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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+i2v.sft filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,320 @@
+---
+base_model:
+- tencent/HunyuanVideo
+library_name: diffusers
+---
+
+HunyuanVideo Keyframe Control Lora is an adapter for HunyuanVideo T2V model for keyframe-based video generation. ​Our architecture builds upon existing models, introducing key enhancements to optimize keyframe-based video generation:​
+*  We modify the input patch embedding projection layer to effectively incorporate keyframe information. By adjusting the convolutional input parameters, we enable the model to process image inputs within the Diffusion Transformer (DiT) framework.​
+*  We apply Low-Rank Adaptation (LoRA) across all linear layers and the convolutional input layer. This approach facilitates efficient fine-tuning by introducing low-rank matrices that approximate the weight updates, thereby preserving the base model's foundational capabilities while reducing the number of trainable parameters.
+* The model is conditioned on user-defined keyframes, allowing precise control over the generated video's start and end frames. This conditioning ensures that the generated content aligns seamlessly with the specified keyframes, enhancing the coherence and narrative flow of the video.​
+
+## Recommended Settings
+1. The model works best on human subjects. Single subject images work slightly better.
+2. It is recommended to use the following image generation resolutions `720x1280`, `544x960`, `1280x720`, `960x544`.
+3. It is recommended to set frames from 33 upto 97. Can go upto 121 frames as well (but not tested much).
+4. Prompting helps a lot but works even without. The prompt can be as simple as just the name of the object you want to generate or can be detailed.
+5. `num_inference_steps` is recommended to be 50, but for fast results you can use 30 as well. Anything less than 30 is not recommended.
+
+## Diffusers
+HunyuanVideo Keyframe Control Lora can be used directly from Diffusers. Install the latest version of Diffusers.
+
+
+```python
+from typing import List, Optional, Tuple, Union
+
+import cv2
+import numpy as np
+import safetensors.torch
+import torch
+import torchvision.transforms.v2 as transforms
+from diffusers import FlowMatchEulerDiscreteScheduler, HunyuanVideoPipeline
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.loaders import HunyuanVideoLoraLoaderMixin
+from diffusers.models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel
+from diffusers.models.attention import Attention
+from diffusers.models.embeddings import apply_rotary_emb
+from diffusers.models.transformers.transformer_hunyuan_video import HunyuanVideoPatchEmbed, HunyuanVideoTransformer3DModel
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import DEFAULT_PROMPT_TEMPLATE, retrieve_timesteps
+from diffusers.pipelines.hunyuan_video.pipeline_output import HunyuanVideoPipelineOutput
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import export_to_video, is_torch_xla_available, load_image, logging, replace_example_docstring
+from diffusers.utils.state_dict_utils import convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
+from PIL import Image
+
+video_transforms = transforms.Compose(
+    [
+        transforms.Lambda(lambda x: x / 255.0),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
+    ]
+)
+
+
+def resize_image_to_bucket(image: Union[Image.Image, np.ndarray], bucket_reso: tuple[int, int]) -> np.ndarray:
+    """
+    Resize the image to the bucket resolution.
