o ?߱iz @s@dZddlZddlZddlmZddlmZmZddlZddl Z ddl m Z ddl m Z ddlmZddlmZmZdd lmZdd lmZdd lmZdd lmZgd Zd gZdZeddGdddZdejde e!fddZ"d#de#de e!de!de$fddZ%  d$de#de e!de!de!de$f dd Z&Gd!d"d"Z'dS)%u8 # Script for generating I2W videos in s3 PYTHONPATH=. python cosmos_predict2/_src/predict2/inference/video2world.py --experiment=Stage-c_pt_4-reason_embeddings-Index-26-Size-2B-Res-720-Fps-16-Note-HQ_V6_from_22_qwen_concat_resume4 --ckpt_path s3://bucket/cosmos_diffusion_v2/official_runs_vid2vid/Stage-c_pt_4-reason_embeddings-Index-26-Size-2B-Res-720-Fps-16-Note-HQ_V6_from_22_qwen_concat_resume4/checkpoints/iter_000045000 --save_root results/cli_debug_from_s3 --input_root /project/cosmos/ybalaji/data/internal_val_set_clean # Script for text2world generation export EXPERIMENT=Stage-c_pt_4-reason_embeddings-Index-26-Size-2B-Res-720-Fps-16-Note-T2V_high_sigma_loss_reweighted CUDA_VISIBLE_DEVICES=0 PYTHONPATH=. python cosmos_predict2/_src/predict2/inference/video2world.py --experiment=${EXPERIMENT} --ckpt_path s3://bucket/cosmos_diffusion_v2/official_runs_vid2vid/${EXPERIMENT}/checkpoints/iter_000025000 --save_root results/base_model/${EXPERIMENT}_025k_seed0_t2w --num_latent_conditional_frames=0 --seed=0 --input_root /project/cosmos/fangyinw/data/pbench/v0 # I2W with context parallel with 8 GPUs: PYTHONPATH=. torchrun --nproc_per_node=8 cosmos_predict2/_src/predict2/inference/video2world.py --experiment=Stage-c_pt_4-reason_embeddings-Index-26-Size-2B-Res-720-Fps-16-Note-HQ_V6_from_22_qwen_concat_resume4 --ckpt_path s3://bucket/cosmos_diffusion_v2/official_runs_vid2vid/Stage-c_pt_4-reason_embeddings-Index-26-Size-2B-Res-720-Fps-16-Note-HQ_V6_from_22_qwen_concat_resume4/checkpoints/iter_000045000 --save_root results/cli_debug_from_s3 --input_root /project/cosmos/ybalaji/data/internal_val_set_clean --context_parallel_size 8 # V2W with context parallel with 8 GPUs: PYTHONPATH=. torchrun --nproc_per_node=8 cosmos_predict2/_src/predict2/inference/video2world.py --experiment=Stage-c_pt_4-reason_embeddings-Index-26-Size-2B-Res-720-Fps-16-Note-HQ_V6_from_22_qwen_concat_resume4 --ckpt_path s3://bucket/cosmos_diffusion_v2/official_runs_vid2vid/Stage-c_pt_4-reason_embeddings-Index-26-Size-2B-Res-720-Fps-16-Note-HQ_V6_from_22_qwen_concat_resume4/checkpoints/iter_000045000 --save_root results/cli_debug_from_s3 --input_root pbench_upsampled_prompts --num_latent_conditional_frames=2 --context_parallel_size=8 Folder structure: We assume the input root contains images and prompts in the following format: input_root/ ├── image_1.jpg ├── image_1.txt ├── image_2.jpg └── image_2.txt └── ... or videos and prompts in the following format: input_root/ ├── video_1.mp4 ├── video_1.txt ├── video_2.mp4 └── video_2.txt └── ... N) dataclass) TYPE_CHECKINGAnyparallel_state)Image)INTERNAL) distributedlog)easy_io)load_model_from_checkpoint)get_text_embedding)z.pngz.jpgz.jpegz.webpz.mp4aThe video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality.T)slotsc@steZdZUdZejeBeBed<ejeBeBed<dZ ejee e fBee BdBed<dZ e e efdBed<dS)CameraConditionInputsz1Typed container for camera conditioning payloads. extrinsics