Update skyreelsinfer/pipelines/pipeline_skyreels_video.py

skyreelsinfer/pipelines/pipeline_skyreels_video.py

@@ -1,425 +1,430 @@
-from typing import Any
-from typing import Callable
-from typing import Dict
-from typing import List
-from typing import Optional
-from typing import Union
-
-import numpy as np
-import torch
-from diffusers import HunyuanVideoPipeline
-from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import DEFAULT_PROMPT_TEMPLATE
-from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import HunyuanVideoPipelineOutput
-from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import MultiPipelineCallbacks
-from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import PipelineCallback
-from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import retrieve_timesteps
-from PIL import Image
-
-
-def resizecrop(image, th, tw):
-    w, h = image.size
-    if h / w > th / tw:
-        new_w = int(w)
-        new_h = int(new_w * th / tw)
-    else:
-        new_h = int(h)
-        new_w = int(new_h * tw / th)
-    left = (w - new_w) / 2
-    top = (h - new_h) / 2
-    right = (w + new_w) / 2
-    bottom = (h + new_h) / 2
-    image = image.crop((left, top, right, bottom))
-    return image
-
-
-def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
-    """
-    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
-    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
-    """
-    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
-    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
-    # rescale the results from guidance (fixes overexposure)
-    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
-    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
-    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
-    return noise_cfg
-
-
-class SkyreelsVideoPipeline(HunyuanVideoPipeline):
-    """
-    support i2v and t2v
-    support true_cfg
-    """
-
-    @property
-    def guidance_rescale(self):
-        return self._guidance_rescale
-
-    @property
-    def clip_skip(self):
-        return self._clip_skip
-
-    @property
-    def do_classifier_free_guidance(self):
-        # return self._guidance_scale > 1 and self.transformer.config.time_cond_proj_dim is None
-        return self._guidance_scale > 1
-
-    def encode_prompt(
-        self,
-        prompt: Union[str, List[str]],
-        do_classifier_free_guidance: bool,
-        negative_prompt: str = "",
-        prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
-        num_videos_per_prompt: int = 1,
-        prompt_embeds: Optional[torch.Tensor] = None,
-        pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        prompt_attention_mask: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_attention_mask: Optional[torch.Tensor] = None,
-        device: Optional[torch.device] = None,
-        dtype: Optional[torch.dtype] = None,
-        max_sequence_length: int = 256,
-    ):
-        num_hidden_layers_to_skip = self.clip_skip if self.clip_skip is not None else 0
-        print(f"num_hidden_layers_to_skip: {num_hidden_layers_to_skip}")
-        if prompt_embeds is None:
-            prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds(
-                prompt,
-                prompt_template,
-                num_videos_per_prompt,
-                device=device,
-                dtype=dtype,
-                num_hidden_layers_to_skip=num_hidden_layers_to_skip,
-                max_sequence_length=max_sequence_length,
-            )
-        if negative_prompt_embeds is None and do_classifier_free_guidance:
-            negative_prompt_embeds, negative_attention_mask = self._get_llama_prompt_embeds(
-                negative_prompt,
-                prompt_template,
-                num_videos_per_prompt,
-                device=device,
-                dtype=dtype,
-                num_hidden_layers_to_skip=num_hidden_layers_to_skip,
-                max_sequence_length=max_sequence_length,
-            )
-        if self.text_encoder_2 is not None and pooled_prompt_embeds is None:
-            pooled_prompt_embeds = self._get_clip_prompt_embeds(
-                prompt,
-                num_videos_per_prompt,
-                device=device,
-                dtype=dtype,
-                max_sequence_length=77,
-            )
-            if negative_pooled_prompt_embeds is None and do_classifier_free_guidance:
-                negative_pooled_prompt_embeds = self._get_clip_prompt_embeds(
-                    negative_prompt,
-                    num_videos_per_prompt,
-                    device=device,
-                    dtype=dtype,
-                    max_sequence_length=77,
-                )
-        return (
-            prompt_embeds,
-            prompt_attention_mask,
-            negative_prompt_embeds,
-            negative_attention_mask,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        )
