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Type a story. Get one continuous video, with sound. Multi-shot scenes render as a single take - no last-frame chaining, no quality loss from shot to shot. That's the whole pitch.
Both workflows now read left to right: numbered lanes, and you only ever touch lanes 2-4. Everything below the main row is optional.
What you need
ComfyUI + this node pack (Manager: MiniMax-H3 Multishot, or the zip on this version).
A MiniMax-H3 checkpoint (links on this page). 24 GB card? Take a GGUF.
SEAMLESS CHAIN - multi-shot scenes as one take
Lane by lane:
README - the quick start lives on the canvas itself.
1 - MODELS - pick your H3 checkpoint and text encoder. VAEs are preset; LoRA slots are empty until you fill one.
2 - ANCHORS (optional) - a photo to open shot 1 on (enable its gate), and a short voice clip to lock the speaker's voice.
3 - YOUR PROMPTS - type your idea in the box, or point the switch at a prompt file. The writer expands it into shot prompts. Writing your own? Set the writer to
passthrough (raw JSON, skip LLM)and paste shots separated by---lines.4 - CONTROLS - size, frames per shot, steps, and
take_seconds(total length; 30 is a good first run). The switches stay off unless you installed the pack a switch names.5 - ENGINE - nothing to change. The remote encoder lives here if you want the text encoder on a second PC: enter its address, flip the encoder switch, free ~15 GB.
6 - OUTPUT - your video and its audio save here.
Optional panels below the main row: reference images (your character, ref2va checkpoints - folder per character + AUTO REFS on), V2V reference (a clip whose look guides the render), FFLF plates (flf_chain mode only), audio spine (a soundtrack the take follows).
EXTEND TAKE - one person talking, as long as you want
Same lanes, different job: one premise becomes ONE continuous speech cut across windows.
1 - MODELS - same as above.
2 - ANCHORS - a photo of your speaker (shot 1 opens on them) and a voice clip. More useful here than anywhere: one person carries the whole take.
3 - YOUR PROMPT - ONE premise, one speaker. The writer writes the whole speech. num_shots 0 = it decides. Passthrough works here too.
4 - CONTROLS -
take_secondsis the star: 30 ships, 60 clears TikTok's minute.windowstays on auto - it sizes itself to your card.5 - ENGINE / 6 - OUTPUT - same as above.
Keep takes to about 4 windows for now - very long takes slowly sharpen.
Rules of thumb (both workflows)
Spoken lines: 8-12 words per shot. Short lines sync; long lines garble.
Say the sounds you want ("rain on the roof, a fridge hum") or it invents its own.
Keep your character's face in frame - faces carry identity between shots.
If something breaks
Red node? Update the pack in Manager, restart, reload the workflow from disk.
Render crawls at low wattage? Lower resolution or frames per shot, or use the remote encoder.
Only one of the two workflows shows in your sidebar? Fixed in 2.6.5 - re-download both.
Still stuck: comment with your console log. I answer.
Deep dives: the two articles linked on this page. Every lane also has a short note on the canvas.
Detailed guide for people that can read good:
Every setting explained: the Seamless Chain deep manual | Civitai
Description
Fixed in 2.1.1 (user-reported, same day)
context_pindied when ComfyUI-H3-Motion-Context was installed. Both packs patched the same ComfyUI method and Motion-Context refuses to stack on an unrecognised wrapper, so its payload patch failed and the chain errored. This pack's wrapper now does everything theirs does and declares their compatibility marker, so whichever loads first owns the site and the other stands down. Load order no longer matters. Verified with a livecontext_pinrender.seamless_tailcrashed mid-chain with Motion-Context installed ("only first/last keyframe anchors are supported") - after your first shot had already rendered. It needs interior keyframe anchors, which conflict with that pack; it now stops before any sampling with the alternatives named: usecontext_pin, orfirst_frame, or remove that pack.seamlessoften reads as a cut - now labeled. It is a legacy latent-only soft pin kept for comparison; the model satisfies it loosely. For a real join usecontext_pinorfirst_frame. The tooltip and settings reference now say so plainly.
Why these never showed in testing here: an install-layout difference disabled the conflict detection on the dev machine. That detection is fixed, and release testing now runs on a packaged clean install so the class cannot slip through again.
Everything you need is in the zip. One download: both node folders, all three workflows, and the full documentation. Nothing else to fetch, nobody to ask.
Install
Unzip. Copy both node folders into
ComfyUI/custom_nodes/:ComfyUI-H3-Multishot/ the sampler and helper nodes ComfyUI_JoyAI_Echo_GGUF_Nodes/ the LLM prompt writer (full workflow only)Restart ComfyUI. ComfyUI v0.30.0 or newer is required — that is the release with native MiniMax-H3 support.
