refactor naming

README.md

@@ -48,7 +48,7 @@ The class provides functionality to compute previous image according to alpha, b
     
 
 **Diffusion Pipeline**: End-to-end pipeline that includes multiple diffusion models, possible text encoders, ...
-*Examples*: GLIDE, Latent-Diffusion, Imagen, DALL-E 2
+*Examples*: Glide, Latent-Diffusion, Imagen, DALL-E 2
 
 <p align="center">
     <img src="https://user-images.githubusercontent.com/10695622/174348898-481bd7c2-5457-4830-89bc-f0907756f64c.jpeg" width="550"/>
@@ -190,7 +190,7 @@ image_pil.save("test.png")
 
 [Diffuser](https://diffusion-planning.github.io/) for planning in reinforcement learning: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1TmBmlYeKUZSkUZoJqfBmaicVTKx6nN1R?usp=sharing)
 
-### 2. `diffusers` as a collection of popular 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).
 

scripts/conversion_glide.py

 import torch
 from torch import nn
 
-from diffusers import ClassifierFreeGuidanceScheduler, DDIMScheduler, GLIDESuperResUNetModel, GLIDETextToImageUNetModel
-from diffusers.pipelines.pipeline_glide import GLIDE, CLIPTextModel
+from diffusers import ClassifierFreeGuidanceScheduler, DDIMScheduler, GlideSuperResUNetModel, GlideTextToImageUNetModel
+from diffusers.pipelines.pipeline_glide import Glide, CLIPTextModel
 from transformers import CLIPTextConfig, GPT2Tokenizer
 
 
@@ -55,7 +55,7 @@ for layer_idx in range(config.num_hidden_layers):
 
 ### Convert the Text-to-Image UNet
 
-text2im_model = GLIDETextToImageUNetModel(
+text2im_model = GlideTextToImageUNetModel(
     in_channels=3,
     model_channels=192,
     out_channels=6,
@@ -80,7 +80,7 @@ text_scheduler = ClassifierFreeGuidanceScheduler(timesteps=1000, beta_schedule="
 # wget https://openaipublic.blob.core.windows.net/diffusion/dec-2021/upsample.pt
 ups_state_dict = torch.load("upsample.pt", map_location="cpu")
 
-superres_model = GLIDESuperResUNetModel(
+superres_model = GlideSuperResUNetModel(
     in_channels=6,
     model_channels=192,
     out_channels=6,
@@ -101,7 +101,7 @@ upscale_scheduler = DDIMScheduler(
     timesteps=1000, beta_schedule="linear", beta_start=0.0001, beta_end=0.02, tensor_format="pt"
 )
 
-glide = GLIDE(
+glide = Glide(
     text_unet=text2im_model,
     text_noise_scheduler=text_scheduler,
     text_encoder=model,

src/diffusers/__init__.py

 # flake8: noqa
 # There's no way to ignore "F401 '...' imported but unused" warnings in this
 # module, but to preserve other warnings. So, don't check this module at all.
-from .utils import is_transformers_available
+from .utils import is_inflect_available, is_transformers_available, is_unidecode_available
 
 
 __version__ = "0.0.4"
@@ -16,8 +16,14 @@ from .schedulers import DDIMScheduler, DDPMScheduler, GradTTSScheduler, PNDMSche
 
 
 if is_transformers_available():
-    from .models.unet_glide import GLIDESuperResUNetModel, GLIDETextToImageUNetModel, GLIDEUNetModel
+    from .models.unet_glide import GlideSuperResUNetModel, GlideTextToImageUNetModel, GlideUNetModel
     from .models.unet_grad_tts import UNetGradTTSModel
-    from .pipelines import GLIDE, GradTTS, LatentDiffusion
+    from .pipelines import Glide, LatentDiffusion
 else:
     from .utils.dummy_transformers_objects import *
+
+
+if is_transformers_available() and is_inflect_available() and is_unidecode_available():
+    from .pipelines import GradTTS
+else:
+    from .utils.dummy_transformers_and_inflect_and_unidecode_objects import *

src/diffusers/models/__init__.py

@@ -17,7 +17,7 @@
 # limitations under the License.
 
