Neodragon: Mobile Video Generation Using Diffusion Transformer
Authors: Animesh Karnewar
Denis Korzhenkov
Ioannis Lelekas
Noor Fathima
Adil Karjauv
Mohsen Ghafoorian
Amir Habibian
Venue: ICLR 2026 Poster (2026)
Keywords: Text to Video Generation
Flow Matching
Diffusion Transformer
Diffusion Models
Mobile Video Generation
Step Distilllation
Block Pruning
Text-Encoder Distillation
Asymmetric Decoder Distillation
Primary area: generative models
TL;DR: DiT based video generation pipeline optimised for Mobile through four novel Machine Learning distillation based optimisations.

Abstract: We propose Neodragon, a video DiT (Diffusion Transformer) designed to run on a low-power NPU present in devices such as phones and laptop computers. We demonstrate that, despite video transformers' huge memory and compute cost, mobile devices can run these models when carefully optimised for efficiency. To achieve this level of efficiency, i) we replace the original large Text-Encoder with a much smaller one with minimal quality loss through our novel distillation framework which doesn’t require any image or video data. ii) We propose an Asymmetric Decoder distillation approach which allows us to replace the native codec-latent-VAE decoder with a more efficient one, without disturbing the generative latent-space of the video generation pipeline. iii) With our Block Pruning strategy, we remove entire blocks from the MMDiT denoiser based on their relative importance and recover original performance through a two-stage distillation process. iv) We reduce the diffusion sampling cost using our novel extended version of DMD (Distribution Matching Distillation) for the Pyramidal Flow-Matching objective. Neodragon generates 49 frames of [640$\times$1024] resolution within 6.7 seconds on the Qualcomm Hexagon NPU with a VBench total score of 81.61, setting a new state-of-the-art for mobile video generation.

Published as a conference paper at ICLR 2026
NEODRAGON: MOBILE VIDEO GENERATION USING DIFFUSION TRANSFORMER
Animesh Karnewar
Denis Korzhenkov loannis Lelekas
Noor Fathima
Adil Karjauv
Mohsen Ghafoorian
Amirhossein Habibian
Qualcomm AI Research" [karnewar, dkorzhen, ilelekas, noor, akar mghafoor, ooo, ahabibia @qua comm, com
Project Page:
ai http::ch..iittu......oeeoeeeee
happy fuzzy panda playing guitar nearby campfire, show mountain in the background
horse taking peaceful walk
turtle swimming
Figure Neodragon produces high-fidelity videos strong semantic alignment input prompts. Shown here is sampler of generations spanning complex motions, and both realistic and imaginative content.
ABSTRACT
We propose Neodragon, a video DiT (Diffusion Transformer) designed to run on low-power NPU present in devices such phones and laptop computers. We demonstrate that, despite video diffusion transformers' huge memory and com- pute cost, mobile devices can run these models when carefully optimised for ef- ficiency. To achieve this level of efficiency, i) we replace the original large Text- Encoder with much smaller one with minimal quality loss through our novel distillation framework which doesn't 't require any image or video data. ii) We pro- pose an Asymmetric Decoder distillation approach which allows us to replace the native codec-latent-VAE decoder with more efficient one, without disturbing the generative latent-space of the video generation pipeline. iii) With our Block Pruming strategy, remove entire blocks from the MMDiT denoiser based on their relative importance and recover original performance through two-stage distillation process. iv) We reduce the diffusion sampling extended version of DMD (Distribution Matching Distillation) for the Pyramidal Flow-Matching objective. Neodragon generates 49 frames of [640% x 1024] res- olution within ~6.7 seconds on the Qualcomm Hexagon NPU with a VBench total score of 81.61, setting new state-of-the the-art for mobile video generation.
using novel
INTRODUCTION
VDMs (Video Diffusion Models) trained internet-scale video data poised become the next transformative leap in artificial intelligence (Brooks et al., 2024), analogous to LLMs (Large Language Models) trained internet-scale text corpora. However, video generation has been an active research area long before the diffusion era, predominantly driven by GAN-based ap-
proaches (Tulyakov al., 2018 Munoz al. 202T Saito et al., 2020 Clark al. 2019; Kam- ran et al. 2021). While GANs suffered from training instability and couldn't 't be scaled to larger
"Qualcomm Al Research initiative of Qualcomm Technologies, Inc.

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Published as a conference paper at ICLR 2026
datasets, Diffusion Models introduced stable training objective that enabled scaling to increas-
ingly large datasets; but at the expense of higher inference complexity (Hyvärinen) 2005 Song &
Ermon 2019 Ho et al. 2020; Song et al., 2021a laala b Karras et al. 2022 Lipman et al. [023 Heitz
et al. |023] Liu et al. (023). Unlike GANS, which generate samples in a single forward pass, diffu-
sion models require numerically integrating learned probability- flloll ODE/SDE through multiple
iterative steps, resulting in significantly higher computational overhead during sampling. Nonethe-
less, given their stability and scalability, diffusion based VDMs (Wan et al. 2025 Jin et al. 2025
Lin et al. 2024 Bar-Tal Tal et al. 2024 Yang et al., 2025 Kong et al., 2024 HaCohen et al. 2024) were preferred over their GAN- based counterparts and are today the contemprary sfate-of-the-arr in
video generation. With Diffusion Models established as the de facto training paradigm for video generation, the next key question concerns the choice of model architecture. Recent systems have transitioned from spatio-temporal UNet backbones (Bar-Ta et al. [024] Ruan et al. [223]] Ho et al.) 2022a) to Transformer based designs (Zhang et al. [025cc] HaCohen et al. 2222; Cheng ct al. 2024).
with DiTs (Diffusiin Transformers) (Peebles & Xiel (023) emerging as the state of the due to their superior scalability, temporal coherence, and visual fidelity (Melnik et al. 2024
Thor 2024).
Although many frontier open-source VDMs exist today (Kong al. 2024 Wan et al. 2025 Ha-
Cohen et al.] 2024; Yang et al.) 22025] Lin et al.] 2024). their heavy computational demands limit accessibility; forcing reliance on cloud infrastructure with latency, privacy, and cost concerns. This causes barriers for creators in low-connectivity or resource-constrained settings. Enabling on-device generation would democratise access by allowing high-quality video synthesis directly on mobile hardware. Mobile video generation is especially important for privacy-presery creative workflows and equitable access in bandwidth or cost-constrained regions; yet is techni- cally challenging due to tight compute, memory, bandwidth, and thermal/power budgets on hand- held devices. To bridge this gap, we propose set of simple yet effective techniques that systemati- cally reduce model and runtime complexity while maintaining fidelity, enabling practical on-device video synthesis. Motivated by this vision, we introduce Neodragon, a DiT-based text-to-video system (see sec. 4 optimised for smartphones, delivering low-latency generation with competitive quality (see fig. D. Our E2E (End-to-End) system runs with only a Peak-Memory usage of ~3.5GB which is well supported by many low-power compute devices today, without affecting the other os critical processes. We enable this mobile VDM execution through our main contributions as summarized below:
always-available
(A) We propose a Text-Encoder Distillation framework that compresses the 4.762B parrameee T5xxı. model by 35x into a lightweight O. 1330-pprametee DT5 (DistilT5) (Wang al., 2025) using a newly trained 0. -330-paaramttrr CA (ContextAdapter) module, without significant quality degradation (see subsec. B.I.
(B) We introduce an Asymmetric Decoder Distillation approach that replaces the native codec- latent-VAE decoder with device-friendly architecture, achieving over 20x parameter reduction and enabling on-device execution with negligible quality impact (see subsec. 3.2)).
(C) We propose a novel MMDiT Block Pruning strategy for the denoiser backbone, yielding >25% reduction with minimal quality loss (see subsec. (33).
(D) We extend DMD to the Pyramidal How-Matching objective (Jin et al. 2025) as our Step Dis- tillation approach, reducing the number of NFEs (Neural Functional Evaluations: from 480 to 21 (>95% reduction) without affecting the VBench score (see subsec. 3.4
RELATED WORK
We cover the most relevant related work for on-device VDMs here, and defer a more thorough cov- erage to the Appendix section B Deploying VDMs on mobile devices is challenging due to limited compute and memory. Most mobilc-optmissed methods extend UNet architectures with aggressive compression and pruning: HuDniinunig (Isobe al. (025) prunes large models via visual- feedback learning, halving parameters with minimal quality loss; MobileVI) (Ben Yahia et al. 2024) adapts Stable Video Diffusion through spatial downscaling, multi-scale temporal representations, and structured pruning, achieving 500x efficiency gains; SnapGen-V (Wu et al. 2025b) combines temporal-laye NAS (Neural Architecture Search) and adversarial step distillation. MoViE (Karjauv) al. 20044) targets mobile video editing by introducing architectural optimisations. lightweight au- toencoder, and multi moltil classifier- free guidance distillation, achieving a 3x on-device speedup. Additionally, it employs adversarial distillation to reduce sampling to a single step, enabling real-
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Prompt
Text Embeddings
MMDET Text Tokens
happy ruzzy
panda slaying
guitee reartyy
comprine, Snow
75
CF
rountain in thu
backeround.
Stable
Unstable
Casen (4, 2)
[EM] Adagner
[KM] Keplace Mode
[ES]]]
[EN]]
[DDES/IOORA Mode
[TDTS] Triisbl--DaalTT
Otherwise
DTS
[KM]
Figure 2: Overview of the proposed Text-Encoder Distillation framework. The original large-scale text- encoder xxx is distilled into light-weight model via trainable CA (ContextAdapter) module, using combination of MSF and Cosine Distance loss to align the embeddings. Multiple modes supported in framework Replace Mode [RM]: where the new CA replaces the original CE (Contex(mmbedder)) Extend Mode [EM]: where the new CA extends the original CE; Lora Mode [LORA]: Where the CA is not a separate
MLP. but LoRA Hu et al. (2022) layers on top of the DT5 text-encoder; and, we allow training the smaller text-encoder v/s keeping trozen via [TDT5] (Trainable-DDF) mode.
time editing at 12 fps on smartphones. In contrast, DiT-based VDMs for on-device deployment re- main nascent: On-device Sora (Kim et al. 2025) applies training-free step reduction, token merging, and dynamic loading, while Wu et al. (Wu al. 2025a) introduce an comepeecompresioonn VAE, KD-guided pruning, and adversarial step distillation to enable mobile video generation. While Mo- bile VDMs share common goal, our techniques are orthogonal to these concurrent works, and their combination could enable powerful application-speeffic deployments.
3 METHOD
3.1 TEXT-ENCODER DISTILLATION
Given the efficiency constraints of our mobile VDM (appendix sec. C explains these and why Pyramidal-Flow is our choice for baseline). we sought baseline latency and initially attempted to port Pyramidal-Flow (Jin et al., 2025 to Qualcomm Hexagon NPU regardless of the time required to generate a video; however, major blocker arose: the x5xx1. Text-Encoder's model footprint (4.726 B parameters) exceeds the mobile device's memory budget, We therefore boiled down our aim to single guiding question: is the full capacity of T5 xxL actually necessary for high-quality lext-to-video generation? A direct operational corollary is whether much smaller encoder can be substituted without perceptible fidelity loss. Motivated by distillation results in text-to-imnen to-i and Lision-language systems (Wang et al.) 2025 Ma al., [2025] Zhang et al.) 2025a Zhao et al.) 2024]-which suggest large encoders are under-utilised for short, descriptive prompts- we hypoth- esise that text-to-video models impose similarly shallow semantic demands. Building DT5 (Dis- tilT5) (Wang et al.] 2025). we propose a prompt-only Text-Encoder distillation framework tailored to video generation (see fig.
We observe that naively matching xxxxL embeddings with DTS proves to be unstable, so we focus on distilling only the components of text understanding from 75xxLL XXL that are relevant video syn- thesis. To this end, we incorporate the CE (ContextEmbedddr) from the MMDiT denoiser, which maps X5XxL embeddings into multi-modal conditioning tokens for the denoiser, and we introduce new learnable module, CA (ContextAdapter), into the distillation setup. Our objective combines MSE and Cosine Distance losses between predicted and ground-truth MMDIT tokens, which we find to be stable with an optimal weighted combination found experimentally.
Laiain (1, 0) = Wimse ||| e112 + wed (1-
t.t
(1)
|t] E8
where, t CE(T and, f CA(DTT5promptt)
(2)
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[KM]
[UARRA]
JECUEE, sappirg
Method
PPrrums
(TE+ CA)
Tot.
VBench Score
Qual. (1)
Sem. (1)
T5 Baseline
4.752 8
50.31
53.08
66.31
D375 C4 [RM] Repbaen
0.260 R
70.64
83.71
63.39
DT3 CA [EM] Extend
0.266
79.16
83.56
61.55
playing
D75 CA [LORA] LoRA
0.136 0
64.74
74,94
24.08
[1101]] Trainble 1725
0.136
79.20
(b)
NICO JIRS Steiteg ERSCE RRIETY Camefure LFE. 2www BERTCAL Io the SEPEENES
Figure 3: Text-Encoder Distillation results. (a) We visualise randomly selected frames from the generated 149x320x512) videos corresonndngg the adjacent text prompts. (b) #Parameters (L) and VBench (t) scores with ContextAdapter [RMJ, [EMJ, [LORA), and [TDT5] modes.
The distillation framework supports multiple configurations, each tailored to explore a specific path over the experimental design space. The ground CE is single linear layer, serving as a fixed reference throughout. In contrast, the newly introduced CAA is a more expressive 4-layer MLP with skip connections every layer, designed to learn the task-specific adaptations. The frame- work operates in four different modes: [RM] Replace-Mode
where CAA substitutes CE entirely; [EM] -odee, where CA complements CE and both of them are cascaded during inference; [TDT5], which makes the DT5 model trainable; and [LORA]. which replaces the MLP-based CA with LoRA (Hu et al.) 2022) adapter layers on top of DT5. Throughout all modes, T5xxI and CE remain frozen to provide consistent ground-truth signals for the distillation. The CA is always trainable, while DT5 is only updated in [TDT5], and in [LORA] mode, only the LoRA adapter layers are trainable.
The setup was trained using ~ -1.4M text-prompts sourced from Common (Wang et al., 2025) DiffusionDB (Wang et al. 2023), , high-aesthetic subset of LAION (score > 6.5) (Schuhmann al., 2022]. and 12ICompbench (Huang et al.] 2023) for 24,000 iterations with values of U'me = 1.0 and Wad 0.1. Appendix section provides more experimental details and exhaustive ablations over the loss-weights and [LORA] mode.
