Every model. What it does, what it can’t, when to use it.
Cloud avatar tools hide their model behind a single “Generate” button. You never learn what your tool can or can’t do — until it fails on the shot you needed. Hybrig is the inverse bet: every model on one rig, every limit out in the open.
Below: the seven image models, five video models, five identity / conditioning layers, and three voice paths Hybrig knows about. Each one with what it’s best for, what it fails at, and which other layers it pairs with. No marketing fluff — if a model is mediocre at hands, this page says so.
Seven still-image models, all local
Every still-image generator on Hybrig runs on your own GPU. There is no cloud image-gen entry on purpose — if you want cloud image gen, the wardrobe-lock and inpainting tools handle specific edits, but free-form generation stays on your rig. Default is Flux dev.
FLUX.2 [dev]
localBlack Forest Labs' open-weight photorealism leader. Heavy — wants a big NVIDIA card. ~30 sec per image on a 4090, ~32 GB GPU memory.
Best for- Photoreal portraits of real people
- Brand stills, product shots, lifestyle frames
- LoRA training base — biggest LoRA ecosystem in the catalog
Fails at- Needs 32GB+ NVIDIA VRAM — won't fit on 24GB consumer cards at full precision
- Not viable on 24GB Macs (even at higher unified memory, FP8 doesn't work on Metal)
- Stylized / painterly / anime — FLUX.2 is photoreal-first; for cartoons use SDXL+Pony or Illustrious
Pairs with- FLUX.2 + a trained character LoRA = best identity lock on the platform
- FLUX.2 + PuLID-Flux = single-photo identity conditioning when no LoRA exists yet
Z-Image Turbo
local6B distilled generator — sub-second on big cards, seconds on Apple Silicon. Apache 2.0 license. The iterate-without-fear model.
Best for- Fast drafts and prompt iteration on 16GB+ GPUs or Apple Silicon
- Cost-free local iteration — sub-second on big cards means you burn through 50 ideas without looking at the clock
- Apache 2.0 — no licensing walls
Fails at- Quality gap vs. FLUX.2 on photoreal close-ups
- Text rendering not as sharp as Qwen-Image 2.0
Pairs with- Z-Image Turbo for ideation, FLUX.2 for the keeper finals
Qwen-Image 2.0
localGeneration + editing unified in one 7B model. Apache 2.0. Native 2K output. Undisputed best at rendering text, signage, and logos.
Best for- Text rendering inside images — signage, logos, branded packaging
- 16GB+ GPUs or Apple Silicon — runs on Macs with 16GB unified memory
- Best-in-class text/logo rendering for branded stills
Fails at- Photoreal faces not as tight as FLUX.2
- Generation + editing in one model means trade-offs on both vs. specialized tools
Qwen-Image-Edit 2511
localMulti-image instruction editing (GGUF Q4 + Lightning 4-step LoRA). Apache 2.0. Powers Hybrig's Logo Swap — scene + logo in, branded variant out, locally.
Best for- Logo Swap at /tools/logo-swap — one scene + N logos → N branded variants, locally
- 16GB+ GPUs or Apple Silicon (GGUF Q4 + Lightning 4-step LoRA means 16GB floor, runs on Macs)
- Instruction-based multi-image editing without cloud upload
Fails at- Not a general-purpose generator — editing-only
- Quality ceiling lower than FLUX.2 inpainting for complex scenes
Pairs with- FLUX.2 or Qwen-Image 2.0 for the base scene, Qwen-Image-Edit 2511 for the logo swap pass
Flux schnell
localSame Flux family as dev but distilled to 4 steps. ~8 sec per image. Slight quality dip, mostly invisible on simple shots.
Best for- Drafts and prompt iteration
- Style search before committing to a finished render
- Anything where a 4x speedup matters more than the last 10% of detail
Fails at- Detail-heavy finals — the 4-step distillation drops fine texture
- Same 12 GB GPU memory floor as dev — schnell isn't the low-VRAM option
Pairs with- Flux schnell for ideation, then re-render the keeper on Flux dev
SDXL + Pony Diffusion v6
localSDXL with the Pony v6 fine-tune. The standard for stylized, painterly, cartoon-adjacent work. ~6.5 GB checkpoint, ~10 GB GPU memory, ~18 sec per image.
