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Hunyuan I2V (#10983)

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This commit is contained in:
Aryan
2025-03-07 12:52:48 +05:30
committed by GitHub
parent d55f41102a
commit 2e5203be04
10 changed files with 1425 additions and 24 deletions
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3
docs/source/en/api/pipelines/hunyuan_video.md
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@@ -49,7 +49,8 @@ The following models are available for the image-to-video pipeline:
| Model name | Description |
|:---|:---|
| [`https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution. Performs best at `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
| [`Skywork/SkyReels-V1-Hunyuan-I2V`](https://huggingface.co/Skywork/SkyReels-V1-Hunyuan-I2V) | Skywork's custom finetune of HunyuanVideo (de-distilled). Performs best with `97x544x960` resolution. Performs best at `97x544x960` resolution, `guidance_scale=1.0`, `true_cfg_scale=6.0` and a negative prompt. |
| [`hunyuanvideo-community/HunyuanVideo-I2V`](https://huggingface.co/hunyuanvideo-community/HunyuanVideo-I2V) | Tecent's official HunyuanVideo I2V model. Performs best at resolutions of 480, 720, 960, 1280. A higher `shift` value when initializing the scheduler is recommended (good values are between 7 and 20) |
## Quantization

113
scripts/convert_hunyuan_video_to_diffusers.py
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@@ -3,11 +3,19 @@ from typing import Any, Dict
import torch
from accelerate import init_empty_weights
from transformers import AutoModel, AutoTokenizer, CLIPTextModel, CLIPTokenizer
from transformers import (
AutoModel,
AutoTokenizer,
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
LlavaForConditionalGeneration,
)
from diffusers import (
AutoencoderKLHunyuanVideo,
FlowMatchEulerDiscreteScheduler,
HunyuanVideoImageToVideoPipeline,
HunyuanVideoPipeline,
HunyuanVideoTransformer3DModel,
)
@@ -134,6 +142,46 @@ VAE_KEYS_RENAME_DICT = {}
VAE_SPECIAL_KEYS_REMAP = {}
TRANSFORMER_CONFIGS = {
"HYVideo-T/2-cfgdistill": {
"in_channels": 16,
"out_channels": 16,
"num_attention_heads": 24,
"attention_head_dim": 128,
"num_layers": 20,
"num_single_layers": 40,
"num_refiner_layers": 2,
"mlp_ratio": 4.0,
"patch_size": 2,
"patch_size_t": 1,
"qk_norm": "rms_norm",
"guidance_embeds": True,
"text_embed_dim": 4096,
"pooled_projection_dim": 768,
"rope_theta": 256.0,
"rope_axes_dim": (16, 56, 56),
},
"HYVideo-T/2-I2V": {
"in_channels": 16 * 2 + 1,
"out_channels": 16,
"num_attention_heads": 24,
"attention_head_dim": 128,
"num_layers": 20,
"num_single_layers": 40,
"num_refiner_layers": 2,
"mlp_ratio": 4.0,
"patch_size": 2,
"patch_size_t": 1,
"qk_norm": "rms_norm",
"guidance_embeds": False,
"text_embed_dim": 4096,
"pooled_projection_dim": 768,
"rope_theta": 256.0,
"rope_axes_dim": (16, 56, 56),
},
}
def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
state_dict[new_key] = state_dict.pop(old_key)
@@ -149,11 +197,12 @@ def get_state_dict(saved_dict: Dict[str, Any]) -> Dict[str, Any]:
return state_dict
def convert_transformer(ckpt_path: str):
def convert_transformer(ckpt_path: str, transformer_type: str):
original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", weights_only=True))
config = TRANSFORMER_CONFIGS[transformer_type]
with init_empty_weights():
transformer = HunyuanVideoTransformer3DModel()
transformer = HunyuanVideoTransformer3DModel(**config)
for key in list(original_state_dict.keys()):
new_key = key[:]
@@ -205,6 +254,10 @@ def get_args():
parser.add_argument("--save_pipeline", action="store_true")
parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved")
parser.add_argument("--dtype", default="bf16", help="Torch dtype to save the transformer in.")