+    """
+    is_pil_image = isinstance(image, Image.Image)
+    if is_pil_image:
+        image_width, image_height = image.size
+    else:
+        image_height, image_width = image.shape[:2]
+
+    if bucket_reso == (image_width, image_height):
+        return np.array(image) if is_pil_image else image
+
+    bucket_width, bucket_height = bucket_reso
+
+    scale_width = bucket_width / image_width
+    scale_height = bucket_height / image_height
+    scale = max(scale_width, scale_height)
+    image_width = int(image_width * scale + 0.5)
+    image_height = int(image_height * scale + 0.5)
+
+    if scale > 1:
+        image = Image.fromarray(image) if not is_pil_image else image
+        image = image.resize((image_width, image_height), Image.LANCZOS)
+        image = np.array(image)
+    else:
+        image = np.array(image) if is_pil_image else image
+        image = cv2.resize(image, (image_width, image_height), interpolation=cv2.INTER_AREA)
+
+    # crop the image to the bucket resolution
+    crop_left = (image_width - bucket_width) // 2
+    crop_top = (image_height - bucket_height) // 2
+    image = image[crop_top : crop_top + bucket_height, crop_left : crop_left + bucket_width]
+
+    return image
+
+
+model_id = "hunyuanvideo-community/HunyuanVideo"
+transformer = HunyuanVideoTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
+pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.bfloat16)
+
+pipe.to("cuda")
+pipe.vae.enable_tiling()
+pipe.vae.enable_slicing()
+
+with torch.no_grad():  # enable image inputs
+    initial_input_channels = pipe.transformer.config.in_channels
+    new_img_in = HunyuanVideoPatchEmbed(
+        patch_size=(pipe.transformer.config.patch_size_t, pipe.transformer.config.patch_size, pipe.transformer.config.patch_size),
+        in_chans=pipe.transformer.config.in_channels * 2,
+        embed_dim=pipe.transformer.config.num_attention_heads * pipe.transformer.config.attention_head_dim,
+    )
+    new_img_in = new_img_in.to(pipe.device, dtype=pipe.dtype)
+    new_img_in.proj.weight.zero_()
+    new_img_in.proj.weight[:, :initial_input_channels].copy_(pipe.transformer.x_embedder.proj.weight)
+
+    if pipe.transformer.x_embedder.proj.bias is not None:
+        new_img_in.proj.bias.copy_(pipe.transformer.x_embedder.proj.bias)
+
+    pipe.transformer.x_embedder = new_img_in
+
+LORA_PATH = "<PATH_TO_CONTROL_LORA_SAFETENSORS>"
+lora_state_dict = pipe.lora_state_dict(LORA_PATH)
+transformer_lora_state_dict = {f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.") and "lora" in k}
+pipe.load_lora_into_transformer(transformer_lora_state_dict, transformer=pipe.transformer, adapter_name="i2v", _pipeline=pipe)
+pipe.set_adapters(["i2v"], adapter_weights=[1.0])
+pipe.fuse_lora(components=["transformer"], lora_scale=1.0, adapter_names=["i2v"])
+pipe.unload_lora_weights()
+
+n_frames, height, width = 77, 1280, 720
+prompt = "a woman"
+cond_frame1 = load_image("https://content.dashtoon.ai/stability-images/e524013d-55d4-483a-b80a-dfc51d639158.png")
+cond_frame1 = resize_image_to_bucket(cond_frame1, bucket_reso=(width, height))
+
+cond_frame2 = load_image("https://content.dashtoon.ai/stability-images/0b29c296-0a90-4b92-96b9-1ed0ae21e480.png")
+cond_frame2 = resize_image_to_bucket(cond_frame2, bucket_reso=(width, height))
+
+cond_video = np.zeros(shape=(n_frames, height, width, 3))
+cond_video[0], cond_video[-1] = np.array(cond_frame1), np.array(cond_frame2)
+
+cond_video = torch.from_numpy(cond_video.copy()).permute(0, 3, 1, 2)
+cond_video = torch.stack([video_transforms(x) for x in cond_video], dim=0).unsqueeze(0)
+
+with torch.no_grad():
+    image_or_video = cond_video.to(device="cuda", dtype=pipe.dtype)
+    image_or_video = image_or_video.permute(0, 2, 1, 3, 4).contiguous()  # [B, F, C, H, W] -> [B, C, F, H, W]
+    cond_latents = pipe.vae.encode(image_or_video).latent_dist.sample()
+    cond_latents = cond_latents * pipe.vae.config.scaling_factor
+    cond_latents = cond_latents.to(dtype=pipe.dtype)
+
+
+@torch.no_grad()
+def call_pipe(
+    pipe,
+    prompt: Union[str, List[str]] = None,
+    prompt_2: Union[str, List[str]] = None,
+    height: int = 720,
+    width: int = 1280,
+    num_frames: int = 129,
+    num_inference_steps: int = 50,
+    sigmas: List[float] = None,
+    guidance_scale: float = 6.0,
+    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,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    attention_kwargs: Optional[Dict[str, Any]] = None,
+    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,
+    image_latents: Optional[torch.Tensor] = None,
+):
+
+    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
+    pipe.check_inputs(
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds,
+        callback_on_step_end_tensor_inputs,
+        prompt_template,
+    )
+
+    pipe._guidance_scale = guidance_scale
+    pipe._attention_kwargs = attention_kwargs
+    pipe._current_timestep = None
+    pipe._interrupt = False
