Merge branch 'main' of https://github.com/huggingface/diffusers

src/diffusers/schedulers/scheduling_ddpm.py

@@ -17,6 +17,7 @@
 import math
 
 import numpy as np
+import torch
 
 from ..configuration_utils import ConfigMixin
 from .scheduling_utils import SchedulerMixin
@@ -76,7 +77,7 @@ 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 +109,7 @@ 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
@@ -142,11 +143,18 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
 
         return pred_prev_sample
 
-    def forward_step(self, original_sample, noise, t):
-        sqrt_alpha_prod = self.alphas_cumprod[t] ** 0.5
-        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[t]) ** 0.5
-        noisy_sample = sqrt_alpha_prod * original_sample + sqrt_one_minus_alpha_prod * noise
-        return noisy_sample
+    def training_step(self, original_samples: torch.Tensor, noise: torch.Tensor, timesteps: torch.Tensor):
+        if timesteps.dim() != 1:
+            raise ValueError("`timesteps` must be a 1D tensor")
+
+        device = original_samples.device
+        batch_size = original_samples.shape[0]
+        timesteps = timesteps.reshape(batch_size, 1, 1, 1)
+
+        sqrt_alpha_prod = self.alphas_cumprod[timesteps] ** 0.5
+        sqrt_one_minus_alpha_prod = (1 - self.alphas_cumprod[timesteps]) ** 0.5
+        noisy_samples = sqrt_alpha_prod.to(device) * original_samples + sqrt_one_minus_alpha_prod.to(device) * noise
+        return noisy_samples
 
     def __len__(self):
         return self.config.timesteps

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

@@ -21,18 +21,18 @@ import unittest
 import numpy as np
 import torch
 
-import pytest
 from diffusers import (
-    BDDM,
-    DDIM,
-    DDPM,
-    GLIDE,
-    PNDM,
+    BDDMPipeline,
+    DDIMPipeline,
     DDIMScheduler,
+    DDPMPipeline,
     DDPMScheduler,
-    GLIDESuperResUNetModel,
-    GLIDETextToImageUNetModel,
-    LatentDiffusion,
+    GlidePipeline,
+    GlideSuperResUNetModel,
+    GlideTextToImageUNetModel,
+    GradTTSPipeline,
+    LatentDiffusionPipeline,
+    PNDMPipeline,
     PNDMScheduler,
     UNetGradTTSModel,
     UNetLDMModel,
@@ -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))
@@ -583,11 +583,11 @@ class PipelineTesterMixin(unittest.TestCase):
         model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32)
         schedular = DDPMScheduler(timesteps=10)
 
-        ddpm = DDPM(model, schedular)
+        ddpm = DDPMPipeline(model, schedular)
 
         with tempfile.TemporaryDirectory() as tmpdirname:
             ddpm.save_pretrained(tmpdirname)
-            new_ddpm = DDPM.from_pretrained(tmpdirname)
+            new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
 
         generator = torch.manual_seed(0)
 
@@ -601,7 +601,7 @@ class PipelineTesterMixin(unittest.TestCase):
     def test_from_pretrained_hub(self):
         model_path = "fusing/ddpm-cifar10"
 
-        ddpm = DDPM.from_pretrained(model_path)
+        ddpm = DDPMPipeline.from_pretrained(model_path)
         ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
 
         ddpm.noise_scheduler.num_timesteps = 10
@@ -624,7 +624,7 @@ class PipelineTesterMixin(unittest.TestCase):
         noise_scheduler = DDPMScheduler.from_config(model_id)
         noise_scheduler = noise_scheduler.set_format("pt")
 
-        ddpm = DDPM(unet=unet, noise_scheduler=noise_scheduler)
+        ddpm = DDPMPipeline(unet=unet, noise_scheduler=noise_scheduler)
         image = ddpm(generator=generator)
 
         image_slice = image[0, -1, -3:, -3:].cpu()
@@ -641,7 +641,7 @@ class PipelineTesterMixin(unittest.TestCase):
         unet = UNetModel.from_pretrained(model_id)
         noise_scheduler = DDIMScheduler(tensor_format="pt")
 
-        ddim = DDIM(unet=unet, noise_scheduler=noise_scheduler)
+        ddim = DDIMPipeline(unet=unet, noise_scheduler=noise_scheduler)
         image = ddim(generator=generator, eta=0.0)
 
         image_slice = image[0, -1, -3:, -3:].cpu()
@@ -660,7 +660,7 @@ class PipelineTesterMixin(unittest.TestCase):
         unet = UNetModel.from_pretrained(model_id)
         noise_scheduler = PNDMScheduler(tensor_format="pt")
 
-        pndm = PNDM(unet=unet, noise_scheduler=noise_scheduler)
+        pndm = PNDMPipeline(unet=unet, noise_scheduler=noise_scheduler)
         image = pndm(generator=generator)
 
         image_slice = image[0, -1, -3:, -3:].cpu()
@@ -674,7 +674,7 @@ class PipelineTesterMixin(unittest.TestCase):
     @slow
     def test_ldm_text2img(self):
         model_id = "fusing/latent-diffusion-text2im-large"
-        ldm = LatentDiffusion.from_pretrained(model_id)
+        ldm = LatentDiffusionPipeline.from_pretrained(model_id)
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
@@ -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 = GlidePipeline.from_pretrained(model_id)
 
         prompt = "a pencil sketch of a corgi"
         generator = torch.manual_seed(0)
@@ -701,11 +701,28 @@ 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 = GradTTSPipeline.from_pretrained(model_id)
+
+        text = "Hello world, I missed you so much."
+        generator = torch.manual_seed(0)
+
+        # generate mel spectograms using text
+        mel_spec = grad_tts(text, generator=generator)
+
+        assert mel_spec.shape == (1, 80, 143)
+        expected_slice = torch.tensor(
+            [-6.6119, -6.5963, -6.2776, -6.7496, -6.7096, -6.5131, -6.4643, -6.4817, -6.7185]
+        )
+        assert (mel_spec[0, :3, :3].flatten() - expected_slice).abs().max() < 1e-2
+
     def test_module_from_pipeline(self):
         model = DiffWave(num_res_layers=4)
         noise_scheduler = DDPMScheduler(timesteps=12)
 
-        bddm = BDDM(model, noise_scheduler)
+        bddm = BDDMPipeline(model, noise_scheduler)
 
         # check if the library name for the diffwave moduel is set to pipeline module
         self.assertTrue(bddm.config["diffwave"][0] == "pipeline_bddm")
@@ -713,6 +730,6 @@ class PipelineTesterMixin(unittest.TestCase):
         # check if we can save and load the pipeline
         with tempfile.TemporaryDirectory() as tmpdirname:
             bddm.save_pretrained(tmpdirname)
-            _ = BDDM.from_pretrained(tmpdirname)
+            _ = BDDMPipeline.from_pretrained(tmpdirname)
             # check if the same works using the DifusionPipeline class
             _ = DiffusionPipeline.from_pretrained(tmpdirname)

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():