parser.add_argument(
"--transformer_type", type=str, default="HYVideo-T/2-cfgdistill", choices=list(TRANSFORMER_CONFIGS.keys())
)
parser.add_argument("--flow_shift", type=float, default=7.0)
return parser.parse_args()
@@ -228,7 +281,7 @@ if __name__ == "__main__":
assert args.text_encoder_2_path is not None
if args.transformer_ckpt_path is not None:
transformer = convert_transformer(args.transformer_ckpt_path)
transformer = convert_transformer(args.transformer_ckpt_path, args.transformer_type)
transformer = transformer.to(dtype=dtype)
if not args.save_pipeline:
transformer.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
@@ -239,19 +292,41 @@ if __name__ == "__main__":
vae.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
if args.save_pipeline:
text_encoder = AutoModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right")
text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16)
tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path)
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
if args.transformer_type == "HYVideo-T/2-cfgdistill":
text_encoder = AutoModel.from_pretrained(args.text_encoder_path, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right")
text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16)
tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path)
scheduler = FlowMatchEulerDiscreteScheduler(shift=args.flow_shift)
pipe = HunyuanVideoPipeline(
transformer=transformer,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
scheduler=scheduler,
)
pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
pipe = HunyuanVideoPipeline(
transformer=transformer,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
scheduler=scheduler,
)
pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")
else:
text_encoder = LlavaForConditionalGeneration.from_pretrained(
args.text_encoder_path, torch_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, padding_side="right")
text_encoder_2 = CLIPTextModel.from_pretrained(args.text_encoder_2_path, torch_dtype=torch.float16)
tokenizer_2 = CLIPTokenizer.from_pretrained(args.text_encoder_2_path)
scheduler = FlowMatchEulerDiscreteScheduler(shift=args.flow_shift)
image_processor = CLIPImageProcessor.from_pretrained(args.text_encoder_path)
pipe = HunyuanVideoImageToVideoPipeline(
transformer=transformer,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
scheduler=scheduler,
image_processor=image_processor,
)
pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB")

2
src/diffusers/__init__.py
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@@ -313,6 +313,7 @@ else:
"HunyuanDiTPAGPipeline",
"HunyuanDiTPipeline",
"HunyuanSkyreelsImageToVideoPipeline",
"HunyuanVideoImageToVideoPipeline",
"HunyuanVideoPipeline",
"I2VGenXLPipeline",
"IFImg2ImgPipeline",
@@ -823,6 +824,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
HunyuanDiTPAGPipeline,
HunyuanDiTPipeline,
HunyuanSkyreelsImageToVideoPipeline,
HunyuanVideoImageToVideoPipeline,
HunyuanVideoPipeline,
I2VGenXLPipeline,
IFImg2ImgPipeline,

12
src/diffusers/models/transformers/transformer_hunyuan_video.py
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@@ -581,7 +581,11 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
self.context_embedder = HunyuanVideoTokenRefiner(
text_embed_dim, num_attention_heads, attention_head_dim, num_layers=num_refiner_layers
)
self.time_text_embed = CombinedTimestepGuidanceTextProjEmbeddings(inner_dim, pooled_projection_dim)
if guidance_embeds:
self.time_text_embed = CombinedTimestepGuidanceTextProjEmbeddings(inner_dim, pooled_projection_dim)
else:
self.time_text_embed = CombinedTimestepTextProjEmbeddings(inner_dim, pooled_projection_dim)
# 2. RoPE
self.rope = HunyuanVideoRotaryPosEmbed(patch_size, patch_size_t, rope_axes_dim, rope_theta)
@@ -708,7 +712,11 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
image_rotary_emb = self.rope(hidden_states)
# 2. Conditional embeddings
temb = self.time_text_embed(timestep, guidance, pooled_projections)
if self.config.guidance_embeds:
temb = self.time_text_embed(timestep, guidance, pooled_projections)
else:
temb = self.time_text_embed(timestep, pooled_projections)
hidden_states = self.x_embedder(hidden_states)
encoder_hidden_states = self.context_embedder(encoder_hidden_states, timestep, encoder_attention_mask)

12
src/diffusers/pipelines/__init__.py
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@@ -222,7 +222,11 @@ else:
"EasyAnimateControlPipeline",
]
_import_structure["hunyuandit"] = ["HunyuanDiTPipeline"]
_import_structure["hunyuan_video"] = ["HunyuanVideoPipeline", "HunyuanSkyreelsImageToVideoPipeline"]
_import_structure["hunyuan_video"] = [
"HunyuanVideoPipeline",
"HunyuanSkyreelsImageToVideoPipeline",
"HunyuanVideoImageToVideoPipeline",
]
_import_structure["kandinsky"] = [
"KandinskyCombinedPipeline",
"KandinskyImg2ImgCombinedPipeline",
@@ -570,7 +574,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
FluxPriorReduxPipeline,
ReduxImageEncoder,
)
from .hunyuan_video import HunyuanSkyreelsImageToVideoPipeline, HunyuanVideoPipeline
from .hunyuan_video import (
HunyuanSkyreelsImageToVideoPipeline,
HunyuanVideoImageToVideoPipeline,
HunyuanVideoPipeline,
)
from .hunyuandit import HunyuanDiTPipeline
from .i2vgen_xl import I2VGenXLPipeline
from .kandinsky import (

2
src/diffusers/pipelines/hunyuan_video/__init__.py
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@@ -24,6 +24,7 @@ except OptionalDependencyNotAvailable:
else:
_import_structure["pipeline_hunyuan_skyreels_image2video"] = ["HunyuanSkyreelsImageToVideoPipeline"]