Best for- Stylized portraits, painterly compositions, concept art
- Huge community LoRA library — character + style
- Faster than Flux when stylization matters more than photorealism
Fails at- Photoreal faces — that is not Pony's job; use Flux instead
- Default style is heavily NSFW-tilted; needs prompt discipline
Pairs with- Pony + IPAdapter (SDXL) for reference-driven stylization
SDXL + Illustrious-XL
localSDXL with the Illustrious-XL fine-tune. The standard for anime / illustration. Trained on Danbooru tags — responds to long comma-separated tag prompts.
Best for- Anime portraits, illustration, manga panels
- Tag-style prompting (Danbooru vocabulary)
- Active community LoRA scene for anime characters
Fails at- Photoreal — faces look painted
- Prose prompts — it expects tags, not full sentences
Stable Diffusion 3.5 Large
localStability AI's flagship. Softer, more cinematic look than Flux. ~6 GB checkpoint, ~12 GB GPU memory, ~35 sec per image.
Best for- Cinematic / painterly stills
- Editorial illustration, stylized portraits
- Single-file checkpoint — simpler install than Flux
Fails at- Photoreal faces lose a touch of detail vs. Flux
- Smaller LoRA ecosystem than Flux or SDXL
HiDream-I1 (Full)
localLate-2024 photoreal model competing with Flux on quality. ~17 GB on disk, ~16 GB GPU memory, ~45 sec per image. Smaller LoRA library because it's newer.
Best for- Photoreal portraits where Flux occasionally falls short on fine detail
- Detail-critical product shots
Fails at- Heavy footprint — 17 GB on disk, 16 GB GPU memory floor
- Newer model — community workflows still settling, fewer LoRAs
- Currently disabled in Hybrig until a dedicated loader is wired
PixArt-Σ
localTiny, fast, prompt-following model. ~2.5 GB on disk, ~6 GB GPU memory. Good for drafts and weak GPUs.
Best for- Quick drafts and prompt iteration
- Weak / low-VRAM GPUs (6 GB minimum)
- Strong prompt-following — does what you asked
Fails at- Visible quality gap vs. Flux / SD 3.5
- Faces and hands degrade fast on complex shots
Four local video models, three cloud polish layers
Default is Wan 2.2 — runs on your GPU, free forever. The auto-fallback chain tries Wan 2.2 → Wan 2.1 → Hunyuan → LTX before reaching for cloud. Cloud has three roles, not one: Seedance 2.0 for face lock, lip-sync rescue and 4K; Seedance 2.5 when a beat has to run 30 seconds without a cut; Gemini Omni for physics + world-knowledge polish on hero beats. All three are polish-after-farming, never the farm engine — cloud structurally can't farm.
Wan 2.7 (local)
localAlibaba's open-weight video flagship — first/last-frame control, instruction-based video editing, tops Wan-Bench 2.0. Successor to Wan 2.1/2.2. ~3-8 min per 5-second clip on a 4090. Free forever — runs on your own GPU.
Best for- Spokesperson clips on your own GPU — no per-second meter
- First/last frame interpolation (weathering time-lapses, before/after shots, age progression)
- Instruction-based video editing — describe the edit, not the keyframes
- Privacy-required work — footage never leaves the machine
Fails at- Needs 24GB+ NVIDIA VRAM — not viable on 24GB Macs (quantized versions may run but quality drops)
- Demanding face-heavy shots vs. Seedance — still ~10-20% behind on tight close-ups
- Hands and crowd scenes weaker than top cloud models
- No native lip-sync — needs a separate lip-sync pass
Pairs with- Wan 2.7 + a trained character LoRA = identity-locked video on free local
- Wan 2.7 → cloud-burst Seedance fallback when 2.7 misses on a shot
LTX-2.3 (local)
localRTX 409024GVRAM12G · fits−0% vs 4090The only open video model with native audio+video in one pass. Deep fine-tuning ecosystem: IC-LoRA, camera-control LoRAs, distilled fast variant. Needs 32GB+ VRAM.
