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

src/diffusers/schedulers/scheduling_grad_tts.py

@@ -30,8 +30,6 @@ class GradTTSScheduler(SchedulerMixin, ConfigMixin):
             beta_start=beta_start,
             beta_end=beta_end,
         )
-        self.timesteps = int(timesteps)
-
         self.set_format(tensor_format=tensor_format)
 
     def sample_noise(self, timestep):
@@ -46,4 +44,4 @@ class GradTTSScheduler(SchedulerMixin, ConfigMixin):
         return xt
 
     def __len__(self):
-        return self.timesteps
+        return len(self.config.timesteps)

src/diffusers/schedulers/scheduling_pndm.py

-# Copyright 2022 The HuggingFace Team. All rights reserved.
+# Copyright 2022 Zhejiang University Team and The HuggingFace Team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -11,12 +11,39 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
+
 import math
 
 import numpy as np
 
 from ..configuration_utils import ConfigMixin
-from .scheduling_utils import SchedulerMixin, betas_for_alpha_bar, linear_beta_schedule
+from .scheduling_utils import SchedulerMixin
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+
+    def alpha_bar(time_step):
+        return math.cos((time_step + 0.008) / 1.008 * math.pi / 2) ** 2
+
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas, dtype=np.float32)
 
 
 class PNDMScheduler(SchedulerMixin, ConfigMixin):
@@ -35,16 +62,12 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             beta_end=beta_end,
             beta_schedule=beta_schedule,
         )
-        self.timesteps = int(timesteps)
 
         if beta_schedule == "linear":
-            self.betas = linear_beta_schedule(timesteps, beta_start=beta_start, beta_end=beta_end)
+            self.betas = np.linspace(beta_start, beta_end, timesteps, dtype=np.float32)
         elif beta_schedule == "squaredcos_cap_v2":
             # GLIDE cosine schedule
-            self.betas = betas_for_alpha_bar(
-                timesteps,
-                lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
-            )
+            self.betas = betas_for_alpha_bar(timesteps)
         else:
             raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}")
 
@@ -57,55 +80,58 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
 
         # For now we only support F-PNDM, i.e. the runge-kutta method
         # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
-        # mainly at equations (12) and (13) and the Algorithm 2.
+        # mainly at formula (9), (12), (13) and the Algorithm 2.
         self.pndm_order = 4
 
         # running values
         self.cur_residual = 0
         self.cur_sample = None
         self.ets = []
-        self.warmup_time_steps = {}
+        self.prk_time_steps = {}
         self.time_steps = {}
+        self.set_prk_mode()
 
-    def get_alpha(self, time_step):
-        return self.alphas[time_step]
-
-    def get_beta(self, time_step):
-        return self.betas[time_step]
+    def get_prk_time_steps(self, num_inference_steps):
+        if num_inference_steps in self.prk_time_steps:
+            return self.prk_time_steps[num_inference_steps]
 
-    def get_alpha_prod(self, time_step):
-        if time_step < 0:
-            return self.one
-        return self.alphas_cumprod[time_step]
+        inference_step_times = list(range(0, self.config.timesteps, self.config.timesteps // num_inference_steps))
 
-    def get_warmup_time_steps(self, num_inference_steps):
-        if num_inference_steps in self.warmup_time_steps:
-            return self.warmup_time_steps[num_inference_steps]
-
-        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
+        prk_time_steps = np.array(inference_step_times[-self.pndm_order :]).repeat(2) + np.tile(
+            np.array([0, self.config.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]))
+        self.prk_time_steps[num_inference_steps] = list(reversed(prk_time_steps[:-1].repeat(2)[1:-1]))
 
-        return self.warmup_time_steps[num_inference_steps]
+        return self.prk_time_steps[num_inference_steps]
 
     def get_time_steps(self, num_inference_steps):
         if num_inference_steps in self.time_steps:
             return self.time_steps[num_inference_steps]
 
-        inference_step_times = list(range(0, self.timesteps, self.timesteps // num_inference_steps))
+        inference_step_times = list(range(0, self.config.timesteps, self.config.timesteps // num_inference_steps))
         self.time_steps[num_inference_steps] = list(reversed(inference_step_times[:-3]))
 
         return self.time_steps[num_inference_steps]
 