_import_structure["pipeline_hunyuan_video"] = ["HunyuanVideoPipeline"]
_import_structure["pipeline_hunyuan_video_image2video"] = ["HunyuanVideoImageToVideoPipeline"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
try:
@@ -35,6 +36,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
else:
from .pipeline_hunyuan_skyreels_image2video import HunyuanSkyreelsImageToVideoPipeline
from .pipeline_hunyuan_video import HunyuanVideoPipeline
from .pipeline_hunyuan_video_image2video import HunyuanVideoImageToVideoPipeline
else:
import sys

860
src/diffusers/pipelines/hunyuan_video/pipeline_hunyuan_video_image2video.py Normal file
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@@ -0,0 +1,860 @@
# Copyright 2024 The HunyuanVideo 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 inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import PIL.Image
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
LlamaTokenizerFast,
LlavaForConditionalGeneration,
)
from ...callbacks import MultiPipelineCallbacks, PipelineCallback
from ...loaders import HunyuanVideoLoraLoaderMixin
from ...models import AutoencoderKLHunyuanVideo, HunyuanVideoTransformer3DModel
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import 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 HunyuanVideoPipelineOutput
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
EXAMPLE_DOC_STRING = """
Examples:
```python
>>> import torch
>>> from diffusers import HunyuanVideoImageToVideoPipeline, HunyuanVideoTransformer3DModel
>>> from diffusers.utils import load_image, export_to_video
>>> model_id = "hunyuanvideo-community/HunyuanVideo-I2V"
>>> transformer = HunyuanVideoTransformer3DModel.from_pretrained(
... model_id, subfolder="transformer", torch_dtype=torch.bfloat16
... )
>>> pipe = HunyuanVideoImageToVideoPipeline.from_pretrained(
... model_id, transformer=transformer, torch_dtype=torch.float16
... )
>>> pipe.vae.enable_tiling()
>>> pipe.to("cuda")
>>> prompt = "A man with short gray hair plays a red electric guitar."
>>> image = load_image(
... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png"
... )
>>> output = pipe(image=image, prompt=prompt).frames[0]
>>> export_to_video(output, "output.mp4", fps=15)
```
"""
DEFAULT_PROMPT_TEMPLATE = {
"template": (
"<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
"1. The main content and theme of the video."
"2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects."
"3. Actions, events, behaviors temporal relationships, physical movement changes of the objects."
"4. background environment, light, style and atmosphere."
"5. camera angles, movements, and transitions used in the video:<|eot_id|>\n\n"
"<|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
),
"crop_start": 103,
"image_emb_start": 5,
"image_emb_end": 581,
"image_emb_len": 576,
"double_return_token_id": 271,
}
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
scheduler,
num_inference_steps: Optional[int] = None,
device: Optional[Union[str, torch.device]] = None,
timesteps: Optional[List[int]] = None,
sigmas: Optional[List[float]] = None,
**kwargs,
):
r"""
Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
Args:
scheduler (`SchedulerMixin`):
The scheduler to get timesteps from.
num_inference_steps (`int`):
The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
must be `None`.
device (`str` or `torch.device`, *optional*):
The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
timesteps (`List[int]`, *optional*):
Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
`num_inference_steps` and `sigmas` must be `None`.
sigmas (`List[float]`, *optional*):
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
`num_inference_steps` and `timesteps` must be `None`.
Returns:
`Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
second element is the number of inference steps.
"""
if timesteps is not None and sigmas is not None:
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
if timesteps is not None:
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accept_sigmas:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" sigmas schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
def retrieve_latents(
encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
):
if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
return encoder_output.latent_dist.sample(generator)
elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
return encoder_output.latent_dist.mode()
elif hasattr(encoder_output, "latents"):
return encoder_output.latents
else:
raise AttributeError("Could not access latents of provided encoder_output")
class HunyuanVideoImageToVideoPipeline(DiffusionPipeline, HunyuanVideoLoraLoaderMixin):
r"""
Pipeline for image-to-video generation using HunyuanVideo.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).
Args:
text_encoder ([`LlavaForConditionalGeneration`]):
[Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
tokenizer (`LlamaTokenizer`):
Tokenizer from [Llava Llama3-8B](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers).
transformer ([`HunyuanVideoTransformer3DModel`]):
Conditional Transformer to denoise the encoded image latents.
scheduler ([`FlowMatchEulerDiscreteScheduler`]):
A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
vae ([`AutoencoderKLHunyuanVideo`]):
Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
text_encoder_2 ([`CLIPTextModel`]):
[CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
tokenizer_2 (`CLIPTokenizer`):
Tokenizer of class
[CLIPTokenizer](https://huggingface.co/docs/transformers/en/model_doc/clip#transformers.CLIPTokenizer).
