diff --git a/scripts/convert_flashvideo_to_diffusers.py b/scripts/convert_flashvideo_to_diffusers.py new file mode 100644 index 000000000000..6e70216278df --- /dev/null +++ b/scripts/convert_flashvideo_to_diffusers.py @@ -0,0 +1,349 @@ +import argparse +from typing import Any, Dict + +import torch +from transformers import T5EncoderModel, T5Tokenizer + +from diffusers import ( + AutoencoderKLCogVideoX, + CogVideoXDDIMScheduler, + CogVideoXImageToVideoPipeline, + CogVideoXPipeline, + CogVideoXTransformer3DModel, +) + + +# NOTE: currently copied from convert_cogvideox_to_diffusers.py + + +def reassign_query_key_value_inplace(key: str, state_dict: Dict[str, Any]): + to_q_key = key.replace("query_key_value", "to_q") + to_k_key = key.replace("query_key_value", "to_k") + to_v_key = key.replace("query_key_value", "to_v") + to_q, to_k, to_v = torch.chunk(state_dict[key], chunks=3, dim=0) + state_dict[to_q_key] = to_q + state_dict[to_k_key] = to_k + state_dict[to_v_key] = to_v + state_dict.pop(key) + + +def reassign_query_key_layernorm_inplace(key: str, state_dict: Dict[str, Any]): + layer_id, weight_or_bias = key.split(".")[-2:] + + if "query" in key: + new_key = f"transformer_blocks.{layer_id}.attn1.norm_q.{weight_or_bias}" + elif "key" in key: + new_key = f"transformer_blocks.{layer_id}.attn1.norm_k.{weight_or_bias}" + + state_dict[new_key] = state_dict.pop(key) + + +def reassign_adaln_norm_inplace(key: str, state_dict: Dict[str, Any]): + layer_id, _, weight_or_bias = key.split(".")[-3:] + + weights_or_biases = state_dict[key].chunk(12, dim=0) + norm1_weights_or_biases = torch.cat(weights_or_biases[0:3] + weights_or_biases[6:9]) + norm2_weights_or_biases = torch.cat(weights_or_biases[3:6] + weights_or_biases[9:12]) + + norm1_key = f"transformer_blocks.{layer_id}.norm1.linear.{weight_or_bias}" + state_dict[norm1_key] = norm1_weights_or_biases + + norm2_key = f"transformer_blocks.{layer_id}.norm2.linear.{weight_or_bias}" + state_dict[norm2_key] = norm2_weights_or_biases + + state_dict.pop(key) + + +def remove_keys_inplace(key: str, state_dict: Dict[str, Any]): + state_dict.pop(key) + + +def replace_up_keys_inplace(key: str, state_dict: Dict[str, Any]): + key_split = key.split(".") + layer_index = int(key_split[2]) + replace_layer_index = 4 - 1 - layer_index + + key_split[1] = "up_blocks" + key_split[2] = str(replace_layer_index) + new_key = ".".join(key_split) + + state_dict[new_key] = state_dict.pop(key) + + +TRANSFORMER_KEYS_RENAME_DICT = { + "transformer.final_layernorm": "norm_final", + "transformer": "transformer_blocks", + "attention": "attn1", + "mlp": "ff.net", + "dense_h_to_4h": "0.proj", + "dense_4h_to_h": "2", + ".layers": "", + "dense": "to_out.0", + "input_layernorm": "norm1.norm", + "post_attn1_layernorm": "norm2.norm", + "time_embed.0": "time_embedding.linear_1", + "time_embed.2": "time_embedding.linear_2", + "ofs_embed.0": "ofs_embedding.linear_1", + "ofs_embed.2": "ofs_embedding.linear_2", + "mixins.patch_embed": "patch_embed", + "mixins.final_layer.norm_final": "norm_out.norm", + "mixins.final_layer.linear": "proj_out", + "mixins.final_layer.adaLN_modulation.1": "norm_out.linear", + "mixins.pos_embed.pos_embedding": "patch_embed.pos_embedding", # Specific to CogVideoX-5b-I2V +} + +TRANSFORMER_SPECIAL_KEYS_REMAP = { + "query_key_value": reassign_query_key_value_inplace, + "query_layernorm_list": reassign_query_key_layernorm_inplace, + "key_layernorm_list": reassign_query_key_layernorm_inplace, + "adaln_layer.adaLN_modulations": reassign_adaln_norm_inplace, + "embed_tokens": remove_keys_inplace, + "freqs_sin": remove_keys_inplace, + "freqs_cos": remove_keys_inplace, + "position_embedding": remove_keys_inplace, +} + +VAE_KEYS_RENAME_DICT = { + "block.": "resnets.", + "down.": "down_blocks.", + "downsample": "downsamplers.0", + "upsample": "upsamplers.0", + "nin_shortcut": "conv_shortcut", + "encoder.mid.block_1": "encoder.mid_block.resnets.0", + "encoder.mid.block_2": "encoder.mid_block.resnets.1", + "decoder.mid.block_1": "decoder.mid_block.resnets.0", + "decoder.mid.block_2": "decoder.mid_block.resnets.1", +} + +VAE_SPECIAL_KEYS_REMAP = { + "loss": remove_keys_inplace, + "up.": replace_up_keys_inplace, +} + +TOKENIZER_MAX_LENGTH = 226 + + +def get_state_dict(saved_dict: Dict[str, Any]) -> Dict[str, Any]: + state_dict = saved_dict + if "model" in saved_dict.keys(): + state_dict = state_dict["model"] + if "module" in saved_dict.keys(): + state_dict = state_dict["module"] + if "state_dict" in saved_dict.keys(): + state_dict = state_dict["state_dict"] + return state_dict + + +def update_state_dict_inplace(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]: + state_dict[new_key] = state_dict.pop(old_key) + + +def convert_transformer( + ckpt_path: str, + num_layers: int, + num_attention_heads: int, + use_rotary_positional_embeddings: bool, + i2v: bool, + dtype: torch.dtype, + init_kwargs: Dict[str, Any], +): + PREFIX_KEY = "model.diffusion_model." + + original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True)) + transformer = CogVideoXTransformer3DModel( + in_channels=32 if i2v else 16, + num_layers=num_layers, + num_attention_heads=num_attention_heads, + use_rotary_positional_embeddings=use_rotary_positional_embeddings, + ofs_embed_dim=512 if (i2v and init_kwargs["patch_size_t"] is not None) else None, # CogVideoX1.5-5B-I2V + use_learned_positional_embeddings=i2v and init_kwargs["patch_size_t"] is None, # CogVideoX-5B-I2V + **init_kwargs, + ).to(dtype=dtype) + + for key in list(original_state_dict.keys()): + new_key = key[len(PREFIX_KEY) :] + for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + transformer.load_state_dict(original_state_dict, strict=True) + return transformer + + +def convert_vae(ckpt_path: str, scaling_factor: float, version: str, dtype: torch.dtype): + init_kwargs = {"scaling_factor": scaling_factor} + if version == "1.5": + init_kwargs.update({"invert_scale_latents": True}) + + original_state_dict = get_state_dict(torch.load(ckpt_path, map_location="cpu", mmap=True)) + vae = AutoencoderKLCogVideoX(**init_kwargs).to(dtype=dtype) + + for key in list(original_state_dict.keys()): + new_key = key[:] + for replace_key, rename_key in VAE_KEYS_RENAME_DICT.items(): + new_key = new_key.replace(replace_key, rename_key) + update_state_dict_inplace(original_state_dict, key, new_key) + + for key in list(original_state_dict.keys()): + for special_key, handler_fn_inplace in VAE_SPECIAL_KEYS_REMAP.items(): + if special_key not in key: + continue + handler_fn_inplace(key, original_state_dict) + + vae.load_state_dict(original_state_dict, strict=True) + return vae + + +def get_transformer_init_kwargs(version: str): + if version == "1.0": + vae_scale_factor_spatial = 8 + init_kwargs = { + "patch_size": 2, + "patch_size_t": None, + "patch_bias": True, + "sample_height": 480 // vae_scale_factor_spatial, + "sample_width": 720 // vae_scale_factor_spatial, + "sample_frames": 49, + } + + elif version == "1.5": + vae_scale_factor_spatial = 8 + init_kwargs = { + "patch_size": 2, + "patch_size_t": 2, + "patch_bias": False, + "sample_height": 300, + "sample_width": 300, + "sample_frames": 81, + } + else: + raise ValueError("Unsupported version of CogVideoX.") + + return init_kwargs + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--transformer_ckpt_path", type=str, default=None, help="Path to original transformer checkpoint" + ) + parser.add_argument("--vae_ckpt_path", type=str, default=None, help="Path to original vae checkpoint") + parser.add_argument("--output_path", type=str, required=True, help="Path where converted model should be saved") + parser.add_argument("--fp16", action="store_true", default=False, help="Whether to save the model weights in fp16") + parser.add_argument("--bf16", action="store_true", default=False, help="Whether to save the model weights in bf16") + parser.add_argument( + "--push_to_hub", action="store_true", default=False, help="Whether to push to HF Hub after saving" + ) + parser.add_argument( + "--text_encoder_cache_dir", type=str, default=None, help="Path to text encoder cache directory" + ) + parser.add_argument( + "--typecast_text_encoder", + action="store_true", + default=False, + help="Whether or not to apply fp16/bf16 precision to text_encoder", + ) + # For CogVideoX-2B, num_layers is 30. For 5B, it is 42 + parser.add_argument("--num_layers", type=int, default=30, help="Number of transformer blocks") + # For CogVideoX-2B, num_attention_heads is 30. For 5B, it is 48 + parser.add_argument("--num_attention_heads", type=int, default=30, help="Number of attention heads") + # For CogVideoX-2B, use_rotary_positional_embeddings is False. For 5B, it is True + parser.add_argument( + "--use_rotary_positional_embeddings", action="store_true", default=False, help="Whether to use RoPE or not" + ) + # For CogVideoX-2B, scaling_factor is 1.15258426. For 5B, it is 0.7 + parser.add_argument("--scaling_factor", type=float, default=1.15258426, help="Scaling factor in the VAE") + # For CogVideoX-2B, snr_shift_scale is 3.0. For 5B, it is 1.0 + parser.add_argument("--snr_shift_scale", type=float, default=3.0, help="Scaling factor in the VAE") + parser.add_argument( + "--i2v", + action="store_true", + default=False, + help="Whether the model to be converted is the Image-to-Video version of CogVideoX.", + ) + parser.add_argument( + "--version", + choices=["1.0", "1.5"], + default="1.0", + help="Which version of CogVideoX to use for initializing default modeling parameters.", + ) + return parser.parse_args() + + +if __name__ == "__main__": + args = get_args() + + transformer = None + vae = None + + if args.fp16 and args.bf16: + raise ValueError("You cannot pass both --fp16 and --bf16 at the same time.") + + dtype = torch.float16 if args.fp16 else torch.bfloat16 if args.bf16 else torch.float32 + + if args.transformer_ckpt_path is not None: + init_kwargs = get_transformer_init_kwargs(args.version) + transformer = convert_transformer( + args.transformer_ckpt_path, + args.num_layers, + args.num_attention_heads, + args.use_rotary_positional_embeddings, + args.i2v, + dtype, + init_kwargs, + ) + if args.vae_ckpt_path is not None: + # Keep VAE in float32 for better quality + vae = convert_vae(args.vae_ckpt_path, args.scaling_factor, args.version, torch.float32) + + text_encoder_id = "google/t5-v1_1-xxl" + tokenizer = T5Tokenizer.from_pretrained(text_encoder_id, model_max_length=TOKENIZER_MAX_LENGTH) + text_encoder = T5EncoderModel.from_pretrained(text_encoder_id, cache_dir=args.text_encoder_cache_dir) + + if args.typecast_text_encoder: + text_encoder = text_encoder.to(dtype=dtype) + + # Apparently, the conversion does