Table 3b presents detailed quantitative evaluation, comparing multiple configurations of the DT5 and its associated CA module. The baseline configuration, which employs the original xx encoder, achieves VBench Total score of 80.31, establishing the upper bound for performance within our setup. Remarkably, when x5xxL. is replaced with the significantly smaller DT5 encoder paired with 4-layer MLP-based CAA operating in [RM], the system maintains high VBench Total score of 79.64 (see fig. 3a.. This reflects a minimal performance drop of just 0.67 percent, while delivering substantial reductions in parameter count and computational overhead.
These findings confirm our hypothesis that the full capacity of x5xxI. is not required for high- quality video synthesis; and a carefully distilled encoder can serve as a viable drop-in replacement without compromising user experience output fidelity. The [RM] configuration is integrated into the final end-to-end Neodragon pipeline (see appendix fig. HT). Notably, while the original T5 XXL text encoder was infeasible for on-device execution, the distilled version achieves latency of just 3ms on Qualcomm Hexagon NPU.
3.2 ASYMMETRIC DECODER DISTILLATION
Having addressed the challenge of the large text encoder, we proceeded to port the model to the device, only to encounter a second bottleneck. Although the decoder in Pyramidal-Flos lightweight in terms of parameters (226M), its forward computation graph requires storing large 4D feature-map buffers. This made it impossible to fit even a single forward pass of the decoder (for generation resolution) on the Qualcomm Hexagon NPU.
relatively
Rather than designing a new mobile-friendly codec-latent- VAE from scratch- would require prohibitively large amounts of data and compute- we frame this challenge another distillation problem. Existing open- source VDMs Yangg et al. 2025 Jin et al., 2023; Kong et al. 2024 Ha- Cohen et al. 2024 Wan al. 2025 Lin et al. 2024) each employ their codec-latent- VAES, resulting in diverse video latent spaces for diffusion. We hypothesize that the decoder from one of
these models may be sufficiently efficient to serve mobile-friendly candidate, which we can
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Table 1: Quantitative Evaluation of Asymmetric Decoder Distillation. We report the DAVIS Puset
cet al. 2017) PSNR (1) using the original Encoder (without modification and finetuning), using our pipeline's
Encoder (after distillation), and VBench scores (1) for evaluating the reconstruction performance and the gen- cration performance respectively.
#Params (1)
GPU Lat. (1)
PSNR (*) Orig. Enc.
PSNR (t)
VBench Score
Method
Decoder
Our Enc.
Tot(1)
Qual(
Sem(")
PF Native Decoder
226M
2.496s
29.12
29.12
80.31
83.68
66.81
WAN modificd
74M
1.666s
31.47
29.18
80.36
83.82
66.55
Cosmos CV [
63M
0,451s
29.34
29.45
79.96
83.37
66.35
LTXVideo modified
237M
1.738s
28.34
29.46
80.34
83.75
66.68
(Our) TinyAEHV modified
10M
0.851s
27.71
28.40
80.25
83,51
67.19
distill into our pipeline. This hypothesis raises two key questions: (i) Are the video latent spaces across different models easily transferable (through lightweight finetuning)? and (ii) How can we reconcile disparities in latent compression factors among these models? To address both questions within a unified empirical framework, we propose an Asymmetric Decoder Distillation approach.
Our proposed framework comprises three components (see appendix fig. FT) First, we introduce asymmetry into the base model's codec-latent- VAE by retaining the original encoder, E to pro- duce latents z = Eene ( (z)) while replacing the original decoder Ede with a new one, Face to re- construct videos as & = (z). Since Faee was originally trained for a different latent space, fine-tuning is essential. However, before fine-tuning, we must resolve the mismatch in compression factors between the native encoder and the asymmetric decoder. Second, we minimally adapt the decoder architecture to match the fixed encoder's compression factor of [8 [8x8x8]. This adjust- ment involves either adding or removing blocks, depending on the decoder's original compression ratio. When new blocks introduced, we reuse the existing architectural design as much pos- sible and minimise additional parameters. Finally third, we fine-tune the entire setup end-to-end using a reconstruction objective, L(x, i)). combining MSE and LPIPS losses (Zhang et al. 2018). The encoder remains frozen to preserve the latent space required by the MMDiT, which also allows us omit the KL regularizer typically employed in VAE training.
We train this setup using dataset of ~550K videos (and corresponding prompts) sourced as: ~553 videos from the Stock Videos subset (Mixkit, Pexels, Pixabay) of OpenSora (Lin et al.] 2024), and ~77K from Panda-70M (Chen et al.) 2024). The training is done for 200,000 itera- tions on eight 80GB Nvidia 1100 GPUs Appendix section DectionFIpro provides details about modifications applied to each decoder, and more details about the training setup.
Table summarises our experiments with the proposed Asymmetric Decoder Distillation frame- work. Remarkably, even with minimal architectural modifications, all decoder variants perform well. The PSNR scores on the DAVIS (Pont- Tuset et al., 2017) test set average above 29 dB, indicating that the asymmetric latent VAE can faithfully reconstruct video signals while operating through the frozen generative latent space of MMDiT. Although these experiments are preliminary, they provide strong empirical evidence for the universal nature of compressive video latent spaces learned by dif- ferent models, demonstrating that such spaces can be transferred between each other with relatively low fine-tuning cost. For our deployment, the AinyAEHV decoder (Boer Bohan 2025) proved be the most parameter-efficient and mobile-friendly option, because of its design and portability; even though other decoders demonstrate better performance. While the native decoder could not run on the Qualcomm Hexagon NPU, the modified version achieves latency of 143 ms when decoding a [ [49x3 x 320 x 512 1 video from latent tensor of shape [7 [7x40> x 64]. This distilled decoder is integrated into our final optimised Neodragon pipeline (see appendix fig. HT).
3.3 MMDIT BLOCK PRUNING
After addressing the two major challenges of the oversized Text-Encoder and the unoptimised de- coder, we successfully obtained our first end-to-end Qualcomm Hexagon NPU latency measurement ~18.22.. While this result demonstrates the feasibility of mobile video generation, the total run- time of approximately three minutes to produce 2-second video relatively small resolution of [320x is far from our goal. Interestingly, the Eext-Encoder and the Decoder, which ini- tially prevented on-device execution, now account for only 0.2s of the total latency. The remaining 184s are required for the tpapioltempyrally pyramidal and causal latent generation performed by the MMDiT denoiser. This observation motivated our next two optimisation directions: (i) reducing
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dog playing in park Published as a conference paper at ICLR 2026
Table 2: Quantitative Evaluation of MMDIT Block Pruning. Performance of MMDiT Block-Prunings across model sizes, reported via VBench scores (1) after Stage-1/2 1/2 fine-tuning. model size (#Params, 4) and Qualcomm Hexagon NPU latency (U). Latency reported only for base and selected 18-block variant used in the final pipeline to minimise profiling costs.
Mobile Latency ())
VBench Score Qual.(t)
Stage
Method
#Parameters ())
Tot,(1)
Sem.(f)
Original model
24 Blocks MMDiT
2.028 B
1.15s
80.31
83.68
66.81
22 Blocks MMDiT
1,858 B
79.82
83.30
65,92
20 Blocks MMDiT
1.688 B
78.65
82.36
63.82
1
18 Blocks MMDiT
1,518 B
0.74s
78.39
81.58
65.63
16 Blocks MMDiT
1,348 B
74.59
78.74
57.99
18 Blocks MMDiT
1,518 B
0.74s
80.21
83.54
66,90
2
16 Blocks MMDiT
1.348 B
78.62
82.40
63.50
Original
Stage-1
Stage-2
24-blocks
22-blocks
20-blocks
18-blocks
16-blocks
18-blocks
16-blocks
A happy fuzzy panda playing guitar nearbyll campfire, snow mountain in the background
Happy dog wearing yellow turtleneck, studio, portrait, facing camera, dark background
turtle swimming in ocean
Figure 4: Qualitative Evaluation of MMDIT Block Pruning. We visualise randomly selected frames from the generated [4 [49x320x512] videos, models with different pruned-sizes, after Stage-1 finetuning. Stage-2 finetuning.
the size of MMDiT in order to accelerate each denoising step; and (ii) reducing the number of de- noising steps required generate the latent video. In this subsection, focus the first direction, while the second direction is explored in the next. To this end, we propose a block boock-pruning strategy inspired by SANA-1.5 (Xie et al. 2025). but adapted the MMDiT architecture and extended with Full Teacher fine-tuning stage.
The MMDIT denoiser, denoted as DD can expressed as a com- position of N blocks operating tokens concatenated from three sources: visual latent tokens (diffused with noise), textual tokens f derived from the prompt (see eq. 2). and the clip embedding token è CLIP(prompt)) Pyramidal-Flow trains the denoiser starting from SD3.5 (Esser al. 2024) MMDiT checkpoint, hence here = 24. Due to the multi-modal nature of the MMDiT archi- tecture, obtain separate block importance scores for the visual and the textual tokens, represented by BIg and BI; respectively, for the htth block.
Analysing Block Importance.
(zk+s, fx411
(z F2,6)
(3)
2k+
BI == 1 - E
and BI := 1 E
(4)
|| 2 24+
[1 t
BIg (BTg. BT()
We compute block-importance scores for each 1tth block in the MMDiT (see eq. 5) defined as the Cosine Distance between the block Dj input and output tokens. To estimate these scores, we use small but diverse calibration set of 100 text prompts, generating five sample videos for each. During

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Table 3: Quantitative Evaluation of Step Distillation. Comparison of step-distillation methods adapted
Pyramidal Nlo--Mttching for video generation under two inference schedules (4-4-4, 1-1-1). The Pyramidal- Flow default configuration is to generate the first frame with 20 steps and rest with 10 at each of the three resolutions. Metrics: VBench-Torie Total (T Quality (Qualt), Semantic (Semf), and Dynamic- Degree (DDt),
Inference Schedule
Distillation Schedule
Mobile
VBeench Score
Method
NFE'(()
Latency (L)
To((T)
Qual(1)
Sem(")
DD(()
(20)1xx3
N/A
118.40s
83.68
64.72
Pyramidal- FFoww Native
(CFG present)
444
N/A
168
41.44s
75.82
79.90
59.49
49.17
1-111
N/A
21
10.36s
59.62
67.90
26.50
6.39
Pyramidal Mean-Flows (CFG present)
4-4-
N/A
168
41.44s
76.25
80.60
58.87
49.17
1-1-1
N/A
21
10.36s
63.44
70,89
33,62
20.00
Pyramidal DMD
4-4
1-1-1
84
20.72s
80.37
85.21
61.01
86.11
1-1-1
1-1-1
21
5.18s
76.48
80.79
59.24
48.89
Pyramidal Progressive Distillation
444
4-4.4
84
20.72s
78.22
82.41
61.46
52.50
1-1-1
1-1-1
21
5. 5.18s
76.17
80.46
59.02
62.22
Pyramidal Adversarial Distillation
444
4-4-4
84
20.72s
78.51
83.19
59.77
87.78
1-1-1
1-1-1
21
5.18s
77.47
81.74
60.39
64.17
this process, we probe the internal token representations of the MMDIT D at every denoising step for both CFG (Classifier-Free Guidance (Ho Salimans] (02T)) passes-one one with descriptive prompt and one with the negative. Interestingly, due to the model's multi-modal nature, the visual and textual importance of a block are not correlated (see appendix sec. G for more details). Therefore, also assess the impact of removing each block on the final generation quality visually. Based on both the importance scores and visual impact, we select the blocks for pruning.
Stage-1 Finetuning. After removing the selected blocks from the MMDiT, we finetune the pruned model using ground-truth data. Specifically, we train the pruned model with our curated ~~55K dataset (see subsec. (32p with the original Flow-Matching objective (Lipman al., 2023). We adopt the default setting of Pyramidal-Flow where only the current frame is denoised while conditioning on past frames sampled from ground truth videos. To improve robustness test-time generations, these history frames are corrupted with gaussian noise during training. Surprisingly we find that only as little as 300 iterations using four 80GB Nvidia H100 GPUs are sufficient for the model to converge in this stage, and training longer (we tried till 3000 iterations) doesn't 't improve the performance further.
Stage-2 Finetuning. After establishing lower bound on the performance achievable by the pruned models in Stage-1, we proceed with Stage-2 finetuning. In this stage, we incorporate feature- matching losses between the Full Teacher model and the pruned Student model. Specifically, apply an MSE loss on visual tokens, Cosine Distance loss on textual tokens, and Flow-Matching losses (Lipman et al., 2023) using both the Teacher model's outputs and ground-truth flow from the data as supervision. Ablations regarding the choice of these losses are detailed in Appendix section G This stage exhibits slower convergence and requires training for 60,000 iterations.
Table and Figure 4 summarise our quantitative and qualitative results respectively. Notably, the 25 % pruned model with 18 blocks achieves VBench score of 80.21, only 0.1 lower than the base 24-block model (80.31), enabling near-lossless compression of the MMDiT denoiser. And hence, we deploy this block pruned MMDiT in our final pipeline (see appendix fig. HI). With this reduction in the MMDiT model size, the latency of a single denoising pass (sum over three pyramidal resolutions) was reduced from 1.15s to 0.74s on Qualcomm Hexagon NPU.
We found it remarkably intriguing that Stage-2 finetuning does not work well when applied directly the pruned model, despite including the data based Flow-Matching loss in Stage-2. We also experimented with various annealed weighting schemes during training, but none matched the per- formance achieved by the currieulum approach of Stage-1 followed by Stage-2. We attribute this behaviour to the optimisation landscape induced by pruning, though deeper investigation could provide valuable insights, which we leave as an interesting direction for future work.
3.4 STEP DISTILLATION
Having pruned approximately 25% of the MMDiT denoiser's parameters, the video generation la- tency on the Qualcomm Hexagon NPU decreased from 184.2s to 118.6s, yielding a saving of 65.8s and significantly improving time-to-video. However this is still bit away from a practically de-

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Published as a conference paper at ICLR 2026
ployable system. Therefore, we aim reduce latency further make the system more practical and user-friende user-friendly, with real-time generation remaining the ultimate goal. Now with the block porcked MMDiT, the iterative latent denoising accounts for 118.4s of the total latency, requiring 480 NFEs (denoising steps). To acceleratee generation by distilling the denoising steps, we propose a pyramidal version of DMD (Yin al. 2024). Given space constraints, we defer the details of the rest of our adaptions to the Section H of the appendix.
