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REVIEW 4 major objections 4 minor 37 references

HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper claims that HPSD lets a text-image-to-video model internalize the quality its privileged image-and-prompt mode elicits, and reproduce it from text alone via hybrid-policy self-distillation.

desk verdict HPSD is a plausible and genuinely interesting hybrid-policy distillation idea with strong but under-validated numbers; the main risk is an unmeasured distribution assumption at the supervision state. read the letter →

arxiv 2608.13205 v1 pith:EDXX3U3T submitted 2026-08-13 cs.CV

Jiazi Bu , Pengyang Ling , Yujie Zhou , Yibin Wang , Yuhang Zang , Xuanlang Dai , Shengyuan Ding , Tianyi Wei
show 5 more authors
Xiaohang Zhan Jiaqi Wang Tong Wu Dahua Lin Xingang Pan
This is my paper · ORCID
classification cs.CV
keywords text-image-to-videoself-distillationhybrid-policydistillationflow-matchingdiffusionmodelsvideogenerationcondition-elicitedcapabilityon-policyvsoff-policyvelocity-levelsupervision
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper asks whether a text-image-to-video model can learn a capability that only shows up when it is given extra privileged inputs — a high-quality first frame and a rewritten detailed prompt — and keep that capability when generating from a plain text prompt alone. The authors propose a self-distillation scheme in which the same model plays both roles: a teacher running in the privileged image-and-enhanced-prompt mode, and a student running in the base text-only mode. The scheme avoids two known failure modes: supervised fine-tuning on teacher outputs is off-policy, while naive on-policy distillation creates a mismatched state by mixing a clean first frame with the student's own rollout. On two TI2V backbones, the method improves text-only video quality on all reported reward metrics and also improves the image-guided mode, suggesting the model's base generation ability is genuinely strengthened.

What carries the argument

The load-bearing object is the hybrid-policy sub-trajectory. Starting from an anchor state $x^{\mathrm{Tea}}_{t_i}$ on the teacher's privileged TI2V trajectory, with the first frame re-noised so all frames share noise level $t_i$, the student evolves the state for $K$ steps under its own T2V velocity $v_\phi(\cdot \mid c_{\mathrm{txt}})$, producing $x^{\mathrm{Hyb}}_{t_{i+K}}$. The teacher is then queried at $\hat{x}^{\mathrm{Hyb}}_{t_{i+K}} = [c_{\mathrm{img}}, x^{\mathrm{Hyb},(2:F)}_{t_{i+K}}]$ in its own TI2V input format, and the student matches the teacher's velocity on frames $2{:}F$. The length $K$ is the interpolation dial: $K=0$ gives off-policy supervision on the teacher trajectory, large $K$ approaches on-policy supervision, and $K=3$ balances anchoring with policy alignment in the reported experiments.

What would settle it

Re-run HPSD with Equation 10 altered so the first frame fed to the teacher is the student's own denoised first frame rather than the privileged $c_{\mathrm{img}}$, keeping everything else fixed; if the reported VideoAlign gains vanish, the clean-frame re-imposition is what carries the method.

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Extended reading notes

Core claim

The central claim is that the quality gap between a TI2V model's privileged (first-frame + enhanced-prompt) mode and its text-only mode is not a fixed property of the architecture: by self-distilling with a hybrid-policy objective, the model can absorb the privileged mode's content and motion priors into its own T2V weights. Concretely, HPSD makes the same TI2V model serve as teacher under privileged conditions and as student under vanilla T2V conditions, supervises the student on states that begin on the teacher's denoising trajectory and then evolve under the student's own velocity, and matches the teacher's velocity field on those hybrid states. In experiments this raises WAN-2.2's T2V VideoAlign from 0.5335 to 1.8753, against 1.2046 for supervised fine-tuning and 0.2613 for on-policy distillation, and also lifts the model's TI2V VideoAlign from 0.7831 to 1.2139.

Load-bearing premise

The method assumes that a video whose first frame is the teacher's clean reference image and whose later frames come from the student's own rollout is a valid input that yields trustworthy teacher guidance; if that mixed state is off-distribution for the model, the teacher's correction signal is corrupted.