not work anymore without this :shrug: + for param in text_encoder.parameters(): + param.data = param.data.contiguous() + + scheduler = CogVideoXDDIMScheduler.from_config( + { + "snr_shift_scale": args.snr_shift_scale, + "beta_end": 0.012, + "beta_schedule": "scaled_linear", + "beta_start": 0.00085, + "clip_sample": False, + "num_train_timesteps": 1000, + "prediction_type": "v_prediction", + "rescale_betas_zero_snr": True, + "set_alpha_to_one": True, + "timestep_spacing": "trailing", + } + ) + if args.i2v: + pipeline_cls = CogVideoXImageToVideoPipeline + else: + pipeline_cls = CogVideoXPipeline + + pipe = pipeline_cls( + tokenizer=tokenizer, + text_encoder=text_encoder, + vae=vae, + transformer=transformer, + scheduler=scheduler, + ) + + # We don't use variant here because the model must be run in fp16 (2B) or bf16 (5B). It would be weird + # for users to specify variant when the default is not fp32 and they want to run with the correct default (which + # is either fp16/bf16 here). + + # This is necessary This is necessary for users with insufficient memory, + # such as those using Colab and notebooks, as it can save some memory used for model loading. + pipe.save_pretrained(args.output_path, safe_serialization=True, max_shard_size="5GB", push_to_hub=args.push_to_hub) diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 30d497892ff5..95e3765c6996 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -192,6 +192,7 @@ "CosmosTransformer3DModel", "DiTTransformer2DModel", "EasyAnimateTransformer3DModel", + "FlashVideoTransformer3DModel", "FluxControlNetModel", "FluxMultiControlNetModel", "FluxTransformer2DModel", @@ -409,6 +410,8 @@ "EasyAnimateControlPipeline", "EasyAnimateInpaintPipeline", "EasyAnimatePipeline", + "FlashVideoPipeline", + "FlashVideoVideoToVideoPipeline", "FluxControlImg2ImgPipeline", "FluxControlInpaintPipeline", "FluxControlNetImg2ImgPipeline", @@ -846,6 +849,7 @@ CosmosTransformer3DModel, DiTTransformer2DModel, EasyAnimateTransformer3DModel, + FlashVideoTransformer3DModel, FluxControlNetModel, FluxMultiControlNetModel, FluxTransformer2DModel, @@ -1038,6 +1042,8 @@ EasyAnimateControlPipeline, EasyAnimateInpaintPipeline, EasyAnimatePipeline, + FlashVideoPipeline, + FlashVideoVideoToVideoPipeline, FluxControlImg2ImgPipeline, FluxControlInpaintPipeline, FluxControlNetImg2ImgPipeline, diff --git a/src/diffusers/models/__init__.py b/src/diffusers/models/__init__.py index cd1df3667a18..a67d7515158e 100755 --- a/src/diffusers/models/__init__.py +++ b/src/diffusers/models/__init__.py @@ -80,6 +80,7 @@ _import_structure["transformers.transformer_cogview4"] = ["CogView4Transformer2DModel"] _import_structure["transformers.transformer_cosmos"] = ["CosmosTransformer3DModel"] _import_structure["transformers.transformer_easyanimate"] = ["EasyAnimateTransformer3DModel"] + _import_structure["transformers.transformer_flashvideo"] = ["FlashVideoTransformer3DModel"] _import_structure["transformers.transformer_flux"] = ["FluxTransformer2DModel"] _import_structure["transformers.transformer_hidream_image"] = ["HiDreamImageTransformer2DModel"] _import_structure["transformers.transformer_hunyuan_video"] = ["HunyuanVideoTransformer3DModel"] @@ -164,6 +165,7 @@ DiTTransformer2DModel, DualTransformer2DModel, EasyAnimateTransformer3DModel, + FlashVideoTransformer3DModel, FluxTransformer2DModel, HiDreamImageTransformer2DModel, HunyuanDiT2DModel, diff --git a/src/diffusers/models/transformers/__init__.py b/src/diffusers/models/transformers/__init__.py index dd8813369b5d..c80b05ee0372 100755 --- a/src/diffusers/models/transformers/__init__.py +++ b/src/diffusers/models/transformers/__init__.py @@ -22,6 +22,7 @@ from .transformer_cogview4 import CogView4Transformer2DModel from .transformer_cosmos import CosmosTransformer3DModel from .transformer_easyanimate import EasyAnimateTransformer3DModel + from .transformer_flashvideo import FlashVideoTransformer3DModel from .transformer_flux import FluxTransformer2DModel from .transformer_hidream_image import HiDreamImageTransformer2DModel from .transformer_hunyuan_video import HunyuanVideoTransformer3DModel diff --git a/src/diffusers/models/transformers/transformer_flashvideo.py b/src/diffusers/models/transformers/transformer_flashvideo.py new file mode 100644 index 000000000000..515bd23569d0 --- /dev/null +++ b/src/diffusers/models/transformers/transformer_flashvideo.py @@ -0,0 +1,588 @@ +# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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. + +from typing import Any, Dict, Optional, Tuple, Union + +import torch +from torch import nn + +from ...configuration_utils import ConfigMixin, register_to_config +from ...loaders import PeftAdapterMixin +from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers +from ...utils.torch_utils import maybe_allow_in_graph +from ..attention import Attention, FeedForward +from ..attention_processor import AttentionProcessor, CogVideoXAttnProcessor2_0, FusedCogVideoXAttnProcessor2_0 +from ..cache_utils import CacheMixin +from ..embeddings import CogVideoXPatchEmbed, TimestepEmbedding, Timesteps +from ..modeling_outputs import Transformer2DModelOutput +from ..modeling_utils import ModelMixin +from ..normalization import AdaLayerNorm, CogVideoXLayerNormZero + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +# Copied from diffusers.models.transformers.cogvideox_transformer_3d.CogVideoXBlock with CogVideoX->FlashVideo +@maybe_allow_in_graph +class FlashVideoBlock(nn.Module): + r""" + Transformer block used in [CogVideoX](https://github.com/THUDM/CogVideo) model. + + Parameters: + dim (`int`): + The number of channels in the input and output. + num_attention_heads (`int`): + The number of heads to use for multi-head attention. + attention_head_dim (`int`): + The number of channels in each head. + time_embed_dim (`int`): + The number of channels in timestep embedding. + dropout (`float`, defaults to `0.0`): + The dropout probability to use. + activation_fn (`str`, defaults to `"gelu-approximate"`): + Activation function to be used in feed-forward. + attention_bias (`bool`, defaults to `False`): + Whether or not to use bias in attention projection layers. + qk_norm (`bool`, defaults to `True`): + Whether or not to use normalization after query and key projections in Attention. + norm_elementwise_affine (`bool`, defaults to `True`): + Whether to use learnable elementwise affine parameters for normalization. + norm_eps (`float`, defaults to `1e-5`): + Epsilon value for normalization layers. + final_dropout (`bool` defaults to `False`): + Whether to apply a final dropout after the last feed-forward layer. + ff_inner_dim (`int`, *optional*, defaults to `None`): + Custom hidden dimension of Feed-forward layer. If not provided, `4 * dim` is used. + ff_bias (`bool`, defaults to `True`): + Whether or not to use bias in Feed-forward layer. + attention_out_bias (`bool`, defaults to `True`): + Whether or not to use bias in Attention output projection layer. + """ + + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + time_embed_dim: int, + dropout: float = 0.0, + activation_fn: str = "gelu-approximate", + attention_bias: bool = False, + qk_norm: bool = True, + norm_elementwise_affine: bool = True, + norm_eps: float = 1e-5, + final_dropout: bool = True, + ff_inner_dim: Optional[int] = None, + ff_bias: bool = True, + attention_out_bias: bool = True, + ): + super().__init__() + + # 1. Self Attention + self.norm1 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True) + + self.attn1 = Attention( + query_dim=dim, + dim_head=attention_head_dim, + heads=num_attention_heads, + qk_norm="layer_norm" if qk_norm else None, + eps=1e-6, + bias=attention_bias, + out_bias=attention_out_bias, + processor=CogVideoXAttnProcessor2_0(), + ) + + # 2. Feed Forward + self.norm2 = CogVideoXLayerNormZero(time_embed_dim, dim, norm_elementwise_affine, norm_eps, bias=True) + + self.ff = FeedForward( + dim, + dropout=dropout, + activation_fn=activation_fn, + final_dropout=final_dropout, + inner_dim=ff_inner_dim, + bias=ff_bias, + ) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor, + temb: torch.Tensor, + image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + attention_kwargs: Optional[Dict[str, Any]] = None, + ) -> torch.Tensor: + text_seq_length = encoder_hidden_states.size(1) + attention_kwargs = attention_kwargs or {} + + # norm & modulate + norm_hidden_states, norm_encoder_hidden_states, gate_msa, enc_gate_msa = self.norm1( + hidden_states, encoder_hidden_states, temb + ) + + # attention + attn_hidden_states, attn_encoder_hidden_states = self.attn1( + hidden_states=norm_hidden_states, + encoder_hidden_states=norm_encoder_hidden_states, + image_rotary_emb=image_rotary_emb, + **attention_kwargs, + ) + + hidden_states = hidden_states + gate_msa * attn_hidden_states + encoder_hidden_states = encoder_hidden_states + enc_gate_msa * attn_encoder_hidden_states + + # norm & modulate + norm_hidden_states, norm_encoder_hidden_states, gate_ff, enc_gate_ff = self.norm2( + hidden_states, encoder_hidden_states, temb + ) + + # feed-forward + norm_hidden_states = torch.cat([norm_encoder_hidden_states, norm_hidden_states], dim=1) + ff_output = self.ff(norm_hidden_states) + + hidden_states = hidden_states + gate_ff * ff_output[:, text_seq_length:] + encoder_hidden_states = encoder_hidden_states + enc_gate_ff * ff_output[:, :text_seq_length] + + return hidden_states, encoder_hidden_states + + +# Copied from diffusers.models.transformers.cogvideox_transformer_3d.CogVideoXTransformer3DModel with CogVideoX->FlashVideo +class FlashVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, CacheMixin): + """ + A Transformer model for video-like data in [CogVideoX](https://github.com/THUDM/CogVideo). + + Parameters: + num_attention_heads (`int`, defaults to `30`): + The number of heads to use for multi-head attention. + attention_head_dim (`int`, defaults to `64`): + The number of channels in each head. + in_channels (`int`, defaults to `16`): + The number of channels in the input. + out_channels (`int`, *optional*, defaults to `16`): + The number of channels in the output. + flip_sin_to_cos (`bool`, defaults to `True`): + Whether to flip the sin to cos in the time embedding. + time_embed_dim (`int`, defaults to `512`): + Output dimension of timestep embeddings. + ofs_embed_dim (`int`, defaults to `512`): + Output dimension of "ofs" embeddings used in CogVideoX-5b-I2B in version 1.5 + text_embed_dim (`int`, defaults to `4096`): + Input dimension of text embeddings from the text encoder. + num_layers (`int`, defaults to `30`): + The number of layers of Transformer blocks to