Pyramidal-Flow decomposes the probability flow into S stages, where the 2th stage operates at 2' smaller resolution than the original, where E .., S 11. Let Down(,,s) and Up(-,s) s) denote
spatial downsampling and upsampling by a factor s, respectively. Each stage is parameterised by pair of noise levels (o and with 1 >> start > and > O, and operates on latents at resolution Down(z, 2'). The start and end distributions for stage are defined as
= (1 dstant) Up( (0on(2,22+1))
+ +a Istart 6,
(6)
itart
= (1 deua) Down(z,2) 2') + o'nd E.
(7)
A different local noise-level Tocal U(0, is used learn the Flow-Matching model at (1
the jth stage, and the global noise-level relates the local noise-level docal oo + o Thus, the overall Pyramidal Flow Matching objective is aggregate over the stagewise objectives: Loyr-FM == Di-1 f.M. Appendix section D provides more details.
For the Pyramidal-DMD. at 7th stage
Baseline
Mean-Flowe
DMDD
Progressive
Advensnial
input 2o (1 )ocal)& and +
foocat the to-be-learned Stu- dent model Do aims to predict the clean latent, parametrized as single- step Euler solver Ey = Zn (o/aatt o'd )Dp((),)))))) since the teacher Score-Model had been trained to approximate the flow de- fined as a derivative w.r.t. Tocal- The so-called Moke-Score-Model D is trained with pyramidal Flow Match- ing objective Lpyr-FM but on the dis- tribution of suudent-predictte latents instead of ground-truth video latents. Having the fake model, the student network is up- dated with DMD loss defined through its gradient V VOLDMD x (D(2r, ( ()) Vožg. The input of teacher and fake model ž, defined as a stage-wise noisy version of student-predicted clean latent, similar to eq. 66 and eq.
Tappy Tutzy panda playerg gutter nearsy curptAre MTON mountain 1n the baskgtzund
playing park
derut
Figure 5: Qualitative evaluation of step distillation. We
visualise randomly selected frames from the generated [49x320x512] vidcos, across different step distillation application on the block-pruned model for 4-4-4 configuration.
clean
Do = (1 d Up( Down(žo. 2), + dstart E,
(8)
Vo and
= (1 d(nd) Eg Jend E,
(9)
Σ = (1 Tioca) Woinn Tocal
(10)
T Ditatt- We follow Yin et al (0224) and define the where 5 ~ (0, and ( and +
Therefore, the sample gets higher weight, if teacher model is capable to estimate its conditional flow with smaller error. In addition we found the supervised Cauchy loss Lieacher log
sample-specific weight of DMD loss as (D(ē,, 7) [Jo2mm Vo
E - Down(z, 21) useful for visual quality and used it with weight 0.5. During training, we update the student and fake model update of per two updates of y.. For student's updates we limit the set of local noise levels Ti to four evenly selected values, and for fake model it is sampled from U(0, 1). To obtain teacher's prediction employ classifier-free guidance with the same hyperparameters as those recommended for Pyramidal-Fie
in alternate manner:
quantitative and qualitative results respectively. Since the pyramidal setting of the base model operates on different resolutions at the time of generation, we specifically target two different configurations, namely 4-4-4 and 1-1-1, where the denoiser spends
Table and Figure 5 summarise
8

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Published as a conference paper at ICLR 2026
Table 4: Comparison with state-of-the-art video generation models. All VBench scores from the compared
methods are extracted from their reported numbers, except for 'Wan2.1* 'Pyramidal Frrmmidl-FFlo*** which are our reproduction scores using the same evaluation pipelines parameters for the generated video
resolution of 320 as we have used for our optimisations.
Platform
Model
Tot(11)
Qua.(1)
Sem.(1)
Flick.())
Aes(1)
Ima.(1)
Obj(())
Scene(1)
Cons.())
Pyramidal-Flow
81.72
84.74
69.62
99.49
63.26
65.01
43.20
26.23
Wan2. 1.38
83.31
85.23
75.65
99.55
63.46
67.01
88.81
41.96
25.50
Wan2.1 1.3B*
82.47
83.33
79.01
99.35
65.05
64.81
89.87
54.20
26.95
Open-Sora V1.2
79.76
81.35
73.39
99.53
56.85
63.34
82.22
42.44
26.85
VogVede
82.01
82.72
79.17
98.53
62.07
65.34
83.42
53.28
27.42
Videoo 5B
81.91
83.05
77.33
78.97
61.88
63,33
85.07
31.96
27.65
Vig 2B
81.55
82.48
77.81
98.85
61.07
62.37
86.48
50.04
27.33
82.64
70.08
99.40
56,94
60.64
25.
LTX-Videe
80.00
82.30
70.79
99.34
59.81
60.28
83.45
51,07
25.19
99.49
57.21
Pyramidal Flow
80.31
83.68
66.81
99:46
60.40
63.90
84.79
45.74
26.31
Snup Mobile Video DIT
81.45
83.12
74.76
98.11
64.16
63.41
92.26
51.06
25.51
Humminghird 16frame
81.35
83,73
71.84
95.24
68.04
71.04
96.36
52.91
28.09
On-device
Hummingbird 26frame
80.31
83.11
69.10
97.64
67.82
69.94
94.86
49.49
27.65
SnapGen
81.14
83.47
71.84
99.37
62.19
92.22
27.42
(Ours) Neodragon E2E
81.61
83.68
73.36
99.27
60.71
59.78
92.37
56.56
28.09
(Ours) Multi Multi-Step
66.90
99.40
63.56
dancer spinning stage with flowing fabric, spotlight following the
giant robot meditating in the middle of ruined futuristic battlefield
Figure Qualitative Evaluation of E2E Neodragon pipeline. We visualise uniformly selected frames from the [49x640x 1024] vidcos (2x super-resolved) generated on Qualcomm Hexagon NPU.
steps and I step on the three denoising resolutions respectively. Except for Pyramidal Mcan-Flows all the other three adaptations provide significant VBench gains compared to the base non-distilled model's performance, especially in the lower step regime of 1-1-1. Since Pyramidal DMD yields the best VBench score of 80.37 for the 4-4-4 setting, we choose to use this step-distilled MMDiT denoiser DD with denoising latency of 20.72s for the Neodragon pipeline (see appendix fig. HT
4 END-TO-END MOBILE VDM
4.1 MODEL COMPONENT INTEGRATION
By applying step distillation, we reduced the end-to-end video generation latency to 25.96s, bringing our system close to the threshold interactive video generation at 26ps. While the model maintains strong VBench score of 80.37, observe noticeable visual-semantic degradation. As illustrated in Fig. our proposed Pyramidal-MMD approach introduces colour saturation artifacts and seman- tic inconsistencies in the first frame. Nevertheless, the generated motion remains smooth and stable, suggesting that this issue is not fully captured by VBench metrics. We hypothesize that these se- mantic artifacts be mitigated by initializing the video with high-quality first frame generated by separate text-to-image model. This strategy would preserve the integrity of the initial frame while leveraging our pipeline to apply coherent and descriptive motion to subsequent frames. To this end, we use SSD-IB (Gupta et al. (024) to generate the first frame in four steps requiring 0.82s including CLIP embedding, then apply our 1-1-1 step-distilled pipeline for subsequent frames. With VAE encoding 0.94s (first frame) and QuickSRNet (Berger et al., 2023) upsampling 5ms, the full pipeline achieves 6.7s latency (see fig. [ and fig. 6). This configuration of ours constitutes our Mo- bile VDM Neodragon (see appendix fig. HI and delivers the highest VBench score 81.61 among on-device solutions (see tab. 4). More qualitative videos are included in supplementary. Table S provides details of the on-device measurements done for running our Neodragon system both Laptop SoC (namely Snapdragon X Elite) and a Mobile SoC (namely Snapdragon Elite Gen4); both powered by the Qualcomm Hexagon NPU.

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Published as a conference paper at ICLR 2026
Table 5: Neodragon On-device Measurements. We report measurements for running Neodragon the lap- top SoC Snapdragon X Elite and mobile SoC Snapdragon Elite Gen4; both powered by Qualcomm Hexagon NPU. Since the VAE Encoder runs only once for the first frame, it is unoptimised. We also report peak RAM usage of each component for the laptop SoC.
Sol:
CLIP
CLIP
DisurTs
SSD1B
VAE
VAE
MMDI++CA
QuickSRNet
Mencurement
G
UNet
Doc.
Eint
Dec.
[7=10-16]
7x220
67x40x44
Snmpdragun XE Eliie / Lat.
53
436
30
1313
378.
941.7
1830
519
1014
550.2
49
Suupdragon /Memm GB
2.44
003
0.17
0.68
021
3.13
Suspdragm Kilie (T.ad.
35
5800
1206.
248.9
4.2 MODEL COMPILATION
Deploying the model on a fixed-point NPU involves the following model compilation steps:
Porting multi-resolution MMDiTs. To compile static graphs, we need to port three MMDiT graphs-low, mid, and high corressisss to the latent resolution of each stage. As illustrated in Appendix Figure DI, the PyTorch model takes past history which dynamically grows with more latent frames being included. This dynamic nature is incompatible with static compilation, hence input paddings are needed for each MMDiT graph. Specifically, we expand the last frame graph and pad zeros when running inference for earlier frames. Doing such steps requires changes the atten- tion mask and positional embedding implementation so that the zero paddings do not contribute to the next frame generation. This step critical because any leakage from padded tokens can degrade temporal consistency.
Precomputation of MMDIT inputs. The input merge function of MMDIT's forward pass computes constant information for each stage. These include input shapes and trainable token lengths specific to the current stage. Besides, as we run a fixed amount of timesteps, we also have timestep embed- dings precomputed as inputs to the MMDiT graph. Precomputing these values not only reduces run- time overhead but also ensures deterministic behaviour across different hardware backends, which is essential for reproducibility.
Reduction of 6D tensors. The MMDiT graph contains temporal and spatial dimensions, which makes the RoPE layer computation 6D tensor mul/add operation. Unlike Torch dynamic graphs, high-dimensional inputs mean more complicated tiling, which usually ends up penalising perfor- mance. To simplify this, reduce along the sin/cos and broadcast dimensions, effectively collaps- ing redundant axes. This enables an extreme performance boost: it reduces MMDiT- [ x 40 x 64) compilation time from nearly a day to less than two hours, while improving inference latency from several seconds to sub-second, Such optimisation is particularly important for real-time applications on edge devices.
Optimising causal mask value. By default, the causal mask value in T5 and MMDIT is set to an extremely large number, which cause problems during device deployment. To mitigate this, adjust the mask value to a more suitable number-large enough to prevent the model from attending to masked tokens, but small enough to avoid overflow. This adjustment subtle yet necessary because fixed- fixed-point arithmetic is highly sensitive to extreme constants.
Rescaling activations in T5. T5 includes res_add and ff-add operations with values exceeding the FP16 numerical range. To ensure numerical stability and maintain functional equivalence, we apply a scaling factor to these residual connections, effectively transforming them without altering the model behaviour. This is a common practice in low- low-precision deployment pipelines and is crucial for preventing saturation in activation maps, which otherwise leads to degraded generation quality.
CONCLUSION
We began with the Pyramidal-Flon pipeline and, through four optimisations. namely Text-Encoderr Distillation, Asymmetric Decoder Distillation, Block Pruning, and Step Distillation, transformed it into Neodragon, an efficient on-device text-to-video generation system. Every component of the pipeline has been either refined or replaced, yet the essence of the original design persists, echoing the paradox of the "Ship of Theseus" (Encyclopaedia Britannica): when every part changes, does the system remain the same? Neodragon reduces latency from minutes to seconds while preserving state-of-the -hte--of-the-rtt quality, marking a significant step towards practical, interactive video generation on consumer hardware. Looking ahead, achieving high-resolution and long- oungduration video generation on-device remains an open challenge, and enabling intelligent video-to-vides editing applications offers exciting directions for future work.
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Published as a conference paper at ICLR 2026
6 ACKNOWLEDGEMENTS
This project has been long and demanding endeavour, supported by countless individuals working diligently behind the scenes, and we take this opportunity extend our gratitude to as many contrib- utors as possible. We thank the Quantisation team Hanwen Xiong and Vancheeswaran Vaidyanathan for their dedicated efforts, as well as the Software team Will Zeng, Rafael Esteves, and Tushar Sing- hal for their invaluable work on building the demos. We are grateful to the Compute Engineering team, including Ashwath Jadhav, Prudhvi Akhil Alahari, and Ashish Chauhan. We further thank Fatih Porikli for his insightful discussions, and the supporting technical leads Frank Mayer, Ork De Rooij, and Chen Zhang for their guidance throughout the project. Our sincere appreciation goes to Michael Hoffmann (Senior Director of Engineering, Qualcomm AI Research Amsterdam) for his ongoing support, and to Lea Heusinger-Jonda for her indispensable assistance with legal approvals as well as open-soureing and publication logistics. Last but by no means least, we acknowledge Monique Hagen for keeping the Amsterdam location running smoothly and enabling an environ- ment where this work could thrive. Finally, we extend our thanks to all others who, in ways large or small, contributed to this project.
REFERENCES
Omer Bal,Tal, Hila Chefer, Omer Tov, al. Lumiere: A space-time diffusion model for video generation. ACM Transactions on Graphics, 2024. 18
Haitam Ben Yahia, Denis Korzhenkov, Ioannis Lelekas, Amir Ghodrati, and Amirhossein Habibian. Mobile video diffusion. In ICCV, 2024.
2
Guillaume Berger, Manik Dhingra, Antoine Mercier, Yashesh Savani, Sunny Panchal, and Fatih Porikli. Quicksrnet: Plain single-image super-resolution architecture for faster inference mo- bile platforms. In Proceedings of the JEEE/CVF conference on computer vision and pattern recognition, pp. 2187-2196, 2023. 29
Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprinl arXiv:2311.15127 2023. 18
Ollin Boer Bohan. Tachv: Tiny autoencoder for hunyuan video.
https://github com/
madebyollin/taeny 2025.
25
Tim Brooks, Bill Peebles, et al. Video generation models as world sim-
ulators.
OpenAI, 2024.
URL. https://opemai: com/research/
video- menio-ggeeraaiiiimmimeee as world- simulators
Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace, Ekaterina Deyneka, Hsiang-wei Chao, Byung Eun Jeon, Yuwei Fang, Hsin-Ying Lee, Jian Ren, Ming-Hsuan Yang, et al. Panda-70m: Captioning 70m videos with multiple cross-modality teachers. In Proceedings of the IEEE/CVF
Conference Computer Vision and Pattern Recognition, pp. 13320-13333 2024. 5
Hao Cheng et al. Videodit: Bridging image diffusion transformers for streamlined video generation.
