From dd76b2b9eaa35a27d324162ae3d989ba97338065 Mon Sep 17 00:00:00 2001 From: LittleNyima Date: Tue, 18 Feb 2025 09:50:55 +0000 Subject: [PATCH 1/5] Initialize DDIM Inversion script --- inference/ddim_inversion.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 inference/ddim_inversion.py diff --git a/inference/ddim_inversion.py b/inference/ddim_inversion.py new file mode 100644 index 0000000..e69de29 From 58d66c8a083030c33ed962371f35ffb17a5451d6 Mon Sep 17 00:00:00 2001 From: LittleNyima Date: Thu, 20 Feb 2025 01:39:12 +0800 Subject: [PATCH 2/5] Implement an unverified version that should be further tested --- inference/ddim_inversion.py | 435 ++++++++++++++++++++++++++++++++++++ 1 file changed, 435 insertions(+) diff --git a/inference/ddim_inversion.py b/inference/ddim_inversion.py index e69de29..fc19b29 100644 --- a/inference/ddim_inversion.py +++ b/inference/ddim_inversion.py @@ -0,0 +1,435 @@ +""" +This script performs DDIM inversion for video frames using a pre-trained model and generates +a video reconstruction based on a provided prompt. It utilizes the CogVideoX pipeline to +process video frames, apply the DDIM inverse scheduler, and produce an output video. + +Usage: + python script.py --model-path /path/to/model --prompt "a prompt" --video-path /path/to/video.mp4 --output-path /path/to/output + +Author: + LittleNyima +""" + +import argparse +import math +import os +from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union, cast + +import decord +import torch +import torch.nn.functional as F +import torchvision.transforms as T +from diffusers.models.attention_processor import Attention, CogVideoXAttnProcessor2_0 +from diffusers.models.autoencoders import AutoencoderKLCogVideoX +from diffusers.models.embeddings import apply_rotary_emb +from diffusers.models.transformers.cogvideox_transformer_3d import CogVideoXBlock, CogVideoXTransformer3DModel +from diffusers.pipelines.cogvideo.pipeline_cogvideox import CogVideoXPipeline, retrieve_timesteps +from diffusers.schedulers import CogVideoXDDIMScheduler, DDIMInverseScheduler +from diffusers.utils import export_to_video + + +class DDIMInversionArguments(TypedDict): + model_path: str + prompt: str + video_path: str + output_path: str + guidance_scale: float + num_inference_steps: int + skip_frames_start: int + skip_frames_end: int + frame_sample_step: Optional[int] + max_num_frames: int + width: int + height: int + fps: int + dtype: torch.dtype + seed: int + device: torch.device + + +def get_args() -> DDIMInversionArguments: + parser = argparse.ArgumentParser() + + parser.add_argument("--model-path", type=str, required=True, help="Path of the pretrained model") + parser.add_argument("--prompt", type=str, required=True, help="Prompt for the direct sample procedure") + parser.add_argument("--video-path", type=str, required=True, help="Path of the video for inversion") + parser.add_argument("--output-path", type=str, default="output", help="Path of the output videos") + parser.add_argument("--guidance-scale", type=float, default=6.0, help="Classifier-free guidance scale") + parser.add_argument("--num-inference-steps", type=int, default=50, help="Number of inference steps") + parser.add_argument("--skip-frames-start", type=int, default=0, help="Number of skipped frames from the start") + parser.add_argument("--skip-frames-end", type=int, default=0, help="Number of skipped frames from the end") + parser.add_argument("--frame-sample-step", type=int, default=None, help="Temporal stride of the sampled frames") + parser.add_argument("--max-num-frames", type=int, default=81, help="Max number of sampled frames") + parser.add_argument("--width", type=int, default=720, help="Resized width of the video frames") + parser.add_argument("--height", type=int, default=480, help="Resized height of the video frames") + parser.add_argument("--fps", type=int, default=8, help="Frame rate of the output videos") + parser.add_argument("--dtype", type=str, default="bf16", choices=["bf16", "fp16"], help="Dtype of the model") + parser.add_argument("--seed", type=int, default=42, help="Seed for the random number generator") + parser.add_argument("--device", type=str, default="cuda", choices=["cuda", "cpu"], help="Device for inference") + + args = parser.parse_args() + args.dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float16 + args.device = torch.device(args.device) + + return DDIMInversionArguments(**vars(args)) + + +class CogVideoXAttnProcessor2_0ForDDIMInversion(CogVideoXAttnProcessor2_0): + def __init__(self): + super().