Standardise outputs for video pipelines (#6626)

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Author: DN6

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -67,10 +67,7 @@
 """
 
 
-def tensor2vid(video: torch.Tensor, processor, output_type="np"):
-    # Based on:
-    # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
-
+def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
     batch_size, channels, num_frames, height, width = video.shape
     outputs = []
     for batch_idx in range(batch_size):
@@ -79,6 +76,15 @@ def tensor2vid(video: torch.Tensor, processor, output_type="np"):
 
         outputs.append(batch_output)
 
+    if output_type == "np":
+        outputs = np.stack(outputs)
+
+    elif output_type == "pt":
+        outputs = torch.stack(outputs)
+
+    elif not output_type == "pil":
+        raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil]")
+
     return outputs
 
 
@@ -805,11 +811,7 @@ def _retrieve_video_frames(self, latents, output_type, return_dict):
             return AnimateDiffPipelineOutput(frames=latents)
 
         video_tensor = self.decode_latents(latents)
-
-        if output_type == "pt":
-            video = video_tensor
-        else:
-            video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
+        video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
 
         if not return_dict:
             return (video,)

src/diffusers/pipelines/stable_video_diffusion/pipeline_stable_video_diffusion.py

@@ -40,10 +40,8 @@ def _append_dims(x, target_dims):
     return x[(...,) + (None,) * dims_to_append]
 
 
-def tensor2vid(video: torch.Tensor, processor, output_type="np"):
-    # Based on:
-    # https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
-
+# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
+def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
     batch_size, channels, num_frames, height, width = video.shape
     outputs = []
     for batch_idx in range(batch_size):
@@ -53,7 +51,13 @@ def tensor2vid(video: torch.Tensor, processor, output_type="np"):
         outputs.append(batch_output)
 
     if output_type == "np":
-        return np.stack(outputs)
+        outputs = np.stack(outputs)
+
+    elif output_type == "pt":
+        outputs = torch.stack(outputs)
+
+    elif not output_type == "pil":
+        raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil]")
 
     return outputs
 

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py

@@ -19,6 +19,7 @@
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer
 
+from ...image_processor import VaeImageProcessor
 from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet3DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
@@ -58,22 +59,26 @@
 """
 
 
-def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]:
-    # This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
-    # reshape to ncfhw
-    mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1)
-    std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1)
-    # unnormalize back to [0,1]
-    video = video.mul_(std).add_(mean)
-    video.clamp_(0, 1)
-    # prepare the final outputs
-    i, c, f, h, w = video.shape
-    images = video.permute(2, 3, 0, 4, 1).reshape(
-        f, h, i * w, c
-    )  # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
-    images = images.unbind(dim=0)  # prepare a list of indvidual (consecutive frames)
-    images = [(image.cpu().numpy() * 255).astype("uint8") for image in images]  # f h w c
-    return images
+# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
+def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
+    batch_size, channels, num_frames, height, width = video.shape
+    outputs = []
+    for batch_idx in range(batch_size):
+        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
+        batch_output = processor.postprocess(batch_vid, output_type)
+
+        outputs.append(batch_output)
+
+    if output_type == "np":
+        outputs = np.stack(outputs)
+
+    elif output_type == "pt":
+        outputs = torch.stack(outputs)
+
+    elif not output_type == "pil":
+        raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil]")
+
+    return outputs
 
 
 class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
@@ -122,6 +127,7 @@ def __init__(
             scheduler=scheduler,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
@@ -717,11 +723,7 @@ def __call__(
             return TextToVideoSDPipelineOutput(frames=latents)
 
         video_tensor = self.decode_latents(latents)
-
-        if output_type == "pt":
-            video = video_tensor
-        else:
-            video = tensor2vid(video_tensor)
+        video = tensor2vid(video_tensor, self.image_processor, output_type)
 
         # Offload all models
         self.maybe_free_model_hooks()

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py

@@ -20,6 +20,7 @@
 import torch
 from transformers import CLIPTextModel, CLIPTokenizer
 
