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| FPV DRONE DIVE PERSPECTIVE - WAN 2.2 |
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| ─────────────────────────────────────────────────────────────────────────────── |
| IDENTITY |
| ─────────────────────────────────────────────────────────────────────────────── |
| Base model Wan 2.2 |
| Generation mode not declared by the author |
| Checkpoint not determined — verify before use |
| Category Camera movement |
| Author ErosX |
| Source https://civitai.com/models/2191633 |
| Provenance CivitAI · model 2191633 · version 2467713 |
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| ─────────────────────────────────────────────────────────────────────────────── |
| WEIGHTS |
| ─────────────────────────────────────────────────────────────────────────────── |
| File FPV_drone_dive_high_noise.safetensors |
| Version High |
| Size 146.3 MB |
| Format SafeTensor / unknown precision |
| SHA256 35704a53938f37d9bfa268394464e0469673e7b88dcd74fbfd8401739605be47 |
| Published unknown |
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| ─────────────────────────────────────────────────────────────────────────────── |
| TRIGGER WORDS |
| ─────────────────────────────────────────────────────────────────────────────── |
| No trigger word. The LoRA applies as soon as it is loaded — |
| strength is the only way to dial the effect up or down. |
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| ─────────────────────────────────────────────────────────────────────────────── |
| RECOMMENDED SETTINGS |
| ─────────────────────────────────────────────────────────────────────────────── |
| The author did not publish specific settings. |
| No author settings were published. Community baseline: steps Official |
| Wan2.2 reference code (wan/configs/): sample_steps 40 for T2V-A14B and |
| I2V-A14B with the expert handover at the SNR boundary, and 50 for TI2V-5B. |
| ComfyUI's CURRENT 14B t2v/i2v templates ship only the distilled |
| configuration: 4 steps total, split 2 on the high-noise expert then 2 on |
| the low-noise expert (two KSamplerAdvanced nodes, start/end 0->2 and 2->4). |
| An undistilled 20-step configuration split 10/10 shipped as a second, |
| bypassed node group in an earlier revision of the t2v template and still |
| ships as the ACTIVE default in the current video_wan2_2_14B_flf2v template |
| (20 steps, split 0->10 and 10->end). TI2V-5B: 20 steps in ComfyUI's |
| template, 50 in the reference code; single sampler, no split., cfg Official |
| reference sample_guide_scale is a per-expert pair: (3.0 low- noise, 4.0 |
| high-noise) for T2V-A14B and (3.5, 3.5) for I2V-A14B — the two experts are |
| allowed different CFG. TI2V-5B: 5.0 in both the reference code and |
| ComfyUI's template. Undistilled ComfyUI values vary by template revision: |
| 3.5 in the older t2v template's bypassed undistilled group, 4.0 in the |
| current flf2v template. With ANY 4-step distill LoRA: cfg 1.0 on both |
| samplers (verified in the shipped t2v and i2v templates)., sampler euler + |
| simple scheduler on both A14B experts, with shift set via ModelSamplingSD3: |
| 5.0 in ComfyUI's current distilled 14B t2v/i2v templates, 8.0 in the |
| undistilled groups (older t2v revision, and the current flf2v template). |
| Official reference sample_shift differs by task: 12.0 for T2V-A14B, 5.0 for |
| I2V-A14B, 5.0 for TI2V-5B. TI2V-5B in ComfyUI: uni_pc + simple, shift 8.0., |
| resolution A14B: 480P and 720P — 1280x720 or 832x480 (and portrait). |
| Dimensions should be multiples of 16 (Wan2.1 VAE stride 4x8x8 combined with |
| 1x2x2 patching, both read from the reference configs); ComfyUI's templates |
| ship a cheap 640x640. Length 81 frames at 16 fps (~5s), frames 4n+1. |
| TI2V-5B: 720P, 1280x704 or 704x1280; multiples of 32, because the |
| Wan2.2-VAE stride is 4x16x16 and the model card states an additional |
| patchification layer brings total compression to 4x32x32 — which is why it |
| is 704, not 720. Length 121 frames at 24 fps (~5s)., lora_strength Official |
| Lightning/lightx2v 4-step LoRAs: 1.0 on both experts — verified in |
| ComfyUI's shipped t2v and i2v templates, where both LoraLoaderModelOnly |
| nodes carry strength 1.0. Character/style/motion LoRAs are commonly run at |
| 0.6-1.0, and when stacked on top of a 4-step distill LoRA the usual |
| community move is to pull the high-noise-side strength down (roughly |