 from .unet import UNetModel
-from .unet_glide import GLIDESuperResUNetModel, GLIDETextToImageUNetModel, GLIDEUNetModel
+from .unet_glide import GlideSuperResUNetModel, GlideTextToImageUNetModel, GlideUNetModel
 from .unet_grad_tts import UNetGradTTSModel
 from .unet_ldm import UNetLDMModel
 from .unet_rl import TemporalUNet

src/diffusers/models/unet_glide.py

@@ -388,7 +388,7 @@ class QKVAttention(nn.Module):
         return a.reshape(bs, -1, length)
 
 
-class GLIDEUNetModel(ModelMixin, ConfigMixin):
+class GlideUNetModel(ModelMixin, ConfigMixin):
     """
     The full UNet model with attention and timestep embedding.
 
@@ -641,7 +641,7 @@ class GLIDEUNetModel(ModelMixin, ConfigMixin):
         return self.out(h)
 
 
-class GLIDETextToImageUNetModel(GLIDEUNetModel):
+class GlideTextToImageUNetModel(GlideUNetModel):
     """
     A UNetModel that performs super-resolution.
 
@@ -734,7 +734,7 @@ class GLIDETextToImageUNetModel(GLIDEUNetModel):
         return self.out(h)
 
 
-class GLIDESuperResUNetModel(GLIDEUNetModel):
+class GlideSuperResUNetModel(GlideUNetModel):
     """
     A UNetModel that performs super-resolution.
 

src/diffusers/pipelines/__init__.py

-from ..utils import is_transformers_available
+from ..utils import is_inflect_available, is_transformers_available, is_unidecode_available
 from .pipeline_bddm import BDDM
 from .pipeline_ddim import DDIM
 from .pipeline_ddpm import DDPM
@@ -6,6 +6,9 @@ from .pipeline_pndm import PNDM
 
 
 if is_transformers_available():
-    from .pipeline_glide import GLIDE
-    from .pipeline_grad_tts import GradTTS
+    from .pipeline_glide import Glide
     from .pipeline_latent_diffusion import LatentDiffusion
+
+
+if is_transformers_available() and is_unidecode_available() and is_inflect_available():
+    from .pipeline_grad_tts import GradTTS

src/diffusers/pipelines/grad_tts_utils.py

@@ -6,20 +6,9 @@ from shutil import copyfile
 
 import torch
 
+import inflect
 from transformers import PreTrainedTokenizer
-
-
-try:
-    from unidecode import unidecode
-except:
-    print("unidecode is not installed")
-    pass
-
-try:
-    import inflect
-except:
-    print("inflect is not installed")
-    pass
+from unidecode import unidecode
 
 
 valid_symbols = [
@@ -234,12 +223,7 @@ def english_cleaners(text):
     return text
 
 
-try:
-    _inflect = inflect.engine()
-except:
-    print("inflect is not installed")
-    _inflect = None
-
+_inflect = inflect.engine()
 _comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
 _decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
 _pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")

src/diffusers/pipelines/pipeline_glide.py

@@ -30,7 +30,7 @@ from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPo
 from transformers.modeling_utils import PreTrainedModel
 from transformers.utils import ModelOutput, add_start_docstrings_to_model_forward, replace_return_docstrings
 
-from ..models import GLIDESuperResUNetModel, GLIDETextToImageUNetModel
+from ..models import GlideSuperResUNetModel, GlideTextToImageUNetModel
 from ..pipeline_utils import DiffusionPipeline
 from ..schedulers import DDIMScheduler, DDPMScheduler
 from ..utils import logging
@@ -711,14 +711,14 @@ def _extract_into_tensor(arr, timesteps, broadcast_shape):
     return res + torch.zeros(broadcast_shape, device=timesteps.device)
 
