rewrite Kandinsky5T2VPipeline to diffusers style

src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py

@@ -75,6 +75,101 @@ EXAMPLE_DOC_STRING = """
         ```
 """
 
+# Copyright 2025 The Wan Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import html
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import regex as re
+import torch
+from transformers import Qwen2TokenizerFast, Qwen2VLProcessor, Qwen2_5_VLForConditionalGeneration, AutoProcessor, CLIPTextModel, CLIPTokenizer
+import torchvision
+from torchvision.transforms import ToPILImage
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...loaders import KandinskyLoraLoaderMixin
+from ...models import AutoencoderKLHunyuanVideo
+from ...models.transformers import Kandinsky5Transformer3DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import KandinskyPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+if is_ftfy_available():
+    import ftfy
+
+
+logger = logging.get_logger(__name__)
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+    
+        ```python
+        >>> import torch
+        >>> from diffusers import Kandinsky5T2VPipeline
+        >>> from diffusers.utils import export_to_video
+
+        >>> pipe = Kandinsky5T2VPipeline.from_pretrained("ai-forever/Kandinsky-5.0-T2V")
+        >>> pipe = pipe.to("cuda")
+
+        >>> prompt = "A cat and a dog baking a cake together in a kitchen."
+        >>> negative_prompt = "Bright tones, overexposed, static, blurred details"
+
+        >>> output = pipe(
+        ...     prompt=prompt,
+        ...     negative_prompt=negative_prompt,
+        ...     height=512,
+        ...     width=768,
+        ...     num_frames=25,
+        ...     num_inference_steps=50,
+        ...     guidance_scale=5.0,
+        ... ).frames[0]
+        >>> export_to_video(output, "output.mp4", fps=6)
+        ```
+"""
+
+
+def basic_clean(text):
+    if is_ftfy_available():
+        text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+def prompt_clean(text):
+    text = whitespace_clean(basic_clean(text))
+    return text
+
 
 class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
     r"""
@@ -96,9 +191,11 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
             Frozen CLIP text encoder.
         tokenizer_2 ([`CLIPTokenizer`]):
             Tokenizer for CLIP.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded video latents.
     """
 
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
 
     def __init__(
@@ -125,6 +222,7 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
 
         self.vae_scale_factor_temporal = vae.config.temporal_compression_ratio
         self.vae_scale_factor_spatial = vae.config.spatial_compression_ratio
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
 
     def _encode_prompt_qwen(
         self,
@@ -132,9 +230,12 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
         device: Optional[torch.device] = None,
         num_videos_per_prompt: int = 1,
         max_sequence_length: int = 256,
+        dtype: Optional[torch.dtype] = None,
     ):
         device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
         prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [prompt_clean(p) for p in prompt]
 
         # Kandinsky specific prompt template
         prompt_template = "\n".join([
@@ -180,16 +281,19 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
         embeds = embeds.repeat(1, num_videos_per_prompt, 1)
         embeds = embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
 
-        return embeds, cu_seqlens
+        return embeds.to(dtype), cu_seqlens
 
     def _encode_prompt_clip(
         self,
         prompt: Union[str, List[str]],
         device: Optional[torch.device] = None,
         num_videos_per_prompt: int = 1,
+        dtype: Optional[torch.dtype] = None,
     ):
         device = device or self._execution_device
+        dtype = dtype or self.text_encoder_2.dtype
         prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [prompt_clean(p) for p in prompt]
 
         inputs = self.tokenizer_2(
             prompt,
@@ -208,7 +312,7 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
         pooled_embed = pooled_embed.repeat(1, num_videos_per_prompt, 1)
         pooled_embed = pooled_embed.view(batch_size * num_videos_per_prompt, -1)
 
-        return pooled_embed
+        return pooled_embed.to(dtype)
 
     def encode_prompt(
         self,
@@ -216,34 +320,151 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
         negative_prompt: Optional[Union[str, List[str]]] = None,
         do_classifier_free_guidance: bool = True,
         num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 512,
         device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
     ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            max_sequence_length (`int`, *optional*, defaults to 512):
+                Maximum sequence length for text encoding.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
         device = device or self._execution_device
 
-        prompt_embeds, prompt_cu_seqlens = self._encode_prompt_qwen(prompt, device, num_videos_per_prompt)
-        pooled_embed = self._encode_prompt_clip(prompt, device, num_videos_per_prompt)
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds[0].shape[0] if isinstance(prompt_embeds, (list, tuple)) else prompt_embeds.shape[0]
 