Load a workflow from
workflows/through the workflow menu.
The full workflow needs five packs (two are in this zip): the writer pack below, ComfyUI-H3-Motion-Context for the context_pin default, RES4LYF for the beta57 scheduler, and ComfyUI-sol-attn + comfyui-minimax-h3-blockcache-T8 for the VRAM panel; ComfyUI-Custom-Scripts adds the script preview. ComfyUI validates every node class before it will queue, so a missing pack stops the whole workflow rather than just its own feature — INSTALL.md lists how to remove each one instead if you would rather not install it. CORE needs none of them, and that is tested on a clean install.
The writer pack is RealRebelAI's (github.com/RealRebelAI/ComfyUI_JoyAI_Echo_GGUF_Nodes), modified so the workflow's join rules actually reach the model; NOTICE_RIFT_MODIFICATIONS.md inside it lists every change. If you already have that pack, replace it with this copy. The CORE workflow does not need it at all.
Models you need
checkpoint MiniMax-H3 ref2va (GGUF Q8_0 / Q5_1 / Q4_0) -> models/diffusion_models
text encoder qwen3vl minimax_h3 (+ its -mmproj sidecar) -> models/text_encoders
video VAE minimax_h3_video_vae -> models/vae
audio VAE minimax_h3_audio_vae -> models/vaeGGUF quants: huggingface.co/joeygambino/MiniMax-H3-GGUF — Q8_0 for 32 GB, Q5_1 for 24 GB, Q4_0 below that.
GGUF encoder pairing. ComfyUI-GGUF matches the -mmproj vision sidecar to the encoder by filename, in the encoder's own folder. Rename either, or split them up, and it loads the encoder without its vision tower — which presents as the model ignoring your reference image. This pack's CLIP loader raises instead of continuing blind, uses the only mmproj beside the encoder when there is exactly one, and takes an mmproj_name widget so you can point at the file directly.
Which workflow
H3_Seamless_Chain_CORE — start here. The same seamless chaining with zero third-party packs. Type shots into the script box and queue.
H3_Seamless_Chain_v2 — everything: master controls, LLM writer, VRAM panel, identity and voice anchors, episode/batch prompt source, boundary plates, audio spine. Optional lanes are gated off by default.
H3_Keyframes — one clip, anchors at chosen frame positions, per-anchor condition strength.
The two things that stop people on the first run
1. The prompt writer needs a model you have pulled
The full workflow points at a local Ollama with model_name = qwen3:14b. If it is not pulled, the first queue stops immediately:
LLM API error 404: model 'qwen3:14b' not foundFix: ollama pull qwen3:14b. Any OpenAI-compatible endpoint works — its URL in base_url, its exact tag in model_name. ollama list prints the tags you have, and it must match character for character.
No LLM at all? Set the master panel's use_file_prompts to manual entry, delete the writer, and feed your own script into the sampler's script input — one prompt per shot, separated by --- on its own line. CORE already works this way.
2. A local writer will fight the video model for the card
Turn on unload_model_after on the writer. It frees its own model from Ollama the moment the script is written. Without it the model stays resident for the server's default five minutes — your whole first shot. ComfyUI's own eviction cannot reach it, because Ollama is a separate process with its own allocator, and Ollama's OpenAI-compatible endpoint has no keep_alive field to ask with; the switch calls the native endpoint, which honours it. On under 32 GB, prefer a remote endpoint entirely.
Settings: start here, change nothing
checkpoint ref2va sampler euler
continuity context_pin scheduler beta57 (full) / beta (CORE)
steps 14 fps 24
frames/shot 362 (~15.1s, the trained maximum)
resolution 1280x736 landscape or 768x1344 verticalbeta57 comes from RES4LYF, not stock ComfyUI. Measured on an identical seed it scored 10/10 for lip-sync against 8/10 for stock beta, with image quality, skin texture, artifacts and audio judged equal — so the full workflow ships it and lists RES4LYF as required, while CORE ships beta and keeps its zero-third-party-pack promise. One widget either way.
Leave every VRAM switch off and the reserve at 0, and try a render before touching any of it. The activation reserve measures each shape and conditioning payload as it renders and sizes the pool itself; it holds on 24 GB cards as well as 32 GB. A hand-set reserve overrides that measurement, so a number that suited one shape becomes wrong for the next. Those switches exist to dig out of a spill the console has already named, not for pre-emptive tuning.