intrinsicsN image_sizemetadata)__name__ __module__ __qualname____doc__torchTensorlisttuple__annotations__rintrdictstrrr r ]/data/cameron/vidgen/cosmos-predict2.5/cosmos_predict2/_src/predict2/inference/video2world.pyrOs &rvideo resolutionc Csx|jd|jd}}|\}}t||||}tt||tt||f}tjj||}tjj ||} | S)a Resizes and crops the input video tensor while preserving aspect ratio. The video is first resized so that the smaller dimension matches the target resolution, preserving the aspect ratio. Then, it's center-cropped to the target resolution. Args: video (torch.Tensor): Input video tensor of shape (T, C, H, W). resolution (list[int]): Target resolution [H, W]. Returns: torch.Tensor: Resized and cropped video tensor of shape (T, C, target_H, target_W). ) shapemaxrmathceil torchvision transforms functionalresize center_crop) r"r#Zorig_horig_wZtarget_hZtarget_w scaling_ratioZresizing_shapeZ video_resizedZ video_croppedr r r! resize_inputYs$r1img_pathnum_video_framesr-cCstj|d}|tvrtd|t|}tjj |}| d}t j |t ||ddddgdd}|dt j}|rGt||}| dddddd}|S) a Reads an image, converts it to a video tensor, and processes it for model input. The image is loaded, converted to a tensor, and replicated to match the `num_video_frames`. It's then optionally resized and permuted to the standard video format (B, C, T, H, W). Args: img_path (str): Path to the input image file. resolution (list[int]): Target resolution [H, W] for resizing. num_video_frames (int): The number of frames the output video tensor should have. resize (bool, optional): Whether to resize the image to the target resolution. Defaults to True. Returns: torch.Tensor: Processed video tensor of shape (1, C, T, H, W). Raises: ValueError: If the image extension is not one of the supported types. zInvalid image extension: rdimo@r$r%)ospathsplitext_IMAGE_EXTENSIONS ValueErrorropenr*r+r, to_tensor unsqueezercat zeros_likerepeattouint8r1permute)r2r#r3r-extimg vid_inputr r r!read_and_process_imagers  ( rJr$ video_pathnum_latent_conditional_framesc CsTtj|d}|tvrtd|zt|\}}t d|j d|Wnt y@}z td|d|d}~wwt |d} | d d dd } | j d} d |dd} t d | d||dvrutd|| j \} } }}t | |||}| | krtd| d| d|| | }| dd|dddddf}||ddd| ddddf<t d| d|d| dd| |kr|ddddddddf}|| }|d|dd}||dd| dddddf<t d|d|dd d d }|dt j}|rt||}|d d d dd d }|S)a Reads a video, processes it for model input. The video is loaded using easy_io, and uses the last 4x(num_latent_conditional_frames - 1) + 1 from the video. If the video is shorter than num_video_frames, it pads with the last frame repeated. The first num_latent_conditional_frames are marked as conditioning frames. Args: video_path (str): Path to the input video file. resolution (list[int]): Target resolution [H, W] for resizing. num_video_frames (int): Number of frames needed by the model (should equal model.tokenizer.get_pixel_num_frames(model.config.state_t)). num_latent_conditional_frames (int): Number of latent conditional frames from the input video (1 or 2). resize (bool, optional): Whether to resize the video to the target resolution. Defaults to True. Returns: torch.Tensor: Processed video tensor of shape (1, C, T, H, W) where T equals