Editorial extensions

If this is right

  • Base text-to-video quality of a TI2V model can be improved substantially (VideoAlign 0.5335 to 1.8753 on WAN-2.2) without video-level human annotations or an external video teacher.
  • The same model's image-guided mode also improves after T2V-oriented distillation (VideoAlign 0.7831 to 1.2139), so the internalized capability is not purely a text-conditioning artifact.
  • K is a genuine interpolation knob: K=0 reduces to off-policy distillation and large K approaches on-policy supervision, with K=3 optimal in the reported experiments.
  • Privileged conditions act additively: distilling from an enhanced prompt plus a synthesized first frame outperforms first-frame-only guidance, so better auxiliary LLM and text-to-image tools should raise the achievable ceiling.
  • Naively applying on-policy distillation to TI2V models can collapse T2V quality (VideoAlign drops to 0.2613 on WAN-2.2), making the hybrid-policy correction necessary rather than optional.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension the paper does not report: compare the teacher's velocity error on hybrid states with its error on true TI2V rollouts at matched noise levels; if the error is much higher on hybrid states, the apparent gains may come from something other than trustworthy teacher correction.
  • The same anchoring idea could apply whenever one conditioning modality is privileged over another in a shared generative model, such as distilling reference-image or depth-conditioned quality into a text-only mode, provided the privileged condition can be converted into a student-compatible state.
  • The paper ablates K only on WAN-2.2; testing the K sweep on LTX-2.3 would show whether K=3 is a global optimum or a backbone-dependent choice.
  • Because the offline stage relies on auxiliary LLM and text-to-image generators, HPSD's ceiling should scale with those generators' quality; this is implied by the paper's additivity ablation but not directly measured across generator strengths.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes HPSD, a self-distillation framework for Text-Image-to-Video (TI2V) diffusion models, with the goal of internalizing the quality gains obtained from privileged conditioning (an enhanced prompt and a high-quality first frame) into the model's base text-to-video (T2V) mode. The teacher is the same TI2V model operating in TI2V mode, while the student operates in T2V mode. During training, the teacher first generates an off-policy anchor trajectory under privileged conditions; the student then rolls out a short K-step sub-trajectory from each anchor state using its own velocity field, and is supervised by the teacher at the resulting hybrid state, where the clean first frame is re-imposed. Experiments on WAN-2.2 and LTX-2.3 report consistent improvements over vanilla T2V, supervised fine-tuning, D-OPSD, and an on-policy distillation baseline on reward-model metrics and VBench.

Significance. The problem addressed is timely and well-motivated: unified TI2V models exhibit a large gap between their conditioned and base generation abilities, and distilling that ability into the base mode is practically valuable. The hybrid-policy idea—anchoring the student on teacher states while letting it evolve under its own policy—is a plausible and novel contribution that generalizes the off-policy/on-policy dichotomy. The paper provides two backbones, ablations on the key hyperparameter K, ablations on the privileged conditions, and a transfer study across first-frame generators, which is a reasonable experimental campaign. The main strengths are the clear formulation of the condition-state mismatch problem, the explicit algorithmic procedure, and the breadth of automatic metrics. However, the evidence for the central mechanism is incomplete: the validity of the hybrid state in Eq. (10) is assumed rather than demonstrated, the on-policy baseline is under-specified, and all quantitative claims rest on single-seed reward-model scores without human evaluation. These issues need to be addressed before the results can be considered robust.