"""
model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
_callback_tensor_inputs = ["latents", "prompt_embeds"]
def __init__(
self,
text_encoder: LlavaForConditionalGeneration,
tokenizer: LlamaTokenizerFast,
transformer: HunyuanVideoTransformer3DModel,
vae: AutoencoderKLHunyuanVideo,
scheduler: FlowMatchEulerDiscreteScheduler,
text_encoder_2: CLIPTextModel,
tokenizer_2: CLIPTokenizer,
image_processor: CLIPImageProcessor,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
image_processor=image_processor,
)
self.vae_scaling_factor = self.vae.config.scaling_factor if getattr(self, "vae", None) else 0.476986
self.vae_scale_factor_temporal = self.vae.temporal_compression_ratio if getattr(self, "vae", None) else 4
self.vae_scale_factor_spatial = self.vae.spatial_compression_ratio if getattr(self, "vae", None) else 8
self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
def _get_llama_prompt_embeds(
self,
image: torch.Tensor,
prompt: Union[str, List[str]],
prompt_template: Dict[str, Any],
num_videos_per_prompt: int = 1,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
max_sequence_length: int = 256,
num_hidden_layers_to_skip: int = 2,
image_embed_interleave: int = 2,
) -> Tuple[torch.Tensor, torch.Tensor]:
device = device or self._execution_device
dtype = dtype or self.text_encoder.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt = [prompt_template["template"].format(p) for p in prompt]
crop_start = prompt_template.get("crop_start", None)
if crop_start is None:
prompt_template_input = self.tokenizer(
prompt_template["template"],
padding="max_length",
return_tensors="pt",
return_length=False,
return_overflowing_tokens=False,
return_attention_mask=False,
)
crop_start = prompt_template_input["input_ids"].shape[-1]
# Remove <|start_header_id|>, <|end_header_id|>, assistant, <|eot_id|>, and placeholder {}
crop_start -= 5
max_sequence_length += crop_start
text_inputs = self.tokenizer(
prompt,
max_length=max_sequence_length,
padding="max_length",
truncation=True,
return_tensors="pt",
return_length=False,
return_overflowing_tokens=False,
return_attention_mask=True,
)
text_input_ids = text_inputs.input_ids.to(device=device)
prompt_attention_mask = text_inputs.attention_mask.to(device=device)
image_embeds = self.image_processor(image, return_tensors="pt").pixel_values.to(device)
prompt_embeds = self.text_encoder(
input_ids=text_input_ids,
attention_mask=prompt_attention_mask,
pixel_values=image_embeds,
output_hidden_states=True,
).hidden_states[-(num_hidden_layers_to_skip + 1)]
prompt_embeds = prompt_embeds.to(dtype=dtype)
image_emb_len = prompt_template.get("image_emb_len", 576)
image_emb_start = prompt_template.get("image_emb_start", 5)
image_emb_end = prompt_template.get("image_emb_end", 581)
double_return_token_id = prompt_template.get("double_return_token_id", 271)
if crop_start is not None and crop_start > 0:
text_crop_start = crop_start - 1 + image_emb_len
batch_indices, last_double_return_token_indices = torch.where(text_input_ids == double_return_token_id)
if last_double_return_token_indices.shape[0] == 3:
# in case the prompt is too long
last_double_return_token_indices = torch.cat(
(last_double_return_token_indices, torch.tensor([text_input_ids.shape[-1]]))
)
batch_indices = torch.cat((batch_indices, torch.tensor([0])))
last_double_return_token_indices = last_double_return_token_indices.reshape(text_input_ids.shape[0], -1)[
:, -1
]
batch_indices = batch_indices.reshape(text_input_ids.shape[0], -1)[:, -1]
assistant_crop_start = last_double_return_token_indices - 1 + image_emb_len - 4
assistant_crop_end = last_double_return_token_indices - 1 + image_emb_len
attention_mask_assistant_crop_start = last_double_return_token_indices - 4
attention_mask_assistant_crop_end = last_double_return_token_indices
prompt_embed_list = []
prompt_attention_mask_list = []
image_embed_list = []
image_attention_mask_list = []
for i in range(text_input_ids.shape[0]):
prompt_embed_list.append(
torch.cat(
[
prompt_embeds[i, text_crop_start : assistant_crop_start[i].item()],
prompt_embeds[i, assistant_crop_end[i].item() :],
]
)
)
prompt_attention_mask_list.append(
torch.cat(
[
prompt_attention_mask[i, crop_start : attention_mask_assistant_crop_start[i].item()],
prompt_attention_mask[i, attention_mask_assistant_crop_end[i].item() :],
]
)
)
image_embed_list.append(prompt_embeds[i, image_emb_start:image_emb_end])
image_attention_mask_list.append(
torch.ones(image_embed_list[-1].shape[0]).to(prompt_embeds.device).to(prompt_attention_mask.dtype)
)
prompt_embed_list = torch.stack(prompt_embed_list)
prompt_attention_mask_list = torch.stack(prompt_attention_mask_list)
image_embed_list = torch.stack(image_embed_list)
image_attention_mask_list = torch.stack(image_attention_mask_list)
if 0 < image_embed_interleave < 6:
image_embed_list = image_embed_list[:, ::image_embed_interleave, :]
image_attention_mask_list = image_attention_mask_list[:, ::image_embed_interleave]
assert (
prompt_embed_list.shape[0] == prompt_attention_mask_list.shape[0]
and image_embed_list.shape[0] == image_attention_mask_list.shape[0]
)
prompt_embeds = torch.cat([image_embed_list, prompt_embed_list], dim=1)
prompt_attention_mask = torch.cat([image_attention_mask_list, prompt_attention_mask_list], dim=1)
return prompt_embeds, prompt_attention_mask
def _get_clip_prompt_embeds(
self,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
max_sequence_length: int = 77,
) -> torch.Tensor:
device = device or self._execution_device
dtype = dtype or self.text_encoder_2.dtype
prompt = [prompt] if isinstance(prompt, str) else prompt