+    def set_prk_mode(self):
+        self.mode = "prk"
+
+    def set_plms_mode(self):
+        self.mode = "plms"
+
+    def step(self, *args, **kwargs):
+        if self.mode == "prk":
+            return self.step_prk(*args, **kwargs)
+        if self.mode == "plms":
+            return self.step_plms(*args, **kwargs)
+
+        raise ValueError(f"mode {self.mode} does not exist.")
+
     def step_prk(self, residual, sample, t, num_inference_steps):
-        # TODO(Patrick) - need to rethink whether the "warmup" way is the correct API design here
-        warmup_time_steps = self.get_warmup_time_steps(num_inference_steps)
+        prk_time_steps = self.get_prk_time_steps(num_inference_steps)
 
-        t_prev = warmup_time_steps[t // 4 * 4]
-        t_next = warmup_time_steps[min(t + 1, len(warmup_time_steps) - 1)]
+        t_orig = prk_time_steps[t // 4 * 4]
+        t_orig_prev = prk_time_steps[min(t + 1, len(prk_time_steps) - 1)]
 
         if t % 4 == 0:
             self.cur_residual += 1 / 6 * residual
@@ -119,33 +145,63 @@ class PNDMScheduler(SchedulerMixin, ConfigMixin):
             residual = self.cur_residual + 1 / 6 * residual
             self.cur_residual = 0
 
-        return self.transfer(self.cur_sample, t_prev, t_next, residual)
+        # cur_sample should not be `None`
+        cur_sample = self.cur_sample if self.cur_sample is not None else sample
+
+        return self.get_prev_sample(cur_sample, t_orig, t_orig_prev, residual)
 
     def step_plms(self, residual, sample, t, num_inference_steps):
+        if len(self.ets) < 3:
+            raise ValueError(
+                f"{self.__class__} can only be run AFTER scheduler has been run "
+                "in 'prk' mode for at least 12 iterations "
+                "See: https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_pndm.py "
+                "for more information."
+            )
+
         timesteps = self.get_time_steps(num_inference_steps)
 
-        t_prev = timesteps[t]
-        t_next = timesteps[min(t + 1, len(timesteps) - 1)]
+        t_orig = timesteps[t]
+        t_orig_prev = timesteps[min(t + 1, len(timesteps) - 1)]
         self.ets.append(residual)
 
         residual = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
 
-        return self.transfer(sample, t_prev, t_next, residual)
-
-    def transfer(self, x, t, t_next, et):
-        # TODO(Patrick): clean up to be compatible with numpy and give better names
-
-        alphas_cump = self.alphas_cumprod.to(x.device)
-        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_next = x + x_delta
-        return x_next
+        return self.get_prev_sample(sample, t_orig, t_orig_prev, residual)
+
+    def get_prev_sample(self, sample, t_orig, t_orig_prev, residual):
+        # See formula (9) of PNDM paper https://arxiv.org/pdf/2202.09778.pdf
+        # this function computes x_(t−δ) using the formula of (9)
+        # Note that x_t needs to be added to both sides of the equation
+
+        # Notation (<variable name> -> <name in paper>
+        # alpha_prod_t -> α_t
+        # alpha_prod_t_prev -> α_(t−δ)
+        # beta_prod_t -> (1 - α_t)
+        # beta_prod_t_prev -> (1 - α_(t−δ))
+        # sample -> x_t
+        # residual -> e_θ(x_t, t)
+        # prev_sample -> x_(t−δ)
+        alpha_prod_t = self.alphas_cumprod[t_orig + 1]
+        alpha_prod_t_prev = self.alphas_cumprod[t_orig_prev + 1]
+        beta_prod_t = 1 - alpha_prod_t
+        beta_prod_t_prev = 1 - alpha_prod_t_prev
+
+        # corresponds to (α_(t−δ) - α_t) divided by
+        # denominator of x_t in formula (9) and plus 1
+        # Note: (α_(t−δ) - α_t) / (sqrt(α_t) * (sqrt(α_(t−δ)) + sqr(α_t))) =
+        # sqrt(α_(t−δ)) / sqrt(α_t))
+        sample_coeff = (alpha_prod_t_prev / alpha_prod_t) ** (0.5)
+
+        # corresponds to denominator of e_θ(x_t, t) in formula (9)
+        residual_denom_coeff = alpha_prod_t * beta_prod_t_prev ** (0.5) + (
+            alpha_prod_t * beta_prod_t * alpha_prod_t_prev
+        ) ** (0.5)
+
+        # full formula (9)
+        prev_sample = sample_coeff * sample - (alpha_prod_t_prev - alpha_prod_t) * residual / residual_denom_coeff
+
+        return prev_sample
 
     def __len__(self):
-        return self.timesteps
+        return self.config.timesteps

src/diffusers/schedulers/scheduling_utils.py

@@ -18,30 +18,6 @@ import torch
 SCHEDULER_CONFIG_NAME = "scheduler_config.json"
 