__init__() + + def calculate_attention( + self, + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + attn: Attention, + batch_size: int, + image_seq_length: int, + text_seq_length: int, + attention_mask: Optional[torch.Tensor], + image_rotary_emb: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor]: + inner_dim = key.shape[-1] + head_dim = inner_dim // attn.heads + + query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2) + + if attn.norm_q is not None: + query = attn.norm_q(query) + if attn.norm_k is not None: + key = attn.norm_k(key) + + # Apply RoPE if needed + if image_rotary_emb is not None: + query[:, :, text_seq_length:] = apply_rotary_emb(query[:, :, text_seq_length:], image_rotary_emb) + if not attn.is_cross_attention: + if key.size(2) == query.size(2): # Attention for reference hidden states + key[:, :, text_seq_length:] = apply_rotary_emb(key[:, :, text_seq_length:], image_rotary_emb) + else: # RoPE should be applied to each group of image tokens + key[:, :, text_seq_length : text_seq_length + image_seq_length] = apply_rotary_emb( + key[:, :, text_seq_length : text_seq_length + image_seq_length], image_rotary_emb + ) + key[:, :, text_seq_length * 2 + image_seq_length :] = apply_rotary_emb( + key[:, :, text_seq_length * 2 + image_seq_length :], image_rotary_emb + ) + + hidden_states = F.scaled_dot_product_attention( + query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False + ) + + hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + encoder_hidden_states, hidden_states = hidden_states.split( + [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1 + ) + return hidden_states, encoder_hidden_states + + def __call__( + self, + attn: Attention, + hidden_states: torch.Tensor, + encoder_hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + image_rotary_emb: Optional[torch.Tensor] = None, + ) -> Tuple[torch.Tensor, torch.Tensor]: + image_seq_length = hidden_states.size(1) + text_seq_length = encoder_hidden_states.size(1) + + hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1) + + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + + if attention_mask is not None: + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1]) + + query = attn.to_q(hidden_states) + key = attn.to_k(hidden_states) + value = attn.to_v(hidden_states) + + query, query_reference = query.chunk(2) + key, key_reference = key.chunk(2) + value, value_reference = value.chunk(2) + + hidden_states, encoder_hidden_states = self.calculate_attention( + query=query, + key=torch.cat((key, key_reference), dim=1), + value=torch.cat((value, value_reference), dim=1), + attn=attn, + batch_size=batch_size, + image_seq_length=image_seq_length, + text_seq_length=text_seq_length, + attention_mask=attention_mask, + image_rotary_emb=image_rotary_emb, + ) + hidden_states_reference, encoder_hidden_states_reference = self.calculate_attention( + query=query_reference, + key=key_reference, + value=value_reference, + attn=attn, + batch_size=batch_size, + image_seq_length=image_seq_length, + text_seq_length=text_seq_length, + attention_mask=attention_mask, + image_rotary_emb=image_rotary_emb, + ) + + return ( + torch.cat((hidden_states, hidden_states_reference)), + torch.cat((encoder_hidden_states, encoder_hidden_states_reference)), + ) + + +class OverrideAttnProcessors: + def __init__(self, transformer: CogVideoXTransformer3DModel): + self.transformer = transformer + self.original_processors = {} + + def __enter__(self): + for block in self.transformer.transformer_blocks: + block = cast(CogVideoXBlock, block) + self.original_processors[id(block)] = block.attn1.get_processor() + block.attn1.set_processor(CogVideoXAttnProcessor2_0ForDDIMInversion()) + + def __exit__(self, _0, _1, _2): + for block in self.transformer.transformer_blocks: + block = cast(CogVideoXBlock, block) + block.attn1.set_processor(self.original_processors[id(block)]) + + +def get_video_frames( + video_path: str, + width: int, + height: int, + skip_frames_start: int, + skip_frames_end: int, + max_num_frames: int, + frame_sample_step: Optional[int], +) -> torch.FloatTensor: + with decord.bridge.use_torch(): + video_reader = decord.VideoReader(uri=video_path, width=width, height=height) + video_num_frames = len(video_reader) + start_frame = min(skip_frames_start, video_num_frames) + end_frame = max(0, video_num_frames - skip_frames_end) + + if end_frame <= start_frame: + indices = [start_frame] + elif end_frame - start_frame <= max_num_frames: + indices = list(range(start_frame, end_frame)) + else: + step = frame_sample_step or (end_frame - start_frame) // max_num_frames + indices = list(range(start_frame, end_frame, step)) + + frames = video_reader.get_batch(indices=indices) + frames = frames[:max_num_frames].float() # ensure that we don't go over the limit + + # Choose first (4k + 1) frames as this is how many is required by the VAE + selected_num_frames = frames.size(0) + remainder = (3 + selected_num_frames) % 4 + if remainder != 0: + frames = frames[:-remainder] + assert frames.size(0) % 4 == 1 + + # Normalize the frames + transform = T.Lambda(lambda x: x / 255.0 * 2.0 - 1.0) + frames = torch.stack(tuple(map(transform, frames)), dim=0) + + return frames.permute(0, 3, 1, 2).contiguous() # [F, C, H, W] + + +def encode_video_frames(vae: AutoencoderKLCogVideoX, video_frames: torch.FloatTensor) -> torch.FloatTensor: + video_frames = video_frames.to(device=vae.device, dtype=vae.dtype) + video_frames = video_frames.unsqueeze(0).permute(0, 2, 1, 3, 4) # [B, C, F, H, W] + latent_dist = vae.encode(x=video_frames).latent_dist.sample().transpose(1, 2) + return latent_dist * vae.config.scaling_factor + + +def export_latents_to_video(pipeline: CogVideoXPipeline, latents: torch.FloatTensor, video_path: str, fps: int): + video = pipeline.decode_latents(latents) + frames = pipeline.video_processor.postprocess_video(video=video, output_type="pil") + export_to_video(video_frames=frames[0], output_video_path=video_path, fps=fps) + + +# Modified from CogVideoXPipeline.__call__ +def sample( + pipeline: CogVideoXPipeline, + latents: torch.FloatTensor, + scheduler: Union[DDIMInverseScheduler, CogVideoXDDIMScheduler], + prompt: Optional[Union[str, List[str]]] = None, + negative_prompt: Optional[Union[str, List[str]]] = None, + num_inference_steps: int = 50, + guidance_scale: float = 6, + use_dynamic_cfg: bool = False, + eta: float = 0.0, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + attention_kwargs: Optional[Dict[str, Any]] = None, + reference_latents: torch.FloatTensor = None, +) -> torch.FloatTensor: + pipeline._guidance_scale = guidance_scale + pipeline._attention_kwargs = attention_kwargs + pipeline._interrupt = False + + device = pipeline._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 = pipeline.encode_prompt( + prompt, + negative_prompt, + do_classifier_free_guidance, + 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(scheduler, num_inference_steps, device) + pipeline._num_timesteps = len(timesteps) + + # 5. Prepare latents. + latents = latents.to(device=device) * scheduler.init_noise_sigma + + # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline + extra_step_kwargs = pipeline.prepare_extra_step_kwargs(generator, eta) + + # 7. Create rotary embeds if required + spatial_scaling_factor = pipeline.vae_scale_factor_spatial * pipeline.transformer.config.patch_size + image_rotary_emb = ( + pipeline._prepare_rotary_positional_embeddings( + height=latents.size(3) * spatial_scaling_factor, + width=latents.size(4) * spatial_scaling_factor, + num_frames=latents.size(1), + device=device, + ) + if