Face7.6Motion8.0Identity7.2Best for- Audio+video synthesis in one pass — no separate lipsync step
- Fine-tuning ecosystem for camera control and style transfer
- When you need native audio baked into the video model
Fails at- Needs 32GB+ VRAM — won't fit on 24GB consumer cards
- Quality ceiling lower than Wan 2.7 on general-purpose video
- Commercial license terms (Lightricks license, not Apache 2.0)
HunyuanVideo (local)
localTencent's HunyuanVideo. Strong on natural motion — body movement, physics, walking, gestures. Slower than Wan: 6-12 min per 5-second clip on a 4090. ~13 GB on disk.
Best for- Action shots, walking, body physics, gesture-heavy scenes
- When Wan's motion looks stiff and you need realistic limb movement
Fails at- Slower than Wan — heavier model
- Identity lock weaker than Wan on tight close-ups
- No native lip-sync
Pairs with- HunyuanVideo for motion + Wan 2.2 keyframe + identity-lock LoRA
LTX-Video (local)
localLightricks' LTX-Video. Small, fast, lower quality. ~9 GB on disk, runs on as little as 12 GB GPU memory. ~1 min per 5-second clip on a 4090.
Best for- Drafting motion direction before committing to a slow model
- Weak GPUs that can't load Wan or Hunyuan
- When you just need to see if the camera move idea works
Fails at- Visibly lower quality than Wan 2.x or Hunyuan
- Identity preservation weaker on close-ups
- Less detailed motion — best for simple shots
Seedance 2.0 (cloud — rescue valve)
cloudByteDance's Seedance 2.0. Top-tier face lock + cinematic VFX. Cloud-only — billed per second of output. The rescue valve when local misses on a critical shot, not the default.
Best for- Premium brand finals where face lock under heavy motion is non-negotiable
- Cinematic VFX and lighting consistency
- Lip-sync passes (native muxed audio support)
Fails at- Mediocre on hands when subject gestures
- Backgrounds drift on clips longer than ~6 seconds
- Per-second billing — failed renders still consume the credit
Pairs with- Wan 2.2 for drafts + Seedance for the keeper finals
- Seedance Standard for finals, Seedance Fast for A/B drafting
Seedance 2.5 (cloud — the long take)
cloudByteDance's newest. The reason to reach for it is length: 30 seconds in ONE generation, where 2.0 caps at 15. A 30s take holds one continuous identity instead of being three clips stitched at seams where the face can drift. Native audio in the same pass. The catch is resolution — 720p ceiling today.
Best for- A full 30s beat in one take — cold opens, monologues, anything where a cut would break continuity
- Multi-shot sequences that need the same character, wardrobe and location across the whole run
- Reference-heavy setups — image, video AND audio references together in one generation
- Native synchronised audio without a separate lip-sync pass
Fails at- 720p ceiling — 1080p and 4K were removed from the endpoint on 2026-08-09. If the cut has to be 4K, this model cannot do it
- ~1.5x the 2.0 token rate ($0.0214 vs $0.014 per 1k) — a 30s 720p take runs about $14
- No fast tier, so there is no cheap draft option on 2.5 the way there is on 2.0
- Unproven here — we have not run a side-by-side against 2.0 yet
Pairs with- Seedance 2.5 for the 30s continuous take, Seedance 2.0 spliced when the cut has to be 4K
- Wan 2.2 locally to lock the blocking and the prompt, then ONE 2.5 take for the keeper
Gemini Omni (cloud — physics polish)
cloudGoogle DeepMind's multimodal video model. Conversational editing, physics-aware motion, world-knowledge compositing. Subscription-gated through Google AI / Google Flow. Polish layer for specific hero beats, never the farm engine — cloud structurally can't farm.