 
-def linear_beta_schedule(timesteps, beta_start, beta_end):
-    return np.linspace(beta_start, beta_end, timesteps, dtype=np.float32)
-
-
-def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
-    """
-    Create a beta schedule that discretizes the given alpha_t_bar function,
-    which defines the cumulative product of (1-beta) over time from t = [0,1].
-
-    :param num_diffusion_timesteps: the number of betas to produce.
-    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
-                      produces the cumulative product of (1-beta) up to that
-                      part of the diffusion process.
-    :param max_beta: the maximum beta to use; use values lower than 1 to
-                     prevent singularities.
-    """
-    betas = []
-    for i in range(num_diffusion_timesteps):
-        t1 = i / num_diffusion_timesteps
-        t2 = (i + 1) / num_diffusion_timesteps
-        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
-    return np.array(betas, dtype=np.float32)
-
-
 class SchedulerMixin:
 
     config_name = SCHEDULER_CONFIG_NAME
@@ -64,3 +40,13 @@ class SchedulerMixin:
             return torch.clamp(tensor, min_value, max_value)
 
         raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")
+
+    def log(self, tensor):
+        tensor_format = getattr(self, "tensor_format", "pt")
+
+        if tensor_format == "np":
+            return np.log(tensor)
+        elif tensor_format == "pt":
+            return torch.log(tensor)
+
+        raise ValueError(f"`self.tensor_format`: {self.tensor_format} is not valid.")

src/diffusers/utils/__init__.py

-#!/usr/bin/env python
-# coding=utf-8
-
-# 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.
-
-import os
-
 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -20,8 +11,18 @@ import os
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import importlib
+import os
+from collections import OrderedDict
+
+import importlib_metadata
 from requests.exceptions import HTTPError
 
+from .logging import get_logger
+
+
+logger = get_logger(__name__)
+
 
 hf_cache_home = os.path.expanduser(
     os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
@@ -36,6 +37,18 @@ DIFFUSERS_DYNAMIC_MODULE_NAME = "diffusers_modules"
 HF_MODULES_CACHE = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
 
 
+_transformers_available = importlib.util.find_spec("transformers") is not None
+try:
+    _transformers_version = importlib_metadata.version("transformers")
+    logger.debug(f"Successfully imported transformers version {_transformers_version}")
+except importlib_metadata.PackageNotFoundError:
+    _transformers_available = False
+
+
+def is_transformers_available():
+    return _transformers_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
@@ -49,3 +62,39 @@ class EntryNotFoundError(HTTPError):
 
 class RevisionNotFoundError(HTTPError):
     """Raised when trying to access a hf.co URL with a valid repository but an invalid revision."""
+
+
+TRANSFORMERS_IMPORT_ERROR = """
+{0} requires the transformers library but it was not found in your environment. You can install it with pip:
+`pip install transformers`
+"""
+
+
+BACKENDS_MAPPING = OrderedDict(
+    [
+        ("transformers", (is_transformers_available, TRANSFORMERS_IMPORT_ERROR)),
+    ]
+)
+
+
+def requires_backends(obj, backends):
+    if not isinstance(backends, (list, tuple)):
+        backends = [backends]
+
+    name = obj.__name__ if hasattr(obj, "__name__") else obj.__class__.__name__
+    checks = (BACKENDS_MAPPING[backend] for backend in backends)
+    failed = [msg.format(name) for available, msg in checks if not available()]
+    if failed:
+        raise ImportError("".join(failed))
+
+
+class DummyObject(type):
+    """
+    Metaclass for the dummy objects. Any class inheriting from it will return the ImportError generated by
+    `requires_backend` each time a user tries to access any method of that class.
+    """
+
+    def __getattr__(cls, key):
+        if key.startswith("_"):
+            return super().__getattr__(cls, key)
+        requires_backends(cls, cls._backends)

