndm_order :]).repeat(2) + np.tile(
+            np.array([0, self.timesteps // num_inference_steps // 2]), self.pndm_order
+        )
         self.warmup_time_steps[num_inference_steps] = list(reversed(warmup_time_steps[:-1].repeat(2)[1:-1]))
 
         return self.warmup_time_steps[num_inference_steps]
@@ -137,7 +139,10 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         at = alphas_cump[t + 1].view(-1, 1, 1, 1)
         at_next = alphas_cump[t_next + 1].view(-1, 1, 1, 1)
 
-        x_delta = (at_next - at) * ((1 / (at.sqrt() * (at.sqrt() + at_next.sqrt()))) * x - 1 / (at.sqrt() * (((1 - at_next) * at).sqrt() + ((1 - at) * at_next).sqrt())) * et)
+        x_delta = (at_next - at) * (
+            (1 / (at.sqrt() * (at.sqrt() + at_next.sqrt()))) * x
+            - 1 / (at.sqrt() * (((1 - at_next) * at).sqrt() + ((1 - at) * at_next).sqrt())) * et
+        )
 
         x_next = x + x_delta
         return x_next

src/diffusers/utils/logging.py

@@ -49,16 +49,16 @@ _tqdm_active = True
 
 def _get_default_logging_level():
     """
-    If TRANSFORMERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
+    If DIFFUSERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
     not - fall back to `_default_log_level`
     """
-    env_level_str = os.getenv("TRANSFORMERS_VERBOSITY", None)
+    env_level_str = os.getenv("DIFFUSERS_VERBOSITY", None)
     if env_level_str:
         if env_level_str in log_levels:
             return log_levels[env_level_str]
         else:
             logging.getLogger().warning(
-                f"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
+                f"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
                 f"has to be one of: { ', '.join(log_levels.keys()) }"
             )
     return _default_log_level
@@ -126,14 +126,14 @@ def get_logger(name: Optional[str] = None) -> logging.Logger:
 
 def get_verbosity() -> int:
     """
-    Return the current level for the 🤗 Transformers's root logger as an int.
+    Return the current level for the 🤗 Diffusers' root logger as an int.
 
     Returns:
         `int`: The logging level.
 
     <Tip>
 
-    🤗 Transformers has following logging levels:
+    🤗 Diffusers has following logging levels:
 
     - 50: `diffusers.logging.CRITICAL` or `diffusers.logging.FATAL`
     - 40: `diffusers.logging.ERROR`
@@ -149,7 +149,7 @@ def get_verbosity() -> int:
 
 def set_verbosity(verbosity: int) -> None:
     """
-    Set the verbosity level for the 🤗 Transformers's root logger.
+    Set the verbosity level for the 🤗 Diffusers' root logger.
 
     Args:
         verbosity (`int`):
@@ -187,7 +187,7 @@ def set_verbosity_error():
 
 
 def disable_default_handler() -> None:
-    """Disable the default handler of the HuggingFace Transformers's root logger."""
+    """Disable the default handler of the HuggingFace Diffusers' root logger."""
 
     _configure_library_root_logger()
 
@@ -196,7 +196,7 @@ def disable_default_handler() -> None:
 
 
 def enable_default_handler() -> None:
-    """Enable the default handler of the HuggingFace Transformers's root logger."""
+    """Enable the default handler of the HuggingFace Diffusers' root logger."""
 
     _configure_library_root_logger()
 
@@ -205,7 +205,7 @@ def enable_default_handler() -> None:
 
 
 def add_handler(handler: logging.Handler) -> None:
-    """adds a handler to the HuggingFace Transformers's root logger."""
+    """adds a handler to the HuggingFace Diffusers' root logger."""
 
     _configure_library_root_logger()
 
@@ -214,7 +214,7 @@ def add_handler(handler: logging.Handler) -> None:
 
 
 def remove_handler(handler: logging.Handler) -> None:
-    """removes given handler from the HuggingFace Transformers's root logger."""
+    """removes given handler from the HuggingFace Diffusers' root logger."""
 