Resolution cannot change mid-chain, and the mux must stay at 24 fps — other rates audibly shift voice accents. Dial-by-dial reference in SETTINGS.md.
Writing a script that chains cleanly
The previous shot's last ~1 second is replayed at the head of the next and discarded. Four rules follow, and breaking them is what produces mid-word chops and pose jumps:
Open holding. Every shot after the first opens in the previous shot's exact closing arrangement, with no dialogue for ~2 seconds. Give it real micro-motion — a breath, a weight shift — so it does not read as a freeze.
Land settled. Every shot ends with ~2 seconds of quiet, back in a stable arrangement, all dialogue finished.
Never split a line across shots. Dialogue plus 4 seconds of hold and settle must fit the shot length. If it does not fit, move the whole line to the next shot.
Repeat descriptions word-for-word. Character appearance and the room/light description, byte-identical in every shot. An unnamed light source gets reinvented per shot, and that is where colour drift starts.
The LLM writer applies these for you. Hand-written scripts must follow them — PROMPTING.md has a worked four-shot example, and example_script.txt is ready to paste.
How the chaining works
context_pin carries the previous shot's last 22 frames as raw latents — never decoded to pixels and re-encoded — placed at interior keyframe coordinates, with a timeline-placed audio reference alongside. The regenerated head is trimmed on decode. Colour, motion and voice cross the boundary as data rather than as a description.
Motion is the clearest case. Hand the next shot a single frame and it knows position but not velocity, so pace can reset at the boundary. Measured on a steady-pace walk: a single-frame anchor with no memory bank wobbled at the join; context_pin held it, and so did the memory bank on its own.
first_frame is the alternative — the model's own trained hand-off, no extra pack, and what CORE ships with. cut for episodic work.
Identity and voice
Nothing wired — the frame relay plus verbatim descriptions hold a face surprisingly well. A ~40 s two-character scene held both faces with no reference images at all.
self_anchor_voice(on) — shot 1's own rendered voice becomes the reference for every later shot. No file needed; write shot 1 with a clean solo line.voice_ref— a clean solo speech clip, pinned across the whole chain including shot 1.reference_images— character portraits carried into every shot as<Picture 1..N>. Bind them in the prompt text.seed_per_shot(leave on) — measured: varying the seed per shot holds the face; one seed for every shot drifted both face and voice. Identity lives in the conditioning, not the seed.
When something goes wrong
404, model not found — the writer's model is not pulled. See above.
A word clips at a join — the script put dialogue too close to a boundary. Move the whole line; do not split it.
Sharpening increases every shot — the texture ratchet. Set
chain_gain_controltoflatten; worth it past about 5 shots.Stalls at 0 steps, or runs several times slower than usual — a VRAM spill, the driver paging to system RAM instead of erroring. The console now names it. Raise the reserve, or drop resolution, frames, or reference payload.
Red or missing nodes — an optional pack is not installed. Delete those nodes, or use CORE.
GGUF architecture error — the pack teaches ComfyUI-GGUF the
minimax_h3architecture at startup. If it persists, runpython apply_gguf_arch_patch.pyfrom the pack folder once and restart.Audio dulls on a very long chain — expected; restart the chain on a scene cut, where a fresh start costs nothing.
What changed in 2.1
The full workflow now works on a clean install. It referenced a prompt-source node that had never been published, and drove the writer through inputs the upstream writer pack does not have — so the boundary rules never reached the model. It rendered, and it rendered worse than it should, with no error to explain why. Both fixed: that node ships here as
RiftPromptSource, and the rules are written into the workflow's own system prompt as well as carried by the writer pack in this zip.The chaining sampler's anchor switches now do something.
voice_ref,reference_images,self_anchor_voice,preview_first_shot,two_pass_upscale,reference_image_sizeand the sampler/scheduler overrides were drawn on the canvas but absent from the class, so ComfyUI stripped them before execution. All real now, render-verified.unload_model_afteron the writer, described above.SHOT COUNT on the master panel drives the sampler and the writer together so they cannot disagree; prompt source switches between a manual scene box and a prompt set, lazily.
Node titles no longer name a checkpoint or a switch position — a title like
H3 model (fl2va)is a lie the moment you change the model.
Two-pass upscale cannot be combined with context_pin or latent_handoff, or with an audio spine: those carry raw latents, or one locked denoise trajectory, across the join, and a two-pass render preserves neither. The node stops with an error naming the conflict rather than quietly producing a weaker join. Two-pass is available on cut, seamless, seamless_tail, first_frame and flf_chain.