+
+    device = pipe._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, pooled_prompt_embeds, prompt_attention_mask = pipe.encode_prompt(
+        prompt=prompt,
+        prompt_2=prompt_2,
+        prompt_template=prompt_template,
+        num_videos_per_prompt=num_videos_per_prompt,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        prompt_attention_mask=prompt_attention_mask,
+        device=device,
+        max_sequence_length=max_sequence_length,
+    )
+
+    transformer_dtype = pipe.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)
+
+    # 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(
+        pipe.scheduler,
+        num_inference_steps,
+        device,
+        sigmas=sigmas,
+    )
+
+    # 5. Prepare latent variables
+    num_channels_latents = pipe.transformer.config.in_channels
+    num_latent_frames = (num_frames - 1) // pipe.vae_scale_factor_temporal + 1
+    latents = pipe.prepare_latents(
+        batch_size * num_videos_per_prompt,
+        num_channels_latents,
+        height,
+        width,
+        num_latent_frames,
+        torch.float32,
+        device,
+        generator,
+        latents,
+    )
+
+    # 6. Prepare guidance condition
+    guidance = torch.tensor([guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device) * 1000.0
+
+    # 7. Denoising loop
+    num_warmup_steps = len(timesteps) - num_inference_steps * pipe.scheduler.order
+    pipe._num_timesteps = len(timesteps)
+
+    with pipe.progress_bar(total=num_inference_steps) as progress_bar:
+        for i, t in enumerate(timesteps):
+            if pipe.interrupt:
+                continue
+
+            pipe._current_timestep = t
+            latent_model_input = latents.to(transformer_dtype)
+            timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+            noise_pred = pipe.transformer(
+                hidden_states=torch.cat([latent_model_input, image_latents], dim=1),
+                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]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = pipe.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(pipe, 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) % pipe.scheduler.order == 0):
+                progress_bar.update()
+    pipe._current_timestep = None
+
+    if not output_type == "latent":
+        latents = latents.to(pipe.vae.dtype) / pipe.vae.config.scaling_factor
+        video = pipe.vae.decode(latents, return_dict=False)[0]
+        video = pipe.video_processor.postprocess_video(video, output_type=output_type)
+    else:
+        video = latents
+
+    # Offload all models
+    pipe.maybe_free_model_hooks()
+
+    if not return_dict:
+        return (video,)
+
+    return HunyuanVideoPipelineOutput(frames=video)
+
+
+video = call_pipe(
+    pipe,
+    prompt=prompt,
+    num_frames=n_frames,
+    num_inference_steps=50,
+    image_latents=cond_latents,
+    width=width,
+    height=height,
+    guidance_scale=6.0,
+    generator=torch.Generator(device="cuda").manual_seed(0),
+).frames[0]
+
+export_to_video(video, "output.mp4", fps=24)
+```

i2v.sft

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b02f04e123365277f5b4a57729759ba96e7b129451788778e78741d1549cc8fd
+size 1146140784

inference.py

@@ -0,0 +1,298 @@
+import safetensors.torch
+import torchvision.transforms.v2 as transforms
+import cv2
+import torch
+import numpy as np
+from typing import List, Optional, Tuple, Union
+from PIL import Image
+from diffusers import HunyuanVideoPipeline, FlowMatchEulerDiscreteScheduler
+from diffusers.models.transformers.transformer_hunyuan_video import HunyuanVideoPatchEmbed, HunyuanVideoTransformer3DModel
+from diffusers.utils import export_to_video
+from diffusers.models.attention import Attention
+from diffusers.utils.state_dict_utils import convert_state_dict_to_diffusers, convert_unet_state_dict_to_peft
+from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
+from diffusers.models.embeddings import apply_rotary_emb
+from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback
+from diffusers.loaders import HunyuanVideoLoraLoaderMixin
+from diffusers.models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import is_torch_xla_available, logging, replace_example_docstring
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.video_processor import VideoProcessor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.hunyuan_video.pipeline_output import HunyuanVideoPipelineOutput
+from diffusers.pipelines.hunyuan_video.pipeline_hunyuan_video import retrieve_timesteps, DEFAULT_PROMPT_TEMPLATE
+from diffusers.utils import load_image
+
+video_transforms = transforms.Compose(
+    [
+        transforms.Lambda(lambda x: x / 255.0),
+        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
+    ]
+)
+
+
+def resize_image_to_bucket(image: Union[Image.Image, np.ndarray], bucket_reso: tuple[int, int]) -> np.ndarray:
+    """
+    Resize the image to the bucket resolution.