major comments (4)
  1. [§4.1, Baselines] The core supervision target in Eq. (10) is the hybrid state [c_img, x^{Hyb,(2:F)}_{t_i+K}], which re-imposes the teacher's clean first frame onto the student's evolved frames. This is structurally identical to the invalid mixed state in Eq. (5) that the paper uses to reject on-policy distillation, except that the frames come from a roll-out anchored at the teacher's trajectory. The paper never measures whether this hybrid state is in-distribution for the teacher, nor whether the teacher's velocity at this state is a meaningful correction target. If the student's roll-out drifts from the teacher's content, the teacher's prediction is corrupted exactly as in Eq. (5), and the stated advantage of HPSD over OPD disappears. Please provide evidence that the hybrid state is plausible, for example by measuring the distribution gap (e.g., Fréchet distance of the mixed state versus the teacher's own TI2V states), by showing that the teacher's velocity at the hybrid state is consistent with the student's content (e.g., via denoising trajectory alignment), or by an ablation that replaces the re-imposed clean first frame with the student's own evolved first frame.
  2. [§4.2, Tab. 1] The on-policy distillation baseline is listed without specifying its conditioning inputs. Equation (4) defines OPD with a single condition c, but in this paper the teacher operates under privileged conditions while the student operates under the vanilla prompt. It is therefore unclear whether the OPD baseline (i) queries the teacher with c_img and the enhanced prompt at student T2V states (thereby inducing the condition-state mismatch), (ii) conditions the teacher on the enhanced prompt only, or (iii) uses some other configuration. This ambiguity matters because the paper attributes the collapse of OPD (VideoAlign 0.2613 on WAN-2.2 versus 0.5335 for vanilla T2V) to condition-state mismatch; under alternative (ii) the baseline would likely not collapse. Please state the exact conditions used for the OPD teacher and, if the mismatch is intended, show that this is the natural adaptation rather than a straw-man configuration.
  3. [§4.3, Tab. 3(a)] All quantitative results are reported as single point estimates, with no standard deviations, no number of seeds, and no significance tests. The headline claim that HPSD 'significantly improves' T2V performance rests on differences in reward-model scores that may be well within run-to-run variance, especially since training uses LoRA and 500 steps on 8 GPUs. In addition, Tab. 3(a) is used to select K=3, and the same evaluation prompt set (VideoDPO/VideoFeedback) appears to be used for both the ablation and the final reported numbers, raising the risk of selection on the test set. Please report means and variances over at least three independent runs, and clarify whether K is chosen on a held-out validation split that is disjoint from the final evaluation set.
  4. [§3.3, Eq. (11)] The loss in Eq. (11) uses the teacher's velocity at the hybrid state as the supervision target, but the paper does not analyze how the teacher improves during training when it is updated by EMA of the student. Since the teacher and student share weights, the teacher's velocity at the hybrid state is partly a function of the student's own policy. This self-referential loop is not circular in itself because the privileged conditions remain fixed, but the interaction between EMA updates and the hybrid state's distribution is not addressed. If the student policy drifts, the EMA teacher may gradually adapt to the student's distribution, weakening the anchoring effect. The paper should either provide a theoretical argument or an empirical diagnostic (e.g., plotting the teacher-student velocity discrepancy over training) to show that the hybrid supervision remains informative.
minor comments (4)
  1. [§4.1] The paper contains several typos and formatting issues, such as 'W AN-2.2' with a space in Table 1 and Figure captions, and 'UR-v2-A, UR-v2-P' expansions that are not consistently defined. Please proofread the manuscript carefully.
  2. [§B.2] The training curve in Figure 9 is described as converging around 500 steps, but the x-axis extends to 800 steps and the text in §B.2 says 'negligible fluctuation thereafter' without specifying the evaluation protocol. Please clarify how the VisionReward score is computed during training and whether the evaluation prompts overlap with the training prompts.
  3. [Related Work] The citation 'Daniel Verdú, 2024' in the related work appears as an author name; the reference entry is formatted as a paper title. Please correct the citation style to match the author-year format used elsewhere.
  4. [§4.1] The evaluation set of 500 prompts is sampled from VideoDPO and VideoFeedback, but the paper does not specify whether any of these prompts also appear in the training set (Pref-GRPO). If there is overlap, the reported numbers may be optimistic. Please state the overlap or ensure disjointness.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: HPSD's central claim is an externally evaluated empirical result, and neither the method's equations nor its citations reduce the reported improvement to a fitted input or a self-referential definition.