Best for- Hero shots that need physics simulation (water, gravity, kinetic objects) local models can't fake
- Compositing beats that need world-knowledge context (a specific historical scene, a cultural reference)
- Iterative conversational refinement on a SINGLE keeper after the local farm has settled the direction
Fails at- Farming — Google AI subscription + per-render metering breaks unit economics at any scale beyond one-off hero shots
- Privacy work — content leaves the machine
- Access — gated behind Google AI subscription, region-limited, allowlist-controlled
Pairs with- Local farm (Wan + LoRA) for the cohort + Omni polish on one or two hero beats per spot
- Use AFTER the render direction is finalized locally — never as the drafting engine
How identity locks across shots
The hardest problem in AI video isn't generating one good frame — it's generating the SAME face across fifty frames, twenty shots, six campaigns. Hybrig gives you five layers, ranked by lock strength: trained LoRA (strongest, slowest to set up) down through PuLID, Redux, IPAdapter, to wardrobe-lock (weakest, narrowest scope).
Per-character LoRA
Train a small adapter (~50-200 MB .safetensors) on 15-30 reference photos of one person. The trained file lives on your drive forever — no subscription, no vendor lock-in.
Best for- Strongest identity lock available on Hybrig — locks across many shots
- Repeat characters across a series, a campaign, a multi-scene project
- Portable: the .safetensors is a file you own
Fails at- Needs 15-30 varied photos — different lighting, angles, expressions, wardrobe. 10 photos in one outfit collapses the LoRA to a generic face.
- Watch for EXIF orientation: rotated photos train the LoRA on sideways faces. Hybrig auto-corrects, but check your inputs.
- 1-2 hours of GPU time to train (one-time cost). Renting a SaaS face slot is faster up front, but you pay forever.
Pairs with- LoRA + Flux dev = strongest still-image identity lock
- LoRA + Wan 2.2 = identity-locked video on free local
- LoRA + IPAdapter (SDXL) when the LoRA is on an SDXL base
PuLID-Flux
Single-photo face-identity conditioning for Flux. Conditions on face geometry only — doesn't drag the reference's backdrop, pose, or clothing into the output. The scene prompt actually steers the render.
Best for- Putting a face into a NEW scene from one reference photo (no LoRA needed)
- Quick identity conditioning when you don't have 15-30 photos to train a LoRA
- Recommended strength 0.9-1.0 (per the PuLID-Flux v0.9.1 README)
Fails at- Identity lock not as tight as a trained LoRA across many shots
- Flux-only — does not work on SDXL family or Wan video models
- Requires the ComfyUI-PuLID-Flux-Enhanced custom node + InsightFace + EVA-CLIP weights
Pairs with- PuLID-Flux + scene prompt at strength 0.9 = new-scene character generation from a single photo
- PuLID-Flux takes precedence over Redux when both are available — PuLID locks identity, Redux pulls style
Flux Redux
Black Forest Labs' style-and-composition conditioning for Flux. Stronger than IPAdapter for Flux because it ships native — no XLabs/PuLID custom node required. Runs on stock ComfyUI.
Best for- Variations of an existing photo — same vibe, different angle
- Style transfer when you have a reference image whose look you want to keep
- Quick drop-in identity conditioning when PuLID isn't installed
Fails at- Redux remakes the entire reference image — same backdrop, same pose, same clothes. Use it for variations of an existing photo, not for putting your face in a new place.
- Won't respect the scene prompt as strongly as PuLID — the reference image's composition wins
- Not a hard identity lock the way PuLID-Flux or a trained LoRA is
Pairs with- Redux for style + a trained LoRA for identity = best of both
- Skip Redux if you have PuLID — PuLID is the better identity path
IPAdapter (SDXL)
Reference-image conditioning for the SDXL family (base, Pony, Illustrious). The current canonical file is ip-adapter-plus_sdxl_vit-h.safetensors via cubiq's ComfyUI_IPAdapter_plus.
Best for- SDXL / Pony / Illustrious reference-image conditioning
- Style + composition reference on stylized models
- Adjustable strength via the IPAdapterAdvanced weight knob
Fails at- SDXL-family only — Flux uses Redux or PuLID instead
- Identity lock weaker than a trained LoRA on close-ups
- Naming varies a lot — Hybrig probes ComfyUI's /object_info to find whatever's actually installed instead of hardcoding a filename
Wardrobe-lock (Gemini)
cloudGoogle Gemini 2.5 Flash Image ("nano-banana") via Hybrig's image-edit provider. Person photo + outfit photo in, person wearing the new outfit out — face preserved.