+from ...image_processor import VaeImageProcessor
 from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet3DConditionModel
 from ...models.lora import adjust_lora_scale_text_encoder
@@ -93,22 +94,26 @@ def retrieve_latents(
         raise AttributeError("Could not access latents of provided encoder_output")
 
 
-def tensor2vid(video: torch.Tensor, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) -> List[np.ndarray]:
-    # This code is copied from https://github.com/modelscope/modelscope/blob/1509fdb973e5871f37148a4b5e5964cafd43e64d/modelscope/pipelines/multi_modal/text_to_video_synthesis_pipeline.py#L78
-    # reshape to ncfhw
-    mean = torch.tensor(mean, device=video.device).reshape(1, -1, 1, 1, 1)
-    std = torch.tensor(std, device=video.device).reshape(1, -1, 1, 1, 1)
-    # unnormalize back to [0,1]
-    video = video.mul_(std).add_(mean)
-    video.clamp_(0, 1)
-    # prepare the final outputs
-    i, c, f, h, w = video.shape
-    images = video.permute(2, 3, 0, 4, 1).reshape(
-        f, h, i * w, c
-    )  # 1st (frames, h, batch_size, w, c) 2nd (frames, h, batch_size * w, c)
-    images = images.unbind(dim=0)  # prepare a list of indvidual (consecutive frames)
-    images = [(image.cpu().numpy() * 255).astype("uint8") for image in images]  # f h w c
-    return images
+# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
+def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
+    batch_size, channels, num_frames, height, width = video.shape
+    outputs = []
+    for batch_idx in range(batch_size):
+        batch_vid = video[batch_idx].permute(1, 0, 2, 3)
+        batch_output = processor.postprocess(batch_vid, output_type)
+
+        outputs.append(batch_output)
+
+    if output_type == "np":
+        outputs = np.stack(outputs)
+
+    elif output_type == "pt":
+        outputs = torch.stack(outputs)
+
+    elif not output_type == "pil":
+        raise ValueError(f"{output_type} does not exist. Please choose one of ['np', 'pt', 'pil]")
+
+    return outputs
 
 
 def preprocess_video(video):
@@ -198,6 +203,7 @@ def __init__(
             scheduler=scheduler,
         )
         self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing
     def enable_vae_slicing(self):
@@ -812,12 +818,11 @@ def __call__(
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.unet.to("cpu")
 
-        video_tensor = self.decode_latents(latents)
+        if output_type == "latent":
+            return TextToVideoSDPipelineOutput(frames=latents)
 
-        if output_type == "pt":
-            video = video_tensor
-        else:
-            video = tensor2vid(video_tensor)
+        video_tensor = self.decode_latents(latents)
+        video = tensor2vid(video_tensor, self.image_processor, output_type)
 
         # Offload all models
         self.maybe_free_model_hooks()

tests/pipelines/animatediff/test_animatediff.py

@@ -262,7 +262,7 @@ def test_free_init(self):
         sum_enabled = np.abs(to_np(frames_normal) - to_np(frames_enable_free_init)).sum()
         max_diff_disabled = np.abs(to_np(frames_normal) - to_np(frames_disable_free_init)).max()
         self.assertGreater(
-            sum_enabled, 1e2, "Enabling of FreeInit should lead to results different from the default pipeline results"
+            sum_enabled, 1e1, "Enabling of FreeInit should lead to results different from the default pipeline results"
         )
         self.assertLess(
             max_diff_disabled,

tests/pipelines/text_to_video_synthesis/test_text_to_video.py

@@ -29,6 +29,7 @@
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     load_numpy,
+    numpy_cosine_similarity_distance,
     require_torch_gpu,
     skip_mps,
     slow,
@@ -141,10 +142,11 @@ def test_text_to_video_default_case(self):
         inputs = self.get_dummy_inputs(device)
         inputs["output_type"] = "np"
         frames = sd_pipe(**inputs).frames
-        image_slice = frames[0][-3:, -3:, -1]
 