-        if do_classifier_free_guidance:
+        if prompt_embeds is None:
+            prompt_embeds_qwen, prompt_cu_seqlens = self._encode_prompt_qwen(
+                prompt=prompt,
+                device=device,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                dtype=dtype,
+            )
+            prompt_embeds_clip = self._encode_prompt_clip(
+                prompt=prompt,
+                device=device,
+                num_videos_per_prompt=num_videos_per_prompt,
+                dtype=dtype,
+            )
+        else:
+            prompt_embeds_qwen, prompt_embeds_clip, prompt_cu_seqlens = prompt_embeds
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
             negative_prompt = negative_prompt or ""
             negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
-            
-            negative_prompt_embeds, negative_cu_seqlens = self._encode_prompt_qwen(negative_prompt, device, num_videos_per_prompt)
-            negative_pooled_embed = self._encode_prompt_clip(negative_prompt, device, num_videos_per_prompt)
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds_qwen, negative_cu_seqlens = self._encode_prompt_qwen(
+                prompt=negative_prompt,
+                device=device,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                dtype=dtype,
+            )
+            negative_prompt_embeds_clip = self._encode_prompt_clip(
+                prompt=negative_prompt,
+                device=device,
+                num_videos_per_prompt=num_videos_per_prompt,
+                dtype=dtype,
+            )
         else:
-            negative_prompt_embeds = None
-            negative_pooled_embed = None
+            negative_prompt_embeds_qwen = None
+            negative_prompt_embeds_clip = None
             negative_cu_seqlens = None
 
-        text_embeds = {
-            "text_embeds": prompt_embeds,
-            "pooled_embed": pooled_embed,
+        prompt_embeds_dict = {
+            "text_embeds": prompt_embeds_qwen,
+            "pooled_embed": prompt_embeds_clip,
         }
-        negative_text_embeds = {
-            "text_embeds": negative_prompt_embeds,
-            "pooled_embed": negative_pooled_embed,
+        negative_prompt_embeds_dict = {
+            "text_embeds": negative_prompt_embeds_qwen,
+            "pooled_embed": negative_prompt_embeds_clip,
         } if do_classifier_free_guidance else None
 
-        return text_embeds, negative_text_embeds, prompt_cu_seqlens, negative_cu_seqlens
+        return prompt_embeds_dict, negative_prompt_embeds_dict, prompt_cu_seqlens, negative_cu_seqlens
+
+    def check_inputs(
+        self,
+        prompt,
+        negative_prompt,
+        height,
+        width,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+    ):
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif negative_prompt is not None and (
+            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
+        ):
+            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
 
     def prepare_latents(
         self,
@@ -252,34 +473,31 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
         height: int = 480,
         width: int = 832,
         num_frames: int = 81,
-        visual_cond: bool = False,
         dtype: Optional[torch.dtype] = None,
         device: Optional[torch.device] = None,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
         if latents is not None:
-            num_latent_frames = latents.shape[1]
-            latents = latents.to(device=device, dtype=dtype)
+            return latents.to(device=device, dtype=dtype)
 
-        else:
-            num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
-            shape = (
-                batch_size,
-                num_latent_frames,
-                int(height) // self.vae_scale_factor_spatial,
-                int(width) // self.vae_scale_factor_spatial,
-                num_channels_latents,
+        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        shape = (
+            batch_size,
+            num_latent_frames,
+            int(height) // self.vae_scale_factor_spatial,
+            int(width) // self.vae_scale_factor_spatial,
+            num_channels_latents,
+        )
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
             )
-            if isinstance(generator, list) and len(generator) != batch_size:
-                raise ValueError(
-                    f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
-                    f" size of {batch_size}. Make sure the batch size matches the length of the generators."
-                )
 
-            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
-                            
-        if visual_cond:
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        
+        if self.transformer.visual_cond:
             # For visual conditioning, concatenate with zeros and mask
             visual_cond = torch.zeros_like(latents)
             visual_cond_mask = torch.zeros(
@@ -291,26 +509,46 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
 
         return latents
 
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1.0
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
     def __call__(
         self,
-        prompt: Union[str, List[str]],
+        prompt: Union[str, List[str]] = None,
         negative_prompt: Optional[Union[str, List[str]]] = None,
         height: int = 512,
         width: int = 768,
-        num_frames: int = 121,
+        num_frames: int = 25,
         num_inference_steps: int = 50,
         guidance_scale: float = 5.0,
         scheduler_scale: float = 10.0,
-        num_videos_per_prompt: int = 1,
-        generator: Optional[torch.Generator] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
         output_type: Optional[str] = "pil",
         return_dict: bool = True,
-        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
         **kwargs,
     ):
         r"""
@@ -318,9 +556,10 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
 