-
-    def image_latents(
-        self,
-        initial_image,
-        batch_size,
-        height,
-        width,
-        device,
-        dtype,
-        num_channels_latents,
-        video_length,
-    ):
-        initial_image = initial_image.unsqueeze(2)
-        image_latents = self.vae.encode(initial_image).latent_dist.sample()
-        if hasattr(self.vae.config, "shift_factor") and self.vae.config.shift_factor:
-            image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
-        else:
-            image_latents = image_latents * self.vae.config.scaling_factor
-        padding_shape = (
-            batch_size,
-            num_channels_latents,
-            video_length - 1,
-            int(height) // self.vae_scale_factor_spatial,
-            int(width) // self.vae_scale_factor_spatial,
-        )
-        latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype)
-        image_latents = torch.cat([image_latents, latent_padding], dim=2)
-        return image_latents
-
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: str,
-        negative_prompt: str = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion",
-        height: int = 720,
-        width: int = 1280,
-        num_frames: int = 129,
-        num_inference_steps: int = 50,
-        sigmas: List[float] = None,
-        guidance_scale: float = 1.0,
-        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,
-        pooled_prompt_embeds: Optional[torch.Tensor] = None,
-        prompt_attention_mask: Optional[torch.Tensor] = None,
-        negative_prompt_embeds: Optional[torch.Tensor] = None,
-        negative_attention_mask: Optional[torch.Tensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-        clip_skip: Optional[int] = 2,
-        callback_on_step_end: Optional[
-            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
-        ] = None,
-        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
-        prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
-        max_sequence_length: int = 256,
-        embedded_guidance_scale: Optional[float] = 6.0,
-        image: Optional[Union[torch.Tensor, Image.Image]] = None,
-        cfg_for: bool = False,
-    ):
-        if hasattr(self, "text_encoder_to_gpu"):
-            self.text_encoder_to_gpu()
-
-        if image is not None and isinstance(image, Image.Image):
-            image = resizecrop(image, height, width)
-
-        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
-            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            None,
-            height,
-            width,
-            prompt_embeds,
-            callback_on_step_end_tensor_inputs,
-            prompt_template,
-        )
-        #  add negative prompt check
-        if 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`:"
-                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
-            )
-
-        if prompt_embeds is not None and negative_prompt_embeds is not None:
-            if prompt_embeds.shape != negative_prompt_embeds.shape:
-                raise ValueError(
-                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
-                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
-                    f" {negative_prompt_embeds.shape}."
-                )
-
-        self._guidance_scale = guidance_scale
-        self._guidance_rescale = guidance_rescale
-        self._clip_skip = clip_skip
-        self._attention_kwargs = attention_kwargs
-        self._interrupt = False
-
-        device = self._execution_device
-
-        # 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.shape[0]
-
-        # 3. Encode input prompt
-        (
-            prompt_embeds,
-            prompt_attention_mask,
-            negative_prompt_embeds,
-            negative_attention_mask,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = self.encode_prompt(
-            prompt=prompt,
-            do_classifier_free_guidance=self.do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            prompt_template=prompt_template,
-            num_videos_per_prompt=num_videos_per_prompt,
-            prompt_embeds=prompt_embeds,
-            prompt_attention_mask=prompt_attention_mask,
-            negative_prompt_embeds=negative_prompt_embeds,
-            negative_attention_mask=negative_attention_mask,
-            device=device,
-            max_sequence_length=max_sequence_length,
-        )
-
-        transformer_dtype = self.transformer.dtype
-        prompt_embeds = prompt_embeds.to(transformer_dtype)
-        prompt_attention_mask = prompt_attention_mask.to(transformer_dtype)
-        if pooled_prompt_embeds is not None:
-            pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype)
-
-        ## Embeddings are concatenated to form a batch.
