move pipelines into folders

src/diffusers/pipeline_utils.py

@@ -57,17 +57,19 @@ class DiffusionPipeline(ConfigMixin):
         from diffusers import pipelines
 
         for name, module in kwargs.items():
-            # check if the module is a pipeline module
-            is_pipeline_module = hasattr(pipelines, module.__module__.split(".")[-1])
-
             # retrive library
             library = module.__module__.split(".")[0]
 
+            # check if the module is a pipeline module
+            pipeline_file = module.__module__.split(".")[-1]
+            pipeline_dir = module.__module__.split(".")[-2]
+            is_pipeline_module = pipeline_file == "pipeline_" + pipeline_dir and hasattr(pipelines, pipeline_dir)
+
             # if library is not in LOADABLE_CLASSES, then it is a custom module.
             # Or if it's a pipeline module, then the module is inside the pipeline
-            # so we set the library to module name.
+            # folder so we set the library to module name.
             if library not in LOADABLE_CLASSES or is_pipeline_module:
-                library = module.__module__.split(".")[-1]
+                library = pipeline_dir
 
             # retrive class_name
             class_name = module.__class__.__name__

src/diffusers/pipelines/__init__.py

@@ -1,20 +1,17 @@
 from ..utils import is_inflect_available, is_transformers_available, is_unidecode_available
-from .pipeline_bddm import BDDMPipeline
-from .pipeline_ddim import DDIMPipeline
-from .pipeline_ddpm import DDPMPipeline
-from .pipeline_latent_diffusion_uncond import LatentDiffusionUncondPipeline
-from .pipeline_pndm import PNDMPipeline
-from .pipeline_score_sde_ve import ScoreSdeVePipeline
-from .pipeline_score_sde_vp import ScoreSdeVpPipeline
-
-
-# from .pipeline_score_sde import ScoreSdeVePipeline
+from .bddm import BDDMPipeline
+from .ddim import DDIMPipeline
+from .ddpm import DDPMPipeline
+from .latent_diffusion_uncond import LatentDiffusionUncondPipeline
+from .pndm import PNDMPipeline
+from .score_sde_ve import ScoreSdeVePipeline
+from .score_sde_vp import ScoreSdeVpPipeline
 
 
 if is_transformers_available():
-    from .pipeline_glide import GlidePipeline
-    from .pipeline_latent_diffusion import LatentDiffusionPipeline
+    from .glide import GlidePipeline
+    from .latent_diffusion import LatentDiffusionPipeline
 
 
 if is_transformers_available() and is_unidecode_available() and is_inflect_available():
-    from .pipeline_grad_tts import GradTTSPipeline
+    from .grad_tts import GradTTSPipeline

src/diffusers/pipelines/bddm/__init__.py

@@ -0,0 +1 @@
+from .pipeline_bddm import BDDMPipeline, DiffWave

src/diffusers/pipelines/bddm/pipeline_bddm.py

@@ -21,9 +21,9 @@ import torch.nn.functional as F
 
 import tqdm
 
-from ..configuration_utils import ConfigMixin
-from ..modeling_utils import ModelMixin
-from ..pipeline_utils import DiffusionPipeline
+from ...configuration_utils import ConfigMixin
+from ...modeling_utils import ModelMixin
+from ...pipeline_utils import DiffusionPipeline
 
 
 def calc_diffusion_step_embedding(diffusion_steps, diffusion_step_embed_dim_in):

src/diffusers/pipelines/ddim/__init__.py

@@ -0,0 +1 @@
+from .pipeline_ddim import DDIMPipeline

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -18,7 +18,7 @@ import torch
 
 import tqdm
 
-from ..pipeline_utils import DiffusionPipeline
+from ...pipeline_utils import DiffusionPipeline
 
 
 class DDIMPipeline(DiffusionPipeline):

src/diffusers/pipelines/ddpm/__init__.py

@@ -0,0 +1 @@
+from .pipeline_ddpm import DDPMPipeline

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -18,7 +18,7 @@ import torch
 
 import tqdm
 
-from ..pipeline_utils import DiffusionPipeline
+from ...pipeline_utils import DiffusionPipeline
 
 
 class DDPMPipeline(DiffusionPipeline):

src/diffusers/pipelines/glide/__init__.py

@@ -0,0 +1,5 @@
+from ...utils import is_transformers_available
+
+
+if is_transformers_available():
+    from .pipeline_glide import CLIPTextModel, GlidePipeline

src/diffusers/pipelines/glide/pipeline_glide.py

@@ -18,7 +18,6 @@ import math
 from dataclasses import dataclass
 from typing import Any, Optional, Tuple, Union
 
-import numpy as np
 import torch
 import torch.utils.checkpoint
 from torch import nn
@@ -30,10 +29,10 @@ 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 ..pipeline_utils import DiffusionPipeline
-from ..schedulers import DDIMScheduler, DDPMScheduler
-from ..utils import logging
+from ...models import GlideSuperResUNetModel, GlideTextToImageUNetModel
+from ...pipeline_utils import DiffusionPipeline
+from ...schedulers import DDIMScheduler, DDPMScheduler
+from ...utils import logging
 