Credits
Prompt writer: RealRebelAI (ComfyUI_JoyAI_Echo_GGUF_Nodes, modified — see the NOTICE in the zip). context_pin: NikoDemon80 (ComfyUI-H3-Motion-Context). Two-pass upscaling: Tr1dae (ComfyUI-MiniMaxH3_LatentUpscaler).
FAQ
Comments (39)
the AIO script seems to be closer to what I want, the ref2a flexibility is really good and I trust it to preserve what I want at the seams more, the only thing lacking is sometimes tricky to get it to seamless transition instead of jump into slightly different positions.
Try latest v2.1 - seamless transitions built in by default.
Thank you for all the work you are putting into these workflows, it's appreciated.
Unfortunately I'm have difficulty with v2.0 as I can't find JoyEcho_LLMEnhance or JoyEcho_PromptSource anywhere. Of course, they just so happen to support the feature I'm eager to try. Any pointers?
Update1: I've installed JoyEcho_LLMEnhance from RealRebelAI's ComfyUI_JoyAI_Echo_GGUF_Nodes pack. Still looking for JoyEcho_PromptSource
Update2: I had to drop joyecho_prompt_source.py from HF joeygambino/joyai-echo-multishot-workflow into custom_nodes\Comfyui_custom_scripts folder. I don't know if it's vital but I removed the second underscore in the file name as that was the name thrown by error in Comfyui.
I think I'm ready to go, I'll leave this here in case it helps anyone else.
v2.1 going up shortly to fix some bugs I didn't catch locally and will take care of this. Sorry!
Both v2.0 and 2.1 do not seem to work with continuity=context_pin even with ComfyUI-H3-Motion-Context installed.
[WARNING] h3_motion_context: another pack has already patched MiniMaxH3.extra_conds (it now comes from '/home/vlady/apps/ComfyUI/custom_nodes/ComfyUI-H3-Multishot.h3_avbank_probe'). Both packs are solving the same keyframe/ref collision and they cannot both own it, so this one is refusing. Disable one of them and restart.
[ERROR] !!! Exception during processing !!! h3_motion_context: the payload patch could not be applied. Without it the audio ref would overwrite the pinned video latents and the motion context would be lost. The reason was logged just above this error.
A different issues found:
continuity=seamless behaves as a cut, not seamless at all.
continuity=seamless_tail errors out with T2V mode AFTER sampling, not before:
[ERROR] !!! Exception during processing !!! only first/last keyframe anchors are supported
I'm in search of seamless T2V (and I2V) clip chaining and could not find working setting in the current version. Both identity anchor gate [OFF] and FFLF PLATES gate [OFF - flf_chain only] are set to T2V
@vladulidlo All three confirmed, and thank you - this is an excellent report. 2.1.1 is up with the fixes.
context_pin + Motion-Context: my pack was grabbing the same patch site Motion-Context needs, before their pack could. Their code publishes a compatibility marker for exactly this situation; mine now honours it, so the two coexist and load order no longer matters. Verified with a live context_pin render.
seamless_tail: real conflict - it needs interior keyframe anchors, which collide with Motion-Context's ownership of that patch math. It now stops before sampling with a clear message instead of dying after your first shot. With Motion-Context installed, use context_pin - it's the stronger mechanism and what that pack is for.
seamless: you're right, and the tooltip now says so - it's a legacy latent-only soft pin kept for comparison, and it often reads as a cut. For seamless T2V chaining use context_pin (or first_frame on an fl2va checkpoint). Both are the measured, working paths.
The bug never showed on my machine because of an install-layout difference that disabled the conflict detection - also fixed, and my release testing now runs on a packaged clean install so this class doesn't slip through again.
@joeygambino
Thank you! For both the fixing and fixing it so quickly!
Seems like the bugs started eating into the bugs, chill, don't rush, take your time, customers can wait.
Ha, thanks. I do tend to rush when I have a new feature to show off. A lot of bugs don't pop up until someone reports them, because the workflows are functioning perfectly for me, but then I realize the things other people just don't have installed.
@joeygambino yeah people are too excited for new model and what they can do. also samples looks good, compared to previous degrading over time was visible, now it looks stable through all 30 secs. will be trying lastest WF later on, good job!
I would like to point to a fork https://github.com/ethanfel/ComfyUI-MiniMaxH3-Contex-Loop
ethanfel's fork is 51 comits ahead of original https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context, but also not (yet) compatible with ComfyUI-H3-Multishot.