-        if self.do_classifier_free_guidance:
-            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
-            negative_attention_mask = negative_attention_mask.to(transformer_dtype)
-            if negative_pooled_prompt_embeds is not None:
-                negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(transformer_dtype)
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
-            if prompt_attention_mask is not None:
-                prompt_attention_mask = torch.cat([negative_attention_mask, prompt_attention_mask])
-            if pooled_prompt_embeds is not None:
-                pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds])
-
-        # 4. Prepare timesteps
-        sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
-        timesteps, num_inference_steps = retrieve_timesteps(
-            self.scheduler,
-            num_inference_steps,
-            device,
-            sigmas=sigmas,
-        )
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.transformer.config.in_channels
-        if image is not None:
-            num_channels_latents = int(num_channels_latents / 2)
-            image = self.video_processor.preprocess(image, height=height, width=width).to(
-                device, dtype=prompt_embeds.dtype
-            )
-        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
-        latents = self.prepare_latents(
-            batch_size * num_videos_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            num_latent_frames,
-            torch.float32,
-            device,
-            generator,
-            latents,
-        )
-        # add image latents
-        if image is not None:
-            image_latents = self.image_latents(
-                image, batch_size, height, width, device, torch.float32, num_channels_latents, num_latent_frames
-            )
-
-            image_latents = image_latents.to(transformer_dtype)
-        else:
-            image_latents = None
-
-        # 6. Prepare guidance condition
-        if self.do_classifier_free_guidance:
-            guidance = (
-                torch.tensor([embedded_guidance_scale] * latents.shape[0] * 2, dtype=transformer_dtype, device=device)
-                * 1000.0
-            )
-        else:
-            guidance = (
-                torch.tensor([embedded_guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device)
-                * 1000.0
-            )
-
-        # 7. Denoising loop
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-        self._num_timesteps = len(timesteps)
-
-        if hasattr(self, "text_encoder_to_cpu"):
-            self.text_encoder_to_cpu()
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if self.interrupt:
-                    continue
-
-                latents = latents.to(transformer_dtype)
-                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
-                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
-                # timestep = t.expand(latents.shape[0]).to(latents.dtype)
-                if image_latents is not None:
-                    latent_image_input = (
-                        torch.cat([image_latents] * 2) if self.do_classifier_free_guidance else image_latents
-                    )
-                    latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=1)
-                timestep = t.repeat(latent_model_input.shape[0]).to(torch.float32)
-                if cfg_for and self.do_classifier_free_guidance:
-                    noise_pred_list = []
-                    for idx in range(latent_model_input.shape[0]):
-                        noise_pred_uncond = self.transformer(
-                            hidden_states=latent_model_input[idx].unsqueeze(0),
-                            timestep=timestep[idx].unsqueeze(0),
-                            encoder_hidden_states=prompt_embeds[idx].unsqueeze(0),
-                            encoder_attention_mask=prompt_attention_mask[idx].unsqueeze(0),
-                            pooled_projections=pooled_prompt_embeds[idx].unsqueeze(0),
-                            guidance=guidance[idx].unsqueeze(0),
-                            attention_kwargs=attention_kwargs,
-                            return_dict=False,
-                        )[0]
-                        noise_pred_list.append(noise_pred_uncond)
-                    noise_pred = torch.cat(noise_pred_list, dim=0)
-                else:
-                    noise_pred = self.transformer(
-                        hidden_states=latent_model_input,
-                        timestep=timestep,
-                        encoder_hidden_states=prompt_embeds,
-                        encoder_attention_mask=prompt_attention_mask,
-                        pooled_projections=pooled_prompt_embeds,
-                        guidance=guidance,
-                        attention_kwargs=attention_kwargs,
-                        return_dict=False,
-                    )[0]
-