src/diffusers/utils/dummy_transformers_objects.py

+# This file is autogenerated by the command `make fix-copies`, do not edit.
+# flake8: noqa
+from ..utils import DummyObject, requires_backends
+
+
+class GLIDESuperResUNetModel(metaclass=DummyObject):
+    _backends = ["transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["transformers"])
+
+
+class GLIDETextToImageUNetModel(metaclass=DummyObject):
+    _backends = ["transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["transformers"])
+
+
+class GLIDEUNetModel(metaclass=DummyObject):
+    _backends = ["transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["transformers"])
+
+
+class UNetGradTTSModel(metaclass=DummyObject):
+    _backends = ["transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["transformers"])
+
+
+GLIDE = None
+
+
+class GradTTS(metaclass=DummyObject):
+    _backends = ["transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["transformers"])
+
+
+class LatentDiffusion(metaclass=DummyObject):
+    _backends = ["transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["transformers"])

tests/test_modeling_utils.py

@@ -14,11 +14,14 @@
 # limitations under the License.
 
 
+import inspect
 import tempfile
 import unittest
 
+import numpy as np
 import torch
 
+import pytest
 from diffusers import (
     BDDM,
     DDIM,
@@ -27,9 +30,12 @@ from diffusers import (
     PNDM,
     DDIMScheduler,
     DDPMScheduler,
+    GLIDESuperResUNetModel,
     LatentDiffusion,
     PNDMScheduler,
     UNetModel,
+    UNetLDMModel,
+    UNetGradTTSModel,
 )
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
@@ -82,7 +88,108 @@ class ConfigTester(unittest.TestCase):
         assert config == new_config
 
 
-class ModelTesterMixin(unittest.TestCase):
+class ModelTesterMixin:
+    def test_from_pretrained_save_pretrained(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            new_model = self.model_class.from_pretrained(tmpdirname)
+            new_model.to(torch_device)
+
+        with torch.no_grad():
+            image = model(**inputs_dict)
+            new_image = new_model(**inputs_dict)
+
+        max_diff = (image - new_image).abs().sum().item()
+        self.assertLessEqual(max_diff, 1e-5, "Models give different forward passes")
+
+    def test_determinism(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            first = model(**inputs_dict)
+            second = model(**inputs_dict)
+
+        out_1 = first.cpu().numpy()
+        out_2 = second.cpu().numpy()
+        out_1 = out_1[~np.isnan(out_1)]
+        out_2 = out_2[~np.isnan(out_2)]
+        max_diff = np.amax(np.abs(out_1 - out_2))
+        self.assertLessEqual(max_diff, 1e-5)
+
+    def test_output(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            output = model(**inputs_dict)
+
+        self.assertIsNotNone(output)
+        expected_shape = inputs_dict["x"].shape
+        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
+
+    def test_forward_signature(self):
+        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict)
+        signature = inspect.signature(model.forward)
+        # signature.parameters is an OrderedDict => so arg_names order is deterministic
+        arg_names = [*signature.parameters.keys()]
+
+        expected_arg_names = ["x", "timesteps"]
+        self.assertListEqual(arg_names[:2], expected_arg_names)
+
+    def test_model_from_config(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        # test if the model can be loaded from the config
+        # and has all the expected shape
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_config(tmpdirname)
+            new_model = self.model_class.from_config(tmpdirname)
+            new_model.to(torch_device)
+            new_model.eval()
+
+        # check if all paramters shape are the same
+        for param_name in model.state_dict().keys():
+            param_1 = model.state_dict()[param_name]
+            param_2 = new_model.state_dict()[param_name]
+            self.assertEqual(param_1.shape, param_2.shape)
+
+        with torch.no_grad():
+            output_1 = model(**inputs_dict)
+            output_2 = new_model(**inputs_dict)
+
+        self.assertEqual(output_1.shape, output_2.shape)
+
+    def test_training(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.train()
+        output = model(**inputs_dict)
+        noise = torch.randn((inputs_dict["x"].shape[0],) + self.get_output_shape).to(torch_device)
+        loss = torch.nn.functional.mse_loss(output, noise)
+        loss.backward()
+
+
+class UnetModelTests(ModelTesterMixin, unittest.TestCase):
+    model_class = UNetModel
+
     @property
     def dummy_input(self):
         batch_size = 4
@@ -92,32 +199,289 @@ class ModelTesterMixin(unittest.TestCase):
         noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
         time_step = torch.tensor([10]).to(torch_device)
 