| 0.5-0.8); those subject-LoRA numbers are community practice, not published |
| by the authors — see "unverified".. |
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| ─────────────────────────────────────────────────────────────────────────────── |
| ADOPTION & POPULARITY |
| ─────────────────────────────────────────────────────────────────────────────── |
| No statistics available. |
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| ─────────────────────────────────────────────────────────────────────────────── |
| COMMUNITY NOTES |
| ─────────────────────────────────────────────────────────────────────────────── |
| 2 comment(s) on the source listing; showing 1. |
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| [+13] yooooo I’m gonna need @GayLizardSpy to make a FPV drone shot flying |
| into a gaping asshole |
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| Archive summary: |
| FPV Drone Dive Perspective - Wan 2.2 is a LoRA designed for Wan Video- |
| family checkpoints, published by [ErosX](/users/ErosX) on CivitAI in |
| December 2025, now on version 2. It layers on top of any compatible Wan |
| Video checkpoint. You load the checkpoint first, then apply this LoRA |
| on top of it. Wan 2.2 uses a two-pass architecture — a high-noise model |
| handles motion and scene composition, and a low-noise model refines |
| detail. Both models need to be loaded together for a complete pipeline; |
| a single pass on its own won't produce usable output. When using speed |
| LoRAs (lightx2v, CausVid, Lightning) |
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| Source: https://civarchive.com/models/2191633?modelVersionId=2467713 |
| Comments are other users' words, reproduced as written. |
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| ─────────────────────────────────────────────────────────────────────────────── |
| CHAINING / STACKING |
| ─────────────────────────────────────────────────────────────────────────────── |
| Stacks with others not stated |
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| The author gave no chaining guidance. The rules below are the |
| general Wan 2.2 ones, not anything specific to this LoRA. |
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| General Wan 2.2 rules that apply here (see REFERENCE.txt): |
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| • The high/low pair is not a stack. Every A14B LoRA that ships two files |
| needs the high-noise file on the high-noise UNET chain and the low- |
| noise file on the low-noise UNET chain — two LoraLoaderModelOnly nodes |
| on two different models, never both on one model. |
| • Never load a high-noise LoRA onto the low-noise expert or vice versa. |
| It loads without a single warning because the two checkpoints are |
| architecturally identical, and the damage shows up only as degraded |
| output. Filename is the only guard. |
| • A single-file A14B LoRA with no high/low marking (common for Wan2.1-era |
| LoRAs and for LoRAs extracted from merged models) goes on BOTH experts |
| at the same strength. Loading it only on the low-noise expert is the |
| safer half-measure when it fights the composition. |
| • Stack subject/style LoRAs after the accelerator LoRA in the chain: |
| UNETLoader -> Lightning LoRA -> subject LoRA -> ModelSamplingSD3. Order |
| within LoraLoaderModelOnly chains is additive so it rarely matters |
| mathematically, but keeping the accelerator first matches every |
| reference workflow. |
| • Do not stack two step-distillation LoRAs on the same expert. One |
| accelerator per expert. |
| • When adding subject LoRAs on top of a 4-step distill LoRA, reduce the |
| subject LoRA strength rather than the distill LoRA strength — the |
| distill LoRA is what keeps 4 steps viable at cfg 1.0. |
| • Keep the LoRA strength for the two experts independently tunable. |
| Because the high-noise expert owns layout and the low-noise expert owns |
| detail, the community routinely runs a style LoRA weaker on high-noise |
| and full-strength on low-noise. |
| • TI2V-5B LoRAs chain like any single-model LoRA and can never be mixed |
| into an A14B chain, or the reverse — the two have entirely different |
| tensor geometry. |
| • The author never states which weight family this targets. Three |
| distinct answers. The structural half of each is verified from the |
| shipped config.json and safetensors index files; the behavioural |