+    """
+    is_pil_image = isinstance(image, Image.Image)
+    if is_pil_image:
+        image_width, image_height = image.size
+    else:
+        image_height, image_width = image.shape[:2]
+
+    if bucket_reso == (image_width, image_height):
+        return np.array(image) if is_pil_image else image
+
+    bucket_width, bucket_height = bucket_reso
+
+    scale_width = bucket_width / image_width
+    scale_height = bucket_height / image_height
+    scale = max(scale_width, scale_height)
+    image_width = int(image_width * scale + 0.5)
+    image_height = int(image_height * scale + 0.5)
+
+    if scale > 1:
+        image = Image.fromarray(image) if not is_pil_image else image
+        image = image.resize((image_width, image_height), Image.LANCZOS)
+        image = np.array(image)
+    else:
+        image = np.array(image) if is_pil_image else image
+        image = cv2.resize(image, (image_width, image_height), interpolation=cv2.INTER_AREA)
+
+    # crop the image to the bucket resolution
+    crop_left = (image_width - bucket_width) // 2
+    crop_top = (image_height - bucket_height) // 2
+    image = image[crop_top : crop_top + bucket_height, crop_left : crop_left + bucket_width]
+
+    return image
+
+
+model_id = "hunyuanvideo-community/HunyuanVideo"
+transformer = HunyuanVideoTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
+pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.bfloat16)
+
+# Enable memory savings
+pipe.vae.enable_tiling()
+pipe.enable_model_cpu_offload()
+
+with torch.no_grad():  # enable image inputs
+    initial_input_channels = pipe.transformer.config.in_channels
+    new_img_in = HunyuanVideoPatchEmbed(
+        patch_size=(pipe.transformer.config.patch_size_t, pipe.transformer.config.patch_size, pipe.transformer.config.patch_size),
+        in_chans=pipe.transformer.config.in_channels * 2,
+        embed_dim=pipe.transformer.config.num_attention_heads * pipe.transformer.config.attention_head_dim,
+    )
+    new_img_in = new_img_in.to(pipe.device, dtype=pipe.dtype)
+    new_img_in.proj.weight.zero_()
+    new_img_in.proj.weight[:, :initial_input_channels].copy_(pipe.transformer.x_embedder.proj.weight)
+
+    if pipe.transformer.x_embedder.proj.bias is not None:
+        new_img_in.proj.bias.copy_(pipe.transformer.x_embedder.proj.bias)
+
+    pipe.transformer.x_embedder = new_img_in
+
+LORA_PATH = "<PATH TO CONTROL LORA>"
+lora_state_dict = pipe.lora_state_dict(LORA_PATH)
+transformer_lora_state_dict = {f'{k.replace("transformer.", "")}': v for k, v in lora_state_dict.items() if k.startswith("transformer.") and "lora" in k}
+pipe.load_lora_into_transformer(transformer_lora_state_dict, transformer=pipe.transformer, adapter_name="i2v", _pipeline=pipe)
+pipe.set_adapters(["i2v"], adapter_weights=[1.0])
+pipe.fuse_lora(components=["transformer"], lora_scale=1.0, adapter_names=["i2v"])
+pipe.unload_lora_weights()
+
+n_frames, height, width = 77, 1280, 720
+prompt = "a woman"
+cond_frame1 = load_image("https://content.dashtoon.ai/stability-images/e524013d-55d4-483a-b80a-dfc51d639158.png")
+cond_frame1 = resize_image_to_bucket(cond_frame1, bucket_reso=(width, height))
+
+cond_frame2 = load_image("https://content.dashtoon.ai/stability-images/0b29c296-0a90-4b92-96b9-1ed0ae21e480.png")
+cond_frame2 = resize_image_to_bucket(cond_frame2, bucket_reso=(width, height))
+
+cond_video = np.zeros(shape=(n_frames, height, width, 3))
+cond_video[0], cond_video[-1] = np.array(cond_frame1), np.array(cond_frame2)
+
+cond_video = torch.from_numpy(cond_video.copy()).permute(0, 3, 1, 2)
+cond_video = torch.stack([video_transforms(x) for x in cond_video], dim=0).unsqueeze(0)
+
+with torch.inference_mode():
+    image_or_video = cond_video.to(device="cuda", dtype=pipe.dtype)
+    image_or_video = image_or_video.permute(0, 2, 1, 3, 4).contiguous()  # [B, F, C, H, W] -> [B, C, F, H, W]
+    cond_latents = pipe.vae.encode(image_or_video).latent_dist.sample()
+    cond_latents = cond_latents * pipe.vae.config.scaling_factor
+    cond_latents = cond_latents.to(dtype=pipe.dtype)
+    assert not torch.any(torch.isnan(cond_latents))
+
+
+@torch.inference_mode()