text_inputs = self.tokenizer_2(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
untruncated_ids = self.tokenizer_2(prompt, padding="longest", return_tensors="pt").input_ids
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
f" {max_sequence_length} tokens: {removed_text}"
)
prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False).pooler_output
return prompt_embeds
def encode_prompt(
self,
image: torch.Tensor,
prompt: Union[str, List[str]],
prompt_2: Union[str, List[str]] = None,
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,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
max_sequence_length: int = 256,
):
if prompt_embeds is None:
prompt_embeds, prompt_attention_mask = self._get_llama_prompt_embeds(
image,
prompt,
prompt_template,
num_videos_per_prompt,
device=device,
dtype=dtype,
max_sequence_length=max_sequence_length,
)
if pooled_prompt_embeds is None:
if prompt_2 is None:
prompt_2 = prompt
pooled_prompt_embeds = self._get_clip_prompt_embeds(
prompt,
num_videos_per_prompt,
device=device,
dtype=dtype,
max_sequence_length=77,
)
return prompt_embeds, pooled_prompt_embeds, prompt_attention_mask
def check_inputs(
self,
prompt,
prompt_2,
height,
width,
prompt_embeds=None,
callback_on_step_end_tensor_inputs=None,
prompt_template=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 prompt_2 is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {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 prompt_2 is not None and (not isinstance(prompt_2, str) and not isinstance(prompt_2, list)):
raise ValueError(f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}")
if prompt_template is not None:
if not isinstance(prompt_template, dict):
raise ValueError(f"`prompt_template` has to be of type `dict` but is {type(prompt_template)}")
if "template" not in prompt_template:
raise ValueError(
f"`prompt_template` has to contain a key `template` but only found {prompt_template.keys()}"
)
def prepare_latents(
self,
image: torch.Tensor,
batch_size: int,
num_channels_latents: int = 32,
height: int = 720,
width: int = 1280,
num_frames: int = 129,
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 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."
)
num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
latent_height, latent_width = height // self.vae_scale_factor_spatial, width // self.vae_scale_factor_spatial
shape = (batch_size, num_channels_latents, num_latent_frames, latent_height, latent_width)
image = image.unsqueeze(2) # [B, C, 1, H, W]
if isinstance(generator, list):
image_latents = [
retrieve_latents(self.vae.encode(image[i].unsqueeze(0)), generator[i]) for i in range(batch_size)
]
else:
image_latents = [retrieve_latents(self.vae.encode(img.unsqueeze(0)), generator) for img in image]
image_latents = torch.cat(image_latents, dim=0).to(dtype) * self.vae_scaling_factor
image_latents = image_latents.repeat(1, 1, num_latent_frames, 1, 1)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
latents = latents.to(device=device, dtype=dtype)
t = torch.tensor([0.999]).to(device=device)
latents = latents * t + image_latents * (1 - t)
return latents, image_latents
def enable_vae_slicing(self):
r"""
Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
"""
self.vae.enable_slicing()
def disable_vae_slicing(self):
r"""
Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_slicing()
def enable_vae_tiling(self):
r"""
Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
processing larger images.
"""
self.vae.enable_tiling()
def disable_vae_tiling(self):
r"""
Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to
computing decoding in one step.
"""
self.vae.disable_tiling()
@property
def guidance_scale(self):
return self._guidance_scale
@property
def num_timesteps(self):
return self._num_timesteps
@property
def attention_kwargs(self):
return self._attention_kwargs
@property
def current_timestep(self):
return self._current_timestep
@property
def interrupt(self):
return self._interrupt
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
image: PIL.Image.Image,
prompt: Union[str, List[str]] = None,
prompt_2: Union[str, List[str]] = None,
negative_prompt: Union[str, List[str]] = None,
negative_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,
true_cfg_scale: float = 1.0,
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_pooled_prompt_embeds: Optional[torch.Tensor] = None,
negative_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,
):
r"""
The call function to the pipeline for generation.
Args:
prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
instead.
prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
will be used instead.
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 `true_cfg_scale` is
not greater than `1`).
negative_prompt_2 (`str` or `List[str]`, *optional*):
The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
`text_encoder_2`. If not defined, `negative_prompt` is used in all the text-encoders.
height (`int`, defaults to `720`):
The height in pixels of the generated image.
width (`int`, defaults to `1280`):
The width in pixels of the generated image.
num_frames (`int`, defaults to `129`):
The number of frames in the generated video.
num_inference_steps (`int`, defaults to `50`):
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
sigmas (`List[float]`, *optional*):
Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in
their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed
will be used.
true_cfg_scale (`float`, *optional*, defaults to 1.0):
When > 1.0 and a provided `negative_prompt`, enables true classifier-free guidance.