full rationale

Walking the derivation chain: the paper's premise is that TI2V conditioning (enhanced prompt plus first frame) elicits higher-quality output from the same model, and HPSD trains the T2V student to match the teacher's velocity on hybrid states formed by anchoring on the teacher trajectory and rolling out K steps with the student's own policy. The loss in Eq. 11 is a standard velocity-matching objective; it does not by construction guarantee the reported gains. Evaluation is performed on held-out prompts from VideoDPO/VideoFeedback using external reward models (VideoAlign, VisionReward, UnifiedReward) and VBench, so the central claim is not a renamed fit or a self-citation chain. The structural resemblance between the criticized condition-state mismatch (Eq. 5) and the supervision state (Eq. 10) is a potential robustness limitation, not a circularity: it challenges the validity of the teacher target, but it is not a case of the paper defining its outcome in terms of its input. Hyperparameters such as K=3, EMA decay, and anchor steps are ablated or reported, and no parameter is fitted to the evaluation metric and then presented as a prediction. The EMA teacher makes the training signal partly self-referential, but that is a property of self-distillation rather than a circular derivation, and the paper's evidence is grounded in external benchmarks. Existing self-citations (e.g., Bytheway, Light-a-video, Pref-GRPO dataset) are contextual or data-source references, not load-bearing justifications of the HPSD mechanism.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a chain of domain assumptions about the validity of hybrid states and the superiority of privileged-condition generation, plus a small set of tuned hyperparameters. No new entities are introduced.

free parameters (4)
  • Sub-trajectory length K = 3
    Chosen as the best value over {0,1,3,5,7} in Tab. 3(a); main results depend on it.
  • Number of anchor steps |A| = 6
    Set in Tab. 4 without an ablation; affects how much teacher content is injected.
  • EMA decay rate = 0.999
    Used to update the teacher in Algorithm 1; no ablation reported.
  • Training steps = 500
    Stopping point chosen from VisionReward plateau in Fig. 9; no held-out criterion described.
assumptions (5)
  • standard math Flow-matching velocity field v_theta and ODE sampling (Eq. 1) describe the model's denoising process.
    The method and losses are formulated inside the flow-matching framework.
  • domain assumption TI2V mode under privileged conditions (enhanced prompt plus first frame) genuinely outperforms base T2V mode for the same weights.
    Motivates the whole distillation target; Fig. 2 shows visuals but no numeric metric table.
  • domain assumption Re-noising the teacher's clean first frame to time t_i produces a valid student-compatible T2V state (Eq. 8).
    No experiment confirms that the resulting mixed state lies on the T2V data manifold.
  • domain assumption The teacher's velocity at the re-imposed first-frame state (Eq. 10) is a useful, non-corrupt training target.
    The central validity assumption; the paper does not measure distribution shift for these states.
  • domain assumption The EMA teacher remains a sufficiently accurate and stable target as the student updates.
    Algorithm 1 line 13; no analysis of teacher-student drift is provided.

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Cite this review

Pith. "Pith review of HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models." pith.science (2026). https://pith.science/paper/EDXX3U3T

@misc{pith2026260813205,
  author       = {Pith},
  title        = {Pith review of: HPSD: Hybrid-Policy Self-Distillation for Text-Image-to-Video Diffusion Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EDXX3U3T}},
  note         = {Machine review of arXiv:2608.13205}
}
read the original abstract

Text-Image-to-Video (TI2V) models are an emerging unified architecture, where a single model simultaneously supports text-to-video (T2V) and image-to-video (I2V) generation. Given a high-quality first frame or a detailed textual prompt, TI2V models unlock substantially better visual quality than their T2V mode, raising a natural question: can the capability elicited by such privileged conditions be internalized into the model's own base generation ability? A common approach toward this goal is model self-distillation. However, the most straightforward solution, supervised fine-tuning, follows an off-policy strategy: its supervision is confined to teacher-generated endpoints from a fixed offline distribution rather than student-visited states, lacking precise correction tailored to the evolving policy. Recent on-policy distillation methods instead suffer from condition-state mismatch, where supervision is steered toward the given first frame instead of the student's actual content, misleading the correction. To achieve self-distillation that absorbs the teacher's privileged prior while retaining precise policy correction, in this work, we propose Hybrid-Policy Self-Distillation (HPSD), a novel self-distillation framework where a single TI2V model acts as both teacher and student under different conditions: the teacher operates in TI2V mode with a high-quality first frame and an enhanced prompt, while the student runs in the base T2V mode with only the vanilla prompt. Specifically, the student inherits off-policy teacher trajectory points as anchors, locally refines them toward its own policy, and finally receives velocity-level supervision on these self-generated roll-outs. Extensive experiments demonstrate that HPSD significantly improves T2V performance while also delivering notable TI2V gains, effectively strengthening the model's base generation ability.