+def call_pipe(
+    pipe,
+    prompt: Union[str, List[str]] = None,
+    prompt_2: Union[str, List[str]] = None,
+    height: int = 720,
+    width: int = 1280,
+    num_frames: int = 129,
+    num_inference_steps: int = 50,
+    sigmas: List[float] = None,
+    guidance_scale: float = 6.0,
+    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,
+    output_type: Optional[str] = "pil",
+    return_dict: bool = True,
+    attention_kwargs: Optional[Dict[str, Any]] = None,
+    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,
+    image_latents: Optional[torch.Tensor] = None,
+):
+
+    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
+    pipe.check_inputs(
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds,
+        callback_on_step_end_tensor_inputs,
+        prompt_template,
+    )
+
+    pipe._guidance_scale = guidance_scale
+    pipe._attention_kwargs = attention_kwargs
+    pipe._current_timestep = None
+    pipe._interrupt = False
+
+    device = pipe._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, pooled_prompt_embeds, prompt_attention_mask = pipe.encode_prompt(
+        prompt=prompt,
+        prompt_2=prompt_2,
+        prompt_template=prompt_template,
+        num_videos_per_prompt=num_videos_per_prompt,
+        prompt_embeds=prompt_embeds,
+        pooled_prompt_embeds=pooled_prompt_embeds,
+        prompt_attention_mask=prompt_attention_mask,
+        device=device,
+        max_sequence_length=max_sequence_length,
+    )
+
+    transformer_dtype = pipe.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)
+
+    # 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(
+        pipe.scheduler,
+        num_inference_steps,
+        device,
+        sigmas=sigmas,
+    )
+
+    # 5. Prepare latent variables
+    num_channels_latents = pipe.transformer.config.in_channels
+    num_latent_frames = (num_frames - 1) // pipe.vae_scale_factor_temporal + 1
+    latents = pipe.prepare_latents(
+        batch_size * num_videos_per_prompt,
+        num_channels_latents,
+        height,
+        width,
+        num_latent_frames,
+        torch.float32,
+        device,
+        generator,
+        latents,
+    )
+
+    # 6. Prepare guidance condition
+    guidance = torch.tensor([guidance_scale] * latents.shape[0], dtype=transformer_dtype, device=device) * 1000.0
+
+    # 7. Denoising loop
+    num_warmup_steps = len(timesteps) - num_inference_steps * pipe.scheduler.order
+    pipe._num_timesteps = len(timesteps)
+
+    with pipe.progress_bar(total=num_inference_steps) as progress_bar:
+        for i, t in enumerate(timesteps):
+            if pipe.interrupt:
+                continue
+
+            pipe._current_timestep = t
+            latent_model_input = latents.to(transformer_dtype)
+            timestep = t.expand(latents.shape[0]).to(latents.dtype)
+
+            noise_pred = pipe.transformer(
+                hidden_states=torch.cat([latent_model_input, image_latents], dim=1),
+                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]
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = pipe.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(pipe, 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) % pipe.scheduler.order == 0):
+                progress_bar.update()
+    pipe._current_timestep = None
+
+    if not output_type == "latent":
+        latents = latents.to(pipe.vae.dtype) / pipe.vae.config.scaling_factor
+        video = pipe.vae.decode(latents, return_dict=False)[0]
+        video = pipe.video_processor.postprocess_video(video, output_type=output_type)
+    else:
+        video = latents
+
+    # Offload all models
+    pipe.maybe_free_model_hooks()
+
+    if not return_dict:
+        return (video,)
+
+    return HunyuanVideoPipelineOutput(frames=video)
+
+
+video = call_pipe(
+    pipe,
+    prompt=prompt,
+    num_frames=n_frames,
+    num_inference_steps=50,
+    image_latents=cond_latents,
+    width=width,
+    height=height,
+    guidance_scale=6.0,
+    generator=torch.Generator(device="cuda").manual_seed(0),
+).frames[0]
+
+export_to_video(video, "output.mp4", fps=24)