num_video_frames. Raises: ValueError: If the video extension is not supported or other validation errors. Note: Uses the last 4x(num_latent_conditional_frames - 1) + 1 frames from the video. If video is shorter, pads with last frame repeated. r4zInvalid video extension: zLoaded video with shape z , metadata: zFailed to load video z: Nr7r%rr$r8z Will extract z$ frames from input video and pad to )r4r$z6num_latent_conditional_frames must be 1 or 2, but got Video has only  frames but needs at least z* frames for num_latent_conditional_frames=zExtracted last z frames from video (frames z to )zPadded z! frames with last extracted frame)r9r:r;lower_VIDEO_EXTENSIONSr=r loadr infor& Exceptionr from_numpyfloatrFzerosrCrDrEr1r@)rKr#r3rLr-rG video_framesvideo_metadatae video_tensoravailable_framesframes_to_extractC_HW full_video start_idxextracted_frames last_framepadding_frameslast_frame_repeatedr r r!read_and_process_videosN    "    ric@sxeZdZdZ      d4dededed ed ed eedBd ed edefddZddZ de dddfde j dedededede dBde j dBfddZddddddde ddd f ded!ee j BdBd"ed#ed$ed%ed&ed'ed(edede dBde j dBd)efd*d+Zdddde ddd fded!ee j BdBd,ed-ed.ed"ed$ed'ed(edede j dBde j dBd)ed/e j fd0d1Zd2d3ZdS)5Video2WorldInferencez Handles the Video2World inference process, including model loading, data preparation, and video generation from an image/video and text prompt. Now supports context parallelism. r4;cosmos_predict2/_src/predict2/configs/video2world/config.pyNFexperiment_name ckpt_paths3_credential_pathcontext_parallel_size config_fileexperiment_optsoffload_diffusion_modeloffload_text_encoderoffload_tokenizerc Cs||_||_||_||_d|_||_||_| |_|rdnd} |jdkr'||dur-g}t s4| d|jr z;Video2WorldInference.generate_vid2world..r$"Resolution must be in 'H,W' formatrr%zProcessing image input: T)r2r#r3r-zProcessing video input: )rKr#r3rLr-Unsupported file extension: z. Supported extensions: Unsupported input_path type: )r"rrrrrrrur|rz+GPU memory usage after getting data_batch: i@z.2fz GBz4[Memory Optimization] Offloading text encoder to CPUrz6[Memory Optimization] Loading tokenizer encoder to GPUr}z6[Memory Optimization] Loading diffusion network to GPUr{)rrz7[Memory Optimization] Starting latent sample generationZuse_loraF)n_samplerrZis_negative_promptrz9[Memory Optimization] Offloading diffusion network to CPUz6[Memory Optimization] Loading tokenizer decoder to GPUr~r5z9[Memory Optimization] Offloading tokenizer decoder to CPUz.[Memory Optimization] Load text encoder to GPU)2rget_video_height_widthsplitrlenrget_pixel_num_framesrstate_trrXrDrErrr9r:r;rQr<r rTrJrRrir=rtyperrumemory_allocatedr is_availablersrrrrtr}rrrr{getattrZ generate_samples_from_batch_loraZgenerate_samples_from_batchr~rdecoderrA)rrrrr3rLrrr#rrrrrhwvideo_resolutionmodel_required_framesrIrGr mem_bytes extra_kwargsZgenerate_samplessample video_listZ sample_chunkZ video_chunkr"r r r!generate_vid2worlds")    "                           z'Video2WorldInference.generate_vid2worldnum_output_frames chunk_size chunk_overlapreturnc4Cs|dkr|j\}}||f}n|d}tdd|D}t|dks(Jd|jj|jjj}|dus:|dkrMt d d ||d|d  t j }nrt |tr{tj|d }|tvrtd |t|}tjj|}|d}|d  t j }|rt||}t j|t | |d d d d gdd }|d!ddd d d}n|t"vrttd|t#$|\}}t %|&d }|!d dd d}|j'd }d|d d }||krt(d|d|||}|dd|dddddf}|j'\}}}}t ||||}||ddd|ddddf<||krO|ddddddddf} ||}!