Figures

Figures reproduced from arXiv: 2608.13205 by the authors.

Figure 1
Figure 1. Introduction to HPSD. (a) Off-policy SFT supervises on fixed teacher endpoints, which drift from the evolving student policy. (b) On-policy distillation queries the teacher at student-visited states, yet suffers from condition-state mismatch in TI2V models. (c) HPSD anchors the student on the teacher trajectory and supervises roll-outs evolved under its own policy. (d) HPSD substantially boosts the base generation q… view at source ↗
Figure 2
Figure 2. Condition-Elicited Capability. Conditioning a TI2V model on a high-quality first frame or an enhanced prompt clearly outperforms vanilla T2V. Their combination yields the best results across visuals and metrics, motivating HPSD’s goal of internalizing this capability into the model. failure we term condition-state mismatch ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Condition-State Mismatch. While standard TI2V/T2V states maintain consistent content across frames, on-policy distillation creates an invalid mixed state by forcing a clean first frame alongside the student’s unaligned T2V rollout, yielding conflicting, corrupted teacher supervision. Distillation Paradigms. Consider a teacher velocity field v˜ and a student vϕ. Off-policy SFT fine￾tunes the student on teacher-genera… view at source ↗
Figures from the paper (28 more)
Figure 4
Figure 4. Figure 4: Overview of HPSD. (a) Offline Stage: Privileged conditions (enhanced prompt and first frame) are synthesized using auxiliary models. (b) Online Stage: The student evolves hybrid-policy sub-trajectories starting from the teacher’s off-policy anchor states. Distillation …
Figure 5
Figure 5. Figure 5: Qualitative Comparisons with Baselines on WAN-2.2. Best viewed zoomed in. Training Details. Unless specified otherwise, all experiments share the following setup. We train for 500 steps on 8 H200 GPUs with a batch size of 1 per GPU, using the AdamW optimizer with a lea…
Figure 6
Figure 6. Figure 6: Qualitative Comparisons with Baselines on LTX-2.3. Best viewed zoomed in. state mismatch. While SFT and D-OPSD produce plausible content, they still lack fine details, con￾sistent subjects, and cinematic lighting. In contrast, HPSD delivers significantly sharper textur…
Figure 7
Figure 7. Figure 7: Effects of HPSD on TI2V mode. The red boxes indicate the shared first frames. Vanilla T2V HPSD (Ours) “man in yellow hazmat suit on a paddleboard in the middle of the ocean.” “coffee swirling in a rainbow cup.” [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Improved Consistency & Physical Plausibility. (Left) Vanilla T2V exhibits limited tem￾poral consistency (paddle appears abruptly), while HPSD ensures coherent subject retention. (Right) Vanilla T2V lacks physical realism (static liquid), whereas HPSD renders natural fl…
Figure 9
Figure 9. Figure 9: Training curve of HPSD. To determine the training budget, we investigate the convergence behavior of HPSD by monitoring its Vi￾sionReward score on WAN-2.2. As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p015_9.png]
Figure 10
Figure 10. Figure 10: Prompt Template for Enhanced Prompt Generation. Prompt Template for First-Frame Prompt Generation Act as an expert AI Visual Director and Prompt Engineer specializing in Image-to-Video (I2V) and Text-to-Image (T2I) workflows. Your task is to design a highly detailed T…
Figure 11
Figure 11. Figure 11: Prompt Template for First-Frame Prompt Generation. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: Comparison between HPSD (with Flux.2-Klein-4B) and vanilla WAN-2.2. C ADDITIONAL QUALITATIVE SAMPLES In this section, we provide further visual results. Specifically, [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Privileged Condition Examples. F PRIVILEGED CONDITION EXAMPLES In this section, we present concrete examples of the privileged conditions utilized during the HPSD training process. As depicted in [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: More qualitative comparison results on WAN-2.2 (1/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “An old wizard with a gray beard, wearing a brown cassock and carrying a staff.” “Man in a business suit with headphones walking in the rain at night.” …