-                # perform guidance
-                if self.do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
-                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(
-                        noise_pred,
-                        noise_pred_text,
-                        guidance_rescale=self.guidance_rescale,
-                    )
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, 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, t, callback_kwargs)
-
-                    latents = callback_outputs.pop("latents", latents)
-                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-        if not output_type == "latent":
-            latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
-            video = self.vae.decode(latents, return_dict=False)[0]
-            video = self.video_processor.postprocess_video(video, output_type=output_type)
-        else:
-            video = latents
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (video,)
-
-        return HunyuanVideoPipelineOutput(frames=video)
+from typing import Any
+from typing import Callable
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import Union
+
+import numpy as np
+import torch
+from diffusers import HunyuanVideoPipeline
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import DEFAULT_PROMPT_TEMPLATE
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import HunyuanVideoPipelineOutput
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import MultiPipelineCallbacks
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import PipelineCallback
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import retrieve_timesteps
+from PIL import Image
+
+
+def resizecrop(image, th, tw):
+    w, h = image.size
+    if h / w > th / tw:
+        new_w = int(w)
+        new_h = int(new_w * th / tw)
+    else:
+        new_h = int(h)
+        new_w = int(new_h * tw / th)
+    left = (w - new_w) / 2
+    top = (h - new_h) / 2
+    right = (w + new_w) / 2
+    bottom = (h + new_h) / 2
+    image = image.crop((left, top, right, bottom))
+    return image
+
+
+def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
+    """
+    Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and
+    Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4
+    """
+    std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True)
+    std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True)
+    # rescale the results from guidance (fixes overexposure)
+    noise_pred_rescaled = noise_cfg * (std_text / std_cfg)
+    # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images
+    noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg
+    return noise_cfg
+
+
+class SkyreelsVideoPipeline(HunyuanVideoPipeline):
+    """
+    support i2v and t2v
+    support true_cfg
+    """
+
+    @property
+    def guidance_rescale(self):
+        return self._guidance_rescale
+
+    @property
+    def clip_skip(self):
+        return self._clip_skip
+
+    @property
+    def do_classifier_free_guidance(self):
+        # return self._guidance_scale > 1 and self.transformer.config.time_cond_proj_dim is None
+        return self._guidance_scale > 1
+
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        do_classifier_free_guidance: bool,
+        negative_prompt: str = "",
+        prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_attention_mask: Optional[torch.Tensor] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        max_sequence_length: int = 256,
+    ):
+        num_hidden_layers_to_skip = self.clip_skip if self.clip_skip is not None else 0
+        print(f"num_hidden_layers_to_skip: {num_hidden_layers_to_skip}")
+        if prompt_embeds is None:
+            prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds(
+                prompt,
+                prompt_template,
+                num_videos_per_prompt,
+                device=device,
+                dtype=dtype,
+                num_hidden_layers_to_skip=num_hidden_layers_to_skip,
+                max_sequence_length=max_sequence_length,
+            )
+        if negative_prompt_embeds is None and do_classifier_free_guidance:
+            negative_prompt_embeds, negative_attention_mask = self._get_llama_prompt_embeds(
+                negative_prompt,
+                prompt_template,
+                num_videos_per_prompt,
+                device=device,
+                dtype=dtype,
+                num_hidden_layers_to_skip=num_hidden_layers_to_skip,
+                max_sequence_length=max_sequence_length,
+            )
+        if self.text_encoder_2 is not None and pooled_prompt_embeds is None:
+            pooled_prompt_embeds = self._get_clip_prompt_embeds(
+                prompt,
+                num_videos_per_prompt,
+                device=device,
+                dtype=dtype,
+                max_sequence_length=77,
+            )
+            if negative_pooled_prompt_embeds is None and do_classifier_free_guidance:
