PIL-ify the pipeline outputs (#111)

src/diffusers/pipeline_utils.py

@@ -19,6 +19,7 @@ import os
 from typing import Optional, Union
 
 from huggingface_hub import snapshot_download
+from PIL import Image
 
 from .configuration_utils import ConfigMixin
 from .utils import DIFFUSERS_CACHE, logging
@@ -189,3 +190,15 @@ class DiffusionPipeline(ConfigMixin):
         # 5. Instantiate the pipeline
         model = pipeline_class(**init_kwargs)
         return model
+
+    @staticmethod
+    def numpy_to_pil(images):
+        """
+        Convert a numpy image or a batch of images to a PIL image.
+        """
+        if images.ndim == 3:
+            images = images[None, ...]
+        images = (images * 255).round().astype("uint8")
+        pil_images = [Image.fromarray(image) for image in images]
+
+        return pil_images

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):
+    def __call__(self, batch_size=1, generator=None, torch_device=None, output_type="numpy"):
         if torch_device is None:
             torch_device = "cuda" if torch.cuda.is_available() else "cpu"
 
@@ -56,5 +56,7 @@ class DDPMPipeline(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/latent_diffusion/pipeline_latent_diffusion.py

@@ -30,6 +30,7 @@ class LatentDiffusionPipeline(DiffusionPipeline):
         eta=0.0,
         guidance_scale=1.0,
         num_inference_steps=50,
+        output_type="numpy",
     ):
         # eta corresponds to η in paper and should be between [0, 1]
 
@@ -86,6 +87,8 @@ class LatentDiffusionPipeline(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/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -13,12 +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,
+        self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50, output_type="numpy"
     ):
         # eta corresponds to η in paper and should be between [0, 1]
 
@@ -47,5 +42,7 @@ class LatentDiffusionUncondPipeline(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/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):
+    def __call__(self, batch_size=1, generator=None, torch_device=None, num_inference_steps=50, output_type="numpy"):
         # 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:
@@ -59,5 +59,7 @@ class PNDMPipeline(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/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):
+    def __call__(self, num_inference_steps=2000, generator=None, output_type="numpy"):
         device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
 
         img_size = self.model.config.image_size
@@ -47,5 +47,7 @@ class ScoreSdeVePipeline(DiffusionPipeline):
 
         sample = sample.clamp(0, 1)
         sample = sample.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            sample = self.numpy_to_pil(sample)
 
         return {"sample": sample}

tests/test_modeling_utils.py

@@ -18,11 +18,11 @@ import inspect
 import math
 import tempfile
 import unittest
-from atexit import register
 
 import numpy as np
 import torch
 
+import PIL
 from diffusers import UNetConditionalModel  # noqa: F401 TODO(Patrick) - need to write tests with it
 from diffusers import (
     AutoencoderKL,
@@ -728,6 +728,26 @@ class PipelineTesterMixin(unittest.TestCase):
 
         assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
 
+    @slow
+    def test_output_format(self):
+        model_path = "google/ddpm-cifar10-32"
+
+        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)
+
+        images = pipe(generator=generator, output_type="pil")["sample"]
+        assert isinstance(images, list)
+        assert len(images) == 1
+        assert isinstance(images[0], PIL.Image.Image)
+
     @slow
     def test_ddpm_cifar10(self):
         model_id = "google/ddpm-cifar10-32"