Rename pipelines (#115)

up

scripts/conversion_ldm_uncond.py

@@ -3,7 +3,7 @@ import argparse
 import OmegaConf
 import torch
 
-from diffusers import UNetLDMModel, VQModel, LatentDiffusionUncondPipeline, DDIMScheduler
+from diffusers import UNetLDMModel, VQModel, LDMPipeline, DDIMScheduler
 
 def convert_ldm_original(checkpoint_path, config_path, output_path):
     config = OmegaConf.load(config_path)
@@ -41,7 +41,7 @@ def convert_ldm_original(checkpoint_path, config_path, output_path):
         clip_sample=False,
     )
 
-    pipeline = LatentDiffusionUncondPipeline(vqvae, unet, noise_scheduler)
+    pipeline = LDMPipeline(vqvae, unet, noise_scheduler)
     pipeline.save_pretrained(output_path)
 
 

scripts/convert_ldm_original_checkpoint_to_diffusers.py

@@ -17,7 +17,7 @@
 import argparse
 import json
 import torch
-from diffusers import VQModel, DDPMScheduler, UNet2DModel, LatentDiffusionUncondPipeline
+from diffusers import VQModel, DDPMScheduler, UNet2DModel, LDMPipeline
 
 
 def shave_segments(path, n_shave_prefix_segments=1):
@@ -326,7 +326,7 @@ if __name__ == "__main__":
         scheduler = DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
         vqvae = VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))
 
-        pipe = LatentDiffusionUncondPipeline(unet=model, scheduler=scheduler, vae=vqvae)
+        pipe = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
         pipe.save_pretrained(args.dump_path)
     except:
         model.save_pretrained(args.dump_path)

src/diffusers/__init__.py

@@ -9,11 +9,11 @@ __version__ = "0.0.4"
 from .modeling_utils import ModelMixin
 from .models import AutoencoderKL, UNet2DConditionModel, UNet2DModel, VQModel
 from .pipeline_utils import DiffusionPipeline
-from .pipelines import DDIMPipeline, DDPMPipeline, LatentDiffusionUncondPipeline, PNDMPipeline, ScoreSdeVePipeline
+from .pipelines import DDIMPipeline, DDPMPipeline, LDMPipeline, PNDMPipeline, ScoreSdeVePipeline
 from .schedulers import DDIMScheduler, DDPMScheduler, PNDMScheduler, SchedulerMixin, ScoreSdeVeScheduler
 
 
 if is_transformers_available():
-    from .pipelines import LatentDiffusionPipeline
+    from .pipelines import LDMTextToImagePipeline
 else:
     from .utils.dummy_transformers_objects import *

src/diffusers/pipelines/__init__.py

@@ -1,10 +1,10 @@
 from ..utils import is_inflect_available, is_transformers_available, is_unidecode_available
 from .ddim import DDIMPipeline
 from .ddpm import DDPMPipeline
-from .latent_diffusion_uncond import LatentDiffusionUncondPipeline
+from .latent_diffusion_uncond import LDMPipeline
 from .pndm import PNDMPipeline
 from .score_sde_ve import ScoreSdeVePipeline
 
 
 if is_transformers_available():
-    from .latent_diffusion import LatentDiffusionPipeline
+    from .latent_diffusion import LDMTextToImagePipeline

src/diffusers/pipelines/latent_diffusion/__init__.py

@@ -2,4 +2,4 @@ from ...utils import is_transformers_available
 
 
 if is_transformers_available():
-    from .pipeline_latent_diffusion import LatentDiffusionPipeline, LDMBertModel
+    from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -14,7 +14,7 @@ from transformers.utils import logging
 from ...pipeline_utils import DiffusionPipeline
 
 
-class LatentDiffusionPipeline(DiffusionPipeline):
+class LDMTextToImagePipeline(DiffusionPipeline):
     def __init__(self, vqvae, bert, tokenizer, unet, scheduler):
         super().__init__()
         scheduler = scheduler.set_format("pt")

src/diffusers/pipelines/latent_diffusion_uncond/__init__.py

@@ -1 +1 @@
-from .pipeline_latent_diffusion_uncond import LatentDiffusionUncondPipeline
+from .pipeline_latent_diffusion_uncond import LDMPipeline

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -5,7 +5,7 @@ from tqdm.auto import tqdm
 from ...pipeline_utils import DiffusionPipeline
 
 
-class LatentDiffusionUncondPipeline(DiffusionPipeline):
+class LDMPipeline(DiffusionPipeline):
     def __init__(self, vqvae, unet, scheduler):
         super().__init__()
         scheduler = scheduler.set_format("pt")

src/diffusers/utils/dummy_transformers_objects.py

@@ -3,42 +3,7 @@
 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"])
-
-
-class GlidePipeline(metaclass=DummyObject):
-    _backends = ["transformers"]
-
-    def __init__(self, *args, **kwargs):
-        requires_backends(self, ["transformers"])
-
-
-class LatentDiffusionPipeline(metaclass=DummyObject):
+class LDMTextToImagePipeline(metaclass=DummyObject):
     _backends = ["transformers"]
 
     def __init__(self, *args, **kwargs):

tests/test_modeling_utils.py

@@ -29,8 +29,8 @@ from diffusers import (
     DDIMScheduler,
     DDPMPipeline,
     DDPMScheduler,
-    LatentDiffusionPipeline,
-    LatentDiffusionUncondPipeline,
+    LDMPipeline,
+    LDMTextToImagePipeline,
     PNDMPipeline,
     PNDMScheduler,
     ScoreSdeVePipeline,
@@ -826,7 +826,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ldm_text2img(self):
-        ldm = LatentDiffusionPipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-text2im-large-256")
+        ldm = LDMTextToImagePipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-text2im-large-256")
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
@@ -842,7 +842,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ldm_text2img_fast(self):
-        ldm = LatentDiffusionPipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-text2im-large-256")
+        ldm = LDMTextToImagePipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-text2im-large-256")
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
@@ -877,7 +877,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
     @slow
     def test_ldm_uncond(self):
-        ldm = LatentDiffusionUncondPipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-celebahq-256")
+        ldm = LDMPipeline.from_pretrained("/home/patrick/google_checkpoints/ldm-celebahq-256")
 
         generator = torch.manual_seed(0)
         image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"]