So if you are seeing:
RuntimeError: continuity=context_pin needs the ComfyUI-H3-Motion-Context pack installed (github.com/NikoDemon80/ComfyUI-H3-Motion-Context)
You could have downloaded the fork instead of https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context
Thank you - you're right, and I've verified it against the fork's source. It registers 18 node ids (MiniMaxH3LoopTrim, the MiniMaxH3Chain* family, the Scheduled* reference nodes) and deliberately does not re-register MiniMaxH3MotionContext. From its own init.py: "The original Motion Context, Save Latent, and Load Latent ids remain exclusively owned by Niko's upstream pack."
So, the fork is a complement rather than a replacement - install both. They're built to coexist, and this pack works with one's runtime patches because all three honor the same patch-ownership markers.
My error message was unhelpful about that, so 2.1.1 now detects the fork and says exactly this instead of just naming a repo you thought you'd installed. It's also documented in INSTALL.md.
That fork looks well worth a look on its own merits, by the way, a disk-backed chain/loop system with review gates and checkpoint resume is solving a different problem than this pack and solving it further than I have.
@joeygambino Good to know! I'm glad you find the forked repo interesting as I do.
Just installed 1.5 last night, after wondering to myself "This new H3 stuff is amazing, just wish I could load more than start/stop keyframes" and looking at civitai for a lark. Blown away by this stuff - once 2.0 or its successors settle out from the bugs, I'll update, but this is already incredible, bordering on revolutionary for me to mess around with. Well done!
Thank you! 2.1.1 should mostly be bug free now - but if you find anything, I try to be quick about fixing things. Sometimes stuff that works perfectly on my machines, don't necessarily work well on others due to different Comfy versions, hardware, node packs installed, etc. I don't know something is broken until someone tells me.
I have noticed that this larryvrh/MiniMax-H3-Turbo-Lora is not burning the output like the lightning one does, anyone else been using it with this workflow?
The only one I've used so far is minimax_h3_turbo_4step_ckpt500.safetensors
And I can't even say I've tested it enough to know it works. When I did test it, it didn't seem to work very well with my workflows unless it was at 12 steps + only Euler - which at the time it was in Alpha so I didn't bother testing further and figure people will use their own anyway. I didn't realize he'd put a hundred more options up though, so I may have to give them a shot.
I've tried to optimize things enough so you don't need a lightning/turbo lora, and 4 steps on a video render seems nuts to me, but I suppose I should give it a shot.
@joeygambino with latest V4 he even says 4 steps is not enough need to be minimum 6-8, at 8 no improvement would be gained. he also uses custom sampler and custom lora loader for those who has less Vram.
Thanks for all the work on this pack, the chaining is genuinely great.
Found a bug though: guide_audio (Audio Spine) with a real voice track outputs static/hiss instead of the audio, on ref2va. Same audio file works perfectly through the native MiniMaxH3ReferenceToVideo node, so it seems isolated to the Audio Spine injection path
On it, next update, coming tonight.
@joeygambino Thanks for the reply! Quick update: I've now tested up through 2.1.6, same result.
The resample fix from 2.1.3 is confirmed working on my end (console shows the resample happening correctly), but the audio is still coming out as static/garbled, no change from before the fix.
I've ruled out continuity mode (context_pin/latent_handoff), checkpoint format (safetensors and GGUF), scheduler (beta/beta57), and LoRA, same result every time. voice_ref works perfectly on the exact same file, so it's specifically the guide_audio path that's still broken for me.
How are you testing this on your end? Trying to figure out what's different about my setup (RTX 5090, ComfyUI 0.32.0).
Got it — and the answer to "how are you testing this on your end" is the bug. I am on ComfyUI 0.30.0. You are on 0.32.0. It works here and cannot work there, and that is entirely on me for not testing across versions.
What changed. 0.32.0 introduced ModelSamplingAV, and ComfyUI now carries the audio half of the audio+video pack scaled onto the video schedule:
process_latent_in audio slice x (shift / audio_shift)
process_latent_out audio slice x 1 / (shift / audio_shift)
For H3 those shifts are 12 and 3, so the audio latent inside the sampler lives in a 4x-scaled domain. The Audio Spine locks its encoded audio into that pack during sampling — and it was writing raw, unscaled values. Every locked column lands 4x too small, which decodes as exactly the static you are hearing. On 0.30.0 there is no such scaling, so raw was correct.
That accounts for everything you found, and your process is what made it findable:
- voice_ref works on the same file — it goes through the text conditioning and never touches the sampler's latent, so the scaling never applies to it.
- The 2.1.3 resample fix fires and changes nothing — you were right, it works. Encoding was never the problem; the problem is one step later.
- Continuity mode, checkpoint format, scheduler, LoRA all irrelevant — none of them touch this path. Ruling them out is what pointed at the sampler.