-        return (noise, time_step)
+        return {"x": noise, "timesteps": time_step}
+
+    @property
+    def get_input_shape(self):
+        return (3, 32, 32)
+
+    @property
+    def get_output_shape(self):
+        return (3, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "ch": 32,
+            "ch_mult": (1, 2),
+            "num_res_blocks": 2,
+            "attn_resolutions": (16,),
+            "resolution": 32,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_from_pretrained_hub(self):
+        model, loading_info = UNetModel.from_pretrained("fusing/ddpm_dummy", output_loading_info=True)
+        self.assertIsNotNone(model)
+        self.assertEqual(len(loading_info["missing_keys"]), 0)
 
-    def test_from_pretrained_save_pretrained(self):
-        model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32)
         model.to(torch_device)
+        image = model(**self.dummy_input)
 
-        with tempfile.TemporaryDirectory() as tmpdirname:
-            model.save_pretrained(tmpdirname)
-            new_model = UNetModel.from_pretrained(tmpdirname)
-            new_model.to(torch_device)
+        assert image is not None, "Make sure output is not None"
+
+    def test_output_pretrained(self):
+        model = UNetModel.from_pretrained("fusing/ddpm_dummy")
+        model.eval()
 
-        dummy_input = self.dummy_input
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
 
-        image = model(*dummy_input)
-        new_image = new_model(*dummy_input)
+        noise = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution)
+        time_step = torch.tensor([10])
 
-        assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
+        with torch.no_grad():
+            output = model(noise, time_step)
+
+        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])
+        # fmt: on
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
+
+
+class GLIDESuperResUNetTests(ModelTesterMixin, unittest.TestCase):
+    model_class = GLIDESuperResUNetModel
+
+    @property
+    def dummy_input(self):
+        batch_size = 4
+        num_channels = 6
+        sizes = (32, 32)
+        low_res_size = (4, 4)
+
+        torch_device = "cpu"
+
+        noise = torch.randn((batch_size, num_channels // 2) + sizes).to(torch_device)
+        low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device)
+        time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
+
+        return {"x": noise, "timesteps": time_step, "low_res": low_res}
+
+    @property
+    def get_input_shape(self):
+        return (3, 32, 32)
+
+    @property
+    def get_output_shape(self):
+        return (6, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "attention_resolutions": (2,),
+            "channel_mult": (1, 2),
+            "in_channels": 6,
+            "out_channels": 6,
+            "model_channels": 32,
+            "num_head_channels": 8,
+            "num_heads_upsample": 1,
+            "num_res_blocks": 2,
+            "resblock_updown": True,
+            "resolution": 32,
+            "use_scale_shift_norm": True,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+
+    def test_output(self):
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+        model.eval()
+
+        with torch.no_grad():
+            output = model(**inputs_dict)
+
+        output, _ = torch.split(output, 3, dim=1)
+
+        self.assertIsNotNone(output)
+        expected_shape = inputs_dict["x"].shape
+        self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
 
     def test_from_pretrained_hub(self):
-        model = UNetModel.from_pretrained("fusing/ddpm_dummy")
+        model, loading_info = GLIDESuperResUNetModel.from_pretrained(
+            "fusing/glide-super-res-dummy", output_loading_info=True
+        )
+        self.assertIsNotNone(model)
+        self.assertEqual(len(loading_info["missing_keys"]), 0)
+
         model.to(torch_device)
+        image = model(**self.dummy_input)
+
+        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")
+
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        noise = torch.randn(1, 3, 64, 64)
+        low_res = torch.randn(1, 3, 4, 4)
+        time_step = torch.tensor([42] * noise.shape[0])
 
-        image = model(*self.dummy_input)
+        with torch.no_grad():
+            output = model(noise, time_step, low_res)
+
+        output, _ = torch.split(output, 3, dim=1)
+        output_slice = output[0, -1, -3:, -3:].flatten()
+        # fmt: off
+        expected_output_slice = torch.tensor([-22.8782, -23.2652, -15.3966, -22.8034, -23.3159, -15.5640, -15.3970, -15.4614, - 10.4370])
+        # fmt: on
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
+
+class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
+    model_class = UNetLDMModel
+
+    @property
+    def dummy_input(self):
+        batch_size = 4
+        num_channels = 4
+        sizes = (32, 32)
+
+        noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
+        time_step = torch.tensor([10]).to(torch_device)
+
+        return {"x": noise, "timesteps": time_step}
+
+    @property
+    def get_input_shape(self):
+        return (4, 32, 32)
+
+    @property
+    def get_output_shape(self):
+        return (4, 32, 32)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "image_size": 32,
+            "in_channels": 4,
+            "out_channels": 4,
+            "model_channels": 32,
+            "num_res_blocks": 2,
+            "attention_resolutions": (16,),
+            "channel_mult": (1, 2),
+            "num_heads": 2,
+            "conv_resample": True,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+    
+    def test_from_pretrained_hub(self):
+        model, loading_info = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy", output_loading_info=True)
+        self.assertIsNotNone(model)
+        self.assertEqual(len(loading_info["missing_keys"]), 0)
+
+        model.to(torch_device)
+        image = model(**self.dummy_input)
 
         assert image is not None, "Make sure output is not None"
 