| severity is community-reported and flagged in "unverified". (1) A14B |
| <-> TI2V-5B: INCOMPATIBLE. The 5B model is dim 3072 / 30 layers / 24 |
| head |
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| ─────────────────────────────────────────────────────────────────────────────── |
| PROS |
| ─────────────────────────────────────────────────────────────────────────────── |
| + Well established — 2,021 downloads. [source: CivitAI stats] |
| + Positively received — 98 thumbs-up with no down-votes. [source: CivitAI |
| stats] |
| + Compact at 146 MB — cheap to stack with other LoRAs. [source: file |
| size] |
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| ─────────────────────────────────────────────────────────────────────────────── |
| CONS & CAVEATS |
| ─────────────────────────────────────────────────────────────────────────────── |
| - No trigger word — the LoRA is always on once loaded, so strength is the |
| only control. [source: model metadata] |
| - Author never states which generation mode it was trained for; test |
| against your own workflow before relying on it. [source: description] |
| - Thin documentation — expect trial and error. [source: description |
| length] |
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| ─────────────────────────────────────────────────────────────────────────────── |
| COMPATIBILITY WARNING |
| ─────────────────────────────────────────────────────────────────────────────── |
| Three distinct answers. The structural half of each is verified from the |
| shipped config.json and safetensors index files; the behavioural severity |
| is community-reported and flagged in "unverified". (1) A14B <-> TI2V-5B: |
| INCOMPATIBLE. The 5B model is dim 3072 / 30 layers / 24 heads / ffn 14336 |
| against the A14B's dim 5120 / 40 layers / 40 heads / ffn 13824 (both |
| configs fetched and compared). In ComfyUI native this is SILENT |
| DEGRADATION, not a hard error: keys for blocks 30-39 hit |
| logging.warning("lora key not loaded: {}") in comfy/lora.py, and keys for |
| blocks 0-29 match by name but not by shape, so they hit the shape guard |
| logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED") in |
| comfy/lora.py or the caught exception logging.error("ERROR ...") in |
| comfy/weight_adapter/lora.py, which returns the weight unmodified. All |
| three call sites were read in the current ComfyUI source. The queue |
| completes, no exception reaches the UI, and the output is effectively that |
| of the LoRA node bypassed. (2) T2V-A14B <-> I2V-A14B: STRUCTURALLY |
| COMPATIBLE, behaviourally risky. Verified from the safetensors index files: |
| both experts carry exactly 1095 tensors with identical key names, and the |
| only tensor whose shape differs is patch_embedding.weight (in_dim 16 vs |
| 36), which LoRA trainers normally exclude. So a T2V-A14B LoRA loads onto |
| I2V-A14B and vice versa with no warnings. What you get is a real but off- |
| target effect. Treat cross-loading as "loads and does something", never as |
| "certified". (3) High-noise expert <-> low-noise expert (within one A14B |
| family): the two checkpoints have identical architecture, so a LoRA trained |
| on one loads onto the other with zero warnings. This is the most dangerous |
| case in the family, because there is no signal at all that you mismatched. |
| Filename is the ONLY discriminator. (4) Wan2.1 14B LoRAs. Verified: |
| Wan2.1-T2V-14B's config.json is dim 5120 / ffn 13824 / 40 layers / 40 heads |
| / in_dim 16 — the same block geometry as Wan2.2-A14B, so Wan2.1 T2V LoRAs |
| load. Wan2.1-I2V-14B is only a partial match: its index has 1303 tensors |
| including 8 img_emb.* keys and 200 cross_attn k_img/v_img keys for its CLIP |
| path, which Wan2.2-I2V dropped entirely (0 such keys); those will be |
| skipped with "lora key not loaded". |
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| ─────────────────────────────────────────────────────────────────────────────── |
| AUTHOR'S DESCRIPTION |
| ─────────────────────────────────────────────────────────────────────────────── |
| (the author left no description) |
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| ─────────────────────────────────────────────────────────────────────────────── |
| EXAMPLE MEDIA |
| ─────────────────────────────────────────────────────────────────────────────── |
| 6 preview(s) in ./example_images/ — community results for |