Also worth knowing: *audio_lock has the identical bug** on 0.32.0, same code path, same cause. It is fixed by the same change.
The fix reads the scale off the live model_sampling object rather than hardcoding 4, so it stays correct if you change the shifts with MiniMaxH3SigmaShift, and it leaves 0.30.0 behaviour byte-identical. It is written and deployed on my side but I have not render-verified it on 0.32.0 yet - I am setting up a 0.32.0 instance to reproduce your exact failure and confirm the cure rather than ship it on code reading alone. It will be in the next release, which is close.
Until then, honestly, there is no clean workaround. voice_ref will hold one voice across shots and is the nearest thing, but it is not the spine — it does not lock every shot to one continuous performance. If you need the spine specifically, 0.30.0 is the only place it currently works, and I would not recommend downgrading a whole install for one feature when the fix is coming.
Thank you for staying with this through 2.1.6 and for testing so carefully. Four ruled-out variables plus "voice_ref works on the same file" is what turned this from a shrug into a one-line fix.
Oh, and, sorry about "coming tonight" - I got sidetracked trying to add too many features at once, which I tend to do. I am going to say it again though... fix is coming tonight (I hope).
Thanks for the deep dive, really appreciate it! No worries about the delay, I'll wait for the fix and try guide_audio again once it's out
attention (gated) and chunk (gated) nodes dont work for me
Can you paste the errors from the terminal?
@joeygambino [WARNING] invalid prompt: {'type': 'missing_node_type', 'message': "Node 'attention patch (gated)' has no class_type. The workflow may be corrupted or a custom node is missing.", 'details': "Node ID '#9'", 'extra_info': {'node_id': '9', 'class_type': None, 'node_title': 'attention patch (gated)'}}
@joeygambino also in multishot wflow now have error
Prompt outputs failed validation: H3MultishotMemorySampler: - Value 4 bigger than max of 3: memory_frames
Это может быть связано со следующим скриптом:
/extensions/comfyui-easy-use/assets/extensions-WrZZZUnM.js
@egin1992654 Sorry for the late response, I missed you replied.
## 1. The gated nodes: two packs to install
The full workflow uses three nodes from two packs I am not allowed to bundle.
I shipped them switched off, assuming that was enough - it is not.
ComfyUI checks that every node class exists before it will queue anything, even a node that is switched off, so a missing pack stops the whole workflow instead of just that one feature.
Install these two and the workflow runs exactly as shipped:
ComfyUI-sol-attn (provides two of the three)
https://github.com/Saganaki22/ComfyUI-sol-attn
comfyui-minimax-h3-blockcache-T8 (provides the third)
https://github.com/T8mars/comfyui-minimax-h3-blockcache-T8
Via ComfyUI Manager (easiest): Manager > Install via Git URL, paste each URL in turn, then restart ComfyUI.
Or by hand:
cd ComfyUI/custom_nodes
git clone https://github.com/Saganaki22/ComfyUI-sol-attn
git clone https://github.com/T8mars/comfyui-minimax-h3-blockcache-T8
then restart ComfyUI. Check the console on startup - if either pack fails to import it will say so there, and that message is the thing to send me.
Reload the workflow afterwards. The three nodes will resolve, and the error goes away. They are speed and memory optimisations, so you will also get a faster render out of it.
One thing worth knowing: those three ship bypassed on the canvas. Installing the packs stops the error. If you then want the speed as well, select each node and press Ctrl+B to un-bypass it, and turn on the matching switch on the FEATURE SWITCHES panel. Leaving them bypassed is fine too - everything renders identically, just slower.
## 2. The memory_frames error
Value 4 bigger than max of 3 - that dial only accepts 0 to 3.
Open the H3MultishotMemorySampler node and set memory_frames to 0 (that is the shipped default), then queue again.
If other dials on that node also look wrong, the workflow file you loaded was saved by an older release. The sampler gained widgets over several versions, and when a saved file has a different number of values than the node has dials,
ComfyUI fills them in order and everything after the mismatch lands on the wrong dial. In that case load H3_Seamless_Chain_v2.json fresh out of the current zip rather than reusing your saved copy - then re-enter any settings you had changed.
You're definitely getting there, man. This is good stuff.
Your own video above has a cut and isn't seamless though. And the image degradation is pretty severe by the end, like WAN 2.2. Seems like you did fix color shift and audio, and there are no overbright frames at the seams, so this is insane progress for only a few days.
Keep up the great work.
Yeah, I am working on the degradation, expecting to have some progress by morning. The cut.. I don't even know what happened there, it's been pretty steadily working for me.
