Merge branch 'main' into latex

README.md

@@ -46,7 +46,7 @@ The class provides functionality to compute previous image according to alpha, b
 
 ## Philosophy
 
-- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code desgin. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
+- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
 - Diffusers is **modality independent** and focusses on providing pretrained models and tools to build systems that generate **continous outputs**, *e.g.* vision and audio.
 - Diffusion models and schedulers are provided as consise, elementary building blocks whereas diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of other library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion).
 
@@ -173,7 +173,7 @@ image_pil = PIL.Image.fromarray(image_processed[0])
 image_pil.save("test.png")
 ```
 
-### 2. `diffusers` as a collection of popula Diffusion systems (GLIDE, Dalle, ...)
+### 2. `diffusers` as a collection of popular Diffusion systems (GLIDE, Dalle, ...)
 
 For more examples see [pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines).
 
@@ -276,6 +276,7 @@ wavwrite("generated_audio.wav", sampling_rate, audio.squeeze().cpu().numpy())
 - Write google colab for training [ ]
 - Write docs / Think about how to structure docs [ ]
 - Add tests to circle ci [ ]
+- Add [Diffusion LM models](https://arxiv.org/pdf/2205.14217.pdf) [ ]
 - Add more vision models [ ]
 - Add more speech models [ ]
 - Add RL model [ ]

examples/README.md

@@ -34,7 +34,7 @@ python -m torch.distributed.launch \
   train_ddpm.py \
   --dataset="huggan/pokemon" \
   --resolution=64 \
-  --output_path="flowers-ddpm" \
+  --output_path="pokemon-ddpm" \
   --batch_size=16 \
   --num_epochs=100 \
   --gradient_accumulation_steps=1 \

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)