 
-class GLIDE(DiffusionPipeline):
+class Glide(DiffusionPipeline):
     def __init__(
         self,
-        text_unet: GLIDETextToImageUNetModel,
+        text_unet: GlideTextToImageUNetModel,
         text_noise_scheduler: DDPMScheduler,
         text_encoder: CLIPTextModel,
         tokenizer: GPT2Tokenizer,
-        upscale_unet: GLIDESuperResUNetModel,
+        upscale_unet: GlideSuperResUNetModel,
         upscale_noise_scheduler: DDIMScheduler,
     ):
         super().__init__()

src/diffusers/schedulers/scheduling_ddim.py

@@ -73,7 +73,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         if beta_schedule == "linear":
             self.betas = np.linspace(beta_start, beta_end, timesteps, dtype=np.float32)
         elif beta_schedule == "squaredcos_cap_v2":
-            # GLIDE cosine schedule
+            # Glide cosine schedule
             self.betas = betas_for_alpha_bar(timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
@@ -132,7 +132,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         std_dev_t = eta * variance ** (0.5)
 
         if use_clipped_residual:
-            # the residual is always re-derived from the clipped x_0 in GLIDE
+            # the residual is always re-derived from the clipped x_0 in Glide
             residual = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
 
         # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf

src/diffusers/schedulers/scheduling_ddpm.py

@@ -76,7 +76,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         elif beta_schedule == "linear":
             self.betas = np.linspace(beta_start, beta_end, timesteps, dtype=np.float32)
         elif beta_schedule == "squaredcos_cap_v2":
-            # GLIDE cosine schedule
+            # Glide cosine schedule
             self.betas = betas_for_alpha_bar(timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
@@ -108,7 +108,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         elif variance_type == "fixed_large":
             variance = self.betas[t]
         elif variance_type == "fixed_large_log":
-            # GLIDE max_log
+            # Glide max_log
             variance = self.log(self.betas[t])
 
         return variance

src/diffusers/schedulers/scheduling_pndm.py

@@ -66,7 +66,7 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
         if beta_schedule == "linear":
             self.betas = np.linspace(beta_start, beta_end, timesteps, dtype=np.float32)
         elif beta_schedule == "squaredcos_cap_v2":
-            # GLIDE cosine schedule
+            # Glide cosine schedule
             self.betas = betas_for_alpha_bar(timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")

src/diffusers/utils/__init__.py

@@ -45,10 +45,34 @@ except importlib_metadata.PackageNotFoundError:
     _transformers_available = False
 
 
+_inflect_available = importlib.util.find_spec("inflect") is not None
+try:
+    _inflect_version = importlib_metadata.version("inflect")
+    logger.debug(f"Successfully imported inflect version {_inflect_version}")
+except importlib_metadata.PackageNotFoundError:
+    _inflect_available = False
+
+
+_unidecode_available = importlib.util.find_spec("unidecode") is not None
+try:
+    _unidecode_version = importlib_metadata.version("unidecode")
+    logger.debug(f"Successfully imported unidecode version {_unidecode_version}")
+except importlib_metadata.PackageNotFoundError:
+    _unidecode_available = False
+
+
 def is_transformers_available():
     return _transformers_available
 
 
+def is_inflect_available():
+    return _inflect_available
+
+
+def is_unidecode_available():
+    return _unidecode_available
+
+
 class RepositoryNotFoundError(HTTPError):
     """
     Raised when trying to access a hf.co URL with an invalid repository name, or with a private repo name the user does
@@ -70,9 +94,23 @@ TRANSFORMERS_IMPORT_ERROR = """
 """
 