Hey, first as almost everyone else has already said. Thanks for your amazing work! And also for being so engaged on follow ups!
I managed to get your Riftcast Studio up and running the other day. This flow was looking like a seamless startup for me after I got rid of a Fantasy Talking GGUF node conflict. Then unfortunately the flow ran to about 85% before throwing a math error. I believe I had everything in place as the models auto-populated when I loaded the H3_Seamless_Chain_v2 flow. This was just with the stock images and prompts.
H3 Multishot Sampler + Memory (long form)
Error log
# ComfyUI Error Report ## Error Details - Node ID: 30 - Node Type: H3MultishotMemorySampler - Exception Type: RuntimeError - Exception Message: RuntimeError: mat1 and mat2 shapes cannot be multiplied (3680x1152 and 3456x1152)
I ran the update patch and it came back success. Apologies if this is one of those long since asked and answered. Feel like I had a pretty solid look around for someone with the same issue and cam up empty.
Thanks
@joeygambino Hi, just to add a little more to this as I've been having the same error. My settings in the master control are 640x960 (2:3) I'm only using one image, or at least it's the only one activated and that's in the Identity Anchor Image Node and the resolution is 1024x1536 (2:3) so from a resolution perspective they should be compatible?
The things I have noticed:
1. It only errors if I'm using a GGUF text encoder, safetensors work fine although it doesn't bring the image in as the first image for the shot.
2. After the error if I look at the parameters for the node, which I assume reflect the state of play when the fatal error occurred, width and height are reported as 764x1344 which isn't 2:3. However, if I do get a successful run (using a safetensor text encoder) the finished video is as specified in the master control i.e. 640x960.
I don't know what any of this means, it's mostly all well above my brain cell count, but I thought I'd let you know incase it helps.
Edit: Just to mention after reading wallmonster's latest comments, my error occurs pretty much as soon as it hits the sampler.
@joeygambino Thanks for the quick response!
Should have clarified but as with Lemming below that was with the gguf models. That first run was with only the place holder 768x768 images in there slots. All use image toggles were turned off which should have defaulted to whatever T2I resolution the workflow was saved at and the example text in place. I do not believe I flipped a single toggle on that run. When I was first starting with comfy I threw a lot of math errors with text encoder mismatches but as you said those where always when the first merge happened. Here it goes pretty much all the way until it is getting ready to move off the ksampler. Rough previews were generating that looked to match your example script. Maybe trying to merge the shots?.
Anyway I'll keep digging through my settings. I do have a pretty robust local machine so I'll give the safetensors version a try. My main interest in gguf version is iteration speed as I am learning, as we see here it is sometimes better to fail fast. Local storage space is another big plus for gguf. Like most of your users I am a bit of a hoarder and hesitant to delete anything. Either I have happy memories of one good run with that file or it is on my mental list to go back and figure out how to optimize later.
Edit here: It was not actually at 85%. The ksampler goes to 100% of the first shot and the error throws on the handoff to the second shot. I made sure the image input toggles were all turned off and for extra security bypassed all of their loaders as well as the audio anchor loader. I tried 1152x1152 hoping for a direct match to the model but something, somewhere is adding a little to the image width no matter what I put in the master. I changed the reference image size toggle from match to max also with no joy.
Edit 2: I thought I kept everything 100% unchanged when I first loaded the workflow but I may have been a little too proactive. When trying to figure out where the extra width is coming from I changed the text encoder sidecar setting from referencing the actual file to auto and at least T2V it was able to join 2 shots and run to completion. It's certainly possible that I populated that field myself on the initial run. Testing I2V now with the sidecar on auto.
Edit 3: It runs to completion I2V with the sidecar set to auto.
Early days after only one run but the initial run off of the same reference image and resolution did not seem to generate the same quality as your Riftcast/JoyEcho workflows. Now that I have completed a run I'll move up to the Q8 model and see how that goes.
Thanks again!
Found it - and it is my bug, not your setup. Ignore my earlier answer about resolutions; that was wrong, sorry for the detour.
It's the mmproj_name widget on the H3 CLIP Loader. Naming a file there went down a different code path than (auto) and skipped the key-renaming step, so the vision tower loaded under names nothing reads. That is why it always died at the shot-2 handoff, why it was GGUF-only, and why nothing you changed about resolution or image toggles helped.
Manual fix, in order of least effort:
1. Set mmproj_name back to (auto). If it loads, you are done.
2. If (auto) then says "No vision sidecar resolved", the pairing is by filename - the mmproj must sit in the same folder as the encoder and contain the encoder's name minus its quant suffix:
So either rename the mmproj to match, or make it the only file with "mmproj" in the name in that folder - the loader falls back to "if there is exactly one, use it."