guidance_scale (`float`, defaults to `1.0`):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality. Note that the only available HunyuanVideo model is
CFG-distilled, which means that traditional guidance between unconditional and conditional latent is
not applied.
num_videos_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
generation deterministic.
latents (`torch.Tensor`, *optional*):
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
prompt_embeds (`torch.Tensor`, *optional*):
Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
provided, text embeddings are generated from the `prompt` input argument.
pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
If not provided, pooled text embeddings will be generated from `prompt` input argument.
negative_prompt_embeds (`torch.FloatTensor`, *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.
negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`HunyuanVideoPipelineOutput`] instead of a plain tuple.
attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
clip_skip (`int`, *optional*):
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
the output of the pre-final layer will be used for computing the prompt embeddings.
callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
callback_on_step_end_tensor_inputs (`List`, *optional*):
The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
`._callback_tensor_inputs` attribute of your pipeline class.
Examples:
Returns:
[`~HunyuanVideoPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`HunyuanVideoPipelineOutput`] 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.
"""
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,
prompt_2,
height,
width,
prompt_embeds,
callback_on_step_end_tensor_inputs,
prompt_template,
)
has_neg_prompt = negative_prompt is not None or (
negative_prompt_embeds is not None and negative_pooled_prompt_embeds is not None
)
do_true_cfg = true_cfg_scale > 1 and has_neg_prompt
self._guidance_scale = guidance_scale
self._attention_kwargs = attention_kwargs
self._current_timestep = None
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. Prepare latent variables
vae_dtype = self.vae.dtype
image_tensor = self.video_processor.preprocess(image, height, width).to(device, vae_dtype)
num_channels_latents = (self.transformer.config.in_channels - 1) // 2
latents, image_latents = self.prepare_latents(
image_tensor,
batch_size * num_videos_per_prompt,
num_channels_latents,
height,
width,
num_frames,
torch.float32,
device,
generator,
latents,
)
image_latents[:, :, 1:] = 0
mask = image_latents.new_ones(image_latents.shape[0], 1, *image_latents.shape[2:])
mask[:, :, 1:] = 0
# 4. Encode input prompt
transformer_dtype = self.transformer.dtype
prompt_embeds, pooled_prompt_embeds, prompt_attention_mask = self.encode_prompt(
image=image,
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,
)
prompt_embeds = prompt_embeds.to(transformer_dtype)
prompt_attention_mask = prompt_attention_mask.to(transformer_dtype)
pooled_prompt_embeds = pooled_prompt_embeds.to(transformer_dtype)
if do_true_cfg:
black_image = PIL.Image.new("RGB", (width, height), 0)
negative_prompt_embeds, negative_pooled_prompt_embeds, negative_prompt_attention_mask = self.encode_prompt(
image=black_image,
prompt=negative_prompt,
prompt_2=negative_prompt_2,
prompt_template=prompt_template,
num_videos_per_prompt=num_videos_per_prompt,
prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=negative_pooled_prompt_embeds,
prompt_attention_mask=negative_prompt_attention_mask,
device=device,
max_sequence_length=max_sequence_length,
)
negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
negative_prompt_attention_mask = negative_prompt_attention_mask.to(transformer_dtype)
negative_pooled_prompt_embeds = negative_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(self.scheduler, num_inference_steps, device, sigmas=sigmas)
# 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
self._current_timestep = t
latent_model_input = torch.cat([latents, image_latents, mask], dim=1).to(transformer_dtype)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0]).to(latents.dtype)
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,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
if do_true_cfg:
neg_noise_pred = self.transformer(
hidden_states=latent_model_input,
timestep=timestep,
encoder_hidden_states=negative_prompt_embeds,
encoder_attention_mask=negative_prompt_attention_mask,
pooled_projections=negative_pooled_prompt_embeds,
attention_kwargs=attention_kwargs,
return_dict=False,
)[0]
noise_pred = neg_noise_pred + true_cfg_scale * (noise_pred - neg_noise_pred)
# 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 XLA_AVAILABLE:
xm.mark_step()
self._current_timestep = None
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 = video[:, :, 4:, :, :]
video = self.video_processor.postprocess_video(video, output_type=output_type)
else:
video = latents[:, :, 1:, :, :]
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (video,)
return HunyuanVideoPipelineOutput(frames=video)

15
src/diffusers/utils/dummy_torch_and_transformers_objects.py
View File

@@ -677,6 +677,21 @@ class HunyuanSkyreelsImageToVideoPipeline(metaclass=DummyObject):
requires_backends(cls, ["torch", "transformers"])
class HunyuanVideoImageToVideoPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch", "transformers"])
class HunyuanVideoPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers"]

65
tests/models/transformers/test_models_transformer_hunyuan_video.py
View File

@@ -154,3 +154,68 @@ class HunyuanSkyreelsImageToVideoTransformer3DTests(ModelTesterMixin, unittest.T
def test_gradient_checkpointing_is_applied(self):
expected_set = {"HunyuanVideoTransformer3DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
class HunyuanVideoImageToVideoTransformer3DTests(ModelTesterMixin, unittest.TestCase):
model_class = HunyuanVideoTransformer3DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
@property
def dummy_input(self):
batch_size = 1
num_channels = 2 * 4 + 1
num_frames = 1
height = 16
width = 16
text_encoder_embedding_dim = 16
pooled_projection_dim = 8
sequence_length = 12
hidden_states = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
encoder_hidden_states = torch.randn((batch_size, sequence_length, text_encoder_embedding_dim)).to(torch_device)
pooled_projections = torch.randn((batch_size, pooled_projection_dim)).to(torch_device)
encoder_attention_mask = torch.ones((batch_size, sequence_length)).to(torch_device)
return {
"hidden_states": hidden_states,
"timestep": timestep,
"encoder_hidden_states": encoder_hidden_states,
"pooled_projections": pooled_projections,
"encoder_attention_mask": encoder_attention_mask,
}
@property
def input_shape(self):
return (8, 1, 16, 16)
@property
def output_shape(self):
return (4, 1, 16, 16)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"in_channels": 2 * 4 + 1,
"out_channels": 4,
"num_attention_heads": 2,
"attention_head_dim": 10,
"num_layers": 1,
"num_single_layers": 1,
"num_refiner_layers": 1,
"patch_size": 1,
"patch_size_t": 1,
"guidance_embeds": False,
"text_embed_dim": 16,
"pooled_projection_dim": 8,
"rope_axes_dim": (2, 4, 4),
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_output(self):
super().test_output(expected_output_shape=(1, *self.output_shape))
def test_gradient_checkpointing_is_applied(self):
expected_set = {"HunyuanVideoTransformer3DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)

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tests/pipelines/hunyuan_video/test_hunyuan_image2video.py Normal file
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# Copyright 2024 The HuggingFace Team.
#
# 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 inspect
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
LlamaConfig,
LlamaModel,
LlamaTokenizer,
)
from diffusers import (
AutoencoderKLHunyuanVideo,
FlowMatchEulerDiscreteScheduler,
HunyuanVideoImageToVideoPipeline,
HunyuanVideoTransformer3DModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, torch_device
from ..test_pipelines_common import PipelineTesterMixin, PyramidAttentionBroadcastTesterMixin, to_np
enable_full_determinism()
class HunyuanVideoImageToVideoPipelineFastTests(
PipelineTesterMixin, PyramidAttentionBroadcastTesterMixin, unittest.TestCase
):
pipeline_class = HunyuanVideoImageToVideoPipeline
params = frozenset(
["image", "prompt", "height", "width", "guidance_scale", "prompt_embeds", "pooled_prompt_embeds"]
)
batch_params = frozenset(["prompt", "image"])
required_optional_params = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback_on_step_end",
"callback_on_step_end_tensor_inputs",
]
)
supports_dduf = False
# there is no xformers processor for Flux
test_xformers_attention = False
test_layerwise_casting = True
test_group_offloading = True
def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1):
torch.manual_seed(0)
transformer = HunyuanVideoTransformer3DModel(
in_channels=2 * 4 + 1,
out_channels=4,
num_attention_heads=2,
attention_head_dim=10,
num_layers=num_layers,
num_single_layers=num_single_layers,
num_refiner_layers=1,
patch_size=1,
patch_size_t=1,
guidance_embeds=False,
text_embed_dim=16,
pooled_projection_dim=8,
rope_axes_dim=(2, 4, 4),
)
torch.manual_seed(0)
vae = AutoencoderKLHunyuanVideo(
in_channels=3,
out_channels=3,
latent_channels=4,
down_block_types=(
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D",
"HunyuanVideoDownBlock3D",
),
up_block_types=(
"HunyuanVideoUpBlock3D",
"HunyuanVideoUpBlock3D",
"HunyuanVideoUpBlock3D",
"HunyuanVideoUpBlock3D",
),
block_out_channels=(8, 8, 8, 8),
layers_per_block=1,
act_fn="silu",
norm_num_groups=4,
scaling_factor=0.476986,
spatial_compression_ratio=8,
temporal_compression_ratio=4,
mid_block_add_attention=True,
)
torch.manual_seed(0)
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
llama_text_encoder_config = LlamaConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=16,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=2,
pad_token_id=1,
vocab_size=1000,
hidden_act="gelu",
projection_dim=32,
)
clip_text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=8,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=2,
pad_token_id=1,
vocab_size=1000,
hidden_act="gelu",
projection_dim=32,
)
torch.manual_seed(0)
text_encoder = LlamaModel(llama_text_encoder_config)
tokenizer = LlamaTokenizer.from_pretrained("finetrainers/dummy-hunyaunvideo", subfolder="tokenizer")
torch.manual_seed(0)
text_encoder_2 = CLIPTextModel(clip_text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
torch.manual_seed(0)
image_processor = CLIPImageProcessor(
crop_size=336,
do_center_crop=True,
do_normalize=True,