+                negative_pooled_prompt_embeds = self._get_clip_prompt_embeds(
+                    negative_prompt,
+                    num_videos_per_prompt,
+                    device=device,
+                    dtype=dtype,
+                    max_sequence_length=77,
+                )
+        return (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_attention_mask,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        )
+
+    def image_latents(
+        self,
+        initial_image,
+        batch_size,
+        height,
+        width,
+        device,
+        dtype,
+        num_channels_latents,
+        video_length,
+    ):
+        initial_image = initial_image.unsqueeze(2)
+        image_latents = self.vae.encode(initial_image).latent_dist.sample()
+        if hasattr(self.vae.config, "shift_factor") and self.vae.config.shift_factor:
+            image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+        else:
+            image_latents = image_latents * self.vae.config.scaling_factor
+        padding_shape = (
+            batch_size,
+            num_channels_latents,
+            video_length - 1,
+            int(height) // self.vae_scale_factor_spatial,
+            int(width) // self.vae_scale_factor_spatial,
+        )
+        latent_padding = torch.zeros(padding_shape, device=device, dtype=dtype)
+        image_latents = torch.cat([image_latents, latent_padding], dim=2)
+        return image_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: str,
+        negative_prompt: str = "Aerial view, aerial view, overexposed, low quality, deformation, a poor composition, bad hands, bad teeth, bad eyes, bad limbs, distortion",
+        height: int = 720,
+        width: int = 1280,
+        num_frames: int = 129,
+        num_inference_steps: int = 50,
+        sigmas: List[float] = None,
+        guidance_scale: float = 1.0,
+        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,
+        pooled_prompt_embeds: Optional[torch.Tensor] = None,
+        prompt_attention_mask: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        negative_attention_mask: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        guidance_rescale: float = 0.0,
+        clip_skip: Optional[int] = 2,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        prompt_template: Dict[str, Any] = DEFAULT_PROMPT_TEMPLATE,
+        max_sequence_length: int = 256,
+        embedded_guidance_scale: Optional[float] = 6.0,
+        image: Optional[Union[torch.Tensor, Image.Image]] = None,
+        cfg_for: bool = False,
+    ):
+        if hasattr(self, "text_encoder_to_gpu"):
+            self.text_encoder_to_gpu()
+
+        if image is not None and isinstance(image, Image.Image):
+            image = resizecrop(image, height, width)
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            None,
+            height,
+            width,
+            prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+            prompt_template,
+        )
+        #  add negative prompt check
+        if 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`:"
+                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
+            )
+
+        if prompt_embeds is not None and negative_prompt_embeds is not None:
+            if prompt_embeds.shape != negative_prompt_embeds.shape:
+                raise ValueError(
+                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
+                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
+                    f" {negative_prompt_embeds.shape}."
+                )
+
+        self._guidance_scale = guidance_scale
+        self._guidance_rescale = guidance_rescale
+        self._clip_skip = clip_skip
+        self._attention_kwargs = attention_kwargs
+        self._interrupt = False
+
+        device = self._execution_device
+
+        # 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.shape[0]
+        pipe.text_encoder.to("cuda")
+
+        # 3. Encode input prompt
+        (
+            prompt_embeds,
+            prompt_attention_mask,
+            negative_prompt_embeds,
+            negative_attention_mask,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            negative_prompt=negative_prompt,
+            prompt_template=prompt_template,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            prompt_attention_mask=prompt_attention_mask,
+            negative_prompt_embeds=negative_prompt_embeds,
+            negative_attention_mask=negative_attention_mask,
+            device=device,
+            max_sequence_length=max_sequence_length,
+        )
+
+        transformer_dtype = self.transformer.dtype
+        prompt_embeds = prompt_embeds.to(transformer_dtype)
+        prompt_attention_mask = prompt_attention_mask.to(transformer_dtype)
+        if pooled_prompt_embeds is not None:
+            pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype)
+
+        ## Embeddings are concatenated to form a batch.