use. + dropout (`float`, defaults to `0.0`): + The dropout probability to use. + attention_bias (`bool`, defaults to `True`): + Whether to use bias in the attention projection layers. + sample_width (`int`, defaults to `90`): + The width of the input latents. + sample_height (`int`, defaults to `60`): + The height of the input latents. + sample_frames (`int`, defaults to `49`): + The number of frames in the input latents. Note that this parameter was incorrectly initialized to 49 + instead of 13 because CogVideoX processed 13 latent frames at once in its default and recommended settings, + but cannot be changed to the correct value to ensure backwards compatibility. To create a transformer with + K latent frames, the correct value to pass here would be: ((K - 1) * temporal_compression_ratio + 1). + patch_size (`int`, defaults to `2`): + The size of the patches to use in the patch embedding layer. + temporal_compression_ratio (`int`, defaults to `4`): + The compression ratio across the temporal dimension. See documentation for `sample_frames`. + max_text_seq_length (`int`, defaults to `226`): + The maximum sequence length of the input text embeddings. + activation_fn (`str`, defaults to `"gelu-approximate"`): + Activation function to use in feed-forward. + timestep_activation_fn (`str`, defaults to `"silu"`): + Activation function to use when generating the timestep embeddings. + norm_elementwise_affine (`bool`, defaults to `True`): + Whether to use elementwise affine in normalization layers. + norm_eps (`float`, defaults to `1e-5`): + The epsilon value to use in normalization layers. + spatial_interpolation_scale (`float`, defaults to `1.875`): + Scaling factor to apply in 3D positional embeddings across spatial dimensions. + temporal_interpolation_scale (`float`, defaults to `1.0`): + Scaling factor to apply in 3D positional embeddings across temporal dimensions. + use_rotary_positional_embeddings (`bool`, defaults to `False`): + Whether to use rotary positional embeddings (RoPE) for the attention layers. + use_learned_positional_embeddings (`bool`, defaults to `False`): + Whether to use learned positional embeddings for the attention layers. Note that at the time of writing + FlashVideo checkpoints do not use learned positional embeddings. + patch_bias (`bool`, defaults to `True`): + Whether to use a bias in the convolutional projection of the patch embedding layer. + use_time_size_embedding (`bool`, defaults to `True`): + Whether to use a new timestep embedding which adds information about the number of latent frames to + the timestep embedding. This is used in FlashVideo Stage 2 (but not Stage 1) checkpoints, and not used by + CogVideoX models. + use_noise_timestep_embedding (`bool`, defaults to `True`): + Whether to add a new timestep embedding which adds information about the noise timestep used for latent + degradation to the timestep embedding. This is used in FlashVideo Stage 2 (but not Stage 1) checkpoints, + and not used by CogVideoX models. + """ + + _skip_layerwise_casting_patterns = ["patch_embed", "norm"] + _supports_gradient_checkpointing = True + _no_split_modules = ["CogVideoXBlock", "CogVideoXPatchEmbed"] + + @register_to_config + def __init__( + self, + num_attention_heads: int = 30, + attention_head_dim: int = 64, + in_channels: int = 16, + out_channels: Optional[int] = 16, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + time_embed_dim: int = 512, + ofs_embed_dim: Optional[int] = None, + text_embed_dim: int = 4096, + num_layers: int = 30, + dropout: float = 0.0, + attention_bias: bool = True, + sample_width: int = 90, + sample_height: int = 60, + sample_frames: int = 49, + patch_size: int = 2, + patch_size_t: Optional[int] = None, + temporal_compression_ratio: int = 4, + max_text_seq_length: int = 226, + activation_fn: str = "gelu-approximate", + timestep_activation_fn: str = "silu", + norm_elementwise_affine: bool = True, + norm_eps: float = 1e-5, + spatial_interpolation_scale: float = 1.875, + temporal_interpolation_scale: float = 1.0, + use_rotary_positional_embeddings: bool = False, + use_learned_positional_embeddings: bool = False, + patch_bias: bool = True, + use_time_size_embedding: bool = True, + use_noise_timestep_embedding: bool = True, + ): + super().__init__() + inner_dim = num_attention_heads * attention_head_dim + + if not use_rotary_positional_embeddings and use_learned_positional_embeddings: + raise ValueError( + "There are no CogVideoX checkpoints available with disable rotary embeddings and learned positional " + "embeddings. If you're using a custom model and/or believe this should be supported, please open an " + "issue at https://github.com/huggingface/diffusers/issues." + ) + + # 1. Patch embedding + self.patch_embed = CogVideoXPatchEmbed( + patch_size=patch_size, + patch_size_t=patch_size_t, + in_channels=in_channels, + embed_dim=inner_dim, + text_embed_dim=text_embed_dim, + bias=patch_bias, + sample_width=sample_width, + sample_height=sample_height, + sample_frames=sample_frames, + temporal_compression_ratio=temporal_compression_ratio, + max_text_seq_length=max_text_seq_length, + spatial_interpolation_scale=spatial_interpolation_scale, + temporal_interpolation_scale=temporal_interpolation_scale, + use_positional_embeddings=not use_rotary_positional_embeddings, + use_learned_positional_embeddings=use_learned_positional_embeddings, + ) + self.embedding_dropout = nn.Dropout(dropout) + + # 2. Time embeddings and ofs embedding(Only CogVideoX1.5-5B I2V have) + + self.time_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift) + self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn) + + self.ofs_proj = None + self.ofs_embedding = None + if ofs_embed_dim: + self.ofs_proj = Timesteps(ofs_embed_dim, flip_sin_to_cos, freq_shift) + self.ofs_embedding = TimestepEmbedding( + ofs_embed_dim, ofs_embed_dim, timestep_activation_fn + ) # same as time embeddings, for ofs + + # TODO: I believe the OFS embedding from CogVideoX1.5 is different from either the time size embedding or the + # noise timestep embedding from FlashVideo. + # NOTE: this is new to the FlashVideo DiT as compared to the base CogVideoX DiT. + # Handle time size and noise timestep embeddings. These have the same shape as the normal time embeddings. + self.time_size_proj = None + self.time_size_embedding = None + if use_time_size_embedding: + self.time_size_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift) + self.time_size_embedding = TimestepEmbedding(inner_dim, time_embed_dim, timestep_activation_fn) + + self.noise_timestep_proj = None + self.noise_timestep_embedding = None + if use_noise_timestep_embedding: + self.noise_timestep_proj = Timesteps(inner_dim, flip_sin_to_cos, freq_shift) + self.noise_timestep_embedding = TimestepEmbedding(inner_dim, flip_sin_to_cos, freq_shift) + + # 3. Define spatio-temporal transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + FlashVideoBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + time_embed_dim=time_embed_dim, + dropout=dropout, + activation_fn=activation_fn, + attention_bias=attention_bias, + norm_elementwise_affine=norm_elementwise_affine, + norm_eps=norm_eps, + ) + for _ in range(num_layers) + ] + ) + self.norm_final = nn.LayerNorm(inner_dim, norm_eps, norm_elementwise_affine) + + # 4. Output blocks + self.norm_out = AdaLayerNorm( + embedding_dim=time_embed_dim, + output_dim=2 * inner_dim, + norm_elementwise_affine=norm_elementwise_affine, + norm_eps=norm_eps, + chunk_dim=1, + ) + + if patch_size_t is None: + # For CogVideox 1.0 + output_dim = patch_size * patch_size * out_channels + else: + # For CogVideoX 1.5 + output_dim = patch_size * patch_size * patch_size_t * out_channels + + self.proj_out = nn.Linear(inner_dim, output_dim) + + self.gradient_checkpointing = False + + @property + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors + def attn_processors(self) -> Dict[str, AttentionProcessor]: + r""" + Returns: + `dict` of attention processors: A dictionary containing all attention processors used in the model with + indexed by its weight name. + """ + # set recursively + processors = {} + + def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): + if hasattr(module, "get_processor"): + processors[f"{name}.processor"] = module.get_processor() + + for sub_name, child in module.named_children(): + fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) + + return processors + + for name, module in self.named_children(): + fn_recursive_add_processors(name, module, processors) + + return processors + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor + def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): + r""" + Sets the attention processor to use to compute attention. + + Parameters: + processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): + The instantiated processor class or a dictionary of processor classes that will be set as the processor + for **all** `Attention` layers. + + If `processor` is a dict, the key needs to define the path to the corresponding cross attention + processor. This is strongly recommended when setting trainable attention processors. + + """ + count = len(self.attn_processors.keys()) + + if isinstance(processor, dict) and len(processor) != count: + raise ValueError( + f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" + f" number of attention layers: {count}. Please make sure to pass {count} processor classes." + ) + + def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): + if hasattr(module, "set_processor"): + if not isinstance(processor, dict): + module.set_processor(processor) + else: + module.set_processor(processor.pop(f"{name}.processor")) + + for sub_name, child in module.named_children(): + fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) + + for name, module in self.named_children(): + fn_recursive_attn_processor(name, module, processor) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedCogVideoXAttnProcessor2_0 + def fuse_qkv_projections(self): + """ + Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) + are fused. For cross-attention modules, key and value projection matrices are fused. + + + + This API is 🧪 experimental. + + + """ + self.original_attn_processors = None + + for _, attn_processor in self.attn_processors.items(): + if "Added" in str(attn_processor.__class__.