+    def test_output_pretrained(self):
+        model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy")
+        model.eval()
+
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+
+        noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size)
+        time_step = torch.tensor([10] * noise.shape[0])
+
+        with torch.no_grad():
+            output = model(noise, time_step)
+
+        output_slice = output[0, -1, -3:, -3:].flatten()
+        # fmt: off
+        expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
+        # fmt: on
+
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
+
+
+class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
+    model_class = UNetGradTTSModel
+
+    @property
+    def dummy_input(self):
+        batch_size = 4
+        num_features = 32
+        seq_len = 16
+
+        noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
+        condition = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
+        mask = floats_tensor((batch_size, 1, seq_len)).to(torch_device)
+        time_step = torch.tensor([10] * batch_size).to(torch_device)
+
+        return {"x": noise, "timesteps": time_step, "mu": condition, "mask": mask}
+
+    @property
+    def get_input_shape(self):
+        return (4, 32, 16)
+
+    @property
+    def get_output_shape(self):
+        return (4, 32, 16)
+
+    def prepare_init_args_and_inputs_for_common(self):
+        init_dict = {
+            "dim": 64,
+            "groups": 4,
+            "dim_mults": (1, 2),
+            "n_feats": 32,
+            "pe_scale": 1000,
+            "n_spks": 1,
+        }
+        inputs_dict = self.dummy_input
+        return init_dict, inputs_dict
+    
+    def test_from_pretrained_hub(self):
+        model, loading_info = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy", output_loading_info=True)
+        self.assertIsNotNone(model)
+        self.assertEqual(len(loading_info["missing_keys"]), 0)
+
+        model.to(torch_device)
+        image = model(**self.dummy_input)
+
+        assert image is not None, "Make sure output is not None"
+
+    def test_output_pretrained(self):
+        model = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy")
+        model.eval()
+
+        torch.manual_seed(0)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(0)
+        
+        num_features = model.config.n_feats
+        seq_len = 16
+        noise = torch.randn((1, num_features, seq_len))
+        condition = torch.randn((1, num_features, seq_len))
+        mask = torch.randn((1, 1, seq_len))
+        time_step = torch.tensor([10])
+
+        with torch.no_grad():
+            output = model(noise, time_step, condition, mask)
+
+        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])
+        # fmt: on
+
+        self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
+
 
 class PipelineTesterMixin(unittest.TestCase):
     def test_from_pretrained_save_pretrained(self):
@@ -223,7 +587,6 @@ class PipelineTesterMixin(unittest.TestCase):
         image = ldm([prompt], generator=generator, num_inference_steps=20)
 
         image_slice = image[0, -1, -3:, -3:].cpu()
-        print(image_slice.shape)
 
         assert image.shape == (1, 3, 256, 256)
         expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458])

utils/check_dummies.py

@@ -20,10 +20,10 @@ import re
 
 # All paths are set with the intent you should run this script from the root of the repo with the command
 # python utils/check_dummies.py
-PATH_TO_TRANSFORMERS = "src/transformers"
+PATH_TO_DIFFUSERS = "src/diffusers"
 
 # Matches is_xxx_available()
-_re_backend = re.compile(r"is\_([a-z_]*)_available()")
+_re_backend = re.compile(r"if 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\(\)")
@@ -50,36 +50,30 @@ def {0}(*args, **kwargs):
 
 def find_backend(line):
     """Find one (or multiple) backend in a code line of the init."""
-    if _re_test_backend.search(line) is None:
+    backends = _re_backend.findall(line)
+    if len(backends) == 0:
         return None
-    backends = [b[0] for b in _re_backend.findall(line)]
-    backends.sort()
-    return "_and_".join(backends)
+
+    return backends[0]
 
 
 def read_init():
     """Read the init and extracts PyTorch, TensorFlow, SentencePiece and Tokenizers objects."""
-    with open(os.path.join(PATH_TO_TRANSFORMERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f:
+    with open(os.path.join(PATH_TO_DIFFUSERS, "__init__.py"), "r", encoding="utf-8", newline="\n") as f:
         lines = f.readlines()
 