In ICLR, 2024. 2
Aidan Clark, Jeff Donahue, and Karen Simonyan. Adversarial video generation on complex datasets. arXiv preprint ar0iv: 06571, 2019.
Encyclopaedia Britannica. Ship of theseus. https://www
Shap--of-Theeeuu--hhloooppp Accessed: 2025-09-22,
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion En- glish, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling rectified flow transformers for high-resolution image synthesis. arXiv preprint 24Xv:2403.03206 2024, URL
https::/arxivv http://wwwww.oroo/////o//////// 22000 03206 6

---

Published as a conference paper at ICLR 2026
Gongfan Fang, Kunjun Li, Xinyin Ma, and Xinchao Wang. Tinyfusion: Diffusion transformers learned shallow. In CVPR, 2025. 18
Zhengyang Geng, Mingyang Deng, Xingjian Bai, Zico Kolter, and Kaiming He. Mean flows for
one-step generative modeling. arXiv preprint arXiv:2505. 2025. 29
Yatharth Gupta, Vishnu V. Jaddipal, Harish Prabhala, Sayak Paul, and Patrick Von Platen. Progres-
sive knowledge distillation of stable diffusion xi using layer level loss, 2024. 9 29
Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Fitan Richardson, Eran Levin, Guy Shiran, Nir Zahari, Ori Gordon, Poriya Panet, Sapir Weissbuch, Victor Kulikov, Yaki Bitterman, Zeev Melumian, and Ofir Bibi. Ltx-video: Realtime video latent diffusion. arXiv
preprint arXiv:2501.0000 50000000,,,, 2024. 2
18
19
22
25
Eric Heitz, Laurent Belcour, and Thomas Chambon. Iterative a-(de)blending: minimalist de- terministic diffusion model. In ACM SIGGRAPH 2023 Conference Proceedings, 2023. doi:
10.1145/7588432.39955544 URL
org hbtpgg/bb/22300 03486
2 22
Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. In NewrIPS 202/ Workshop on Deep Generative Models and Downstream Application, 2021. URLhttps: httlhhttps:/ppeeevviee
net / forum? ( qw=
27
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems, volume 33, pp. 6840-6851, 2020. 2 22
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David
Fleet. Video diffusion models. 0034552 2022a. 2
Jonathan Ho, Tim Salimans, Alexey Gritsenko, et al. Imagen video: High definition video generation with diffusion models. In arXiv preprint arXiv:2210.02303,, 2022b.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. In International Con- ference on Learning Representations, 2022. URL https: pettps:/oopene http:///ppeeeeeeeeeeeeee htttps://openeeview..et
id=nZevKeeFYE.
Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems, 36:78723-78747 787447, 2023.
Aapo Hyvärinen. Estimation of non-nommalized statistical models by score matching. Journal of Machine Learning Research, 6:695-709, 2005. URL http: //www. jmlr orq/papers/
volume http:///awaaaneeaaaaaaaa......a
22
Takashi Isobe, He Cui, Dong Zhou, Mengmeng Ge, Dong Li, and Emad Barsoum. Amd- hummingbird: Towards an efficient text-to-video model. arXiv preprint arXhv:2503.18559 2025.
Yang Jin, Zhicheng Sun, Ningyuan Li, Kun Xu, Kun Xu, Hao Jiang, Nan Zhuang, Quzhe Huang, Yang Song. Yadong MU, and Zhouchen Lin. Pyramidal flow matching for efficient video gener- ative modeling. In The Thirtentth International Conference on Learning Representations, 2025. URL httppp/:///peeeeeiee.... wtt.eeww.eeeeeooeeeeee www..eeeeeooomeee66eeeeeee
19 22
23
24
26 29
Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, Stewart Lee Zuckerbrod, Ken- ton M Sanders, and Salah A Baker. Rv-gan: segmenting retinal vascular structure in fundus pho- tographs using novel multi-scale generative adversarial network. International Conference on Medical Image Computing and Computer-Assistel Intervention, pp. 34-44. Springer, 2021.1
Adil Karjauv, Noor Fathima, loannis Lelekas, Fatih Porikli, Amir Ghodrati, and Amirhossein Habib- ian. Movie: Mobile diffusion for video editing. arXiv preprint 16.65724 2024. 2
12

---

Published as a conference paper at ICLR 2026
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine. Elucidating the design space of diffusion- based generative models. In Advances in Neural Information Processing Systems (NeurIPS), 2022. doi: 10.48550/rXX1v2220000033 URL https:/ httpas///arxivio wwwaariivv..... http:o/gg/abb////////s
Bosung Kim, Kyuhwan Lee, Isu Jeong, Jungmin Cheon, Yeojin Lee, and Seulki Lee. On-device sora: Enabling training-free diffusion-based text-to-video generation for mobile devices. arXiv preprint arXiv:2502.0445 043633, 2025.
W. Kong, Q Tian, Z. Zhang, R. Min, Z. Dai, J. Zhou, J. Xiong, X. Li. B. Wu, J. Zhang, et al. Hunyuanvideo: A systematic framework for large video generative models. arXiv preprint arXiv:2412.03603, 2024. URL
03603
18
9
Jae Kwon and Ethan Buchman. Cosmos whitepaper. A Nenw. Distrib. Ledgers, 27:1-32, 2019. 25
Bin Lin, Yunyang Ge, Xinhua Cheng, et al. Open-sora plan: Open-source large video generation model. arXiv preprint arXiv:2412.0013. 2024.
18 19
Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In The Eleventh International Conference on Learning Rep- resentations, 2023. URL URL
net wew.eeeoorr.eeeeeeeeeeeeeee.eeee www.orunniddPpwwWRCUUT..
22 28
Xingchao Liu, Chengyue Gong, and qiang liu. Flow straight and fast: Learning to generate and transfer data with rectified flow. In The Eleventh International Conference Learning Repre- sentations, 2023. URL https:://// hptpt/eee///pnreiie...... wwwpnnrviewweneeeeeeeeee net. forummrdrm
http://wwww...et/ooooom..///---. XVJjnInw2Z
22
Jian Ma, Qirong Peng, Xu Guo, Chen Chen, Haonan Lu, and Zhenyu Yang. X2i: Scamless integra- tion of multimodal understanding into diffusion transformer via attention distillation. In ICCV, 2025. S
Andrew Melnik et al. Video diffusion models: A survey, arXiv preprint arXiv:2405.03150. 2024. 2
Andres Munoz, Mohammadreza Zolfaghari, Max Argus, and Thomas Brox. Temporal shift gan for large scale video generation. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3179-3188 1188, 2021.
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort. Up or down? Adaptive rounding for post-training quantization. In Hal Daumé III and Aarti Singh (eds.), Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pp. 7197-7206. PMLR, 13-18 Jul 2020. URL httpps:/pproeeddnnee http:////mm.m.ppeeee////////////
html
31
William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 4195-4205, October 2023. 2
Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Corkine-Hornunt and Lue Van Gool. The 2017 davis challenge on video object segmentation. arXiv preprint arXiv:1704.00675, 2017.
Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. Advances in neural information processing systems, 32, 2019. 19
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High- resolution image synthesis with latent diffusion models. Proceedings the the IEEE/CVF confer-
ence on computer vision and puttern recognition, pp. 10684-10695, 2022. 19
Yuxuan Ruan et al. Mm-diffusion: Learning multi-mode diffusion models for joint audio and video generation. In CVPR, 2023.
13

---

Published as a conference paper at ICLR 2026
Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi. Train sparsely, gener- ate densely: Memory-efficieee unsupervised training of high-resolution temporal gan. In In- ternational Journal of Computer Vision, May 2020. URL httpa:://c...oog///////
s s11263- 020-01333-
Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models. In ICLR, 2022.
18
29
30
Axel Sauer, Dominik Lorenz, Andreas Blattmann, and Robin Rombach. Adversarial diffusion dis-
tillation. In European Conference on Computer Vision, pp. 87-103. Springer, 2024. 29
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarezyk, and Jenia Jitsev, LAION-5b: An open large-scale dataset for training next generation image-text models. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022. URLbttpa opwnrevieeeeeeeeeeeeeeeeee net/ www.mfirrmmioooroeor (im.mmiddiMMY foee:/oormmi G=Y3744MMMMY
Sangeetha Siddegowda, Marios Fournarakis, Markus Nagel, Tijmen Blankevoort, Chirag Patel, and Abhijit Khobare. Neural network quantization with ai model efficiency toolkit (aimet), 2022.
URL https: org. abs/ 2201. 08442 31
Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In Interna-
tional Conference on Learning Representationn (ICLR), 2021a. doi: 10.45550/axiiv22000.50222
URL huttps://arxtte org abs/2010. 02502
22
Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution.
In Advances in Neural Information Processing Systems, volume 32, 2019. doi: 10.48550/arXiv.
05600
22
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben
Poole. Score-based generative modeling through stochastic differential equations. In Interna- tional Conference on Learning Representatiinss (ICLR), 2021b. doi: (0.48550/arxi.2211..333 URL https:://aat
org/ abs///asll.l.//////// 13401.1..145566
Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. In Proceedings of the 40th International Conference Machine Learning, pp. 1-3255..... PMLR, 23 29 Jul 2023. URL
mlr 22r mtsssss/220///ogg2333
18
Wei-Ming Thor.
U-net vs transformer comparison for diffusion.
https:
7/apxml. www/
chapter- 3
vs wtt.:anttttsstttssstttteereeeere di difffusion
2024. 2
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1526-1535, 2018
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural informa- tion processing systems, 30, 2017. 19
Team Wan, Ang Wang. Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang. Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang. Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang. Tianyi Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wenmeng Zhou, Wente Wang. Wenting Shen, Wenyuan Yu, Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang. Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large- video generative models.
arXiv preprint 2503 203.203144 14, 2025.
T9
22
25
14

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Published as a conference paper at ICLR 2026
Lifu Wang, Daqing Liu, Xinchen Liu, and Xiaodong He. Scaling down text encoders of text-to- image diffusion models. In Proceedings of the Computer Vision and Pattern Recognition Confer- ence, pp. 18424-18433, 2025.
Zijie Wang, Evan Montoya, David Munechika, Haoyang Yang. Benjamin Hoover, and Duen Horng Chau. DiffusionDB: A large-scale prompt gallery dataset for text-to-image gen- erative models. In Proceedings of the 6 Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp.
2023. doi: 10. 10.11863/v1/2023.11 10.1665//1/2033.aalloo....1 URL https::///// http:://aaaaanttooooooeoeee org. 2023 acl 1onll-oon..5.
Yushu Wu, Yanyu Li, Anil Kag, Ivan Skorokhodov. Willi Menapace, Ke Ma, Arpit Sahni, Ju Hu, Aliaksandr Siarohin, Dhritiman Sagar, Yanzhi Wang, and Sergey Tulyakov. Taming diffusion transformer for real-time mobile video generation. arXiv preprint arXiv:2507. 13343, 2025a. URI.
https::/errs www.iii.orrgoooooooo
org/ abs abs/ /200. 13343
Yushu Wu, Zhixing Zhang, Yanyu Li, Yanwu Xu, Anil Kag, Yang Sui, Huseyin Coskun, Ke Ma, Aleksei Lebedev, Ju Hu, el. Snapgen-v: Generating five-second video within five seconds mobile device. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp.
2479-2490, 2025b.
Enze Xic, Junsong Chen, Yuyang Zhao, Jincheng YU, Ligeng Zhu, Yujun Lin, Zhekai Zhang, Muyang Li, Junyu Chen, Han Cai, Bingchen Liu, Daquan Zhou, and Song Han. SANA 1.5: Efficient scaling of training-tine and inference-time compute linear diffusion trans- former. In Forty-second International Conference on Machine Learning, 2025. URL https:
6 27
Z. Yang et al. Clip-kd: An empirical study of clip model distillation. arXiv:2307.12732 2024. CVPR 2024.
arXiv preprint
Zhuoyi Yang. Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang, Guanyu Feng. Da Yin, Yuxuan. Zhang, Weihan Wang, Yean Cheng, Bin Xu, Xiaotao Gu, Yuxiao Dong, and Jie Tang. Cogvideox: Text-to-video diffusion models with an expert transformer. In The Thirteenth International Conference on Learning Representationns, 2025. htthhttp://
net wett/forumm..dd112266666
8
19
22
Fulong Ye, Guang Liu, Xinya Wu, and Ledell Wu. Altdiffusion: a multilingual text-to-image diffu- sion model. In Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Sym- posium on Educational Advances in Artificial Intelligence, 2024. doi: 10. 10.1609/aaii, ht099//aa.v388..... 338.1.28887777
URL https:://oooio
10. 1609/ aaai. v3817 28487
17
Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William Freeman, and Taesung Park. One-step diffusion with distribution matching distillation. In Proceedings of the JEEF/CVF conference on computer vision and paltern recognition, pp. 6613-6623, 2024, g
29
Jiahui Yu, Xin Li, Jing Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved VQGAN. In International Conference on Learning Representations, 2022. URL https:
//openreview. wwt./forrmuuurt.m wwy http://ew.......oooooooooooooooa
19
Richard Zhang, Phillip Isola, Alexei Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595, 2018.
25 26
Tiezheng Zhang, Yitong Li, Yu-cheng Chou, Jieneng Chen, Alan Yuille, Chen Wei, and Junfei Xiao. Vision- lan.laae- outo-encoder: Scalable knowledge distillation from diffusion models. arXiv preprint 7xXx::2007.07144 2025a. URL
org/ wttgga/b///000000 2500/5507000 07100
Yang Zhang, Er Jin, Yanfei Dong, Ashkan Khakzar, Philip Torr, Johannes Stegmaier, and Kenji Kawaguchi. Effortless efficiency: Low-cost pruning of diffusion models. In ICIR, 2025b. 18
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Zhenghao Zhang et al. Tora: Traject diffusion transformer for video generation. In CVPR, 2025c. 2
Zhixing Zhang, Yanyu Li, Yushu Wu, Anil Kag, Ivan Skorokhodov, Willi Menapace, Aliaksandr Siarohin, Junli Cao, Dimitris Metaxas, Sergey Tulyakov, al. Sf-v: Single forward video gen- eration model. Advances in Neural Information Processing Systems, 37:103599-103618 2024.