do_resize=True,
image_mean=[0.48145466, 0.4578275, 0.40821073],
image_std=[0.26862954, 0.26130258, 0.27577711],
resample=3,
size=336,
)
components = {
"transformer": transformer,
"vae": vae,
"scheduler": scheduler,
"text_encoder": text_encoder,
"text_encoder_2": text_encoder_2,
"tokenizer": tokenizer,
"tokenizer_2": tokenizer_2,
"image_processor": image_processor,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image_height = 16
image_width = 16
image = Image.new("RGB", (image_width, image_height))
inputs = {
"image": image,
"prompt": "dance monkey",
"prompt_template": {
"template": "{}",
"crop_start": 0,
},
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 4.5,
"height": image_height,
"width": image_width,
"num_frames": 9,
"max_sequence_length": 16,
"output_type": "pt",
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
video = pipe(**inputs).frames
generated_video = video[0]
# NOTE: The expected video has 4 lesser frames because they are dropped in the pipeline
self.assertEqual(generated_video.shape, (5, 3, 16, 16))
expected_video = torch.randn(5, 3, 16, 16)
max_diff = np.abs(generated_video - expected_video).max()
self.assertLessEqual(max_diff, 1e10)
def test_callback_inputs(self):
sig = inspect.signature(self.pipeline_class.__call__)
has_callback_tensor_inputs = "callback_on_step_end_tensor_inputs" in sig.parameters
has_callback_step_end = "callback_on_step_end" in sig.parameters
if not (has_callback_tensor_inputs and has_callback_step_end):
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_subset(pipe, i, t, callback_kwargs):
# iterate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
def callback_inputs_all(pipe, i, t, callback_kwargs):
for tensor_name in pipe._callback_tensor_inputs:
assert tensor_name in callback_kwargs
# iterate over callback args
for tensor_name, tensor_value in callback_kwargs.items():
# check that we're only passing in allowed tensor inputs
assert tensor_name in pipe._callback_tensor_inputs
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
# Test passing in a subset
inputs["callback_on_step_end"] = callback_inputs_subset
inputs["callback_on_step_end_tensor_inputs"] = ["latents"]
output = pipe(**inputs)[0]
# Test passing in a everything
inputs["callback_on_step_end"] = callback_inputs_all
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
output = pipe(**inputs)[0]
def callback_inputs_change_tensor(pipe, i, t, callback_kwargs):
is_last = i == (pipe.num_timesteps - 1)
if is_last:
callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
return callback_kwargs
inputs["callback_on_step_end"] = callback_inputs_change_tensor
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
output = pipe(**inputs)[0]
assert output.abs().sum() < 1e10
def test_attention_slicing_forward_pass(
self, test_max_difference=True, test_mean_pixel_difference=True, expected_max_diff=1e-3
):
if not self.test_attention_slicing:
return
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
for component in pipe.components.values():
if hasattr(component, "set_default_attn_processor"):
component.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
output_without_slicing = pipe(**inputs)[0]
pipe.enable_attention_slicing(slice_size=1)
inputs = self.get_dummy_inputs(generator_device)
output_with_slicing1 = pipe(**inputs)[0]
pipe.enable_attention_slicing(slice_size=2)
inputs = self.get_dummy_inputs(generator_device)
output_with_slicing2 = pipe(**inputs)[0]
if test_max_difference:
max_diff1 = np.abs(to_np(output_with_slicing1) - to_np(output_without_slicing)).max()
max_diff2 = np.abs(to_np(output_with_slicing2) - to_np(output_without_slicing)).max()
self.assertLess(
max(max_diff1, max_diff2),
expected_max_diff,
"Attention slicing should not affect the inference results",
)
def test_vae_tiling(self, expected_diff_max: float = 0.2):
# Seems to require higher tolerance than the other tests
expected_diff_max = 0.6
generator_device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to("cpu")
pipe.set_progress_bar_config(disable=None)
# Without tiling
inputs = self.get_dummy_inputs(generator_device)
inputs["height"] = inputs["width"] = 128
output_without_tiling = pipe(**inputs)[0]
# With tiling
pipe.vae.enable_tiling(
tile_sample_min_height=96,
tile_sample_min_width=96,
tile_sample_stride_height=64,
tile_sample_stride_width=64,
)
inputs = self.get_dummy_inputs(generator_device)
inputs["height"] = inputs["width"] = 128
output_with_tiling = pipe(**inputs)[0]
self.assertLess(
(to_np(output_without_tiling) - to_np(output_with_tiling)).max(),
expected_diff_max,
"VAE tiling should not affect the inference results",
)
# TODO(aryan): Create a dummy gemma model with smol vocab size
@unittest.skip(
"A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
)
def test_inference_batch_consistent(self):
pass
@unittest.skip(
"A very small vocab size is used for fast tests. So, any kind of prompt other than the empty default used in other tests will lead to a embedding lookup error. This test uses a long prompt that causes the error."
)
def test_inference_batch_single_identical(self):
pass
@unittest.skip(
"Encode prompt currently does not work in isolation because of requiring image embeddings from image processor. The test does not handle this case, or we need to rewrite encode_prompt."
)
def test_encode_prompt_works_in_isolation(self):
pass