Update pr_test_peft_backend.yml to use 1 process for testing

docs/source/en/api/models/autoencoderkl.md

@@ -33,9 +33,6 @@ model = AutoencoderKL.from_single_file(url)
 ## AutoencoderKL
 
 [[autodoc]] AutoencoderKL
-    - decode
-    - encode
-    - all
 
 ## AutoencoderKLOutput
 

examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py

@@ -1279,7 +1279,7 @@ def main(args):
         for name, param in text_encoder_one.named_parameters():
             if "token_embedding" in name:
                 # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
-                param.data = param.to(dtype=torch.float32)
+                param = param.to(dtype=torch.float32)
                 param.requires_grad = True
                 text_lora_parameters_one.append(param)
             else:
@@ -1288,7 +1288,7 @@ def main(args):
         for name, param in text_encoder_two.named_parameters():
             if "token_embedding" in name:
                 # ensure that dtype is float32, even if rest of the model that isn't trained is loaded in fp16
-                param.data = param.to(dtype=torch.float32)
+                param = param.to(dtype=torch.float32)
                 param.requires_grad = True
                 text_lora_parameters_two.append(param)
             else:
@@ -1725,19 +1725,19 @@ def main(args):
         num_train_epochs_text_encoder = int(args.train_text_encoder_frac * args.num_train_epochs)
     elif args.train_text_encoder_ti:  # args.train_text_encoder_ti
         num_train_epochs_text_encoder = int(args.train_text_encoder_ti_frac * args.num_train_epochs)
-    # flag used for textual inversion
-    pivoted = False
+
     for epoch in range(first_epoch, args.num_train_epochs):
         # if performing any kind of optimization of text_encoder params
         if args.train_text_encoder or args.train_text_encoder_ti:
             if epoch == num_train_epochs_text_encoder:
                 print("PIVOT HALFWAY", epoch)
                 # stopping optimization of text_encoder params
-                # this flag is used to reset the optimizer to optimize only on unet params
-                pivoted = True
+                # re setting the optimizer to optimize only on unet params
+                optimizer.param_groups[1]["lr"] = 0.0
+                optimizer.param_groups[2]["lr"] = 0.0
 
             else:
-                # still optimizing the text encoder
+                # still optimizng the text encoder
                 text_encoder_one.train()
                 text_encoder_two.train()
                 # set top parameter requires_grad = True for gradient checkpointing works
@@ -1747,12 +1747,6 @@ def main(args):
 
         unet.train()
         for step, batch in enumerate(train_dataloader):
-            if pivoted:
-                # stopping optimization of text_encoder params
-                # re setting the optimizer to optimize only on unet params
-                optimizer.param_groups[1]["lr"] = 0.0
-                optimizer.param_groups[2]["lr"] = 0.0
-
             with accelerator.accumulate(unet):
                 prompts = batch["prompts"]
                 # encode batch prompts when custom prompts are provided for each image -
@@ -1891,7 +1885,8 @@ def main(args):
 
                 # every step, we reset the embeddings to the original embeddings.
                 if args.train_text_encoder_ti:
-                    embedding_handler.retract_embeddings()
+                    for idx, text_encoder in enumerate(text_encoders):
+                        embedding_handler.retract_embeddings()
 
             # Checks if the accelerator has performed an optimization step behind the scenes
             if accelerator.sync_gradients:

src/diffusers/pipelines/animatediff/pipeline_animatediff.py

@@ -67,7 +67,10 @@ EXAMPLE_DOC_STRING = """
 """
 
 
-def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
+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
+
     batch_size, channels, num_frames, height, width = video.shape
     outputs = []
     for batch_idx in range(batch_size):
@@ -76,15 +79,6 @@ def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", 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
 
 
@@ -811,7 +805,11 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdap
             return AnimateDiffPipelineOutput(frames=latents)
 
         video_tensor = self.decode_latents(latents)
-        video = tensor2vid(video_tensor, self.image_processor, output_type=output_type)
+
+        if output_type == "pt":
+            video = video_tensor
+        else:
+            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

@@ -40,8 +40,10 @@ def _append_dims(x, target_dims):
     return x[(...,) + (None,) * dims_to_append]
 