pipeline.transformer.config.use_rotary_positional_embeddings + else None + ) + + # 8. Denoising loop + num_warmup_steps = max(len(timesteps) - num_inference_steps * scheduler.order, 0) + + trajectory = torch.zeros_like(latents).unsqueeze(0).repeat(len(timesteps), 1, 1, 1, 1, 1) + with pipeline.progress_bar(total=num_inference_steps) as progress_bar: + for i, t in enumerate(timesteps): + if pipeline.interrupt: + continue + + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + if reference_latents is not None: + reference = reference_latents[i] + reference = torch.cat([reference] * 2) if do_classifier_free_guidance else reference + latent_model_input = torch.cat([latent_model_input, reference]) + latent_model_input = 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 = pipeline.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: + pipeline._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 + pipeline.guidance_scale * (noise_pred_text - noise_pred_uncond) + + # compute the noisy sample x_t-1 -> x_t + latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0] + latents = latents.to(prompt_embeds.dtype) + trajectory[i] = latents + + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0): + progress_bar.update() + + # Offload all models + pipeline.maybe_free_model_hooks() + + return trajectory + + +@torch.no_grad() +def ddim_inversion( + model_path: str, + prompt: str, + video_path: str, + output_path: str, + guidance_scale: float, + num_inference_steps: int, + skip_frames_start: int, + skip_frames_end: int, + frame_sample_step: Optional[int], + max_num_frames: int, + width: int, + height: int, + fps: int, + dtype: torch.dtype, + seed: int, + device: torch.device, +): + pipeline: CogVideoXPipeline = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device=device) + video_frames = get_video_frames( + video_path=video_path, + width=width, + height=height, + skip_frames_start=skip_frames_start, + skip_frames_end=skip_frames_end, + max_num_frames=max_num_frames, + frame_sample_step=frame_sample_step, + ).to(device=device) + video_latents = encode_video_frames(vae=pipeline.vae, video_frames=video_frames) + inverse_scheduler = DDIMInverseScheduler(**pipeline.scheduler.config) + inverse_latents = sample( + pipeline=pipeline, + latents=video_latents, + scheduler=inverse_scheduler, + prompt="", + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + generator=torch.Generator(device=device).manual_seed(seed=seed), + ) + with OverrideAttnProcessors(transformer=pipeline.transformer): + recon_latents = sample( + pipeline=pipeline, + latents=torch.randn_like(video_latents), + scheduler=inverse_scheduler, + prompt=prompt, + num_inference_steps=num_inference_steps, + guidance_scale=guidance_scale, + generator=torch.Generator(device=device).manual_seed(seed=seed), + reference_latents=reversed(inverse_latents), + ) + filename = os.path.splitext(os.path.basename(video_path))[0] + inverse_video_path = os.path.join(output_path, f"{filename}_inversion.mp4") + recon_video_path = os.path.join(output_path, f"{filename}_reconstruction.mp4") + export_latents_to_video(pipeline, inverse_latents[-1], inverse_video_path, fps) + export_latents_to_video(pipeline, recon_latents[-1], recon_video_path, fps) + + +if __name__ == "__main__": + arguments = get_args() + ddim_inversion(**arguments) From 250a0bce4561905983affaba93b7b0ef669ff48c Mon Sep 17 00:00:00 2001 From: LittleNyima Date: Thu, 20 Feb 2025 05:03:15 +0000 Subject: [PATCH 3/5] stable version --- inference/ddim_inversion.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/inference/ddim_inversion.py b/inference/ddim_inversion.py index fc19b29..309c374 100644 --- a/inference/ddim_inversion.py +++ b/inference/ddim_inversion.py @@ -160,6 +160,7 @@ class CogVideoXAttnProcessor2_0ForDDIMInversion(CogVideoXAttnProcessor2_0): query, query_reference = query.chunk(2) key, key_reference = key.chunk(2) value, value_reference = value.chunk(2) + batch_size = batch_size // 2 hidden_states, encoder_hidden_states = self.calculate_attention( query=query, @@ -295,6 +296,8 @@ def sample( ) if do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + if reference_latents is not None: + prompt_embeds = torch.cat([prompt_embeds] * 2, dim=0) # 4. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(scheduler, num_inference_steps, device) @@ -305,13 +308,14 @@ def sample( # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = pipeline.prepare_extra_step_kwargs(generator, eta) + if isinstance(scheduler, DDIMInverseScheduler): # Inverse scheduler does not accept extra kwargs + extra_step_kwargs = {} # 7. Create rotary embeds if required - spatial_scaling_factor = pipeline.vae_scale_factor_spatial * pipeline.transformer.config.patch_size image_rotary_emb = ( pipeline._prepare_rotary_positional_embeddings( - height=latents.size(3) * spatial_scaling_factor, - width=latents.size(4) * spatial_scaling_factor, + height=latents.size(3) * pipeline.vae_scale_factor_spatial, + width=latents.size(4) * pipeline.vae_scale_factor_spatial, num_frames=latents.size(1), device=device, ) @@ -332,7 +336,7 @@ def sample( if reference_latents is not None: reference = reference_latents[i] reference = torch.cat([reference] * 2) if do_classifier_free_guidance else reference - latent_model_input = torch.cat([latent_model_input, reference]) + latent_model_input = torch.cat([latent_model_input, reference], dim=0) latent_model_input = scheduler.scale_model_input(latent_model_input, t) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML @@ -349,6 +353,9 @@ def sample( )[0] noise_pred = noise_pred.float() + if reference_latents is not None: # Recover the original batch size + noise_pred, _ = noise_pred.chunk(2) + # perform guidance if use_dynamic_cfg: pipeline._guidance_scale = 1 + guidance_scale * ( @@ -410,20 +417,20 @@ def ddim_inversion( prompt="", num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, - generator=torch.Generator(device=device).manual_seed(seed=seed), + generator=torch.Generator(device=device).manual_seed(seed), ) with OverrideAttnProcessors(transformer=pipeline.transformer): recon_latents = sample( pipeline=pipeline, latents=torch.randn_like(video_latents), - scheduler=inverse_scheduler, + scheduler=pipeline.scheduler, prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, - generator=torch.Generator(device=device).manual_seed(seed=seed), + generator=torch.Generator(device=device).manual_seed(seed), reference_latents=reversed(inverse_latents), ) - filename = os.path.splitext(os.path.basename(video_path))[0] + filename, _ = os.path.splitext(os.path.basename(video_path)) inverse_video_path = os.path.join(output_path, f"{filename}_inversion.mp4") recon_video_path = os.path.join(output_path, f"{filename}_reconstruction.mp4") export_latents_to_video(pipeline, inverse_latents[-1], inverse_video_path, fps) From e0bf3954589ada7e9786b3a7aec9b09ba328b4d1 Mon Sep 17 00:00:00 2001 From: LittleNyima Date: Sun, 23 Feb 2025 19:41:21 +0800 Subject: [PATCH 4/5] make the style of argparser consistent with repo --- inference/ddim_inversion.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/inference/ddim_inversion.py b/inference/ddim_inversion.py index 309c374..7be35b3 100644 --- a/inference/ddim_inversion.py +++ b/inference/ddim_inversion.py @@ -50,16 +50,16 @@ class DDIMInversionArguments(TypedDict): def get_args() -> DDIMInversionArguments: parser = argparse.ArgumentParser() - parser.add_argument("--model-path", type=str, required=True, help="Path of the pretrained model") + parser.add_argument("--model_path", type=str, required=True, help="Path of the pretrained model") parser.add_argument("--prompt", type=str, required=True, help="Prompt for the direct sample procedure") - parser.add_argument("--video-path", type=str, required=True, help="Path of the video for inversion") - parser.add_argument("--output-path", type=str, default="output", help="Path of the output videos") - parser.add_argument("--guidance-scale", type=float, default=6.0, help="Classifier-free guidance