         Args:
             prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the video generation.
+                The prompt or prompts to guide the video generation. If not defined, pass `prompt_embeds` instead.
             negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to avoid during video generation.
+                The prompt or prompts to avoid during video generation. If not defined, pass `negative_prompt_embeds`
+                instead. Ignored when not using guidance (`guidance_scale` < `1`).
             height (`int`, defaults to `512`):
                 The height in pixels of the generated video.
             width (`int`, defaults to `768`):
@@ -335,82 +574,109 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
                 Scale factor for the custom flow matching scheduler.
             num_videos_per_prompt (`int`, *optional*, defaults to 1):
                 The number of videos to generate per prompt.
-            generator (`torch.Generator`, *optional*):
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
                 A torch generator to make generation deterministic.
             latents (`torch.Tensor`, *optional*):
                 Pre-generated noisy latents.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generated video.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`KandinskyPipelineOutput`].
-            callback_on_step_end (`Callable`, *optional*):
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
                 A function that is called at the end of each denoising step.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function.
+            max_sequence_length (`int`, defaults to `512`):
+                The maximum sequence length for text encoding.
         
         Examples:
         
         Returns:
             [`~KandinskyPipelineOutput`] or `tuple`:
                 If `return_dict` is `True`, [`KandinskyPipelineOutput`] is returned, otherwise a `tuple` is returned where
-                the first element is a list with the generated images and the second element is a list of `bool`s
-                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
+                the first element is a list with the generated images.
         """
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 0. Reset embeddings dtype
         self.transformer.time_embeddings.reset_dtype()
         self.transformer.text_rope_embeddings.reset_dtype()
         self.transformer.visual_rope_embeddings.reset_dtype()
-        
-        dtype = self.transformer.dtype
 
-        if height % 16 != 0 or width % 16 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            negative_prompt,
+            height,
+            width,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+        )
 
-        if isinstance(prompt, str):
-            batch_size = 1
-        else:
-            batch_size = len(prompt)
-
-        device = self._execution_device
-        do_classifier_free_guidance = guidance_scale > 1.0
-        
         if num_frames % self.vae_scale_factor_temporal != 1:
             logger.warning(
                 f"`num_frames - 1` has to be divisible by {self.vae_scale_factor_temporal}. Rounding to the nearest number."
             )
             num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
         num_frames = max(num_frames, 1)
-        
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self.scheduler.timesteps
-        
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
 
-        text_embeds, negative_text_embeds, prompt_cu_seqlens, negative_cu_seqlens = self.encode_prompt(
+        self._guidance_scale = guidance_scale
+        self._interrupt = False
+
+        device = self._execution_device
+        dtype = self.transformer.dtype
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds[0].shape[0] if isinstance(prompt_embeds, (list, tuple)) else prompt_embeds.shape[0]
+
+        # 3. Encode input prompt
+        prompt_embeds_dict, negative_prompt_embeds_dict, prompt_cu_seqlens, negative_cu_seqlens = self.encode_prompt(
             prompt=prompt,
             negative_prompt=negative_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
             num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
             device=device,
+            dtype=dtype,
         )
 
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
         num_channels_latents = 16
         latents = self.prepare_latents(
-            batch_size=batch_size * num_videos_per_prompt,
-            num_channels_latents=16,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            visual_cond=self.transformer.visual_cond,
-            dtype=dtype,
-            device=device,
-            generator=generator,
-            latents=latents,
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            num_frames,
+            dtype,
+            device,
+            generator,
+            latents,
         )
-        
-        visual_cond = latents[:, :, :, :, 16:]
 
+        # 6. Prepare rope positions
+        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
         visual_rope_pos = [
-            torch.arange(num_frames // 4 + 1, device=device),
-            torch.arange(height // 8 // 2, device=device),
-            torch.arange(width // 8 // 2, device=device),
+            torch.arange(num_latent_frames, device=device),
+            torch.arange(height // self.vae_scale_factor_spatial // 2, device=device),
+            torch.arange(width // self.vae_scale_factor_spatial // 2, device=device),
         ]
         
         text_rope_pos = torch.arange(prompt_cu_seqlens[-1].item(), device=device)
@@ -421,52 +687,72 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
             else None
         )
 
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
                 timestep = t.unsqueeze(0).flatten()
 