Figure 15
Figure 15. Figure 15: More qualitative comparison results on WAN-2.2 (2/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “a ship sailing in the ocean.” “A gray Power Ranger, adorned with a purple cape, is running at a high speed against a green background.” [PITH_FULL_IMA…
Figure 16
Figure 16. Figure 16: More qualitative comparison results on WAN-2.2 (3/6). Best viewed zoomed in. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_16.png]
Figure 17
Figure 17. Figure 17: More qualitative comparison results on WAN-2.2 (4/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “steampunk themed chefs cooking beside the sea, high detail rpg.” “A boy is picking up a coin from the road in a 3D cartoon.” [PITH_FULL_IMAGE:figures/…
Figure 18
Figure 18. Figure 18: More qualitative comparison results on WAN-2.2 (5/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “A car with wings is flying through a city.” “A man rides a bike, escaping a lava tsunami, while pigeons fly away in the background and the camera circl…
Figure 19
Figure 19. Figure 19: More qualitative comparison results on WAN-2.2 (6/6). Best viewed zoomed in. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_19.png]
Figure 20
Figure 20. Figure 20: Comparison between HPSD and vanilla WAN-2.2 (1/3). Best viewed zoomed in. 21 [PITH_FULL_IMAGE:figures/full_fig_p021_20.png]
Figure 21
Figure 21. Figure 21: Comparison between HPSD and vanilla WAN-2.2 (2/3). Best viewed zoomed in. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_21.png]
Figure 22
Figure 22. Figure 22: Comparison between HPSD and vanilla WAN-2.2 (3/3). Best viewed zoomed in. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_22.png]
Figure 23
Figure 23. Figure 23: More qualitative comparison results on LTX-2.3 (1/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “an alligator swimming in Hagerstown City Park.” “a young guy executive on the phone walking across street intersection, lots of people, traffice.” [PI…
Figure 24
Figure 24. Figure 24: More qualitative comparison results on LTX-2.3 (2/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “People walking on the moon surface, Earth is seen in the sky, Futuristic Homes.” “Two teddy bears are talking to each other as they walk into a forest.…
Figure 25
Figure 25. Figure 25: More qualitative comparison results on LTX-2.3 (3/6). Best viewed zoomed in. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_25.png]
Figure 26
Figure 26. Figure 26: More qualitative comparison results on LTX-2.3 (4/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “Animation of drifting waves, with moving pink clouds at sunset.” “A black Labrador and a gray Norwegian Forest cat play on the lawn in the backyard.” …
Figure 27
Figure 27. Figure 27: More qualitative comparison results on LTX-2.3 (5/6). Best viewed zoomed in. Vanilla T2V OPD D-OPSD SFT HPSD (Ours) “A 4K, 3D animated squirrel named Sammy is shown hopping around in the forest, looking at flowers.” “cool looking pilot flying a red plane under a suspe…
Figure 28
Figure 28. Figure 28: More qualitative comparison results on LTX-2.3 (6/6). Best viewed zoomed in. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_28.png]
Figure 29
Figure 29. Figure 29: Comparison between HPSD and vanilla LTX-2.3 (1/2). Best viewed zoomed in. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_29.png]
Figure 30
Figure 30. Figure 30: Comparison between HPSD and vanilla LTX-2.3 (2/2). Best viewed zoomed in. 27 [PITH_FULL_IMAGE:figures/full_fig_p027_30.png]
Figure 31
Figure 31. Figure 31: Generated results using same prompts and different seeds on LTX-2.3. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_31.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.