     _configure_library_root_logger()
 
@@ -233,7 +233,7 @@ def disable_propagation() -> None:
 
 def enable_propagation() -> None:
     """
-    Enable propagation of the library log outputs. Please disable the HuggingFace Transformers's default handler to
+    Enable propagation of the library log outputs. Please disable the HuggingFace Diffusers' default handler to
     prevent double logging if the root logger has been configured.
     """
 
@@ -243,7 +243,7 @@ def enable_propagation() -> None:
 
 def enable_explicit_format() -> None:
     """
-    Enable explicit formatting for every HuggingFace Transformers's logger. The explicit formatter is as follows:
+    Enable explicit formatting for every HuggingFace Diffusers' logger. The explicit formatter is as follows:
     ```
         [LEVELNAME|FILENAME|LINE NUMBER] TIME >> MESSAGE
     ```
@@ -258,7 +258,7 @@ def enable_explicit_format() -> None:
 
 def reset_format() -> None:
     """
-    Resets the formatting for HuggingFace Transformers's loggers.
+    Resets the formatting for HuggingFace Diffusers' loggers.
 
     All handlers currently bound to the root logger are affected by this method.
     """
@@ -270,10 +270,10 @@ def reset_format() -> None:
 
 def warning_advice(self, *args, **kwargs):
     """
-    This method is identical to `logger.warninging()`, but if env var TRANSFORMERS_NO_ADVISORY_WARNINGS=1 is set, this
+    This method is identical to `logger.warninging()`, but if env var DIFFUSERS_NO_ADVISORY_WARNINGS=1 is set, this
     warning will not be printed
     """
-    no_advisory_warnings = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS", False)
+    no_advisory_warnings = os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS", False)
     if no_advisory_warnings:
         return
     self.warning(*args, **kwargs)

tests/test_modeling_utils.py

@@ -19,7 +19,18 @@ import unittest
 
 import torch
 
-from diffusers import DDIM, DDPM, PNDM, GLIDE, BDDM, DDIMScheduler, DDPMScheduler, LatentDiffusion, PNDMScheduler, UNetModel
+from diffusers import (
+    BDDM,
+    DDIM,
+    DDPM,
+    GLIDE,
+    PNDM,
+    DDIMScheduler,
+    DDPMScheduler,
+    LatentDiffusion,
+    PNDMScheduler,
+    UNetModel,
+)
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.pipeline_bddm import DiffWave
@@ -214,6 +225,21 @@ class PipelineTesterMixin(unittest.TestCase):
         expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458])
         assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
 
+    @slow
+    def test_glide_text2img(self):
+        model_id = "fusing/glide-base"
+        glide = GLIDE.from_pretrained(model_id)
+
+        prompt = "a pencil sketch of a corgi"
+        generator = torch.manual_seed(0)
+        image = glide(prompt, generator=generator, num_inference_steps_upscale=20)
+
+        image_slice = image[0, :3, :3, -1].cpu()
+
+        assert image.shape == (1, 256, 256, 3)
+        expected_slice = torch.tensor([0.7119, 0.7073, 0.6460, 0.7780, 0.7423, 0.6926, 0.7378, 0.7189, 0.7784])
+        assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
+
     def test_module_from_pipeline(self):
         model = DiffWave(num_res_layers=4)
         noise_scheduler = DDPMScheduler(timesteps=12)
@@ -229,17 +255,3 @@ class PipelineTesterMixin(unittest.TestCase):
             _ = BDDM.from_pretrained(tmpdirname)
             # check if the same works using the DifusionPipeline class
             _ = DiffusionPipeline.from_pretrained(tmpdirname)
-    @slow
-    def test_glide_text2img(self):
-        model_id = "fusing/glide-base"
-        glide = GLIDE.from_pretrained(model_id)
-
-        prompt = "a pencil sketch of a corgi"
-        generator = torch.manual_seed(0)
-        image = glide(prompt, generator=generator, num_inference_steps_upscale=20)
-
-        image_slice = image[0, :3, :3, -1].cpu()
-
-        assert image.shape == (1, 256, 256, 3)
-        expected_slice = torch.tensor([0.7119, 0.7073, 0.6460, 0.7780, 0.7423, 0.6926, 0.7378, 0.7189, 0.7784])
-        assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2