| d |!d d }"|"|dd|dddddf<|!d ddd }|d  t j }|rgt||}|d!ddd d d}nKt(d|t |t j)r|}|j'd|kr||j'd}!t |j'd|j'd |!|j'd |j'd |j*}#t j||#gdd }n t(dt+|g}$||}%||}&|&dkrd }'n d |&|%d |%}'td|'d|d|d|d |,}(t-|'D]})|)|%}*t.|*||}+|+|*},|*|kr ntd|)d d|'d|*d|+|(dddd|*|+ddddf}-|,|kr^||,}!t |-j'd|-j'd |!|-j'd |-j'd |-j*}#t j|-|#gdd }-|)dkrf|}.n|}.|j/||-|||.|| |)| | | | d }/|/ddddd|,ddddf}/|)dkr|$0|/n|$0|/dddd|dddddf|)|'d kr|/dd 1d!d"d  t j }0|*|.}1|+}2|0dddd|.dddddf|(dddd|1|2ddddf<qt j|$dd }3td#|3j'|3S)$aP Generate video using autoregressive sliding window approach. Args: prompt: The text prompt describing the desired video content/style. input_path: Path to the input image or video file or a torch.Tensor. num_output_frames: Total number of frames to generate in the final output. chunk_size: Number of frames per chunk (model's native capacity). chunk_overlap: Number of overlapping frames between chunks. guidance: Classifier-free guidance scale. num_latent_conditional_frames: Number of latent conditional frames. resolution: Target video resolution in "H,W" format. seed: Random seed for reproducibility. negative_prompt: Custom negative prompt. camera: Target camera extrinsics and intrinsics for the K output videos. action: Target robot action for the K output videos. num_steps: Number of generation steps. Returns: torch.Tensor: The generated video tensor (B, C, T, H, W) in the range [-1, 1]. rrcSrr rrr r r!rrzKVideo2WorldInference.generate_autoregressive_from_batch..r$rNrr4r%z+Processing image input for autoregressive: r7r5r8z+Processing video input for autoregressive: rMrNrPrrz Generating z chunks with chunk_size=z, chunk_overlap=z for z total frameszProcessing chunk /z , frames -) rrrr3rLr#rrrrrg@g?gg?z!Generated final video with shape )2rrrrrrrrrrrXrDrErrr9r:r;rQr<r rTrr>r*r+r,r?r@r1rArBrCrFrRr rSrVrWr&r=rrrclonerangeminrrclamp)4rrrrrrrrLr#rrrrrrrrrZfull_input_videorGrHrYr`r\r]r^rdrer_rarbrcrfrgrhpaddingZgenerated_chunksZeffective_chunk_sizeZremaining_after_first num_chunksZcurrent_input_video chunk_idxZ start_frameZ end_frameZactual_chunk_sizeZ chunk_inputZchunk_num_conditionalZ chunk_videoZchunk_video_uint8Z update_startZ update_endZ final_videor r r!"generate_autoregressive_from_batchs&      (           &&  &  ,  &z7Video2WorldInference.generate_autoregressive_from_batchcCsB|jdkrddlm}ddlm}|r||dSdS)zClean up distributed resources.r4rNr)rotorch.distributedr megatron.coreris_initializeddestroy_model_paralleldestroy_process_group)rdistrr r r!cleanups    zVideo2WorldInference.cleanup)r4rkNFFF)rrrrrrrboolrr_DEFAULT_NEGATIVE_PROMPTrrrrrrrr r r r!rjs    z R       W        ^rj)T)r$T)(rr(r9 dataclassesrtypingrrrr*rrPILr%cosmos_predict2._src.imaginaire.flagsr%cosmos_predict2._src.imaginaire.utilsr r -cosmos_predict2._src.imaginaire.utils.easy_ior Z3cosmos_predict2._src.interactive.utils.model_loaderr rZ2cosmos_predict2._src.predict2.inference.get_t5_embr Z0cosmos_predict2._src.predict2.utils.model_loaderr<rRrrrrrr1rrrJrirjr r r r!sH&         1 [