Yang Zhao, Yanwu Xu, Zhisheng Xiao, Haolin Jia, and Tingbo Hou. Mobilediifusiin: Instant text- to-image generation mobile devices. In ECCV, 2024, doi: 10. 10110077978-33-01--73333--71 URL /ttt:p///ooo..orr/00000000777333
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APPENDIX
A ADDITIONAL QUALITATIVE VIDEO SAMPLES
Please unzip the adjoining supplementane aataeenttrymmmttriiiaal mwtamialaaaaa..aaam zip and open the index. html file to view the supplementary video results provided to demonstrate the fidelity of our final generations. We also include qualitative video results for each of the four optimisations that we introduce to demonstrate the effectiveness of the methods that apply.
B MORE RELATED WORK
Vixeoo Diffusion Models. Text-to-video (T2V) generation has rapidly advanced with diffusion-based architectures, which surpass GAN-based methods in temporal consistency and scalability. Early approaches extended text-to-image diffusion models by adding temporal layers U-Net backbones, as seen in models like LTX-Video (HaCohen et al. 2024) and Open-Sora Plan (Lin et al.) 2024). However, these designs struggled with long-range temporal coherence and overall video quality. Recent models increasingly adopt transformer-bas
architectures for their superior ability to model global spatio-temporal dependencies. For example, Cog VideoX (Yang al. 2025) employs a diffusion transformer with 3D VAE and expert transformer layers for strong text-video alignment. Similarly, Wan (Wan et al., 2025) adopts large-scale transformer- based design for high-quality video synthesis, and Yuan Video (Kong et al. 2024) focuses on high-fidelity generation with joint image-vided video training and optimised text encoders. Our work builds upon Pyramidal-FNI Flow (Jin et al. 2025). which introduces a pyramidal flow matching strategy that progressively refines latents across spatial and temporal scales. Unlike cascaded pipelines, unifies generation in a single diffusion transformer and supports autoregressive video generation with temporal pyramids. These inductive biaresa-ierarchical spatio-temporal modelling and autoregressive conditioning- make it a strong baseline for efficient, coherent video synthesis.
Text-encoder Distillation. Large text encoders, such as T5xxI or CLIP, are widely used in diffusion-based generative models to capture rich semantic representations. However, their size and computational cost pose significant challenges for on on-device deployment and real-time genera- tion. To address this, DistilITS (Wang al. 2025) introduces a vision- vision-guided guided knowledge distillation framework that compresses large T5-based encoders into smaller variants (e.g., T5-Base) while preserving semantic alignment with the visual domain. The method employs multi-stage distilla- tion using curated datasets optimised for image quality, semantic understanding. and text rendering, achieving up to 50x size reduction with minimal performance degradation. Related efforts in mul- timodal settings, such as CLIP distillation (Yang et al., 2024) and multilingual encoder distillation in AltDiffusion (Ye et al. 2024), further demonstrate the effectiveness of encoder compression for improving efficiency in diffusion pipelines.
Within concurrent works on mobilc-optimised T2V generation, text encoder optimisation remains largely overlooked. On-device Sora (Kim al., 2025) applies dynamic loading to T5 reduce memory footprint but does not modity the encoder architecture itself. Similarly, Wi er al. (Wul al.) 2025a) focuses on optimising the denoising backbone and VAE components, without introducing contributions towards text encoder compression. This highlights an open research gap in systematically distilling or compressing text encoders for efficient on-device video diffusion models that we attempt to fill with our novel contribution.
Video Decoder Optimisation. While most research on efficiency in video diffusion models focuses on latent compression or denoising acceleration, decoder-side optimisation has received compar- atively less attention. Existing works primarily explore architectural or inference-level strategies to reduce decoding overhead. -TXeVideo (HaCohen et al., 2024) introduces a decoder that per- forms the final denoising step, effectively shifting part of the refinement process from the diffusion backbone to the VAE decoder, reducing the number of diffusion iterations. WF-VAE in Open-Son Plan (Lin et al. 2024) proposes block-wise decoding with Causal Cache mechanism to enable tiled inference for high-resolution videos under memory constraints. Similarly, PyramidalFlow (Jin al. 2025) implements tile-enabled decoding and sequential offloading between CPU and GPU
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to support large-scale video generation on limited hardware. Cascaded approaches such Imagen Video (Ho et al. 2022b) and Lumiere (Bar- Tal et al. 2024) adopt multi-stage super-resolution de- coders for progressive refinement, though these designs prioritise quality over on-device efficiency.
In contrast, our work introduces an asymmetric decoder distillation strategy that substitutes the base model's native decoder with device-optimised architecture while preserving the original video encoding scheme. Unlike prior methods that rely on tiling or caching for memory savings, our method directly targets decoder complexity through knowledge distillation, enabling efficient deployment without altering the latent representation or retraining the diffusion backbone.
Block Pruning. Pruning for diffusion transformers has recently gained attention as means to re- duce inference cost without retraining from scratch. TinyFusion (Fang et al.) 2025) introduces a learnable depth-pruning framework for DiTs, where layer masks are optimised jointly with a re- coverability objective and refined through masked knowledge distillation. Similarly, Effortless Ef- ficiency (Zhang et al., (2025)) proposes a model-agnostic structural pruning approach for diffusion models, learning pruning masks across the denoising process to remove redundant layers with min- imal fine-tuning. For video diffusion transformers, the parallel work by Wu el al. (Wu et al. 2025a) adopts a sensitivity- layer components, attention heads, and entire blocks-guided by knowledge distillation and sensi- tivity analysis, as part of a broader system-level optimisation for real-time mobile generation.
tri-level pruning strategy that prunes at multiple granularities withhin
In contrast, method focuses block-level pruming tailored to the MMDIT denoiser. We introduce a three-stage pipeline comprising block-importane scoring, short fine-tuning, and full meacher-model distillation. Unlike TinyFusion's differentiable depth pruning or Wu et al.'s multi- granularity censitivity-base approach, our strategy aligns pruning units with the natural MMDiT block structure to preserve spatio-temports attention pathways critical for video generation, while simplifying the pruning process for practical deployment.
Step Distillation. Reducing the number of denoising steps in diffusion models is critical for im- proving inference efficiency, and several step distillation strategies have been proposed. Progressive Distillation (Salimans & Ho 2022) is a seminal approach that iteratively halves the number of steps by training a student to mimic the teacher's trajectory, achieving substantial speedups while pre- serving quality. Subsequent works explore alternative paradigms, such as adversarial step distilla- tion (Zhang et al., 2024). which augments the distillation objective with adversarial losses to enhance perceptual fidelity: this strategy has been adopted in video generation pipelines such as (HaCohen et al. 2024 Wu et al. 2025a). Another interesting direction is Distribution Matching Distillation (DMD) (Salimans & Ho 2022 Song al. 2023). D which aligns the student's output distribution with that of the teacher across noise levels, providing principled framework for step reduction without progressive halving. Building on this foundation, we are the first to adapt DMD-based step distil- lation method to a pyramidal flow-matching denoiser (Jin et al. (2025) enabling efficient inference while preserving the model's hierarchical spatio-temporal structure.
C MOBILE EFFICIENCY REQUIREMENTS
Table C1 summarises recently released text-to-video diffusion models that are publicly available. For building& mobile texi-to-video system, we consider the constraints imposed by mobile hard- ware platforms. The primary limitations are twofold: (i) the total model size, which must fit within the DRAM capacity of the target device, and (ii) the computational complexity, as this directly influ- ences both the power consumption and the inference latency. Regarding the first constraint, as shown in Table [T all models except Hunyuan Video Kong et al. (2024) offer relatively compact check- points, providing some flexibility in model selection. However, the second a more careful analysis. In the following discussion, we explain how the two features of the model from Pyramidal-Flos namely Causal Attention and Token-Saving (in the form of Pyramid) contribute to the biible--iiedlliness of it, and thereby motivating why we select it the foundation of our Neodragon system.
censtrintt-ommpee
complexity-requiiee
Latent video diffusion models Blattmann et al. (2023): Yang et al. (2025): Jin et al. (025); Kong
al. 2024); HaCohen et al. (2024) : Wan et al." 2025) Lin et (2024), building on the framework
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Table Cl: Comparison of Recent Text-to- Video Models. Rows sorted by release date (ascending).
Model
Generation Resolution
Model-Size
Causal?
Toleen-Savings!
Cogvideo x Yang et al.
2015
19 2024
49x768x1361
2,08
K1.55
A
Pyramidal-Foooe Fiow
2025
29 Oct
19x381x512
2,08
81.56
Hunyuan Video Kong et
2014
Dec 2024
129x544 x60
[3.0R
85.09
X
X
L.TVVideo HaConeen
30 Dec 2024
81x512x x
2.0B
82.30
Wan Wan a[ 2025
25 Feb 2025
31x480x932
1.3B
84.26
X
OpenSOKA <ta.. 2024
13 Mar 2025
129x766x1365
1.18
77.70
X
introduced by Rombach et al. Rombacc et al.
2022) for images, generate videos in two stages. First, codec-latent-Ve VAB compresses the input from pixel space lower-dimensional latent space
using architectures such as VQ-VAE Razavi et al. (2019) or VQ-GAN Yu et al. (2022). Second, a diffusion model is trained on these compressed latents, enabling efficient yet high-quality generation. This design balances computational efficiency with generative capacity for high-resolution video synthesis. Intuitively, the diffusion model handles the more challenging task of composing scene layout, objects, and their spatial relationships, while the VAE focuses on reconstructing textures and perceptual details. Formally, during training, an input video æ E 2 is mapped to a latent representation
Exne( where = T/Ji is the number of latent frames, and h = H/fs. w W/fs are the spatial dimensions reduced by a factor/ f. (typically 8). and are the number of latent channels. The temporal compression factor fe is usually or 8, slightly different from fa (Although for Pyramidal-Fioh it is also 8). During inference, the diffusion model generates latent video é E Rtxhxuxe starting from Gaussian noise, which is then decoded into RGB frames by: a = Edec(
R(xhxexe via a spatio-temporal encoder:
As summarised in Table most open-sourced video diffusion models models-except for Pyramidal-Flow emmllo fully bidirectional self-attention Vaswani et al. (2017) within transformers to generate or denoise the 1xhxwxx latent representations. Due the all-to-all nature of self-attention, the com- putational complexity of such bidirectional video transformers is given by Cui in Equation CT Here we can amortize the computational complexity as the number of dot-products computed by a single self-attention Vaswani et al. (2017) operation in the Transformer network. As we know, the number of dot- pot-products are equal to the square of the number of tokens input to the attention operation, due to the all-to-all nature of the operation. In contrast, causal frame-by- by-frame transformer operating on the same latent size has complexity Ceausal, as derived in Equation C5 Since causal transformers achieve approximately a 2 reduction in compute compared to fully bidirectional counterparts (see eq. C6). Pyramidal- Flow Jin et al. (2025) emerges as a desirable foundation model for our system. Apart from the 2x speedup, the ability to generate videos in a streaming fashion the fly is another characteristic of the causal attention transformer which adds to the desirability. Now going one step further, show that the temporal pyramidal conditioning for the causal frame-by-frame generation of Pyramidal-Flow, actually gives us 32x compute saving as opposed to the 2 saving of vanilla non-pyramidal causal model (see fig. DIp
Bidirectional Attention:
Cbi = (hwwt)
(CI)
Causal Attention: Ceausal
x
(h- w
(C2)
tokons in frame &
tokens in frames k..
(hw)) k
(C3)
1=1
= =(hw)²
k
(C4)
k=1
E(t ( 1) 2
= (lew)2
(C5)
(C6)
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Coi
(
2t
Speedup causall
causal
Crausal
(hw)). ((++))
1+1
23 2x as 0
(C7)
Temporally Pyramidal Causal latent generation (general St > S). We a temporal pyramid with S stages indexed by i E {O..... --1: -}}.
Stage i corresponds to a spatial resolution that is downsampled by 2 per dimension relative the highest resolution. If full-resolution frame has M = h t tokens, then the number of tokens contributed by a frame at stage i is
M
M2
= 4
i i= 0, 0., S-1,
since each 2x reduction per spatial dimension reduces the token count by a factor of 4. We refer stage the highest (full-resolution) stage and stage S-1 the lowest stage. For a query frame i and a history frame j S k, let the temporal distance be == k j. The number of tokens contributed by this particular history frame are:
M,
d=
0 (self),
M
1 -1,
T(d)=
4d-1
M
d S.
45-11
Each query frame has M query tokens, so the dot-product cost contributed by a (k. pair is M T(d). Summing over all ordered pairs (k,j) with < j < k: < t is equivalent summing over distances d and counting how many pairs have that distance: for a fixed d, there are exactly (t d) pairs (k, with k - d.
Total complexity (general S). Let = be the token downsampling factor, and define the finite sums
S-2
1 5-1 4 ) (1-4-18-1))
A(S) Σ n
1 -
S-1
1 (S) -1 (S 1
D(S) Σ dr d-1
(1
With u t- (and t > S so 2 1), we obtain
s Σ (1 d)
(lr((,, ΣΣ
M T(k - j] = M
8 (
j=
self (d=
d=s
goometric ramp (d=1...-1))
hulk stage (d2
ulu 1) =M2 tA(S) D(S) + 45-1
(C8)
Asymptotically as (with fixed S).
M²
Cni
(t. 4--1 12 + O(t),
Speedupper(St Cpyr (t, S)
2
4-1
1-000
(C9)
Specialisation S = 3 (matches Fig. D1. For S 3, equation C9 becomes:
Speeduprempora
Syreeduppy 3) 2 (3-1) 32
(C10)
The 32x compute saving from the Temporally Pyramidal Causal attention is already major boost, but the Pyramidal-Flowd model goes further by also denoising each frame in spatial pyramid (coarse-to-fine) fashion. This spatial pyramidal structure is orthogonal to the temporal pyramid and provides an additional speedup. We now derive this spatial speedup and then combine it with the temporal speedup obtain the total compute savings full bidirectional attention (see Fig. DT
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Spatial pyramid setup. Assume that the denoising process allocates fractions ps of the total de- noising steps each stage, with Σ Pt 1.