-        assert frames[0].shape == (32, 32, 3)
-        expected_slice = np.array([192.0, 44.0, 157.0, 140.0, 108.0, 104.0, 123.0, 144.0, 129.0])
+        image_slice = frames[0][0][-3:, -3:, -1]
+
+        assert frames[0][0].shape == (32, 32, 3)
+        expected_slice = np.array([0.7537, 0.1752, 0.6157, 0.5508, 0.4240, 0.4110, 0.4838, 0.5648, 0.5094])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -183,7 +185,7 @@ def test_progress_bar(self):
 class TextToVideoSDPipelineSlowTests(unittest.TestCase):
     def test_two_step_model(self):
         expected_video = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy"
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/video_2step.npy"
         )
 
         pipe = TextToVideoSDPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b")
@@ -192,10 +194,8 @@ def test_two_step_model(self):
         prompt = "Spiderman is surfing"
         generator = torch.Generator(device="cpu").manual_seed(0)
 
-        video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="pt").frames
-        video = video_frames.cpu().numpy()
-
-        assert np.abs(expected_video - video).mean() < 5e-2
+        video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").frames
+        assert numpy_cosine_similarity_distance(expected_video.flatten(), video_frames.flatten()) < 1e-4
 
     def test_two_step_model_with_freeu(self):
         expected_video = []
@@ -207,10 +207,9 @@ def test_two_step_model_with_freeu(self):
         generator = torch.Generator(device="cpu").manual_seed(0)
 
         pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.2, b2=1.4)
-        video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="pt").frames
-        video = video_frames.cpu().numpy()
-        video = video[0, 0, -3:, -3:, -1].flatten()
+        video_frames = pipe(prompt, generator=generator, num_inference_steps=2, output_type="np").frames
+        video = video_frames[0, 0, -3:, -3:, -1].flatten()
 
-        expected_video = [-0.3102, -0.2477, -0.1772, -0.648, -0.6176, -0.5484, -0.0217, -0.056, -0.0177]
+        expected_video = [0.3643, 0.3455, 0.3831, 0.3923, 0.2978, 0.3247, 0.3278, 0.3201, 0.3475]
 
         assert np.abs(expected_video - video).mean() < 5e-2

tests/pipelines/text_to_video_synthesis/test_video_to_video.py

@@ -157,10 +157,10 @@ def test_text_to_video_default_case(self):
         inputs = self.get_dummy_inputs(device)
         inputs["output_type"] = "np"
         frames = sd_pipe(**inputs).frames
-        image_slice = frames[0][-3:, -3:, -1]
+        image_slice = frames[0][0][-3:, -3:, -1]
 
-        assert frames[0].shape == (32, 32, 3)
-        expected_slice = np.array([162.0, 136.0, 132.0, 140.0, 139.0, 137.0, 169.0, 134.0, 132.0])
+        assert frames[0][0].shape == (32, 32, 3)
+        expected_slice = np.array([0.6391, 0.5350, 0.5202, 0.5521, 0.5453, 0.5393, 0.6652, 0.5270, 0.5185])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -214,9 +214,11 @@ def test_two_step_model(self):
 
         prompt = "Spiderman is surfing"
 
-        video_frames = pipe(prompt, video=video, generator=generator, num_inference_steps=3, output_type="pt").frames
-
-        expected_array = np.array([-0.9770508, -0.8027344, -0.62646484, -0.8334961, -0.7573242])
-        output_array = video_frames.cpu().numpy()[0, 0, 0, 0, -5:]
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        video_frames = pipe(prompt, video=video, generator=generator, num_inference_steps=3, output_type="np").frames
 
-        assert numpy_cosine_similarity_distance(expected_array, output_array) < 1e-2
+        expected_array = np.array(
+            [0.17114258, 0.13720703, 0.08886719, 0.14819336, 0.1730957, 0.24584961, 0.22021484, 0.35180664, 0.2607422]
+        )
+        output_array = video_frames[0, 0, :3, :3, 0].flatten()
+        assert numpy_cosine_similarity_distance(expected_array, output_array) < 1e-3