__name__): + raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") + + self.original_attn_processors = self.attn_processors + + for module in self.modules(): + if isinstance(module, Attention): + module.fuse_projections(fuse=True) + + self.set_attn_processor(FusedCogVideoXAttnProcessor2_0()) + + # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections + def unfuse_qkv_projections(self): + """Disables the fused QKV projection if enabled. + + + + This API is 🧪 experimental. + + + + """ + if self.original_attn_processors is not None: + self.set_attn_processor(self.original_attn_processors) + + def forward( + self, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor, + timestep: Union[int, float, torch.LongTensor], + timestep_cond: Optional[torch.Tensor] = None, + noise_timestep: Optional[Union[int, float, torch.LongTensor]] = None, + time_size_tensor: Optional[torch.LongTensor] = None, + ofs: Optional[Union[int, float, torch.LongTensor]] = None, + image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + attention_kwargs: Optional[Dict[str, Any]] = None, + return_dict: bool = True, + ): + if attention_kwargs is not None: + attention_kwargs = attention_kwargs.copy() + lora_scale = attention_kwargs.pop("scale", 1.0) + else: + lora_scale = 1.0 + + if USE_PEFT_BACKEND: + # weight the lora layers by setting `lora_scale` for each PEFT layer + scale_lora_layers(self, lora_scale) + else: + if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None: + logger.warning( + "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective." + ) + + batch_size, num_frames, channels, height, width = hidden_states.shape + + # 1. Time embedding + timesteps = timestep + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=hidden_states.dtype) + emb = self.time_embedding(t_emb, timestep_cond) + + if self.ofs_embedding is not None: + ofs_emb = self.ofs_proj(ofs) + ofs_emb = ofs_emb.to(dtype=hidden_states.dtype) + ofs_emb = self.ofs_embedding(ofs_emb) + emb = emb + ofs_emb + + # NOTE: this is new to the FlashVideo DiT as compared to the base CogVideoX DiT. + # Handle time step and noise timestep embeddings, if using + if self.time_size_embedding is not None: + # Explicitly prepare num_frames as a 1D tensor for the sinusoidal timestep embedding + if time_size_tensor is not None: + time_size_tensor = torch.full((batch_size,), num_frames) + time_size_emb = self.time_size_proj(time_size_tensor) + time_size_emb = time_size_emb.to(dtype=hidden_states.dtype) + time_size_emb = self.time_size_embedding(time_size_emb) + emb = emb + time_size_emb + + if self.noise_timestep_embedding is not None: + # Explicitly prepare noise_timestep as a 1D tensor for the sinusoidal timestep embedding + if not isinstance(noise_timestep, torch.Tensor): + noise_timestep = torch.full((batch_size,), noise_timestep) + noise_timestep_emb = self.noise_timestep_proj(noise_timestep) + noise_timestep_emb = noise_timestep_emb.to(dtype=hidden_states.dtype) + noise_timestep_emb = self.noise_timestep_embedding(noise_timestep_emb) + emb = emb + noise_timestep_emb + + # 2. Patch embedding + hidden_states = self.patch_embed(encoder_hidden_states, hidden_states) + hidden_states = self.embedding_dropout(hidden_states) + + text_seq_length = encoder_hidden_states.shape[1] + encoder_hidden_states = hidden_states[:, :text_seq_length] + hidden_states = hidden_states[:, text_seq_length:] + + # 3. Transformer blocks + for i, block in enumerate(self.transformer_blocks): + if torch.is_grad_enabled() and self.gradient_checkpointing: + hidden_states, encoder_hidden_states = self._gradient_checkpointing_func( + block, + hidden_states, + encoder_hidden_states, + emb, + image_rotary_emb, + attention_kwargs, + ) + else: + hidden_states, encoder_hidden_states = block( + hidden_states=hidden_states, + encoder_hidden_states=encoder_hidden_states, + temb=emb, + image_rotary_emb=image_rotary_emb, + attention_kwargs=attention_kwargs, + ) + + hidden_states = self.norm_final(hidden_states) + + # 4. Final block + hidden_states = self.norm_out(hidden_states, temb=emb) + hidden_states = self.proj_out(hidden_states) + + # 5. Unpatchify + p = self.config.patch_size + p_t = self.config.patch_size_t + + if p_t is None: + output = hidden_states.reshape(batch_size, num_frames, height // p, width // p, -1, p, p) + output = output.permute(0, 1, 4, 2, 5, 3, 6).flatten(5, 6).flatten(3, 4) + else: + output = hidden_states.reshape( + batch_size, (num_frames + p_t - 1) // p_t, height // p, width // p, -1, p_t, p, p + ) + output = output.permute(0, 1, 5, 4, 2, 6, 3, 7).flatten(6, 7).flatten(4, 5).flatten(1, 2) + + if USE_PEFT_BACKEND: + # remove `lora_scale` from each PEFT layer + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (output,) + return Transformer2DModelOutput(sample=output) diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index c8fbdf0c6c29..84c80de49454 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -231,6 +231,7 @@ "EasyAnimateInpaintPipeline", "EasyAnimateControlPipeline", ] + _import_structure["flashvideo"] = ["FlashVideoPipeline", "FlashVideoVideoToVideoPipeline"] _import_structure["hidream_image"] = ["HiDreamImagePipeline"] _import_structure["hunyuandit"] = ["HunyuanDiTPipeline"] _import_structure["hunyuan_video"] = [ @@ -608,6 +609,7 @@ EasyAnimateInpaintPipeline, EasyAnimatePipeline, ) + from .flashvideo import FlashVideoPipeline, FlashVideoVideoToVideoPipeline from .flux import ( FluxControlImg2ImgPipeline, FluxControlInpaintPipeline, diff --git a/src/diffusers/pipelines/flashvideo/__init__.py b/src/diffusers/pipelines/flashvideo/__init__.py new file mode 100644 index 000000000000..a3ace6c8a5b1 --- /dev/null +++ b/src/diffusers/pipelines/flashvideo/__init__.py @@ -0,0 +1,50 @@ +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_import_structure = {} + + +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_flashvideo"] = ["FlashVideoPipeline"] + _import_structure["pipeline_flashvideo_video2video"] = ["FlashVideoVideoToVideoPipeline"] + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * + else: + from .pipeline_flashvideo import FlashVideoPipeline + from .pipeline_flashvideo_video2video import FlashVideoVideoToVideoPipeline + +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/flashvideo/pipeline_flashvideo.py b/src/diffusers/pipelines/flashvideo/pipeline_flashvideo.py new file mode 100644 index 000000000000..f79a77ec169a --- /dev/null +++ b/src/diffusers/pipelines/flashvideo/pipeline_flashvideo.py @@ -0,0 +1,791 @@ +# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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 +import math +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +from transformers import T5EncoderModel, T5Tokenizer + +from ...callbacks import MultiPipelineCallbacks, PipelineCallback +from ...loaders import CogVideoXLoraLoaderMixin +from ...models import AutoencoderKLCogVideoX, FlashVideoTransformer3DModel +from ...models.embeddings import get_3d_rotary_pos_embed +from ...pipelines.pipeline_utils import DiffusionPipeline +from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler +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_output import FlashVideoPipelineOutput + + +# NOTE: copied from CogVideoXPipelineOutput currently with CogVideoX->FlashVideo + + +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 CogVideoXPipeline + >>> from diffusers.utils import export_to_video + + >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b" + >>> pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda") + >>> prompt = ( + ... "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " + ... "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " + ... "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " + ... "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " + ... "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " + ... "atmosphere of this unique musical performance." + ... ) + >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0] + >>> export_to_video(video, "output.mp4", fps=8) + ``` +""" + + +# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid +def get_resize_crop_region_for_grid(src, tgt_width, tgt_height): + tw = tgt_width + th = tgt_height + h, w = src + r = h / w + if r > (th / tw): + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + +# 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 + + +class FlashVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin): + r""" + Pipeline for text-to-video generation using FlashVideo, which itself is based on CogVideoX. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. + text_encoder ([`T5EncoderModel`]): + Frozen text-encoder. CogVideoX uses + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the + [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant. + tokenizer (`T5Tokenizer`): + Tokenizer of class + [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + transformer ([`FlashVideoTransformer3DModel`]): + A text conditioned `FlashVideoTransformer3DModel` to denoise the encoded video latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `transformer` to denoise the encoded video latents. + """ + + _optional_components = [] + model_cpu_offload_seq = "text_encoder->transformer->vae" + + _callback_tensor_inputs = [ + "latents", + "prompt_embeds", + "negative_prompt_embeds", + ] + + def __init__( + self, + tokenizer: T5Tokenizer, + text_encoder: T5EncoderModel, + vae: AutoencoderKLCogVideoX, + transformer: FlashVideoTransformer3DModel, + scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler], + ): + super().__init__() + + self.register_modules( + tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler + ) + self.vae_scale_factor_spatial = ( + 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 + ) + self.vae_scale_factor_temporal = ( + self.vae.config.temporal_compression_ratio if getattr(self, "vae", None) else 4 + ) + self.vae_scaling_factor_image = self.vae.config.scaling_factor if getattr(self, "vae", None) else 0.7 + + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) + + def _get_t5_prompt_embeds( + self, + prompt: Union[str, List[str]] = None, + num_videos_per_prompt: int = 1, + max_sequence_length: int = 226, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + device = device or self._execution_device + dtype = dtype or self.text_encoder.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(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.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because `max_sequence_length` is set to " + f" {max_sequence_length} tokens: {removed_text}" + ) + + prompt_embeds = self.text_encoder(text_input_ids.to(device))[0] + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + # duplicate text embeddings for each generation per prompt, using mps friendly method + _, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) + + return prompt_embeds + + def encode_prompt( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + do_classifier_free_guidance: bool = True, + num_videos_per_prompt: int = 1, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + max_sequence_length: int = 226, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): + Whether to use classifier free guidance or not. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + Number of videos that should be generated per prompt. torch device to place the resulting embeddings on + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + device: (`torch.device`, *optional*): + torch device + dtype: (`torch.dtype`, *optional*): + torch dtype + """ + device = device or self._execution_device + + prompt = [prompt] if isinstance(prompt, str) else prompt + if prompt is not None: + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + prompt_embeds = self._get_t5_prompt_embeds( + prompt=prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + if do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + + negative_prompt_embeds = self._get_t5_prompt_embeds( + prompt=negative_prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + return prompt_embeds, negative_prompt_embeds + + def prepare_latents( + self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None + ): + 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." + ) + + shape = ( + batch_size, + (num_frames - 1) // self.vae_scale_factor_temporal + 1, + num_channels_latents, + height // self.vae_scale_factor_spatial, + width // self.vae_scale_factor_spatial, + ) + + if latents is None: + latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + else: + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def decode_latents(self, latents: torch.Tensor) -> torch.Tensor: + latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width] + latents = 1 / self.vae_scaling_factor_image * latents + + frames = self.vae.decode(latents).sample + return frames + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + # Copied from diffusers.pipelines.latte.pipeline_latte.LattePipeline.check_inputs + def check_inputs( + self, + prompt, + height, + width, + negative_prompt, + callback_on_step_end_tensor_inputs, + prompt_embeds=None, + negative_prompt_embeds=None, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 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 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)}") + + if prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + 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}." + ) + + def fuse_qkv_projections(self) -> None: + r"""Enables fused QKV projections.""" + self.fusing_transformer = True + self.transformer.fuse_qkv_projections() + + def unfuse_qkv_projections(self) -> None: + r"""Disable QKV projection fusion if enabled.""" + if not self.fusing_transformer: + logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.") + else: + self.transformer.unfuse_qkv_projections() + self.fusing_transformer = False + + def _prepare_rotary_positional_embeddings( + self, + height: int, + width: int, + num_frames: int, + device: torch.device, + ) -> Tuple[torch.Tensor, torch.Tensor]: + grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) + grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) + + p = self.transformer.config.patch_size + p_t = self.transformer.config.patch_size_t + + base_size_width = self.transformer.config.sample_width // p + base_size_height = self.transformer.config.sample_height // p + + if p_t is None: + # CogVideoX 1.0 + grid_crops_coords = get_resize_crop_region_for_grid( + (grid_height, grid_width), base_size_width, base_size_height + ) + freqs_cos, freqs_sin = get_3d_rotary_pos_embed( + embed_dim=self.transformer.config.attention_head_dim, + crops_coords=grid_crops_coords, + grid_size=(grid_height, grid_width), + temporal_size=num_frames, + device=device, + ) + else: + # CogVideoX 1.5 + base_num_frames = (num_frames + p_t - 1) // p_t + + freqs_cos, freqs_sin = get_3d_rotary_pos_embed( + embed_dim=self.transformer.config.attention_head_dim, + crops_coords=None, + grid_size=(grid_height, grid_width), + temporal_size=base_num_frames, + grid_type="slice", + max_size=(base_size_height, base_size_width), + device=device, + ) + + return freqs_cos, freqs_sin + + @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, + prompt: Optional[Union[str, List[str]]] = None, + negative_prompt: Optional[Union[str, List[str]]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_frames: Optional[int] = None, + num_inference_steps: int = 50, + timesteps: Optional[List[int]] = None, + guidance_scale: float = 6, + use_dynamic_cfg: bool = False, + num_videos_per_prompt: int = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: 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"], + max_sequence_length: int = 226, + ) -> Union[FlashVideoPipelineOutput, Tuple]: + """ + Function invoked when calling 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. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial): + The height in pixels of the generated image. This is set to 480 by default for the best results. + width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial): + The width in pixels of the generated image. This is set to 720 by default for the best results. + num_frames (`int`, defaults to `48`): + Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will + contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where + num_seconds is 6 and fps is 8. However, since videos can be saved at any fps, the only condition that + needs to be satisfied is that of divisibility mentioned above. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + guidance_scale (`float`, *optional*, defaults to 7.0): + 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. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of videos to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *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 will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.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. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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). + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + 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. + max_sequence_length (`int`, defaults to `226`): + Maximum sequence length in encoded prompt. Must be consistent with + `self.transformer.config.max_text_seq_length` otherwise may lead to poor results. + + Examples: + + Returns: + [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] or `tuple`: + [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a + `tuple`. When returning a tuple, the first element is a list with the generated images. + """ + + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial + width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial + num_frames = num_frames or self.transformer.config.sample_frames + + num_videos_per_prompt = 1 + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + prompt, + height, + width, + negative_prompt, + callback_on_step_end_tensor_inputs, + prompt_embeds, + negative_prompt_embeds, + ) + self._guidance_scale = guidance_scale + self._attention_kwargs = attention_kwargs + self._current_timestep = None + self._interrupt = False + + # 2. Default 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] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds, negative_prompt_embeds = self.encode_prompt( + prompt, + negative_prompt, + do_classifier_free_guidance, + num_videos_per_prompt=num_videos_per_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + max_sequence_length=max_sequence_length, + device=device, + ) + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) + self._num_timesteps = len(timesteps) + + # 5. Prepare latents + latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + + # For CogVideoX 1.5, the latent frames should be padded to make it divisible by patch_size_t + patch_size_t = self.transformer.config.patch_size_t + additional_frames = 0 + if patch_size_t is not None and latent_frames % patch_size_t != 0: + additional_frames = patch_size_t - latent_frames % patch_size_t + num_frames += additional_frames * self.vae_scale_factor_temporal + + latent_channels = self.transformer.config.in_channels + latents = self.prepare_latents( + batch_size * num_videos_per_prompt, + latent_channels, + num_frames, + height, + width, + prompt_embeds.dtype, + device, + generator, + latents, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Create rotary embeds if required + image_rotary_emb = ( + self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device) + if self.transformer.config.use_rotary_positional_embeddings + else None + ) + + # 8. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + # for DPM-solver++ + old_pred_original_sample = None + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + self._current_timestep = t + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latent_model_input.shape[0]) + + # predict noise model_output + noise_pred = self.transformer( + hidden_states=latent_model_input, + encoder_hidden_states=prompt_embeds, + timestep=timestep, + image_rotary_emb=image_rotary_emb, + attention_kwargs=attention_kwargs, + return_dict=False, + )[0] + noise_pred = noise_pred.float() + + # perform guidance + if use_dynamic_cfg: + self._guidance_scale = 1 + guidance_scale * ( + (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 + ) + if 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) + + # compute the previous noisy sample x_t -> x_t-1 + if not isinstance(self.scheduler, CogVideoXDPMScheduler): + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + else: + latents, old_pred_original_sample = self.scheduler.step( + noise_pred, + old_pred_original_sample, + t, + timesteps[i - 1] if i > 0 else None, + latents, + **extra_step_kwargs, + return_dict=False, + ) + latents = latents.to(prompt_embeds.dtype) + + # call the callback, if provided + 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) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + + 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": + # Discard any padding frames that were added for CogVideoX 1.5 + latents = latents[:, additional_frames:] + video = self.decode_latents(latents) + video = self.video_processor.postprocess_video(video=video, output_type=output_type) + else: + video = latents + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (video,) + + return FlashVideoPipelineOutput(frames=video) diff --git a/src/diffusers/pipelines/flashvideo/pipeline_flashvideo_video2video.py b/src/diffusers/pipelines/flashvideo/pipeline_flashvideo_video2video.py new file mode 100644 index 000000000000..fcd88d2729e8 --- /dev/null +++ b/src/diffusers/pipelines/flashvideo/pipeline_flashvideo_video2video.py @@ -0,0 +1,916 @@ +# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI 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 +import math +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +from PIL import Image +from transformers import T5EncoderModel, T5Tokenizer + +from ...callbacks import MultiPipelineCallbacks, PipelineCallback +from ...loaders import CogVideoXLoraLoaderMixin +from ...models import AutoencoderKLCogVideoX, FlashVideoTransformer3DModel +from ...models.embeddings import get_3d_rotary_pos_embed +from ...pipelines.pipeline_utils import DiffusionPipeline +from ...schedulers import CogVideoXDDIMScheduler, CogVideoXDPMScheduler +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_output import FlashVideoPipelineOutput + + +# NOTE: copied from CogVideoXPipelineOutput currently with CogVideoX->FlashVideo + + +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 CogVideoXPipeline + >>> from diffusers.utils import export_to_video + + >>> # Models: "THUDM/CogVideoX-2b" or "THUDM/CogVideoX-5b" + >>> pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16).to("cuda") + >>> prompt = ( + ... "A panda, dressed in a small, red jacket and a tiny hat, sits on a wooden stool in a serene bamboo forest. " + ... "The panda's fluffy paws strum a miniature acoustic guitar, producing soft, melodic tunes. Nearby, a few other " + ... "pandas gather, watching curiously and some clapping in rhythm. Sunlight filters through the tall bamboo, " + ... "casting a gentle glow on the scene. The panda's face is expressive, showing concentration and joy as it plays. " + ... "The background includes a small, flowing stream and vibrant green foliage, enhancing the peaceful and magical " + ... "atmosphere of this unique musical performance." + ... ) + >>> video = pipe(prompt=prompt, guidance_scale=6, num_inference_steps=50).frames[0] + >>> export_to_video(video, "output.mp4", fps=8) + ``` +""" + + +# Similar to diffusers.pipelines.hunyuandit.pipeline_hunyuandit.get_resize_crop_region_for_grid +def get_resize_crop_region_for_grid(src, tgt_width, tgt_height): + tw = tgt_width + th = tgt_height + h, w = src + r = h / w + if r > (th / tw): + resize_height = th + resize_width = int(round(th / h * w)) + else: + resize_width = tw + resize_height = int(round(tw / w * h)) + + crop_top = int(round((th - resize_height) / 2.0)) + crop_left = int(round((tw - resize_width) / 2.0)) + + return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width) + + +# 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 FlashVideoVideoToVideoPipeline(DiffusionPipeline, CogVideoXLoraLoaderMixin): + r""" + Pipeline for text-to-video generation using FlashVideo, which itself is based on CogVideoX. + + This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the + library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations. + text_encoder ([`T5EncoderModel`]): + Frozen text-encoder. CogVideoX uses + [T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5EncoderModel); specifically the + [t5-v1_1-xxl](https://huggingface.co/PixArt-alpha/PixArt-alpha/tree/main/t5-v1_1-xxl) variant. + tokenizer (`T5Tokenizer`): + Tokenizer of class + [T5Tokenizer](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Tokenizer). + transformer ([`FlashVideoTransformer3DModel`]): + A text conditioned `FlashVideoTransformer3DModel` to denoise the encoded video latents. + scheduler ([`SchedulerMixin`]): + A scheduler to be used in combination with `transformer` to denoise the encoded video latents. + """ + + _optional_components = [] + model_cpu_offload_seq = "text_encoder->transformer->vae" + + _callback_tensor_inputs = [ + "latents", + "prompt_embeds", + "negative_prompt_embeds", + ] + + def __init__( + self, + tokenizer: T5Tokenizer, + text_encoder: T5EncoderModel, + vae: AutoencoderKLCogVideoX, + transformer: FlashVideoTransformer3DModel, + scheduler: Union[CogVideoXDDIMScheduler, CogVideoXDPMScheduler], + ): + super().__init__() + + self.register_modules( + tokenizer=tokenizer, text_encoder=text_encoder, vae=vae, transformer=transformer, scheduler=scheduler + ) + self.vae_scale_factor_spatial = ( + 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8 + ) + self.vae_scale_factor_temporal = ( + self.vae.config.temporal_compression_ratio if getattr(self, "vae", None) else 4 + ) + self.vae_scaling_factor_image = self.vae.config.scaling_factor if getattr(self, "vae", None) else 0.7 + + self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial) + + def _get_t5_prompt_embeds( + self, + prompt: Union[str, List[str]] = None, + num_videos_per_prompt: int = 1, + max_sequence_length: int = 226, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + device = device or self._execution_device + dtype = dtype or self.text_encoder.dtype + + prompt = [prompt] if isinstance(prompt, str) else prompt + batch_size = len(prompt) + + text_inputs = self.tokenizer( + prompt, + padding="max_length", + max_length=max_sequence_length, + truncation=True, + add_special_tokens=True, + return_tensors="pt", + ) + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer(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.batch_decode(untruncated_ids[:, max_sequence_length - 1 : -1]) + logger.warning( + "The following part of your input was truncated because `max_sequence_length` is set to " + f" {max_sequence_length} tokens: {removed_text}" + ) + + prompt_embeds = self.text_encoder(text_input_ids.to(device))[0] + prompt_embeds = prompt_embeds.to(dtype=dtype, device=device) + + # duplicate text embeddings for each generation per prompt, using mps friendly method + _, seq_len, _ = prompt_embeds.shape + prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1) + prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1) + + return prompt_embeds + + def encode_prompt( + self, + prompt: Union[str, List[str]], + negative_prompt: Optional[Union[str, List[str]]] = None, + do_classifier_free_guidance: bool = True, + num_videos_per_prompt: int = 1, + prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.Tensor] = None, + max_sequence_length: int = 226, + device: Optional[torch.device] = None, + dtype: Optional[torch.dtype] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + prompt (`str` or `List[str]`, *optional*): + prompt to be encoded + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + do_classifier_free_guidance (`bool`, *optional*, defaults to `True`): + Whether to use classifier free guidance or not. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + Number of videos that should be generated per prompt. torch device to place the resulting embeddings on + prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.Tensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + device: (`torch.device`, *optional*): + torch device + dtype: (`torch.dtype`, *optional*): + torch dtype + """ + device = device or self._execution_device + + prompt = [prompt] if isinstance(prompt, str) else prompt + if prompt is not None: + batch_size = len(prompt) + else: + batch_size = prompt_embeds.shape[0] + + if prompt_embeds is None: + prompt_embeds = self._get_t5_prompt_embeds( + prompt=prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + if do_classifier_free_guidance and negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + + if prompt is not None and type(prompt) is not type(negative_prompt): + raise TypeError( + f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" + f" {type(prompt)}." + ) + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" + f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + + negative_prompt_embeds = self._get_t5_prompt_embeds( + prompt=negative_prompt, + num_videos_per_prompt=num_videos_per_prompt, + max_sequence_length=max_sequence_length, + device=device, + dtype=dtype, + ) + + return prompt_embeds, negative_prompt_embeds + + def prepare_latents( + self, + video: Optional[torch.Tensor] = None, + batch_size: int = 1, + num_channels_latents: int = 16, + height: int = 1080, # Should be the target height in pixels + width: int = 1920, # Should be the target width in pixels + dtype: Optional[torch.dtype] = None, + device: Optional[torch.device] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.Tensor] = None, + timestep: Optional[torch.Tensor] = None, + interpolation_mode: str = "bilinear", + sample_mode: str = "sample", + ): + 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_frames = (video.size(2) - 1) // self.vae_scale_factor_temporal + 1 if latents is None else latents.size(1) + + # NOTE: the shape here corresponds to the latent shape of the target size, which is in general different from + # the latent shape of the input `video`. + shape = ( + batch_size, + num_frames, + num_channels_latents, + height // self.vae_scale_factor_spatial, + width // self.vae_scale_factor_spatial, + ) + + if latents is None: + # First upscale the conditioning video to the desired shape. + upscaled_video = self.upscale_latents(latents, height, width, interpolation_mode) + + # Now encode the upscaled video using the 3D VAE encoder. + latents = self.encode_latents(upscaled_video, batch_size, generator, sample_mode) + + # If timestep is not supplied, latent degradation for FlashVideo Stage 2 will be disabled. However, the + # authors found that latent degradation is important for the final video quality, so it is recommended to + # supply a timestep. + if timestep is not None: + latent_noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) + # NOTE: the original implementation allows the noise to be modified using shift/scale factors. This is + # currently not implemented, and the original code defaults to using shift/scale values that would + # keep the original noise level from the scheduler. + latents = self.scheduler.add_noise(latents, latent_noise, timestep) + else: + # NOTE: in line with similar pipelines like CogVideoXVideoToVideoPipeline, supplied latents are expected + # to be directly usable at the start of the denoising loop. In particular, the latents must have the + # correct shape and already be noised (assuming latent degradation is used.) + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def decode_latents(self, latents: torch.Tensor) -> torch.Tensor: + latents = latents.permute(0, 2, 1, 3, 4) # [batch_size, num_channels, num_frames, height, width] + latents = 1 / self.vae_scaling_factor_image * latents + + frames = self.vae.decode(latents).sample + return frames + + def encode_latents( + self, + video: torch.Tensor, + batch_size: int = 1, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + sample_mode: str = "sample", + ): + if isinstance(generator, list): + latents = [ + retrieve_latents(self.vae.encode(video[i].unsqueeze(0)), generator[i], sample_mode) for i in range(batch_size) + ] + else: + latents = [retrieve_latents(self.vae.encode(vid.unsqueeze(0)), generator, sample_mode) for vid in video] + latents = torch.cat(latents, dim=0).permute(0, 2, 1, 3, 4) # [B, F, C, H, W] + latents = latents * self.vae_scaling_factor_image + return latents + + def upscale_latents( + self, + latents: torch.Tensor, + target_height: int = 1080, + target_width: int = 1920, + interpolation_mode: str = "bilinear", + ): + """ + Upscales the low-resolution video from Stage 1 for to the target height/width use in Stage 2 high-resolution + video enhancement. + """ + upscaled_latents = torch.nn.functional.interpolate( + latents, + size=(target_height, target_width), + mode=interpolation_mode, + align_corners=False, + antialias=True, + ) + return upscaled_latents + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + video, + prompt, + negative_prompt, + height, + width, + latents, + prompt_embeds, + negative_prompt_embeds, + callback_on_step_end_tensor_inputs, + ): + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 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 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)}") + + if prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `prompt`: {prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + 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}." + ) + + if video is None and latents is None: + raise ValueError( + "At least one of `video` and `latents` must be supplied, but neither is available." + ) + + def fuse_qkv_projections(self) -> None: + r"""Enables fused QKV projections.""" + self.fusing_transformer = True + self.transformer.fuse_qkv_projections() + + def unfuse_qkv_projections(self) -> None: + r"""Disable QKV projection fusion if enabled.""" + if not self.fusing_transformer: + logger.warning("The Transformer was not initially fused for QKV projections. Doing nothing.") + else: + self.transformer.unfuse_qkv_projections() + self.fusing_transformer = False + + def _prepare_rotary_positional_embeddings( + self, + height: int, + width: int, + num_frames: int, + device: torch.device, + ) -> Tuple[torch.Tensor, torch.Tensor]: + grid_height = height // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) + grid_width = width // (self.vae_scale_factor_spatial * self.transformer.config.patch_size) + + p = self.transformer.config.patch_size + p_t = self.transformer.config.patch_size_t + + base_size_width = self.transformer.config.sample_width // p + base_size_height = self.transformer.config.sample_height // p + + if p_t is None: + # CogVideoX 1.0 + grid_crops_coords = get_resize_crop_region_for_grid( + (grid_height, grid_width), base_size_width, base_size_height + ) + freqs_cos, freqs_sin = get_3d_rotary_pos_embed( + embed_dim=self.transformer.config.attention_head_dim, + crops_coords=grid_crops_coords, + grid_size=(grid_height, grid_width), + temporal_size=num_frames, + device=device, + ) + else: + # CogVideoX 1.5 + base_num_frames = (num_frames + p_t - 1) // p_t + + freqs_cos, freqs_sin = get_3d_rotary_pos_embed( + embed_dim=self.transformer.config.attention_head_dim, + crops_coords=None, + grid_size=(grid_height, grid_width), + temporal_size=base_num_frames, + grid_type="slice", + max_size=(base_size_height, base_size_width), + device=device, + ) + + return freqs_cos, freqs_sin + + @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, + video: List[Image.Image] = None, + prompt: Optional[Union[str, List[str]]] = None, + negative_prompt: Optional[Union[str, List[str]]] = None, + height: Optional[int] = None, + width: Optional[int] = None, + num_frames: Optional[int] = None, + num_inference_steps: int = 50, + timesteps: Optional[List[int]] = None, + noise_timestep: Optional[int] = 675, + guidance_scale: float = 6, + use_dynamic_cfg: bool = False, + num_videos_per_prompt: int = 1, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + latents: Optional[torch.FloatTensor] = None, + prompt_embeds: Optional[torch.FloatTensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + output_type: 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"], + max_sequence_length: int = 226, + ) -> Union[FlashVideoPipelineOutput, Tuple]: + """ + Function invoked when calling the pipeline for generation. + + Args: + video (`List[PIL.Image.Image]`, *optional*): + The input video to condition the generation on. Must be a list of images/frames of the video. If not + supplied, `latents` must be supplied instead. + 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. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + height (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial): + The height in pixels of the generated image. This is set to 480 by default for the best results. + width (`int`, *optional*, defaults to self.transformer.config.sample_height * self.vae_scale_factor_spatial): + The width in pixels of the generated image. This is set to 720 by default for the best results. + num_frames (`int`, defaults to `48`): + Number of frames to generate. Must be divisible by self.vae_scale_factor_temporal. Generated video will + contain 1 extra frame because CogVideoX is conditioned with (num_seconds * fps + 1) frames where + num_seconds is 6 and fps is 8. However, since videos can be saved at any fps, the only condition that + needs to be satisfied is that of divisibility mentioned above. + num_inference_steps (`int`, *optional*, defaults to 50): + The number of denoising steps. More denoising steps usually lead to a higher quality image at the + expense of slower inference. + timesteps (`List[int]`, *optional*): + Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument + in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is + passed will be used. Must be in descending order. + noise_timestep (`int`, *optional*, defaults to `675`) + The timestep corresponding to the noise level used for latent degradation. This controls how much + degradation is applied to the latents before the denoising loop (with higher values meaning more + degradation); the FlashVideo authors recommend a value in the range of 650-750. If `None`, latent + degradation will not be applied (for example, in the Stage 1 model). Note that we could express this + as a strength value (e.g. 0.675) like in []`CogVideoXVideoToVideoPipeline`], but unlike pipelines which + have a strength argument we always use the full timestep schedule for denoising. + noise_shift_scale (`float` or `Tuple[float, float]`, *optional*, defaults to 1.0): + Shift and scale values to modify the noise level for latent degradation. The default value of `1.0` + corresponds to using the default noise level of noise_timestep according to the pipeline's scheduler. + guidance_scale (`float`, *optional*, defaults to 7.0): + 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. + num_videos_per_prompt (`int`, *optional*, defaults to 1): + The number of videos to generate per prompt. + generator (`torch.Generator` or `List[torch.Generator]`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + latents (`torch.FloatTensor`, *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 will ge generated by sampling using the supplied random `generator`. + prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.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. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] 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). + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + 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. + max_sequence_length (`int`, defaults to `226`): + Maximum sequence length in encoded prompt. Must be consistent with + `self.transformer.config.max_text_seq_length` otherwise may lead to poor results. + + Examples: + + Returns: + [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] or `tuple`: + [`~pipelines.cogvideo.pipeline_cogvideox.CogVideoXPipelineOutput`] if `return_dict` is True, otherwise a + `tuple`. When returning a tuple, the first element is a list with the generated images. + """ + + if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): + callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs + + height = height or self.transformer.config.sample_height * self.vae_scale_factor_spatial + width = width or self.transformer.config.sample_width * self.vae_scale_factor_spatial + num_frames = num_frames or self.transformer.config.sample_frames + + num_videos_per_prompt = 1 + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + video, + prompt, + negative_prompt, + height, + width, + latents, + prompt_embeds, + negative_prompt_embeds, + callback_on_step_end_tensor_inputs, + ) + self._guidance_scale = guidance_scale + self._attention_kwargs = attention_kwargs + self._current_timestep = None + self._interrupt = False + + # 2. Default 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] + + device = self._execution_device + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. Encode input prompt + prompt_embeds, negative_prompt_embeds = self.encode_prompt( + prompt, + negative_prompt, + do_classifier_free_guidance, + num_videos_per_prompt=num_videos_per_prompt, + prompt_embeds=prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + max_sequence_length=max_sequence_length, + device=device, + ) + if do_classifier_free_guidance: + prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + + # Also prepare inputs for other embeddings (time size, noise timestep) used by FlashVideo Stage 2 here. + # For efficiency purposes, since the time size and noise timestep stay the same throughout the denoising loop, + # prepare them ahead of time here (if using). These are used only in Stage 2 models. + time_size_tensor = None + noise_timesteps = None + if self.transformer.config.use_time_size_embedding: + time_size_tensor = torch.full((batch_size * num_videos_per_prompt,), latent_frames, device=latents.device) + if self.transformer.config.use_noise_timestep_embedding: + noise_timesteps = torch.full((batch_size * num_videos_per_prompt,), noise_timestep, device=latents.device) + + # 4. Prepare timesteps + timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) + self._num_timesteps = len(timesteps) + + # 5. Prepare latents + latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1 + + # For CogVideoX 1.5, the latent frames should be padded to make it divisible by patch_size_t + patch_size_t = self.transformer.config.patch_size_t + additional_frames = 0 + if patch_size_t is not None and latent_frames % patch_size_t != 0: + additional_frames = patch_size_t - latent_frames % patch_size_t + num_frames += additional_frames * self.vae_scale_factor_temporal + + if latents is None: + video = self.video_processor.preprocess_video(video, height=height, width=width) + video = video.to(device=device, dtype=prompt_embeds.dtype) + + latent_channels = self.transformer.config.in_channels + latents = self.prepare_latents( + video=video, + batch_size=batch_size * num_videos_per_prompt, + num_channels_latents=latent_channels, + height=height, + width=width, + dtype=prompt_embeds.dtype, + device=device, + generator=generator, + latents=latents, + timestep=noise_timesteps, + ) + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + # 7. Create rotary embeds if required + image_rotary_emb = ( + self._prepare_rotary_positional_embeddings(height, width, latents.size(1), device) + if self.transformer.config.use_rotary_positional_embeddings + else None + ) + + # 8. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) + + with self.progress_bar(total=num_inference_steps) as progress_bar: + # for DPM-solver++ + old_pred_original_sample = None + for i, t in enumerate(timesteps): + if self.interrupt: + continue + + self._current_timestep = t + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timestep = t.expand(latent_model_input.shape[0]) + + # predict noise model_output + noise_pred = self.transformer( + hidden_states=latent_model_input, + encoder_hidden_states=prompt_embeds, + timestep=timestep, + noise_timestep=noise_timesteps, + time_size_tensor=time_size_tensor, + image_rotary_emb=image_rotary_emb, + attention_kwargs=attention_kwargs, + return_dict=False, + )[0] + noise_pred = noise_pred.float() + + # perform guidance + if use_dynamic_cfg: + self._guidance_scale = 1 + guidance_scale * ( + (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 + ) + if 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) + + # compute the previous noisy sample x_t -> x_t-1 + if not isinstance(self.scheduler, CogVideoXDPMScheduler): + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + else: + latents, old_pred_original_sample = self.scheduler.step( + noise_pred, + old_pred_original_sample, + t, + timesteps[i - 1] if i > 0 else None, + latents, + **extra_step_kwargs, + return_dict=False, + ) + latents = latents.to(prompt_embeds.dtype) + + # call the callback, if provided + 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) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + + 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": + # Discard any padding frames that were added for CogVideoX 1.5 + latents = latents[:, additional_frames:] + video = self.decode_latents(latents) + video = self.video_processor.postprocess_video(video=video, output_type=output_type) + else: + video = latents + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (video,) + + return FlashVideoPipelineOutput(frames=video) diff --git a/src/diffusers/pipelines/flashvideo/pipeline_output.py b/src/diffusers/pipelines/flashvideo/pipeline_output.py new file mode 100644 index 000000000000..85dc55e76c41 --- /dev/null +++ b/src/diffusers/pipelines/flashvideo/pipeline_output.py @@ -0,0 +1,21 @@ +from dataclasses import dataclass + +import torch + +from diffusers.utils import BaseOutput + + +# Copied from diffusers.pipelines.cogvideo.pipeline_output.CogVideoXPipelineOutput with CogVideoX->FlashVideo +@dataclass +class FlashVideoPipelineOutput(BaseOutput): + r""" + Output class for FlashVideo pipelines. + + Args: + frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): + List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing + denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape + `(batch_size, num_frames, channels, height, width)`. + """ + + frames: torch.Tensor diff --git a/tests/pipelines/flashvideo/__init__.py b/tests/pipelines/flashvideo/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/pipelines/flashvideo/test_flashvideo.py b/tests/pipelines/flashvideo/test_flashvideo.py new file mode 100644 index 000000000000..4f140690d189 --- /dev/null +++ b/tests/pipelines/flashvideo/test_flashvideo.py @@ -0,0 +1,365 @@ +# 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 gc +import inspect +import unittest + +import numpy as np +import torch +from transformers import AutoTokenizer, T5EncoderModel + +from diffusers import AutoencoderKLCogVideoX, CogVideoXPipeline, CogVideoXTransformer3DModel, DDIMScheduler +from diffusers.utils.testing_utils import ( + enable_full_determinism, + numpy_cosine_similarity_distance, + require_torch_accelerator, + slow, + torch_device, +) + +from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS +from ..test_pipelines_common import ( + PipelineTesterMixin, + PyramidAttentionBroadcastTesterMixin, + check_qkv_fusion_matches_attn_procs_length, + check_qkv_fusion_processors_exist, + to_np, +) + + +# NOTE: currently copied from test_cogvideox.py + + +enable_full_determinism() + + +class CogVideoXPipelineFastTests(PipelineTesterMixin, PyramidAttentionBroadcastTesterMixin, unittest.TestCase): + pipeline_class = CogVideoXPipeline + params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"} + batch_params = TEXT_TO_IMAGE_BATCH_PARAMS + image_params = TEXT_TO_IMAGE_IMAGE_PARAMS + image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS + required_optional_params = frozenset( + [ + "num_inference_steps", + "generator", + "latents", + "return_dict", + "callback_on_step_end", + "callback_on_step_end_tensor_inputs", + ] + ) + test_xformers_attention = False + test_layerwise_casting = True + test_group_offloading = True + + def get_dummy_components(self, num_layers: int = 1): + torch.manual_seed(0) + transformer = CogVideoXTransformer3DModel( + # Product of num_attention_heads * attention_head_dim must be divisible by 16 for 3D positional embeddings + # But, since we are using tiny-random-t5 here, we need the internal dim of CogVideoXTransformer3DModel + # to be 32. The internal dim is product of num_attention_heads and attention_head_dim + num_attention_heads=4, + attention_head_dim=8, + in_channels=4, + out_channels=4, + time_embed_dim=2, + text_embed_dim=32, # Must match with tiny-random-t5 + num_layers=num_layers, + sample_width=2, # latent width: 2 -> final width: 16 + sample_height=2, # latent height: 2 -> final height: 16 + sample_frames=9, # latent frames: (9 - 1) / 4 + 1 = 3 -> final frames: 9 + patch_size=2, + temporal_compression_ratio=4, + max_text_seq_length=16, + ) + + torch.manual_seed(0) + vae = AutoencoderKLCogVideoX( + in_channels=3, + out_channels=3, + down_block_types=( + "CogVideoXDownBlock3D", + "CogVideoXDownBlock3D", + "CogVideoXDownBlock3D", + "CogVideoXDownBlock3D", + ), + up_block_types=( + "CogVideoXUpBlock3D", + "CogVideoXUpBlock3D", + "CogVideoXUpBlock3D", + "CogVideoXUpBlock3D", + ), + block_out_channels=(8, 8, 8, 8), + latent_channels=4, + layers_per_block=1, + norm_num_groups=2, + temporal_compression_ratio=4, + ) + + torch.manual_seed(0) + scheduler = DDIMScheduler() + text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5") + tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5") + + components = { + "transformer": transformer, + "vae": vae, + "scheduler": scheduler, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + } + 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) + inputs = { + "prompt": "dance monkey", + "negative_prompt": "", + "generator": generator, + "num_inference_steps": 2, + "guidance_scale": 6.0, + # Cannot reduce because convolution kernel becomes bigger than sample + "height": 16, + "width": 16, + "num_frames": 8, + "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] + + self.assertEqual(generated_video.shape, (8, 3, 16, 16)) + expected_video = torch.randn(8, 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_inference_batch_single_identical(self): + self._test_inference_batch_single_identical(batch_size=3, expected_max_diff=1e-3) + + 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): + 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_overlap_factor_height=1 / 12, + tile_overlap_factor_width=1 / 12, + ) + 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", + ) + + def test_fused_qkv_projections(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + pipe = self.pipeline_class(**components) + pipe = pipe.to(device) + pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inputs(device) + frames = pipe(**inputs).frames # [B, F, C, H, W] + original_image_slice = frames[0, -2:, -1, -3:, -3:] + + pipe.fuse_qkv_projections() + assert check_qkv_fusion_processors_exist( + pipe.transformer + ), "Something wrong with the fused attention processors. Expected all the attention processors to be fused." + assert check_qkv_fusion_matches_attn_procs_length( + pipe.transformer, pipe.transformer.original_attn_processors + ), "Something wrong with the attention processors concerning the fused QKV projections." + + inputs = self.get_dummy_inputs(device) + frames = pipe(**inputs).frames + image_slice_fused = frames[0, -2:, -1, -3:, -3:] + + pipe.transformer.unfuse_qkv_projections() + inputs = self.get_dummy_inputs(device) + frames = pipe(**inputs).frames + image_slice_disabled = frames[0, -2:, -1, -3:, -3:] + + assert np.allclose( + original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3 + ), "Fusion of QKV projections shouldn't affect the outputs." + assert np.allclose( + image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3 + ), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled." + assert np.allclose( + original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2 + ), "Original outputs should match when fused QKV projections are disabled." + + +@slow +@require_torch_accelerator +class CogVideoXPipelineIntegrationTests(unittest.TestCase): + prompt = "A painting of a squirrel eating a burger." + + def setUp(self): + super().setUp() + gc.collect() + torch.cuda.empty_cache() + + def tearDown(self): + super().tearDown() + gc.collect() + torch.cuda.empty_cache() + + def test_cogvideox(self): + generator = torch.Generator("cpu").manual_seed(0) + + pipe = CogVideoXPipeline.from_pretrained("THUDM/CogVideoX-2b", torch_dtype=torch.float16) + pipe.enable_model_cpu_offload(device=torch_device) + prompt = self.prompt + + videos = pipe( + prompt=prompt, + height=480, + width=720, + num_frames=16, + generator=generator, + num_inference_steps=2, + output_type="pt", + ).frames + + video = videos[0] + expected_video = torch.randn(1, 16, 480, 720, 3).numpy() + + max_diff = numpy_cosine_similarity_distance(video, expected_video) + assert max_diff < 1e-3, f"Max diff is too high. got {video}"