 
+UNIDECODE_IMPORT_ERROR = """
+{0} requires the unidecode library but it was not found in your environment. You can install it with pip:
+`pip install Unidecode`
+"""
+
+
+INFLECT_IMPORT_ERROR = """
+{0} requires the inflect library but it was not found in your environment. You can install it with pip:
+`pip install inflect`
+"""
+
+
 BACKENDS_MAPPING = OrderedDict(
     [
         ("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
+        ("unidecode", (is_unidecode_available, UNIDECODE_IMPORT_ERROR)),
+        ("inflect", (is_inflect_available, INFLECT_IMPORT_ERROR)),
     ]
 )
 

src/diffusers/utils/dummy_transformers_and_inflect_and_unidecode_objects.py

+# This file is autogenerated by the command `make fix-copies`, do not edit.
+# flake8: noqa
+from ..utils import DummyObject, requires_backends
+
+
+class GradTTS(metaclass=DummyObject):
+    _backends = ["transformers", "inflect", "unidecode"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["transformers", "inflect", "unidecode"])

src/diffusers/utils/dummy_transformers_objects.py

@@ -3,21 +3,21 @@
 from ..utils import DummyObject, requires_backends
 
 
-class GLIDESuperResUNetModel(metaclass=DummyObject):
+class GlideSuperResUNetModel(metaclass=DummyObject):
     _backends = ["transformers"]
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["transformers"])
 
 
-class GLIDETextToImageUNetModel(metaclass=DummyObject):
+class GlideTextToImageUNetModel(metaclass=DummyObject):
     _backends = ["transformers"]
 
     def __init__(self, *args, **kwargs):
         requires_backends(self, ["transformers"])
 
 
-class GLIDEUNetModel(metaclass=DummyObject):
+class GlideUNetModel(metaclass=DummyObject):
     _backends = ["transformers"]
 
     def __init__(self, *args, **kwargs):
@@ -31,10 +31,7 @@ class UNetGradTTSModel(metaclass=DummyObject):
         requires_backends(self, ["transformers"])
 
 
-GLIDE = None
-
-
-class GradTTS(metaclass=DummyObject):
+class Glide(metaclass=DummyObject):
     _backends = ["transformers"]
 
     def __init__(self, *args, **kwargs):

tests/test_modeling_utils.py

@@ -21,17 +21,17 @@ import unittest
 import numpy as np
 import torch
 
-import pytest
 from diffusers import (
     BDDM,
     DDIM,
     DDPM,
-    GLIDE,
+    Glide,
     PNDM,
     DDIMScheduler,
     DDPMScheduler,
-    GLIDESuperResUNetModel,
-    GLIDETextToImageUNetModel,
+    GlideSuperResUNetModel,
+    GlideTextToImageUNetModel,
+    GradTTS,
     LatentDiffusion,
     PNDMScheduler,
     UNetGradTTSModel,
@@ -247,13 +247,13 @@ class UnetModelTests(ModelTesterMixin, unittest.TestCase):
 
         output_slice = output[0, -1, -3:, -3:].flatten()
         # fmt: off
-        expected_output_slice = torch.tensor([ 0.2891, -0.1899,  0.2595, -0.6214,  0.0968, -0.2622,  0.4688,  0.1311, 0.0053])
+        expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
         # fmt: on
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
 
 
-class GLIDESuperResUNetTests(ModelTesterMixin, unittest.TestCase):
-    model_class = GLIDESuperResUNetModel
+class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
+    model_class = GlideSuperResUNetModel
 
     @property
     def dummy_input(self):
@@ -309,7 +309,7 @@ class GLIDESuperResUNetTests(ModelTesterMixin, unittest.TestCase):
         self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
     def test_from_pretrained_hub(self):
-        model, loading_info = GLIDESuperResUNetModel.from_pretrained(
+        model, loading_info = GlideSuperResUNetModel.from_pretrained(
             "fusing/glide-super-res-dummy", output_loading_info=True
         )
         self.assertIsNotNone(model)
@@ -321,7 +321,7 @@ class GLIDESuperResUNetTests(ModelTesterMixin, unittest.TestCase):
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
-        model = GLIDESuperResUNetModel.from_pretrained("fusing/glide-super-res-dummy")
+        model = GlideSuperResUNetModel.from_pretrained("fusing/glide-super-res-dummy")
 
         torch.manual_seed(0)
         if torch.cuda.is_available():
@@ -342,8 +342,8 @@ class GLIDESuperResUNetTests(ModelTesterMixin, unittest.TestCase):
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
 