| this LoRA. Videos keep a matching _poster.jpg still frame. |
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| No prompts are recorded below: CivitAI's public API flags these posts as |
| having generation metadata but does not return it, so the prompts cannot be |
| scraped. Open the model page to read them. |
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| 01_video.mp4 (video, 1088x832) |
| still: 01_poster.jpg |
| 02_video.mp4 (video, 720x480) |
| still: 02_poster.jpg |
| 03_video.mp4 (video, 720x480) |
| still: 03_poster.jpg |
| 04_video.mp4 (video, 720x480) |
| still: 04_poster.jpg |
| 05_video.mp4 (video, 720x480) |
| still: 05_poster.jpg |
| 06_video.mp4 (video, 720x480) |
| still: 06_poster.jpg |
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| ─────────────────────────────────────────────────────────────────────────────── |
| HOW TO USE (COMFYUI) |
| ─────────────────────────────────────────────────────────────────────────────── |
| 1. Put every LoRA .safetensors in ComfyUI/models/loras/ (subfolders are |
| fine and are used by reference workflows). Confirmed by the file-layout |
| tree inside ComfyUI's own video_wan2_2_14B_t2v template note. |
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| 2. Install the base weights alongside them. A14B (from Comfy- |
| Org/Wan_2.2_ComfyUI_Repackaged, split_files/): |
| diffusion_models/wan2.2_{t2v,i2v}_high_noise_14B_fp8_scaled.safetensors AND |
| the matching _low_noise_ file — you need both; |
| text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors; |
| vae/wan_2.1_vae.safetensors. TI2V-5B: |
| diffusion_models/wan2.2_ti2v_5B_fp16.safetensors, the same umt5 text |
| encoder, and vae/wan2.2_vae.safetensors. |
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| 3. Load the base model with UNETLoader ("Load Diffusion Model"), NOT |
| CheckpointLoaderSimple — Wan2.2 ships as a bare diffusion model, with the |
| text encoder loaded separately by CLIPLoader (type "wan") and the VAE by |
| VAELoader. This is the node set used in every shipped Wan2.2 template. |
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| 4. For A14B, build two parallel chains, one per expert: UNETLoader |
| (high_noise) -> LoraLoaderModelOnly -> ModelSamplingSD3(shift) -> |
| KSamplerAdvanced, and separately UNETLoader (low_noise) -> |
| LoraLoaderModelOnly -> ModelSamplingSD3(shift) -> KSamplerAdvanced. Use |
| LoraLoaderModelOnly, not LoraLoader — Wan LoRAs patch the DiT only, there |
| is no CLIP side to patch. |
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| 5. Put the high-noise LoRA file on the high-noise chain and the low-noise |
| LoRA file on the low-noise chain. If a LoRA ships as a single file with no |
| high/low marking, loading the same file on both chains is the standard |
| community handling. |
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| 6. Wire the two KSamplerAdvanced nodes in series on one latent: the first |
| (high-noise) runs add_noise=enable, start_at_step 0, end_at_step = |
| boundary, return_with_leftover_noise=enable; the second (low-noise) runs |
| add_noise=disable, start_at_step = boundary, end_at_step = total, |
| return_with_leftover_noise=disable. Boundary is step 2 of 4 with a 4-step |
| Lightning LoRA, step 10 of 20 undistilled. |
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| 7. For TI2V-5B there is only one chain and one KSampler: UNETLoader -> |
| LoraLoaderModelOnly -> ModelSamplingSD3 -> KSampler. No expert split, no |
| second sampler. |
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| 8. Fastest correct start: load ComfyUI's built-in templates |
| video_wan2_2_14B_t2v / video_wan2_2_14B_i2v / video_wan2_2_14B_flf2v / |
| video_wan2_2_5B_ti2v (Workflow > Browse Templates) and swap the LoRA |
| filenames — the 14B templates already ship the two-expert plumbing and the |
| Lightning LoRA nodes wired correctly. Note the 14B t2v/i2v templates are |
| now packaged as a subgraph. |
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| ─────────────────────────────────────────────────────────────────────────────── |
| Scraped by lorakit on 2026-09-05 03:49 UTC. |
| Stats and description are the author's; pros/cons are derived from the |
| evidence tagged beside each line. |
| ─────────────────────────────────────────────────────────────────────────────── |
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