Per-frame cost scaling, At stage i, both the query and the effective K/V token counts scale by 1/4' relative full resolution. Since attention cost is bilinear queries and keys, the per-frame cost at stage scales as
1
1
Cost factor stage
16
Thus, c denotes the per-frame cost under the Temporally Pyramidal Causal setup (with queries at full resolution), then the spatially adjusted per-frame cost is
8-1
Pi 16
Chemp
(p)
(k) Cremp=
Bs(p)
Pi
spatial-temp temp
16
1=0
Spatial speedup. speedup is
The relative compute multiplier in the spatial dimension is (.s(p) < 1, so the
1
Speedup Spatial (p)
Bs (p)
Uniform allocation across stages. then
If denoising steps split uniformly across stages, ps S+
8
1
1-11--1--
-
16-5
16
15S
As
Speedup spatial
S
16*
S
15S
16(1
16 16-8)
1=0
16
For large S, this approaches 8g
16
and Speedup spatial
15
S.
155
16
Specialisation to S =
With three spatial stages and uniform allocation pi = 3
1 B3 = 1
273
768
= 0.3555,
Speedup spatial
2 2.81 x.
3
16
256
768
83
273
Combined -eapooltemporal speedup. asymptotic speedup of
The temporal pyramid (with S 3 stages) yields an
Speedup temporall 32x
relative to full bidirectional attention. tion) yields
The spatial pyramid (with S = 3 stages and uniform alloca-
Speedup speciua spatial R 2.81x
relative to the temporal-only baseline. Since these optimisations act on orthogonal dimensions (tem- poral spatial), the combined speedup is multiplicative:
Speedup combined ~ 32 x 2.81 & x0
Thus, Spatio-temporally Pyramidal Causal latent generation setup can reduce the dominant tention complexity by nearly two orders of magnitude (90x) compared to a full-resolution, fully bidirectional attention. These efficiency gains are not merely theoretical; they enable practical scal- ing of autoregressive video diffusion to longer sequences and higher resolutions without prohibitive compute costs. For this reason, we adopt Pyramidal-Fon Jiin et al. (2025) as the foundation for Neodragon system, leveraging its hicrarchical structure to deliver both computational efficiency and strong generative performance.
For the pre-super-res output, the spatial resolution targeted in our setup is [320x512). Thus the starting point for us is the Pyramidal-Fii model, which achieves a total VBench score of 80.31. This score is obtained using the inference parameters provided by the authors in their leased code, applied to the 480p low-resolution checkpoint- note note that no official VBench score is reported for this checkpoint. The officially reported score for Pyramidal-Floo corresponds to its
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nigher-resolution 720p checkpoint, which achieves 81.72. It is important to acknowledge that the Pyramidal-Flow model, which we adopt as our foundation, was trained with comparatively fewer GPU hours than some of the more resource-intensive open-source models such as Wan Wan et al. (202)), VogVideoX Yang et al. (205), and Video HaCohen et al. (2024). Consequently, any inherent limitations in Pyramid-Flon Flow's generations are inherited by system. However, the suite of optimisations we introduce to enable efficient deployment on our platform is broadly applicable and can be extended other models well.
D PYRAMIDAL. FLOW-MATCHING FOR VIDEO GENERATION
It is fascinating to observe how the training objectives for diffusson models have evolved-from early Score Matching approaches (Hyvärinen) 2005 Song Ermon 2019), which required on the order of 1,000 denoising steps for generation, to the current frate-of-the-eoe Flow-Matching meth- ods (Lipman et al. 2023 Heitz al. 2023 Liu et al. 2023), which enable high-quality synthesis in as few as 50 steps. Along this trajectory, numerous influential works (Ho et al.) 2020 [ong al.)
2021a bj Karras et al., 2022) have contributed to making diffusion training both simpler and more scalable.
Flow-Matching. Modern video diffusion models adopt a remarkably straightforward yet highly scalable training algorithm, capable of handling datasets with upwards of ~ ~100M videos. Starting from video latents x ~ Pdam( extracted via a fixed codec-latent- VAE, we construct noisy samples
2g (1 - o) + de,
where a ~ (O0, 1) denotes the noise level (0 clean, = fully noisy) and e ~ (N I) is Gaussian noise. This defines a continuous probability flow over ž., whose instantaneous velocity is given by dž, = do c
v(
Interestingly, this velocity does not depend on a due to the linearity of the flow, in contrast to earlier formulations such as DDPM Ho et al. (2020). The MMDiT denoiser D is trained to predict these velocities, conditioned on the noise level a,, via the Flow Matching objective:
LFM E.,,, [I[((E,,)) (1
Once trained, generation reduces to solving the ODE
= Jo=1 D(2,, da,
typically using a first-order solver such as discrete Euler with 50 steps, though higher-order solvers are also applicable. In practice, D is further conditioned on text prompt embeddings i and CLIP embeddings e
Pyramidal Flow-Matching (Jin et al. 2025
decomposes the probability flow into S stages, where the 7thh stage (i E {O......$ S -T>) operates at a resolution that is gi-times smaller than the original. Let Down(-,s) 3) and Up(-, s) denote downsampling and upsampling by a factor s, respectively. Each stage is parameterised by a pair of noise levels (Astatt: dena) with > o Sttat > dend > 0, and operates on latents at resolution Down(z, 2t). The start and end distributions for stage i are defined as
Suaa := (1 (aam) Up( DDwnnz,, 2'+ 2) + o² o'tart
(D1)
gg and
(1 (1-d1 (1-o Down(z, 2') o'nn C,
(D2)
where e ~ (N, T). By the universality of the Flow Matching objective, the stage-wise loss LiM is well defined to learn the flow between the above start and end distributions at stage i. A different local noise-level olocal ~ U(0, 1) is used to learn the Flow-Matching model at the zth stage, and the global noise-level o¹ relates to the local noise-level ooocal as a' = j Thus, the overall Pyramidal Flow Matching objective an aggregate over the stagewise objectives:
C FM
Σ CPM-
==0
For clarity, we omitted explicit timestep conditioning in earlier sections.
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CLIP L+G Text
TS-XXL Text
128x4095
VAE decoder
Generated video
Encoder
Denolser
16x774Cи64
Latent tensor
18xhe40x64
3od 520xx512
Frame
Frame
Frame
Frame
18m40
18
10440V04
x 16x40x64
4440664
xx 10x20x32
10x
denose
16x10xx16
denoae
TONGUN02
History
16x20x32
25x
10x10xx1
Novory
20s
10x
10s
6 16x10xx6
denicio
10x
Pyramidal Autoregressive Video Diffusion Pipeline. The pyramidal autore- gressive video diffusion scheme (Jin et al. (2025) differs from the conventional latent-diffusie in how the the latent-video frames are generated (iteratively denoised). The latent frames are autoregressively generated one- by-one by denoising the curent fraam while conditioning applied in the denoising process as: firstly the denoising of the current frame starts from lower resolution and proceeds to reach the highest native latent-resolution; and secondly, each denoising step is conditioned on past history, where the frames from the further past are spatially downsampled.
Figure D1: Overview
the past history. spatio-tempon temporal pyramid
Intuitively, the Flow-Matching model at 1thh stage flows from a noisy and pixelated version of latent video to less noisy and less pixelated version. Note that the model is not only learning the denoising objective, but also the super-resolution objective when matching the ground truth instan- taneous flow. The most ingenious contribution from Pyramidal-Flow is that the noise-levels of the different stages are not disjoint, but overlapping, which are obtained as:
Fend
(D3)
20 2+1 end
of start
(D4)
1 1 Fnnd
And thus, after training, the same MMDIT denoiser network D be used generate the samples by flowing through all the stages and jumping resolution across stages using the following equations:
2 - d'tart
T v3
Up(2-111, Up(ž 111, 2) +
(D5)
dtars
2
2
7
such that, é' E EN(00,)) E') and Ey Block
7
(D6)
7
7
In practice (code), the value for gamma has been set to y = -1/3 for minimising the noise added
to the upsampled pixels in the neighbourhood of 2 after nearest neighbour upsampling in order
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to continue the probability flow trajectory. We note that this specific solution is obtained for nearest neighbour upsampling where the pixel values are replicated as the reoslution is increased. Although deriving this relation for other interpolation schemes such as bilinear and bicubic could be an inter- esting academic exercise, we stick to using nearest neighbour due to the demonstrated high-quality empirical results by Pyramidal-Fion (Jin et al. 2025 -Ochhamms Razor in action.
E MORE DETAILS ABOUT TEXT-ENCODER DISTILLATION
WWW
oramn
www DESEEMNL
JUAC
An TAA
240 parla
ACONA Sor
MAai oralidancy
Lando Ae ondy
sueiill waccartt
SETeo Scleiin
qutaa nár putz
Sctwring
rota aroohheus
Jenj jronn udle
pon 3.55
[100]] 17
(a)
(b)
Figure E1: Ablations for Text-Encoder Distillation. We ablate the loss weights Wmee and wed for the [RM] mode in (a); and ablate the two controllable hyperparameters the LoRA layers, namely dimensions (dims)
and the scale (alpha) of [LORA] mode in (b).
Our proposed setup was trained using the Adam optimiser with relatively higher learning rate of 3e -3 3, but decayed via cosine schedule to 3e Training was conducted over 24,000 iterations on four 80GB H100 GPUs, with batch size of 512 GPU, resulting in total batch size of 2048. The complete training process took approximately 16 hours. As our default value, we set Wmse = 1.0 and Wad 0.1, but provide the ablation over these for the 4LMLP [RM] in Figure Ela This setup enabled efficient convergence of the distilled encoder using only text data, without requiring any image video supervision.
In Figure El we present a couple of ablations of our proposed Distillation Framework. Chosen for its superior empirical performance, natural question regarding [RM] that arises, is how the two distinct loss functions in Equation [ contribute to the overall optimisation landscape. Figure Ela illustrates various combinations of weights applied the sum of these losses. When either Wisse or Wed is set to zero, the corresponding loss is effectively disabled. We note that disabling the cosine distance results of the radar-plot). This underscores that the Cosine Distance loss is essential for stabilising the training. Lastly, as can be observed, the best performance is obtained when we use Winee 1.0 and Wed 0.1.
divergence, denoted by the purple curve in Figure Ela (observe the centre
Next, following the analysis of loss weighting, we investigate how the architecture of the CA in- fluences the adaptability of the distilled text encoder DT5. A comprehensive exploration of the architectural design space is beyond the scope of this work, so we ablate the number LoRA di- mensions and the LoRA scale in Figure Eib for the [LORA] mode. Our findings indicate that even minimal visual quality in this mode necessitates a High-Ran Rank Approximation rather than a Low-Rank one. This observation further motivates the need for full fine-tuning of the DT5 model's weights, which we address in the [TDT5) mode.
In comparison, the Extend-Mode configuration- where the CA augments rather than places the original CE (Contextmmbeddde)) incurs a slightly higher parameter overhead due to the dual-module setup. Interestingly, this configuration yields marginally lower VBench score than
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Replace-Modd which we attribute to the rigidity of the frozen CE embedding space, potentially limiting the adaptability the extended context representation.
For the [LORA] variant, observe that achieving even minimum viable video generation quality requires a significantly increased number of low-rank dimensions and high scalar a (see fig. ETb). Even with these adjustments, the resulting VBench score of 64.47, while enabling some visual fidelity, falls short of the performance achieved by other configurations. This motivated further experimentation with a trainable DT5 encoder ([TDT5)). Notably, this setup achieves strong VBench score of 79.20 with only half the parameter count of the Replace-Mode configuration. However, we ultimately select [RM] as our final deployment choice, prioritising even marginal gains in VBench score to maximise generation quality, despite the higher parameter count relative to the [TDT5] variant. Figure 3a presents qualitative examples that align with our experimental findings. Notably, the [LORA] mode frequently overlooks key semantic cues from the text prompts- for instance, generating teddy bear instead of panda, or failing adhere to the lack-and-white constraint specified in the prompt. In contrast, the remaining three modes perform comparably, with only occasional semantic mismatches.
F MORE DETAILS ABOUT ASYMMETRIC DECODER DISTILLATION
L(x,8) MSE (x.8) (t.
Pyramid-Fiowe VAE Encoder
ThyAEH/VEE Decoder
Conr3
Causat00
Canaeti0 RosBlok
Mid Eboo20
Torow
20 Nembioo
Upcompa20
TCCr
Canzd
3x
3x
W
lpplt
Pretminee boyer
Figure F1: Overview of the proposed Asymmetric Decoder Distillation framework. A asymmetric decoder from a different pretrained diiet-vvidooddiffuiinn model is distilled into our pipeline by: firstly modify- ing the decnder architecture to match the fixed [8 8 x 8] compressed latent-space of our model; and secondly by finetuning this asymmetric VAE with video data using MSE and LPIPS (Zhang et al.) (018)) losses. The encoder kept frozen that the generative latent-space of the video diffusion backbone is undisturbed. We note TinyAHHV (Boer Bohan 2025) decoder is visualised here, but the framework works with other models as well.
As shown in Figure F1 apply minimal modifications to integrate different asymmetric decoders into our pipeline. For the TinyAEHY decoder (Boer Bohan 2025), we modify the first TGrow (tem- poral upsampler) layer perform 2x temporal upsampling instead of its default 1x (no) upsam- pling, reinitialising the parameters this block with random weights. This single change suffices match our latent compression factor. For the Cosmos decoder (Kwon Buchman, 2019).. we use the Continuous Tokens variant with 8 x 8 compression, requiring no architectural changes. For LTXVideo (HaCohen al. 2024), we remove the decoder's unpatchification layer and update the conv.out layer with new weights. Additionally, to accommodate our 16-dimensional latents, we replace the conv in layer. Finally, for the Wan decoder (Wan et al., (025). similar to TinyAEHY we modify the first upsampling block to perform 2x spatio-temporal upsampling instead of the de- fault 2x spatial-only upsampling. These minimal adjustments enable us to distill diverse asymmetric decoders into out generation pipeline.
