make PIL the default output type

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -28,7 +28,7 @@ class DDIMPipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50):
+    def __call__(self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50, output_type="pil"):
         # eta corresponds to η in paper and should be between [0, 1]
         if torch_device is None:
             torch_device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -55,5 +55,7 @@ class DDIMPipeline(DiffusionPipeline):
 
         image = (image / 2 + 0.5).clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
 
         return {"sample": image}

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -28,7 +28,7 @@ class DDPMPipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, generator=None, torch_device=None, output_type="numpy"):
+    def __call__(self, batch_size=1, generator=None, torch_device=None, output_type="pil"):
         if torch_device is None:
             torch_device = "cuda" if torch.cuda.is_available() else "cpu"
 

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -30,7 +30,7 @@ class LatentDiffusionPipeline(DiffusionPipeline):
         eta=0.0,
         guidance_scale=1.0,
         num_inference_steps=50,
-        output_type="numpy",
+        output_type="pil",
     ):
         # eta corresponds to η in paper and should be between [0, 1]
 

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -13,7 +13,7 @@ class LatentDiffusionUncondPipeline(DiffusionPipeline):
 
     @torch.no_grad()
     def __call__(
-        self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50, output_type="numpy"
+        self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50, output_type="pil"
     ):
         # eta corresponds to η in paper and should be between [0, 1]
 

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -28,7 +28,7 @@ class PNDMPipeline(DiffusionPipeline):
         self.register_modules(unet=unet, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, batch_size=1, generator=None, torch_device=None, num_inference_steps=50, output_type="numpy"):
+    def __call__(self, batch_size=1, generator=None, torch_device=None, num_inference_steps=50, output_type="pil"):
         # For more information on the sampling method you can take a look at Algorithm 2 of
         # the official paper: https://arxiv.org/pdf/2202.09778.pdf
         if torch_device is None:

src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

@@ -11,7 +11,7 @@ class ScoreSdeVePipeline(DiffusionPipeline):
         self.register_modules(model=model, scheduler=scheduler)
 
     @torch.no_grad()
-    def __call__(self, num_inference_steps=2000, generator=None, output_type="numpy"):
+    def __call__(self, num_inference_steps=2000, generator=None, output_type="pil"):
         device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
         img_size = self.model.config.image_size

tests/test_modeling_utils.py

@@ -704,9 +704,9 @@ class PipelineTesterMixin(unittest.TestCase):
 
         generator = torch.manual_seed(0)
 
-        image = ddpm(generator=generator)["sample"]
+        image = ddpm(generator=generator, output_type="numpy")["sample"]
         generator = generator.manual_seed(0)
-        new_image = new_ddpm(generator=generator)["sample"]
+        new_image = new_ddpm(generator=generator, output_type="numpy")["sample"]
 
         assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
 
@@ -722,9 +722,9 @@ class PipelineTesterMixin(unittest.TestCase):
 
         generator = torch.manual_seed(0)
 
-        image = ddpm(generator=generator)["sample"]
+        image = ddpm(generator=generator, output_type="numpy")["sample"]
         generator = generator.manual_seed(0)
-        new_image = ddpm_from_hub(generator=generator)["sample"]
+        new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
 
         assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
 
@@ -735,10 +735,6 @@ class PipelineTesterMixin(unittest.TestCase):
         pipe = DDIMPipeline.from_pretrained(model_path)
 
         generator = torch.manual_seed(0)
-        images = pipe(generator=generator)["sample"]
-        assert images.shape == (1, 32, 32, 3)
-        assert isinstance(images, np.ndarray)
-
         images = pipe(generator=generator, output_type="numpy")["sample"]
         assert images.shape == (1, 32, 32, 3)
         assert isinstance(images, np.ndarray)
@@ -748,6 +744,11 @@ class PipelineTesterMixin(unittest.TestCase):
         assert len(images) == 1
         assert isinstance(images[0], PIL.Image.Image)
 
+        # use PIL by default
+        images = pipe(generator=generator)["sample"]
+        assert isinstance(images, list)
+        assert isinstance(images[0], PIL.Image.Image)
+
     @slow
     def test_ddpm_cifar10(self):
         model_id = "google/ddpm-cifar10-32"
@@ -759,7 +760,7 @@ class PipelineTesterMixin(unittest.TestCase):
         ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
 
         generator = torch.manual_seed(0)
-        image = ddpm(generator=generator)["sample"]
+        image = ddpm(generator=generator, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -777,7 +778,7 @@ class PipelineTesterMixin(unittest.TestCase):
         ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
 
         generator = torch.manual_seed(0)
-        image = ddpm(generator=generator)["sample"]
+        image = ddpm(generator=generator, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -795,7 +796,7 @@ class PipelineTesterMixin(unittest.TestCase):
         ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
 
         generator = torch.manual_seed(0)
-        image = ddim(generator=generator, eta=0.0)["sample"]
+        image = ddim(generator=generator, eta=0.0, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -812,7 +813,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
         pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
         generator = torch.manual_seed(0)
-        image = pndm(generator=generator)["sample"]
+        image = pndm(generator=generator, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -826,7 +827,9 @@ class PipelineTesterMixin(unittest.TestCase):
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
-        image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20)["sample"]
+        image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy")[
+            "sample"
+        ]
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -840,7 +843,7 @@ class PipelineTesterMixin(unittest.TestCase):
 
         prompt = "A painting of a squirrel eating a burger"
         generator = torch.manual_seed(0)
-        image = ldm([prompt], generator=generator, num_inference_steps=1)["sample"]
+        image = ldm([prompt], generator=generator, num_inference_steps=1, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -861,7 +864,7 @@ class PipelineTesterMixin(unittest.TestCase):
         sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler)
 
         torch.manual_seed(0)
-        image = sde_ve(num_inference_steps=300)["sample"]
+        image = sde_ve(num_inference_steps=300, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]
 
@@ -874,7 +877,7 @@ class PipelineTesterMixin(unittest.TestCase):
         ldm = LatentDiffusionUncondPipeline.from_pretrained("CompVis/ldm-celebahq-256")
 
         generator = torch.manual_seed(0)
-        image = ldm(generator=generator, num_inference_steps=5)["sample"]
+        image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"]
 
         image_slice = image[0, -3:, -3:, -1]