+        if self.do_classifier_free_guidance:
+            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
+            negative_attention_mask = negative_attention_mask.to(transformer_dtype)
+            if negative_pooled_prompt_embeds is not None:
+                negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(transformer_dtype)
+            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
+            if prompt_attention_mask is not None:
+                prompt_attention_mask = torch.cat([negative_attention_mask, prompt_attention_mask])
+            if pooled_prompt_embeds is not None:
+                pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds])
+
+        # 4. Prepare timesteps
+        sigmas = np.linspace(1.0, 0.0, num_inference_steps + 1)[:-1] if sigmas is None else sigmas
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            sigmas=sigmas,
+        )
+
+        # 5. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels
+        if image is not None:
+            num_channels_latents = int(num_channels_latents / 2)
+            image = self.video_processor.preprocess(image, height=height, width=width).to(
+                device, dtype=prompt_embeds.dtype
+            )
+        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            num_latent_frames,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+        # add image latents
+        if image is not None:
+            image_latents = self.image_latents(
+                image, batch_size, height, width, device, torch.float32, num_channels_latents, num_latent_frames
+            )
+
+            image_latents = image_latents.to(transformer_dtype)
+        else:
+            image_latents = None
+
+        # 6. Prepare guidance condition
+        if self.do_classifier_free_guidance:
+            guidance = (
+                torch.tensor([embedded_guidance_scale] * latents.shape[0] * 2, dtype=transformer_dtype, device=device)
+                * 1000.0
+            )
+        else:
+            guidance = (
+                torch.tensor([embedded_guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device)
+                * 1000.0
+            )
+
+        # 7. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
+        if hasattr(self, "text_encoder_to_cpu"):
+            self.text_encoder_to_cpu()
+        pipe.text_encoder.to("cpu")
+        pipe.vae.to("cpu")
+        torch.cuda.empty_cache()
+        
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                latents = latents.to(transformer_dtype)
+                latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
+                # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+                # timestep = t.expand(latents.shape[0]).to(latents.dtype)
+                if image_latents is not None:
+                    latent_image_input = (
+                        torch.cat([image_latents] * 2) if self.do_classifier_free_guidance else image_latents
+                    )
+                    latent_model_input = torch.cat([latent_model_input, latent_image_input], dim=1)
+                timestep = t.repeat(latent_model_input.shape[0]).to(torch.float32)
+                if cfg_for and self.do_classifier_free_guidance:
+                    noise_pred_list = []
+                    for idx in range(latent_model_input.shape[0]):
+                        noise_pred_uncond = self.transformer(
+                            hidden_states=latent_model_input[idx].unsqueeze(0),
+                            timestep=timestep[idx].unsqueeze(0),
+                            encoder_hidden_states=prompt_embeds[idx].unsqueeze(0),
+                            encoder_attention_mask=prompt_attention_mask[idx].unsqueeze(0),
+                            pooled_projections=pooled_prompt_embeds[idx].unsqueeze(0),
+                            guidance=guidance[idx].unsqueeze(0),
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )[0]
+                        noise_pred_list.append(noise_pred_uncond)
+                    noise_pred = torch.cat(noise_pred_list, dim=0)
+                else:
+                    noise_pred = self.transformer(
+                        hidden_states=latent_model_input,
+                        timestep=timestep,
+                        encoder_hidden_states=prompt_embeds,
+                        encoder_attention_mask=prompt_attention_mask,
+                        pooled_projections=pooled_prompt_embeds,
+                        guidance=guidance,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0]
+
+                # perform guidance
+                if self.do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                if self.do_classifier_free_guidance and self.guidance_rescale > 0.0:
+                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
+                    noise_pred = rescale_noise_cfg(
+                        noise_pred,
+                        noise_pred_text,
+                        guidance_rescale=self.guidance_rescale,
+                    )
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, 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, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+        if not output_type == "latent":
+            pipe.vae.to("cuda")
+            latents = latents.to(self.vae.dtype) / self.vae.config.scaling_factor
+            video = self.vae.decode(latents, return_dict=False)[0]
+            video = self.video_processor.postprocess_video(video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return HunyuanVideoPipelineOutput(frames=video)