Best for- Swapping outfits without changing the face
- Fast (single API call, ~10-20 sec) compared to re-rendering from scratch
- Honest about being cloud — Gemini is the only model in this category that solves wardrobe swap reliably right now
Fails at- Cloud-only — files leave the device for this one step (Gemini terms apply)
- Outfit fidelity drops on heavily patterned or branded garments
- Doesn't change pose or scene — wardrobe only
Pairs with- Gemini wardrobe-edit → re-shoot the edited image into a LoRA training set when you want a new wardrobe permanently locked into the character
Cloned, recorded, or local
Hybrig's voice path is honest: ElevenLabs is the production clone today (cloud, $22/mo), bring-your-own-audio is the free path if you'd rather record yourself, and local TTS (XTTS / Piper) is on the roadmap as the privacy-first replacement for ElevenLabs.
ElevenLabs (cloud)
cloudProduction voice-clone TTS path. Eleven Multilingual v2 by default. ~$22/mo subscription. Hybrig clones once from a 30-second sample, then synthesizes any script.
Best for- Voice cloning from a 30-second reference recording
- Multilingual scripts (v2 model)
- Production-grade prosody and emotion
Fails at- Cloud-only — script + voice sample upload to ElevenLabs servers
- Subscription required ($22/mo at writing) — unlike the rest of Hybrig, this one isn't free
- Failed renders still consume credits
Pairs with- ElevenLabs voice clone → Wan 2.2 video + lipsync pass = full local-first spokesperson on free local video, paid only for the voice
Bring-your-own audio
Skip TTS entirely. Upload a pre-recorded audio file and Hybrig muxes it onto the video. Authentic voice, no clone artifacts, no ElevenLabs cost.
Best for- When you'd rather record yourself than clone yourself
- Languages or accents the TTS doesn't nail
- Zero-cost voice path — the upload is local, the mux is local
Fails at- Requires you to actually record clean audio (Sony A7 IV's XLR or any USB mic does the job)
- No clone — you have to record every script yourself
Local TTS (XTTS / Piper)
roadmapFully local voice synthesis. Not yet wired into Hybrig's pipeline — flagged here because James plans to add it. XTTS clones from a short reference; Piper is fast non-clone TTS.
Best for- Privacy-required projects where the voice can't leave the machine
- Offline workflows (planes, no-wifi shoots, Faraday environments)
- Replacing the $22/mo ElevenLabs cost when quality is good enough
Fails at- Not yet integrated — currently a roadmap item, not shippable
- XTTS clone quality below ElevenLabs on prosody and emotion
- Piper sounds robotic next to a clone — it's a non-clone TTS
What stacks, what conflicts
Identity layers don’t all play nice together. Two layers conditioning the same Flux pathway will fight; a Flux layer and an SDXL layer just don’t see each other. Below: which combinations stack cleanly, which conflict.
| Layer | LoRA | PuLID-Flux | Flux Redux | IPAdapter (SDXL) |
|---|---|---|---|---|
| LoRA | — | stacks | stacks | stacks |
| PuLID-Flux | stacks | — | conflicts | — |
| Flux Redux | stacks | conflicts | — | — |
| IPAdapter (SDXL) | stacks | — | — | — |
- LoRA: LoRA is the strongest layer. Anything else stacks on top — LoRA owns identity, the other layer adds style or reference.
- PuLID-Flux: PuLID and Redux both condition the same Flux pathway — Hybrig picks PuLID when both are available because PuLID respects the prompt; Redux drags the reference image's composition.
- Flux Redux: Redux + LoRA stacks fine — Redux pulls style, LoRA holds identity. Redux + PuLID conflicts (same conditioning slot).
- IPAdapter (SDXL): IPAdapter is SDXL-family only. It stacks with SDXL-base LoRAs but never sees a Flux model.