scale") - parser.add_argument("--num-inference-steps", type=int, default=50, help="Number of inference steps") - parser.add_argument("--skip-frames-start", type=int, default=0, help="Number of skipped frames from the start") - parser.add_argument("--skip-frames-end", type=int, default=0, help="Number of skipped frames from the end") - parser.add_argument("--frame-sample-step", type=int, default=None, help="Temporal stride of the sampled frames") - parser.add_argument("--max-num-frames", type=int, default=81, help="Max number of sampled frames") + parser.add_argument("--video_path", type=str, required=True, help="Path of the video for inversion") + parser.add_argument("--output_path", type=str, default="output", help="Path of the output videos") + parser.add_argument("--guidance_scale", type=float, default=6.0, help="Classifier-free guidance scale") + parser.add_argument("--num_inference_steps", type=int, default=50, help="Number of inference steps") + parser.add_argument("--skip_frames_start", type=int, default=0, help="Number of skipped frames from the start") + parser.add_argument("--skip_frames_end", type=int, default=0, help="Number of skipped frames from the end") + parser.add_argument("--frame_sample_step", type=int, default=None, help="Temporal stride of the sampled frames") + parser.add_argument("--max_num_frames", type=int, default=81, help="Max number of sampled frames") parser.add_argument("--width", type=int, default=720, help="Resized width of the video frames") parser.add_argument("--height", type=int, default=480, help="Resized height of the video frames") parser.add_argument("--fps", type=int, default=8, help="Frame rate of the output videos") From 2c33c0982bfc1e615401913cd8e4d300d03908f3 Mon Sep 17 00:00:00 2001 From: LittleNyima Date: Wed, 26 Feb 2025 15:54:58 +0800 Subject: [PATCH 5/5] fix import order and deprecate for CVX 2B models --- inference/ddim_inversion.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/inference/ddim_inversion.py b/inference/ddim_inversion.py index 7be35b3..e932bf4 100644 --- a/inference/ddim_inversion.py +++ b/inference/ddim_inversion.py @@ -3,8 +3,17 @@ This script performs DDIM inversion for video frames using a pre-trained model a a video reconstruction based on a provided prompt. It utilizes the CogVideoX pipeline to process video frames, apply the DDIM inverse scheduler, and produce an output video. +**Please notice that this script is based on the CogVideoX 5B model, and would not generate +a good result for 2B variants.** + Usage: - python script.py --model-path /path/to/model --prompt "a prompt" --video-path /path/to/video.mp4 --output-path /path/to/output + python ddim_inversion.py + --model-path /path/to/model + --prompt "a prompt" + --video-path /path/to/video.mp4 + --output-path /path/to/output + +For more details about the cli arguments, please run `python ddim_inversion.py --help`. Author: LittleNyima @@ -15,7 +24,6 @@ import math import os from typing import Any, Dict, List, Optional, Tuple, TypedDict, Union, cast -import decord import torch import torch.nn.functional as F import torchvision.transforms as T @@ -27,6 +35,10 @@ from diffusers.pipelines.cogvideo.pipeline_cogvideox import CogVideoXPipeline, r from diffusers.schedulers import CogVideoXDDIMScheduler, DDIMInverseScheduler from diffusers.utils import export_to_video +# Must import after torch because this can sometimes lead to a nasty segmentation fault, or stack smashing error. +# Very few bug reports but it happens. Look in decord Github issues for more relevant information. +import decord # isort: skip + class DDIMInversionArguments(TypedDict): model_path: str @@ -399,6 +411,8 @@ def ddim_inversion( device: torch.device, ): pipeline: CogVideoXPipeline = CogVideoXPipeline.from_pretrained(model_path, torch_dtype=dtype).to(device=device) + if not pipeline.transformer.config.use_rotary_positional_embeddings: + raise NotImplementedError("This script supports CogVideoX 5B model only.") video_frames = get_video_frames( video_path=video_path, width=width,