 
-class GLIDETextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
-    model_class = GLIDETextToImageUNetModel
+class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
+    model_class = GlideTextToImageUNetModel
 
     @property
     def dummy_input(self):
@@ -401,7 +401,7 @@ class GLIDETextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
         self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
     def test_from_pretrained_hub(self):
-        model, loading_info = GLIDETextToImageUNetModel.from_pretrained(
+        model, loading_info = GlideTextToImageUNetModel.from_pretrained(
             "fusing/unet-glide-text2im-dummy", output_loading_info=True
         )
         self.assertIsNotNone(model)
@@ -413,7 +413,7 @@ class GLIDETextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
         assert image is not None, "Make sure output is not None"
 
     def test_output_pretrained(self):
-        model = GLIDETextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy")
+        model = GlideTextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy")
 
         torch.manual_seed(0)
         if torch.cuda.is_available():
@@ -431,7 +431,7 @@ class GLIDETextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
         output, _ = torch.split(output, 3, dim=1)
         output_slice = output[0, -1, -3:, -3:].flatten()
         # fmt: off
-        expected_output_slice = torch.tensor([  2.7766, -10.3558, -14.9149,  -0.9376, -14.9175, -17.7679,  -5.5565, -12.9521, -12.9845])
+        expected_output_slice = torch.tensor([2.7766, -10.3558, -14.9149, -0.9376, -14.9175, -17.7679, -5.5565, -12.9521, -12.9845])
         # fmt: on
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
 
@@ -571,7 +571,7 @@ class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
 
         output_slice = output[0, -3:, -3:].flatten()
         # fmt: off
-        expected_output_slice = torch.tensor([-0.0690, -0.0531,  0.0633, -0.0660, -0.0541,  0.0650, -0.0656, -0.0555, 0.0617])
+        expected_output_slice = torch.tensor([-0.0690, -0.0531, 0.0633, -0.0660, -0.0541, 0.0650, -0.0656, -0.0555, 0.0617])
         # fmt: on
 
         self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
@@ -689,7 +689,7 @@ class PipelineTesterMixin(unittest.TestCase):
     @slow
     def test_glide_text2img(self):
         model_id = "fusing/glide-base"
-        glide = GLIDE.from_pretrained(model_id)
+        glide = Glide.from_pretrained(model_id)
 
         prompt = "a pencil sketch of a corgi"
         generator = torch.manual_seed(0)
@@ -701,6 +701,20 @@ class PipelineTesterMixin(unittest.TestCase):
         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
 
+    @slow
+    def test_grad_tts(self):
+        model_id = "fusing/grad-tts-libri-tts"
+        grad_tts = GradTTS.from_pretrained(model_id)
+
+        text = "Hello world, I missed you so much."
+
+        # generate mel spectograms using text
+        mel_spec = grad_tts(text)
+
+        assert mel_spec.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 (mel_spec.flatten() - expected_slice).abs().max() < 1e-2
+
     def test_module_from_pipeline(self):
         model = DiffWave(num_res_layers=4)
         noise_scheduler = DDPMScheduler(timesteps=12)

utils/check_dummies.py

@@ -23,10 +23,9 @@ import re
 PATH_TO_DIFFUSERS = "src/diffusers"
 
 # Matches is_xxx_available()
-_re_backend = re.compile(r"if is\_([a-z_]*)_available\(\)")
+_re_backend = re.compile(r"is\_([a-z_]*)_available\(\)")
 # Matches from xxx import bla
 _re_single_line_import = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
-_re_test_backend = re.compile(r"^\s+if\s+not\s+is\_[a-z]*\_available\(\)")
 
 
 DUMMY_CONSTANT = """
@@ -54,7 +53,7 @@ def find_backend(line):
     if len(backends) == 0:
         return None
 
-    return backends[0]
+    return "_and_".join(backends)
 
 
 def read_init():