The setup was trained using the AdamW optimiser with a fixed learning rate of le-4. Compared to the earlier Text-Encoder Distillation experiments (ref subsec. 3.1). these runs were significantly more GPU-intensive. Training was performed for 200,000 iterations on eight 80 GB H100 GPUs, with PPr batch sizes ranging from 2 to 6 (depending on decoder size), resulting in an effective batch size of 16-48 We used the default patch size of [33x2 x53x256 62256] sampled from a corpus of approximately 350K videos. The full training process took about 1120- 140 hours. For the
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1.00
0.85
prompt scores
Neg text-prmpp
0.70
Visual tokens
0.60
accore
0.40
0.20
0.08
0.06
0.02
0.00
10
12
14
16
17
19
21
23
Block#
Figure G1: Block Importance Scores v/s Block-ids. Block Importance Scores for the 24 MMDiT blocks in
the denoiser backbone, caleulated using equation G5] Textual scores computed for 23 blocks, excluding the final block where text tokens ignored. The plot visualises token-level importance forward passes:
two CFG
descriptive text-prompts orange negative text-prompts. Visual token scores (green) are shown only once, as they remain identical across both passes.
na-prune Scure: N/A
prune. id: Scure: 0,703
pruno. id: 2
prune. id: Score: 0.023
prunc. idd: 4
prunc, id: Score: 0.016
Soure: 0.0585
Score: 0.018
prune_id: Score: 0.012
prune__ id: Score: 0.019
prune Score: 0.013
prune_ide _i:: Score: 0.023
prune_ie Score: 0.019
prune_ id: Score: 0.014
prune id: 12
id: Score: 0.024
prune 1d: 14
prune 1d: Score: 0.044
prune id: 16 Score: 0.038
prune_ prune_id: 17 Score: 0.037
Score: 0.016
Score: 0.026
prune id: 18 Score: 0.025
prune id:
prune id: 20 Scorc: 0.020
prune_id Score: 0.012
prune id: 22
prune 1d: 23 Score: 0.239
Score: 0.008
Seore: 0.018
Figure G2: Visual guidance for Block Pruning. We visualise randomly selected frames from the generated [49x 320x512] videos, across 24 different models in which the prunc_id" MMDIT block is dropped from the model. choose the blocks highlighted with boxes for pruning, giving us 25% model size reduction.
reconstruction objective, followed Pyramidal-Flow' default weighting: 10.0 for the MSE loss and 1.0 for the LPIPS loss (Zhang et al. 2018).
G MORE DETAILS ABOUT MMDIT BLOCK PRUNING
Analysing Block Importance Scores. The MMDiT denoiser, denoted D, can be expressed
as a composition of N blocks operating tokens concatenated from three sources: visual latent tokens (diffused with noise), textual tokens derived from the prompt (ref eq. 2). and the clip embedding token é. In our base model Pyramidal-Fii Jin et al. (2025). N = 24.
D(2, é) DN DN Di... (z,
(GI)
where, è ē:= CLIP(prompt)
(G2)
Due to the multi-modal nature of the MMDIT architecture, we obtain separate block importance scores for the visual and the textual tokens, represented by BT and BT; respectively, for the Ath
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Table G1: MMDIT Block-Pruning Stage-2 Ablations. We ablate the choices the three losses, namely Feature Loss (oken-matching). Teacher FM Loss (fow-matching using Teacher's predicted flow), and Data FM Loss (flow-machiing using ground truth data flow); and which Mapping to use when Feature Loss
is active using VBench scores (t).
Feature
Teacher
Data
Block
VBncch Score
Loss
FM Loss
FM Loss
Mapping
Tot.(1)
Qual.(f)
Sem.(f)
N/A
78.39
81.58
65.63
N/A
80.00
83.52
65.89
Next-Block
79.86
82.92
67.61
Next-Block
79.93
82.90
68.02
N/A
80.04
83.52
66.11
Next-Block
80.35
83.82
66.48
Next-Block
80.11
83.44
66.79
Simple Simple
80.10
83.31
67.28
80.21
83.54
66.90
NiTlack Mals
Bock
Mlack
Block
Mlacc
Elock
Rlace
Rlack
Koock
Rlace
Twrse Mardin
Famus klcchin
(a) Simple Block Mapping
(b) Next-Block Mapping
Figure G3: Stage-2 Simple Block Mapping v/s Next-Block Mapping. For minimising the token-matching
losses between the Full Teacher model the Pruned Student model, (a) Simple Block Mapping maps the output of each of the present block in the student model to the corresponding one in the Teacher model; whereas, (b) Next-Blocd Block Mapping maps the output of each present block in the Student model to the input of the next- available block in the Teacher model (except for the final block which always matches final output).
block.
2k 1x41,tx44 fxt: (zk: :0.k Ek. C)
(G3)
2k 2K+
fx [10xx (44
BI% ;= 1 - -E
and BI == 1 -
(G4)
|| 2z 12 224
BIg = (BI k BI))
(G5)
As shown in Figure G1 we compute inok--impoteance scores for each 1 block in the MMDiT (see Eq. G5). defined as the Cosine Distance between the block D3 's input and output tokens. To estimate these scores, we use a small but diverse calibration set 100 text prompts, generating five sample videos for each. During this process, we probe the internal token representations of the MMDiT D at every denoising step for both CFG (Classificr-Fret Guidance Ho Salimans (202T) passes- one with descriptive prompts and
with negative prompts. Consistent with observations for SANA- 1.5 [ie et al. (2025), we find that the initial and final blocks are more influential, while intermediate blocks contribute less, they induce minimal residual changes to the tokens. Interestingly, due to the model's multi-modal nature, the visual and textual importance of a block are not correlated. Therefore, as illustrated in Figure G2 we also assess the impact of removing each block on the final generation quality. Based on both the importance scores and visual impact, we select six highlighted blocks for pruning, while also experimenting with smaller and slightly larger sets to explore the trade-off between quality and model size.
Stage-1 Finetuning. This setup was trained using the Adam optimiser with fixed learning rate of 3e-5 on four 80GB NVIDIA H100 GPUs, with per-GPU batch size of 4, resulting in effective batch size of 16. Since training was limited to 300 iterations, the process took only about 1-2 hours. Although experimented with longer training (up to 3K iterations), observed significant
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Original
Textual scores
Visual scores
Average scores Visual guidance
A happy fuzzy panda playing guitar nearby campfire; snow mountain in the background
Happy dog wearing yellow turtleneck, studio, portrait, facing camera, dark background
turtle swimming in ocean
A dog playing in park
A donut
Figure G4: Different BI-scores based block-pruning compared visual guidance. We visualise randomly selected frames from the generated [49x320x512) videos corresponding to the adjacent text prompts, across different 18-blocks pruned versions of the original model. Using only the textual-score or visual-scores or even average of the two (see fig. GT) directly results in lot of semantic distortion in the generated samples as first stage finetuning. Whereas, upon choosing the blocks prune based on the average scores well the visual impact (see fig. causes minimal semantic distortion.
performance gains. Remarkably, even with such minimal fine-tuning, we were able to recover most of the lost performance, underscoring the effectiveness of our block selection strategy based importance scores and visual inspection.
Also, shown in Figure G4| removing the 6 blocks using either just the textual scores, the visual scores and taking an average of them. We clearly from the figure how much difference the use of visual impact makes the the Stage-1 finetuned model.
apply Stage-1 Finetuning to the 18-blocks model obtained by
Stage-2 Finetuning. After establishing lower bound the performance achievable by the pruned models in 1tage-1, we proceed with Stage-2 finetuning of the pruned model. In this stage, incorporate feature-matching losses between the Full Teacher model and the pruned Student model. Specifically, we apply an MSE loss on visual tokens, a cosine distance loss on textual fea- tures, and Flow-Matching losses Lipman et (023) using both the Teacher model's outputs and ground-truth flow from the data as supervision. Table 2 reports the scores obtained after Stage-2 finetuning (second section). Notably, the 25% pruned model with 18 blocks achieves a VBench score of 80.21, only 0.1% lower than the base 24-block model (80.31), enabling near-lossless com- pression of the MMDiT denoiser. Figure 4 shows qualitative examples of the kind of generations that can be obtained by the differently sized pruned models after the Stage- and Stage-2 finetuning.
Table presents an ablation study on the design choices for loss functions used during Stage-2 finetuning. We also compare two block-mapping strategies: Simple Block mapping and Next-Block mapping, which determine how each block in the pruned Student model is paired with a block in the Full Teacher model for applying feature-matching losses. Figure G3 illustrates the difference between these two schemes. All experiments are conducted on the T8-block model. While all reasonable configurations perform well in principle, some yield slightly better results in practice. Interestingly, the model trained with Next-Blan Block Mapping and only data- based Flow-Mane motching loss achieves the highest VBench score of 80.35 (even surpassing the base model). However, this config-
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SSD1B
CLIP L
15700
Super Reselution
VAE Encoder
CA
Optinised
VAE Decoderr
Block Dencieer
Latent
1bdenbb54
Charnshecbe
Figure HI: Neodragon E2E full pipeline. Neodragon integrates all four of our proposed optimisations namely, Distilled small Text Text-Encoder [3T] Asymmetric VAE Decoder 3.2 Block-Pruned MMDiT [3.3] and Step-Distilled scheduler 3.44 (not shown here). For hoosting the visual fidelity of the generations, we also include SSDIB (Gupta et al. 2024) for generating the first image, and QuickSRNet (Berger et al. 2023 for 2x
super-resolution.
uration introduces artifacts in some generations and occasionally produces black videos for certain prompts. Therefore, we adopt the model trained with all losses and Simple Block Mapping as the final version for deployment in the Neodragon pipeline (see Fig. HT. As reported in Table 2 start- ing from the full 24-block MMDiT with Qualcomm Hexagon NPUlatency of 1.15s, we reduce the latency 0.92s with minimal impact VBench performance.
H MORE DETAILS ABOUT STEP DISTILLATION
To further accelerate generation, we explore diffusion step-distillation techniques in this section, which aim to reduce the NFE requirement of the Denoising Scheduler. To this end, we begin by detailing the training objective of Pyramidal Flow-/Matching (Jin et al. (025)), followed by an ex- planation of how we adapt four different Flow-Matching step-distillation techniques from the lit- erature to this pyramidal setting. The selected techniques include DMD (Yin et al.] (022)) Direct Progressive Distillation (Salimans & Ho, 2022). and Adversarial Distillation (Sauer al. 2024) as discrete aep-distillaatinn methods, and the recent Mean- Mean-Flows (Geng et al.) 2025) as a continuous consistency-based approach. While Mean-Flows was originally proposed for training models from scratch, we adapt to operate in a distillation setting by applying it to an already trained model.
Pyramidal Mean-Flows Mean-FLows Geng al. (2025) proposed two changes to the learning al- gorithm of Flow-Matching models. First, they propose to model not the instantancous velocity field v(ā,) of the underlying probability-flow ODE, but instead the Mean velocity field Umean ( (2., B, which denotes the average velocity of the trajectory going from 3 to (such that, > B8. A direct implication of which is that the denoiser network now needs also condition on 5 apart from a, i.e. the mean- predicted velocity is now (( B, or. Through very interesting derivation, Mean-Fil -lows shows that the learning objective for such Mean-Flows model is given by:
Lm Eg,9, D( B, Umean (2., B, 01
Where, the target ground-truth Umean is computed as:
Umean ( B) v(z.. (a ()((E (),_D++,D)
Where the latter term is computed as JVP in code. In Our Pyramidal Mean-Flows version, we extend the Pyramidal Flow Matching loss for each of the 7th stage such that the Umean is computed as,
Umean--yr := v o) - (a ()(,,, + (o )0,
Note the scaling of the last term, which accounts for the scaled version the instantaneous velocities which are learned by the Pyramidal-Flow Matching model. Thus, with this correction to the Umean-pyrs can define the squared 1.2 loss per stage obtain MMP and then giving us the aggregate loss
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function Lyr-pyz In practice of course since finetuning the MMDiT D from pretrained Flow-Matching model, we first only finetune it with the second starting point conditioning 3 while only training for Flow-Matching objective, i.c. setting 3 σ, and later training the Mean-Flows objective. Also as a key detail, the training needs to have only 25% of the batch-samples that Mean-Flows (i.e. * d)) while the rest still Flow-Matching samples so that the training stabilises. From our experiments, we found that fine-tuning the model in such a way very soon leads to a loss that is overpowered by Flow-Matching rather than Mean-Flows, while increasing more Mean-Flow batch samples leads to unstable training. Further exploring the cause of this instability constitues an interesting direction for future work.
Pyramidal Progressive Distillation We apply the Progressive Distillation Salimans & Ho (2022)
to Pyramidal Flow-Matching. Firstly, we found that stage-wise 2x distillation is not necessary for Pyramidal Flow-Matching model, which already can do the generation with 20 steps for a single resolution, thereby doing denoising of single frame 60 steps (given pyramidal resolutions). Thus, for per stage we setup two networks: student network D3 (to be distilled) and the teacher network D for supervision. Then, we obtain synthetic generated videos for the ~350K prompts that we had curated to form our dataset that we never leave the support of the probability distribution that is learned by the teacher network D.
We quantise the steps of the Teacher model 16 uniformly sampled o² [o 14 steps.... o values (Euler solver) for the 7th stage. The Student then either learns single step (for 1-1-1 con- figuration) or 4 steps (for 4-4-4 configuration) uniformly subsampled from the set of the teacher steps. We detail the loss objective for the 4-4-4 configuration below, but note that the 1-1-1 follows directly from it, or any other distillation configuration as long as the number of teacher steps are are perfectly divisible by the number of student steps that are be distilled.
Given a sampled
dicchr we take the next j steps (in this case 4 steps) to fix the last sigma from the teacher inference as o "iuchh The ground truth end-point from the teacher inference trajectory given the starting point م, is computed by running no-grad mode Euler inference of the Teacher:
a+i (gistl
Σ
o(t)D((
seuch
toac
Then, the student model's prediction to match the giots in one step is computed again as an Euler step:
+ (alets - DD) mm
asd
Thus, having computed the noisy simulations from the Teacher as well as the student's one-step noisy predictions, the loss function for this distillation is defined as
Lpyr-prog := E,,,, [01.,..[11__-- aud
(1,t, - - [
woch
Intuitively, we basically run the teacher model 'D for four Euler steps to get the teacher's prediction, without gradients and then train the student model to match this output. Once distilled, the student model can then be used to run the inference in the distilled number of steps. While in all this, as explained mathematically, we ensure that the velocities are still being scaled per resolution (stage) correctly and that use the local noise-levels in the teacher and student simulations.
Pyramidal Adversarial Step Distillation Finally in the Pyramidal Adversarial Step Distillation approach, we follow the same setup the progressive one, but also add a patchwise GAN loss on top of the squared L. loss of the Progressive distillation approach.
Eppr-ady Wrecon -pyr-prgg + Wady LGNN (2 (م+,)
Texh
We use the Hinge-GAr GAN loss for the LGAN which speeds up the distillation process by not only focussing on the pixelwise distance, but also matching the distributions of the noisy tokens adver- sarially.
As is standard practice with Adversarial step distillation, we use the Features extracted from the Teacher network D passed 4layer MLP as the Discriminator architecture for the GAN loss. The teacher network always remains frozen and only the MLP is trainable. We empirically found the values of Wrecon 10.0 and WGAN 1.0 converge well.