 
 #####################
@@ -594,7 +593,7 @@ class CLIPTextTransformer(nn.Module):
         bsz, seq_len = input_shape
         # CLIP's text model uses causal mask, prepare it here.
         # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
-        causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len).to(hidden_states.device)
+        #        causal_attention_mask = self._build_causal_attention_mask(bsz, seq_len).to(hidden_states.device)
 
         # expand attention_mask
         if attention_mask is not None:

src/diffusers/pipelines/grad_tts/__init__.py

@@ -0,0 +1,6 @@
+from ...utils import is_inflect_available, is_transformers_available, is_unidecode_available
+
+
+if is_transformers_available() and is_unidecode_available() and is_inflect_available():
+    from .grad_tts_utils import GradTTSTokenizer
+    from .pipeline_grad_tts import GradTTSPipeline, TextEncoder

src/diffusers/pipelines/grad_tts/pipeline_grad_tts.py

@@ -6,10 +6,10 @@ import torch
 from torch import nn
 
 import tqdm
-from diffusers import DiffusionPipeline
-from diffusers.configuration_utils import ConfigMixin
-from diffusers.modeling_utils import ModelMixin
 
+from ...configuration_utils import ConfigMixin
+from ...modeling_utils import ModelMixin
+from ...pipeline_utils import DiffusionPipeline
 from .grad_tts_utils import GradTTSTokenizer  # flake8: noqa
 
 

src/diffusers/pipelines/latent_diffusion/__init__.py

@@ -0,0 +1,5 @@
+from ...utils import is_transformers_available
+
+
+if is_transformers_available():
+    from .pipeline_latent_diffusion import AutoencoderKL, LatentDiffusionPipeline, LDMBertModel

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -7,20 +7,15 @@ import torch.nn as nn
 import torch.utils.checkpoint
 
 import tqdm
+from transformers.activations import ACT2FN
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
 
-
-try:
-    from transformers.activations import ACT2FN
-    from transformers.configuration_utils import PretrainedConfig
-    from transformers.modeling_outputs import BaseModelOutput
-    from transformers.modeling_utils import PreTrainedModel
-    from transformers.utils import logging
-except ImportError:
-    raise ImportError("Please install the transformers.")
-
-from ..configuration_utils import ConfigMixin
-from ..modeling_utils import ModelMixin
-from ..pipeline_utils import DiffusionPipeline
+from ...configuration_utils import ConfigMixin
+from ...modeling_utils import ModelMixin
+from ...pipeline_utils import DiffusionPipeline
 
 
 ################################################################################

src/diffusers/pipelines/latent_diffusion_uncond/__init__.py

@@ -0,0 +1 @@
+from .pipeline_latent_diffusion_uncond import LatentDiffusionUncondPipeline

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -6,9 +6,9 @@ import torch.nn as nn
 
 import tqdm
 
-from ..configuration_utils import ConfigMixin
-from ..modeling_utils import ModelMixin
-from ..pipeline_utils import DiffusionPipeline
+from ...configuration_utils import ConfigMixin
+from ...modeling_utils import ModelMixin
+from ...pipeline_utils import DiffusionPipeline
 
 
 def get_timestep_embedding(timesteps, embedding_dim):

src/diffusers/pipelines/pndm/__init__.py

@@ -0,0 +1 @@
+from .pipeline_pndm import PNDMPipeline

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -18,7 +18,7 @@ import torch
 
 import tqdm
 
-from ..pipeline_utils import DiffusionPipeline
+from ...pipeline_utils import DiffusionPipeline
 
 
 class PNDMPipeline(DiffusionPipeline):

src/diffusers/pipelines/score_sde_ve/__init__.py

@@ -0,0 +1 @@
+from .pipeline_score_sde_ve import ScoreSdeVePipeline

src/diffusers/pipelines/score_sde_vp/__init__.py

@@ -0,0 +1 @@
+from .pipeline_score_sde_vp import ScoreSdeVpPipeline

tests/test_modeling_utils.py

@@ -47,7 +47,7 @@ from diffusers import (
 )
 from diffusers.configuration_utils import ConfigMixin
 from diffusers.pipeline_utils import DiffusionPipeline
-from diffusers.pipelines.pipeline_bddm import DiffWave
+from diffusers.pipelines.bddm.pipeline_bddm import DiffWave
 from diffusers.testing_utils import floats_tensor, slow, torch_device
 
 
@@ -1005,11 +1005,13 @@ class PipelineTesterMixin(unittest.TestCase):
         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")
+        self.assertTrue(bddm.config["diffwave"][0] == "bddm")
 
         # check if we can save and load the pipeline
         with tempfile.TemporaryDirectory() as tmpdirname:
             bddm.save_pretrained(tmpdirname)
             _ = BDDMPipeline.from_pretrained(tmpdirname)
             # check if the same works using the DifusionPipeline class
-            _ = DiffusionPipeline.from_pretrained(tmpdirname)
+            bddm = DiffusionPipeline.from_pretrained(tmpdirname)
+
+        self.assertTrue(bddm.config["diffwave"][0] == "bddm")