3. If you would rather not touch your model folder, one line in custom_nodes/ComfyUI-H3-Multishot/h3_multishot_utils.py. Find:
if mmproj_name and mmproj_name != "(auto)":
and change it to:
if False and mmproj_name and mmproj_name != "(auto)":
That makes the widget inert and forces the working path. Restart ComfyUI. It is a workaround, not the fix - the real one keeps the widget working for people with split folders.
Or just wait. It is already fixed and verified on my side, and the next release is close — it also carries two other things that stop the workflow running for anyone who installed from here: the accelerator nodes shipped switched on (so a clean install could not queue at all), and a widget mismatch that threw "The value 1 for reference_image_size is not available". If you are not blocked today, the update will be the cleaner path.
Thanks again - @wallmonster151, your Edit 2 is what found this. It would have stayed hidden for a long time otherwise.
@LemmingWolf01 - the 768x1344 you saw on the node after the error is a display quirk, not the cause: width and height are driven by links from MASTER CONTROLS, so the widget keeps showing its own stored default. Your render really was 640x960. Separately, tell me which safetensors encoder you used when the image did not come in as the first frame and I will chase that one too.
Found it - and it is my bug, not your setup. Ignore my earlier answer about resolutions; that was wrong and I am sorry for the detour.
It is the mmproj_name widget on the H3 CLIP Loader. Naming a file there went down a different code path than (auto) and skipped the key-renaming step, so the vision tower loaded under names nothing reads. That is why it always died at the shot-2 handoff, why it was GGUF-only, and why nothing you changed about resolution or image toggles helped.
Manual fix, in order of least effort:
1. Set mmproj_name back to (auto). If it loads, you are done.
2. If (auto) then says "No vision sidecar resolved", the pairing is by filename - the mmproj must sit in the same folder as the encoder and contain the encoder's name minus its quant suffix:
MiniMax-H3-encoder-Q5_K_M.gguf + MiniMax-H3-encoder-mmproj-F16.gguf pairs
MiniMax-H3-encoder-Q5_K_M.gguf + mmproj-F16.gguf does not
So either rename the mmproj to match, or make it the only file with "mmproj" in the name in that folder - the loader falls back to "if there is exactly one, use it."
3. If you would rather not touch your model folder, one line in custom_nodes/ComfyUI-H3-Multishot/h3_multishot_utils.py. Find:
if mmproj_name and mmproj_name != "(auto)":
and change it to:
if False and mmproj_name and mmproj_name != "(auto)":
That makes the widget inert and forces the working path. Restart ComfyUI. It is a workaround, not the fix - the real one keeps the widget working for people with split folders.
Or just wait. It is already fixed and verified on my side, and the next release is close — it also carries two other things that stop the workflow running for anyone who installed from here: the accelerator nodes shipped switched on (so a clean install could not queue at all), and a widget mismatch that threw "The value 1 for reference_image_size is not available". If you are not blocked today, the update will be the cleaner path.
Thanks again - @wallmonster151, your Edit 2 is what found this. It would have stayed hidden for a long time otherwise.
@LemmingWolf01 - the 768x1344 you saw on the node after the error is a display quirk, not the cause: width and height are driven by links from MASTER CONTROLS, so the widget keeps showing its own stored default. Your render really was 640x960. Separately, tell me which safetensors encoder you used when the image did not come in as the first frame and I will chase that one too.
@joeygambino Thanks man! Coincidentally I saw you were updating that file on git when I was digging around. I almost threw in the new utils file to test. I will do that on the next run. In truth I spent a fair amount of time hacking around in that file last night with no joy so I did a full revert to confirm the issue before reaching out.
@joeygambino Hi, thanks for the updates and I can confirm that GGUF text encoders work now. With regard to the first frame issue, the safetensors encoder I was using was just the stock "qwen3vl_32b_minimax_h3_nvfp4_awq" I've since tried with a GGUF + mmproj and still no first frame from my Identify anchor image. Perhaps I'm not fully understanding the process and it's something I'm doing wrong. As it stands I have the:
Reference Gate OFF,
FFLF Plate Gate OFF,
Identity Anchor gate ON (with image),
Continuity = first_frame,
and using a fl2va model (I have tried a ref2va model as well, though in this instance it shouldn't be required, should it?)
As I understand things, that should produce a shot with the first frame as per my Identity Anchor image.
As I said, maybe it's something I'm doing or not doing.
Thanks for all your fantastic work and help.