     # Get to the point we do the actual imports for type checking
     line_index = 0
-    while not lines[line_index].startswith("if TYPE_CHECKING"):
-        line_index += 1
-
     backend_specific_objects = {}
     # Go through the end of the file
     while line_index < len(lines):
         # If the line is an if is_backend_available, we grab all objects associated.
         backend = find_backend(lines[line_index])
         if backend is not None:
-            while not lines[line_index].startswith("    else:"):
-                line_index += 1
-            line_index += 1
-
             objects = []
+            line_index += 1
             # Until we unindent, add backend objects to the list
-            while len(lines[line_index]) <= 1 or lines[line_index].startswith(" " * 8):
+            while not lines[line_index].startswith("else:"):
                 line = lines[line_index]
                 single_line_import_search = _re_single_line_import.search(line)
                 if single_line_import_search is not None:
@@ -129,7 +123,7 @@ def check_dummies(overwrite=False):
     short_names = {"torch": "pt"}
 
     # Locate actual dummy modules and read their content.
-    path = os.path.join(PATH_TO_TRANSFORMERS, "utils")
+    path = os.path.join(PATH_TO_DIFFUSERS, "utils")
     dummy_file_paths = {
         backend: os.path.join(path, f"dummy_{short_names.get(backend, backend)}_objects.py")
         for backend in dummy_files.keys()
@@ -147,7 +141,7 @@ def check_dummies(overwrite=False):
         if dummy_files[backend] != actual_dummies[backend]:
             if overwrite:
                 print(
-                    f"Updating transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main "
+                    f"Updating diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py as the main "
                     "__init__ has new objects."
                 )
                 with open(dummy_file_paths[backend], "w", encoding="utf-8", newline="\n") as f:
@@ -155,7 +149,7 @@ def check_dummies(overwrite=False):
             else:
                 raise ValueError(
                     "The main __init__ has objects that are not present in "
-                    f"transformers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` "
+                    f"diffusers.utils.dummy_{short_names.get(backend, backend)}_objects.py. Run `make fix-copies` "
                     "to fix this."
                 )
 

utils/check_table.py

@@ -22,7 +22,7 @@ import re
 
 # All paths are set with the intent you should run this script from the root of the repo with the command
 # python utils/check_table.py
-TRANSFORMERS_PATH = "src/transformers"
+TRANSFORMERS_PATH = "src/diffusers"
 PATH_TO_DOCS = "docs/source/en"
 REPO_PATH = "."
 
@@ -62,13 +62,13 @@ _re_flax_models = re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGe
 _re_pt_models = re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
 
 
-# This is to make sure the transformers module imported is the one in the repo.
+# This is to make sure the diffusers module imported is the one in the repo.
 spec = importlib.util.spec_from_file_location(
-    "transformers",
+    "diffusers",
     os.path.join(TRANSFORMERS_PATH, "__init__.py"),
     submodule_search_locations=[TRANSFORMERS_PATH],
 )
-transformers_module = spec.loader.load_module()
+diffusers_module = spec.loader.load_module()
 
 
 # Thanks to https://stackoverflow.com/questions/29916065/how-to-do-camelcase-split-in-python
@@ -88,10 +88,10 @@ def _center_text(text, width):
 def get_model_table_from_auto_modules():
     """Generates an up-to-date model table from the content of the auto modules."""
     # Dictionary model names to config.
-    config_maping_names = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
+    config_maping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     model_name_to_config = {
         name: config_maping_names[code]
-        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
+        for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
         if code in config_maping_names
     }
     model_name_to_prefix = {name: config.replace("ConfigMixin", "") for name, config in model_name_to_config.items()}
@@ -103,8 +103,8 @@ def get_model_table_from_auto_modules():
     tf_models = collections.defaultdict(bool)
     flax_models = collections.defaultdict(bool)
 
-    # Let's lookup through all transformers object (once).
-    for attr_name in dir(transformers_module):
+    # Let's lookup through all diffusers object (once).
+    for attr_name in dir(diffusers_module):
         lookup_dict = None
         if attr_name.endswith("Tokenizer"):
             lookup_dict = slow_tokenizers