 
-# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.tensor2vid
-def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", output_type: str = "np"):
+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
+
     batch_size, channels, num_frames, height, width = video.shape
     outputs = []
     for batch_idx in range(batch_size):
@@ -51,13 +53,7 @@ def tensor2vid(video: torch.Tensor, processor: "VaeImageProcessor", 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 np.stack(outputs)
 
     return outputs
 

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth.py

@@ -19,7 +19,6 @@ import numpy as np
 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
@@ -59,26 +58,22 @@ EXAMPLE_DOC_STRING = """
 """
 
 
-# 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 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
 
 
 class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
@@ -127,7 +122,6 @@ class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lora
             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):
@@ -723,7 +717,11 @@ class TextToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lora
             return TextToVideoSDPipelineOutput(frames=latents)
 
         video_tensor = self.decode_latents(latents)
-        video = tensor2vid(video_tensor, self.image_processor, output_type)
+
+        if output_type == "pt":
+            video = video_tensor
+        else:
+            video = tensor2vid(video_tensor)
 
         # Offload all models
         self.maybe_free_model_hooks()

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_synth_img2img.py

@@ -20,7 +20,6 @@ import PIL.Image
 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
@@ -94,26 +93,22 @@ def retrieve_latents(
         raise AttributeError("Could not access latents of provided encoder_output")
 
 
-# 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 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
 
 
 def preprocess_video(video):
@@ -203,7 +198,6 @@ class VideoToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lor
             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):
@@ -818,11 +812,12 @@ class VideoToVideoSDPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lor
         if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
             self.unet.to("cpu")
 
-        if output_type == "latent":
-            return TextToVideoSDPipelineOutput(frames=latents)
-
         video_tensor = self.decode_latents(latents)
-        video = tensor2vid(video_tensor, self.image_processor, output_type)
+
+        if output_type == "pt":
+            video = video_tensor
+        else:
+            video = tensor2vid(video_tensor)
 
         # Offload all models
         self.maybe_free_model_hooks()

tests/pipelines/animatediff/test_animatediff.py

@@ -262,7 +262,7 @@ class AnimateDiffPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         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, 1e1, "Enabling of FreeInit should lead to results different from the default pipeline results"
+            sum_enabled, 1e2, "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

@@ -29,7 +29,6 @@ from diffusers.utils import is_xformers_available
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     load_numpy,
-    numpy_cosine_similarity_distance,
     require_torch_gpu,
     skip_mps,
     slow,
@@ -142,11 +141,10 @@ class TextToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         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][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 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])
 
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -185,7 +183,7 @@ class TextToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
 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")
@@ -194,8 +192,10 @@ class TextToVideoSDPipelineSlowTests(unittest.TestCase):
         prompt = "Spiderman is surfing"
         generator = torch.Generator(device="cpu").manual_seed(0)
 
-        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
+        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
 
     def test_two_step_model_with_freeu(self):
         expected_video = []
@@ -207,9 +207,10 @@ class TextToVideoSDPipelineSlowTests(unittest.TestCase):
         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="np").frames
-        video = video_frames[0, 0, -3:, -3:, -1].flatten()
+        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()
 
-        expected_video = [0.3643, 0.3455, 0.3831, 0.3923, 0.2978, 0.3247, 0.3278, 0.3201, 0.3475]
+        expected_video = [-0.3102, -0.2477, -0.1772, -0.648, -0.6176, -0.5484, -0.0217, -0.056, -0.0177]
 
         assert np.abs(expected_video - video).mean() < 5e-2

tests/pipelines/text_to_video_synthesis/test_video_to_video.py

@@ -157,10 +157,10 @@ class VideoToVideoSDPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         inputs = self.get_dummy_inputs(device)
         inputs["output_type"] = "np"
         frames = sd_pipe(**inputs).frames
-        image_slice = frames[0][0][-3:, -3:, -1]
+        image_slice = frames[0][-3:, -3:, -1]
 
-        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 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 np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
 
@@ -214,11 +214,9 @@ class VideoToVideoSDPipelineSlowTests(unittest.TestCase):
 
         prompt = "Spiderman is surfing"
 
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        video_frames = pipe(prompt, video=video, generator=generator, num_inference_steps=3, output_type="np").frames
+        video_frames = pipe(prompt, video=video, generator=generator, num_inference_steps=3, output_type="pt").frames
 
-        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
+        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:]
+
+        assert numpy_cosine_similarity_distance(expected_array, output_array) < 1e-2