-                with torch.autocast(device_type="cuda", dtype=dtype):
-                    pred_velocity = self.transformer(
-                        hidden_states=latents,
-                        encoder_hidden_states=text_embeds["text_embeds"],
-                        pooled_projections=text_embeds["pooled_embed"],
-                        timestep=timestep,
+                                
+
+                # Predict noise residual
+                # with torch.autocast(device_type="cuda", dtype=dtype):
+                pred_velocity = self.transformer(
+                    hidden_states=latents.to(dtype),
+                    encoder_hidden_states=prompt_embeds_dict["text_embeds"].to(dtype),
+                    pooled_projections=prompt_embeds_dict["pooled_embed"].to(dtype),
+                    timestep=timestep.to(dtype),
+                    visual_rope_pos=visual_rope_pos,
+                    text_rope_pos=text_rope_pos,
+                    scale_factor=(1, 2, 2), 
+                    sparse_params=None,
+                    return_dict=True
+                ).sample
+
+                if self.do_classifier_free_guidance and negative_prompt_embeds_dict is not None:
+                    uncond_pred_velocity = self.transformer(
+                        hidden_states=latents.to(dtype),
+                        encoder_hidden_states=negative_prompt_embeds_dict["text_embeds"].to(dtype),
+                        pooled_projections=negative_prompt_embeds_dict["pooled_embed"].to(dtype),
+                        timestep=timestep.to(dtype),
                         visual_rope_pos=visual_rope_pos,
-                        text_rope_pos=text_rope_pos,
-                        scale_factor=(1, 2, 2), 
+                        text_rope_pos=negative_text_rope_pos,
+                        scale_factor=(1, 2, 2),
                         sparse_params=None,
                         return_dict=True
                     ).sample
 
-                    if guidance_scale > 1.0 and negative_text_embeds is not None:
-                        uncond_pred_velocity = self.transformer(
-                            hidden_states=latents,
-                            encoder_hidden_states=negative_text_embeds["text_embeds"],
-                            pooled_projections=negative_text_embeds["pooled_embed"],
-                            timestep=timestep,
-                            visual_rope_pos=visual_rope_pos,
-                            text_rope_pos=negative_text_rope_pos,
-                            scale_factor=(1, 2, 2),
-                            sparse_params=None,
-                            return_dict=True
-                        ).sample
-
-                        pred_velocity = uncond_pred_velocity + guidance_scale * (
-                            pred_velocity - uncond_pred_velocity
-                        )
+                    pred_velocity = uncond_pred_velocity + guidance_scale * (
+                        pred_velocity - uncond_pred_velocity
+                    )
                 
-                latents[:, :, :, :, :16] = self.scheduler.step(pred_velocity, t, latents[:, :, :, :, :16], return_dict=False)[0]
+                # Compute previous sample
+                latents[:, :, :, :, :16] = self.scheduler.step(
+                    pred_velocity, t, latents[:, :, :, :, :16], return_dict=False
+                )[0]
 
                 if callback_on_step_end is not None:
                     callback_kwargs = {}
                     for k in callback_on_step_end_tensor_inputs:
                         callback_kwargs[k] = locals()[k]
-                    callback_outputs = callback_on_step_end(self, i, timestep, callback_kwargs)
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
                     latents = callback_outputs.pop("latents", latents)
-                
+                    prompt_embeds_dict = callback_outputs.pop("prompt_embeds", prompt_embeds_dict)
+                    negative_prompt_embeds_dict = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds_dict)
+
                 if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                     progress_bar.update()
-                    
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        # 8. Post-processing
         latents = latents[:, :, :, :, :16]
 
         # 9. Decode latents to video
@@ -477,26 +763,23 @@ class Kandinsky5T2VPipeline(DiffusionPipeline, KandinskyLoraLoaderMixin):
                 batch_size,
                 num_videos_per_prompt,
                 (num_frames - 1) // self.vae_scale_factor_temporal + 1,
-                height // 8,
-                width // 8,
+                height // self.vae_scale_factor_spatial,
+                width // self.vae_scale_factor_spatial,
                 16,
             )
             video = video.permute(0, 1, 5, 2, 3, 4)  # [batch, num_videos, channels, frames, height, width]
-            video = video.reshape(batch_size * num_videos_per_prompt, 16, (num_frames - 1) // self.vae_scale_factor_temporal + 1, height // 8, width // 8)
+            video = video.reshape(
+                batch_size * num_videos_per_prompt, 
+                16, 
+                (num_frames - 1) // self.vae_scale_factor_temporal + 1, 
+                height // self.vae_scale_factor_spatial, 
+                width // self.vae_scale_factor_spatial
+            )
             
             # Normalize and decode
             video = video / self.vae.config.scaling_factor
             video = self.vae.decode(video).sample
-            video = ((video.clamp(-1.0, 1.0) + 1.0) * 127.5).to(torch.uint8)
-            # Convert to output format
-            if output_type == "pil":
-                if num_frames == 1:
-                    # Single image
-                    video = [ToPILImage()(frame.squeeze(1)) for frame in video]
-                else:
-                    # Video frames
-                    video = [video[i] for i in range(video.shape[0])]
-
+            video = self.video_processor.postprocess_video(video, output_type=output_type)
         else:
             video = latents