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Human Preference %
Prompt Dimension
Neodragon
No preference
PyramidalFlow
Appearance Style
Color
61.5
38.5
0.0
Homan Action
73.
23.1
Multiple Objects
73.1
23.1
3.8
Object Class
84.6
15.4
0.0
Overall Consistency
19.2
Scene
38.5
23.1
38.5
Spatial Relationship
Subject Consistency
53.8
34.6
11.5
Temporal Flickering
15.4
Temporal Style
63.5
9.6
26.9
Total
48.3
29.8
21.8
Noodragon
preference
Hummingbird
Appearance Style
20.5
Color
96.2
1,9
1.9
Human Action
80.8
19.2
0.0
Multiple Objects
71.8
25.6
2.6
Object Class
90.4
5.8
3.8
Overall Consistnncy
Scene
94.2
5.8
0.0
Subject Consistency
92.3
Temporal Style
76.9
23.1
0.0
Total
83.7
12.0
4.3
Table II: Results of the method-blinded human visual preference study Rows correspond subsets filtered by different VBench prompt dimensions.
750 paired video comparisons.
Table J1: Neodragon Quantization Scheme Used. We report the quantization scheme used for each modules here.
Modale
CLIF
CLIF
DGaitS
SSPIB
VAE
VAE
XMMDICCA
QuiESRNN
17:40-64 20
Calibration Duta Size
NA N4
NA
NA
505
400
40 4hdh
40
900
500 36th
deploy SNR
N4
NA
1140
1548
2441
25/B
I MORE DETAILS ABOUT END-TO-END INTEGRATION
Figure HT details our full end-to-end pipeline that we ship as part of the Neodragon Mobile VDM. It provides highly modular mechanism for generating videos on device.
USER STUDIES
Table I1 summarizes the results of a method-blinded human preference study. In this study, we pre- sented participants with unlabeled video pairs generated by two competing methods using identical input prompts. Participants were asked to select their preferred video or indicate no significant dif- ference as third option. To minimize potential bias, the order of method appearances within each pair was randomized. The set of prompts for comparison where randomly selected from the VBench prompts set. The results indicate that Neodragon is more often preferred over Pyramidal-FF while its preference rate dramatically increases when compared to AMD- MMD-Hummiggbird.
J MODEL PIPELINE QUANTIZATION
Fixed point quantization. The quantization scheme used for deploying the various modules and calibration results are described in table JI We use AIMET Al Model Efficiency Toolkit [Sidde- gowda et al. (2022) to perform Post Training Quantization (PTQ). W8A16 quantization scheme is used for but QuickSRNet. We apply AIMET AdaRound Nagel et al. (002)). weight rounding technique, to QuickSRNet which ends up adding 7+dB SQNR. In this table, SQNR is calculated between original FP models vs. the deployed models
It's worth pointing out that the complexity of pipeline has impact end-to-end integra- tion SQNR as the quantization loss, despite minimal for each model, compounds quickly. In DiT pipeline, for example, have FP embeddings coming from text encoders and first frame generated
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by SSDIB pipeline. Then it goes through 3 stages of DiTs mutiplied by the number of timesteps each stage. After which it's decoded by VAE decoder and upsampled by QuickSRNet. This is where the step distillation mentioned in previous section comes to assistance. Reduction to only timestep on each stage doesn't 't just help reduce latency but also greatly mitigates the compounding quantization loss along the pipeline.
K DISCUSSION OF LIMITATIONS
Despite its promising results, Neodragon has few limitations that highlight opportunities for future work.
First, the overall performance of the system is bounded by the capabilities of the underlying Pyramidal-Flow baseline. While our optimisations significantly reduce latency and memory foot- print, they do not fundamentally alter the generative capacity of the base model. Consequently, Neo- dragon inherits constraints video resolution, temporal length, and semantic fidelity, as reflected in VBench scores. For instance, our pipeline currently supports videos of frames at 640x1024 resolution, which, although competitive for mobile deployment, falls short of the high-resolution and long-duration outputs achievable by server-scale models such as Wan or Cog VideoX. Extend- ing Neodragon support longer sequences and higher resolutions without compromising efficiency remains an open challenge.
Second, our MMDiT block pruning strategy introduces a practical bottleneck: the selection of blocks to prune currently requires some visual inspection although having the guidance from the BI scores. While approach leverages block-importance scores and visual inspection to identify low-impact blocks, this process is labour-intensive and lacks scalability. Moreover, the multi-modal nature of MMDiT means that visual and textual importance scores are often uncorrelated, compli- cating automated decision-making. To address this, We are investigating heuristic-driven pruning strategies that incorporate perceptual metrics such as LPIPS, aiming automate block selection while preserving semantic alignment and temporal consistency.
Third, the step distillation process-critical for reducing the number of denoising steps-remaing an unresolved research problem. Our adaptation of pyramidal DMD achieves substantial speedups, reducing NFEs from 480 to 21 and cutting latency from 147s to 6.4s in the 1-1-1 configuration. How- ever, this acceleration comes at the cost of visual artifacts and semantic inconsistencies, particularly in the first frame. To mitigate these issues, we currently employ a hybrid architecture that generates the initial frame using SSD-IB and applies motion synthesis via the block-pruned MMDiT. While effective, this workaround introduces architectural complexity and deviates from the ideal of uni- fied model. Developing robust, single- mogel solution for step distillation that preserves fidelity across all frames is an active area research and a key goal for future iterations of Neodragon.
In summary, Neodragon demonstrates that mobile video generation is feasible, but achieving high- resolution, long-duration and artifact-free outputs challenge. Addressing these limitations will require advances in automated pruning, principled step distillation, and scalable architectural design, paving the way for truly interactive and high-quality video synthesis consumer hardware.
fully unified architecture remains an open
L USE OF LARGE LANGUAGE MODELS
We used Microsoft Copilot (a large language model) to aid in polishing the writing of this sub- mission. The model was employed solely for improving clarity and readability; all ideas, technical content, and conclusions are our own.
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Reviews:
[{"review_id": "sFBXUMzzgQ", "reviewer": "Reviewer_C59U", "summary": "The paper proposes a practical recipe for on-device text-to-video generation built on an MMDiT backbone, targeting Qualcomm Hexagon NPU. The framework includes four techniques: 1) Text encoder distillation: replace the original 5B T5-XXL text encoder with a tiny 130M distillT5 + context adapter. 2) Asymmetric VAE decoder distillation with only a 10M parameter model for efficient latent decoding. 3) block pruning for MMDiT by analyzing block importance to reduce parameters for MMDiT, and 4) extend the distribution matching distillation to the pyramidal flow-matching setting.", "questions": "- Do you have a latency breakdown for each module of the pipeline?\n- Have you conducted a user study or human evaluation to compare the base model with the smaller/pruned model?\n- Is there an ablation study showing the effectiveness of block pruning based on importance scores?\n- typos:\n  - `neogradon` -> `neodragon` in the abstract.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The paper presents a clear, step-by-step framework for on-device video generation with competitive Vbench.\n- Text-only distillation for the text-encoder is well-explained and practically motivated.\n- The step-distillation for pyramidal model adaptation is novel and empirically effective under 4‑4‑4 Tab.3, with a dqualitative result of tradeoff.", "weaknesses": "- The paper does not include a user study or human evaluation of the generated videos, which would strengthen the perceptual quality claims.\n- The hardware/deployment setup is under-specified — key details such as peak memory, per-module latency, data types/quantization, and runtime environment are missing.\n- The claim that the visual and textual importance of a block are uncorrelated is interesting, but currently under-supported; additional evidence/analysis would make this argument more convincing.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wnLquizJMm", "reviewer": "Reviewer_fTZz", "summary": "The paper proposes Neogradon, a video DiT (Diffusion Transformer) designed to run on a low-power NPU present in devices such as phones and laptop computers. It reduces the diffusion sampling cost using their novel extended version of DMD (Distribution Matching Distillation) for the Pyramidal Flow-Matching objective.", "questions": "For specific issues, please refer to the points listed in the Weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper successfully transforms a large, server-side Diffusion Transformer into a highly efficient, deployable mobile solution.\n2. The paper is well-structured, featuring a data-driven abstract and logical presentation of the methodology. \n3. The paper provides a finding that the Cosine Distance loss is indispensable for stabilizing the text encoder distillation process. This highlights the crucial role of preserving the directional coherence of text embeddings for the downstream attention mechanisms.", "weaknesses": "1. The final performance claim (VBench 81.61) is achieved by a hybrid pipeline that uses external models (SSD-IB for high-quality first-frame initialization and QuickSRNet for super-resolution) to compensate for artifacts caused by aggressive step distillation in the core model.\n2. The key assumptions underpinning the core compression strategies, such as the \"universality of compressed video latent space\" and the concept of \"similarly shallow semantic demands\" for large language models, are supported primarily by empirical results rather than formal theoretical proofs.\n3. The paper lacks critical quantitative data, such as comparisons of NPU peak memory usage and real-time decoding latency, to fully justify the final selection of certain components (e.g., choosing the TinyAEHV decoder despite its lower PSNR).\n4. The necessity of the two-stage curriculum used for MMDiT block pruning is only proven empirically (i.e., direct Stage 2 fails), but a definitive theoretical or optimization-landscape explanation for why this two-step curriculum is mandatory is missing.\n5. The block importance metric used for MMDiT pruning is insufficient on its own and requires the inclusion of subjective visual impact assessment to determine the final blocks to remove, suggesting the need for a more comprehensive, objective metric.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GlmMfWdmPv", "reviewer": "Reviewer_eXNS", "summary": "This paper proposed Neogradon, a video DiT designed to run on a low-power NPU present in devices. It designed a lightweight text encoder, video decoder, pruned diffusion transformer, and step distillation method for applying Distribution Matching Distillation for the Pyramidal Flow-Matching objective. Experiments shown that Neodragon generates 49 frames of 640×1024 resolution videos within 7.6 seconds on the Qualcomm Hexagon NPU with a VBench total score of 81.61.", "questions": "Please see above weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation is clear and meaningful as a on-device design of video generation model are important for phones or laptops.\n\n2. The experiments are thorough, providing a detailed exploration of various compression techniques.\n\n3. The paper has adapted various components in the video generation model on the edge-device, which is a very systematic project.\n\n4. The proposed model has good performance and can achieve advanced generation performance on the edge-device.", "weaknesses": "1. The display of some relational data in the paper is not intuitive, such as some key metrics for end-to-end deployment such as memory consumption and latency, which are only explained in text. Providing some tables or charts would be more obvious.\n\n2. The paper should provide clearer references to some of the baseline choices made in the appendix regarding the baseline method, such as why Pyramidal Flow was chosen.\n\n3. More efficiency should be reported, such as memory consumption and latency of other comparison methods.\n\n4. The balance between the performance and efficiency of different proposed components on the final model should be reported.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "r0l7ksViYS", "reviewer": "Reviewer_oTLa", "summary": "This paper proposes Neodragon, an efficient on-device video diffusion transformer designed to run entirely on a mobile NPU. The method starts from the Pyramidal-Flow framework and introduces four complementary optimizations:\n1. Text-Encoder Distillation: Compresses the large T5-XXL encoder into a lightweight “DT5 + ContextAdapter” model with minimal quality loss.\n2. Asymmetric Decoder Distillation: Replaces the original heavy VAE decoder with a much smaller decoder transferred from other models, keeping the encoder and DiT frozen.\n3. MMDiT Block Pruning: Removes less important blocks from the diffusion transformer while maintaining fidelity through two-stage fine-tuning.\n4. Step Distillation for Pyramidal-Flow: Reduces the number of denoising steps from 480 to 21 using an extended Distribution Matching Distillation (DMD) strategy.\n\nThese combined optimizations enable real-time text-to-video generation on the Qualcomm Hexagon NPU, setting a new state of the art for mobile video generation.", "questions": "1. The paper perform full-stack, end-to-end optimization on pyramid-flow model. I wonder whether the similar techiques like can be applied--or have been tried--on other video diffusion models such as Wan or CogVideoX, which target consumer-level GPUs. Would the authors expect comparable performance gains or face new challenges due to architectural differences?\n2. I am experienced in CUDA programming and are curious about NPU programming. Could the author compare the differences or GPU and NPU programming and provide some implementation insight?\n3. The step distillation process seems to offer much greater acceleration than block pruning. Can the author provide some insights in how to combine step distillation and block pruning to achieve best speed accuracy tradeoff? Does the speed gain of block pruning worh the accuracy loss?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "1. **Well-structured and practical problem analysis:** I appreciate the way the paper systematically analyzes bottlenecks in the video diffusion pipeline. It first identifies that memory (not compute) is the primary constraint, and addresses this through text encoder and decoder distillation. Once memory becomes manageable, the authors shift focus to speed via block pruning and denoising step distillation. This staged approach shows deep understanding of real deployment challenges and practical prioritization.\n2. **Targeted and effective design choices.** Each optimization directly addresses a critical performance bottleneck—encoder size, latent reconstruction cost, model depth, and step count—without losing model compatibility. The work shows excellent engineering sense in solving exactly the key part at each stage.\n3. **Comprehensive ablation studies.** Nearly every design component is supported by specific ablations. For example, Figure 1 (b) compares different text encoder distilltation method, Table 1 compares multiple decoder architectures under asymmetric fine-tuning; Table 2 analyzes MMDiT pruning ratios; and Table 3 reports performance across step distillation configurations. This makes the conclusions well supported and convincing.\n4. **Rich visual evidence.** The paper provides abundant qualitative results, both in the main paper and in the appendix, including frame-by-frame comparisons and long video samples. These visuals clearly demonstrate that the model preserves temporal consistency and visual quality even after aggressive compression.", "weaknesses": "1. Although the experiment is rich and supportive, the setting of experiment is sometimes not clearly stated, For example, I do not realize what base model the paper use before a second read. Second example is that I was confused for a while about what \"Neodragon E2E\" and \"T2V Multi-Step\" are, and later find in line 470 and 471 what these mean. The author is encouraged to add a specific section to clarify the setting of experiment conducted throughout the paper and I believe this would greatly increase the clarity of the paper. Possibly this is due to limited spaces. Also, some table/figure (like table 4) is not referred in the text. \n2. Lack of NPU Implementation Detail. While the paper reports on-device latency and memory numbers, it omits critical information such as hardware numbers (memory, flops), runtime environment, or hardware-specific optimizations.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)