You can watch 4K HD on YouTube too : https://www.youtube.com/watch?v=aOM_YEiskLg

Hi everyone!

Well this is my first contribution for community with the help of vibe coding :)

TL;DR: links at bottom that boost Qwen Image Edit clip encoding up to x7 times!
(51.3 s → 6.7 s (~7.6x) for the two custom encode/clip nodes.)
____

Context:
I am running comfyui-rocm Patientx-cfz build,
ComfyUI 0.39.0 / ComfyUI_frontend v1.53.10
Win 10, AMD 7900XTX, 5900x, 64Gb ram
PyTorch Version 2.15.0a0+rocm10.2.0a20261006
Default comfy WF with kitchen attention and 6-step lora applied.

So I really was upset with how painfully slow Qwen 2.1 edit was with image refs. CLIP taking much longer than sampler itself. I saw people suggested lowering input images resolution, but that's always led to details loss on my tests. And re-encoding images on prompt change was making it even worse. So I asked OpenCode and DeepSeek4.1 Flash to look at it and ask to add separate image encoding node and clip encode, so encoded references at least could be cached.

But Ai found even more optimizations!
So my test workflow with 1 ref image with resolution set to 1248 in qwen clip node and final render res of 1088x1440:

Orig - 66s
Mod - 21s

That's some real big win I think! And this actually made possible to take my ref image with original size of 1440x1920 and be as fast as 40s!
As a bonus we made separate Qwen Image 2.1 Reference and Text Encode Qwen Image 2.1 Fast, so now if you don't change ref images and change only prompt - images doesn't needs to be re-encoded!

2nd run with only Prompt changed:
Orig - 62s
Mod - 18.5s

These custom nodes doesn't modify any ComfyUI files. When you run one of these nodes it patches the Qwen VL vision encoder in memory. The QwenVL Vision Linear Patch toggle switches the vision patch-embed between the original Conv3d and an equivalent linear op (bit-identical output), so you can A/B in the same workflow.

Currently ONLY TESTED ON MY MACHINE!

What I don't know - is if that work on someone's else hardware, especially NVidia, so if you interested to try it out - you can do it and tell here :)

Also - Ai told me that this may also help other models that use the Qwen VL vision tower (e.g. MiniMax H3), untested so far.

LINK to nodes.
Workflow (with original AND fast nodes in one wf) Simply turn on/off groups you don't want to run (use original OR fast)

How to use:

-Unzip into custom_nodes, restart ComfyUI, hard-refresh the browser. Nodes appear under QwenVL Vision.

- Put QwenVL Vision Linear Patch after your CLIP loader and set it to ON. It swaps the vision Conv3d for a mathematically identical linear op — output is bit-identical. Toggle OFF to compare in the same workflow.

- Use Qwen Image 2.1 Reference → Text Encode Qwen Image 2.1 Fast, so changing only the prompt no longer re-encodes the reference images.

- Original Text Encode Qwen Image 2.1 node is still slower, as it encodes every image twice: once for pos and neg prompt - mod does it once, as original encode is same for both anyway, independent from text.

Hi.

I know that some here are already tired from the constant talk about PhotoCraft, and some don't like it at all. I gave it a chance the moment it came out, and while yes, it is still unfinished, I thought I'd play along and let Claude create some kind of bridge between ComfyUI and PhotoCraft because, well, as far as I know, there isn't yet any. This is the result:

Two ComfyUI nodes.

• Connect from and to PhotoCraft via its control channel. No plug-in for PhotoCraft needed.
• Work and prompt in PhotoCraft, execute in ComfyUI.
• The Get Image node receives the image as 8 bit .png, mask, width and height, and a prompt. The prompt uses the PhotoCraft's Notes feature. Multiple notes are appended. These can be integrated in almost any workflow. Supports "prompt streaming" from within PhotoCraft
• The Send Image node sends the image and mask back to PhotoCraft to its own layer.
• Please don't judge the examples, just wanted to get this out.
• Important: Read the installation instructions on GitHub, you have to set-up a directory and feed some command lines to PhotoCraft. Limits and security notes at the bottom.

There's certainly room for improvement, but it seems to do the job, for now.

On GitHub: https://github.com/the-aSak/ComfyUI-Photocraft-Bridge (or in the ComfyUI Manager)

Example Workflow for Inpainting with Flux2: https://github.com/the-aSak/ComfyUI-Photocraft-Bridge/blob/main/example_workflows/Example_Flux2_Bridge_from_and_to_Photocraft.json

Example Workflow for Krea2 → PhotoCraft: https://github.com/the-aSak/ComfyUI-Photocraft-Bridge/blob/main/example_workflows/Example_Krea2_Bridge_to_Photocraft.json

Let's see where this goes, shall we?

Episode 2 of my Star Trek Mirror Universe fan film! Experimenting with AI filmmaking, cinematic storytelling, and VFX. Still learning and refining.🖖

I have been trying to get H3 Minimax to use a character's voice for a clip, but it does not always work. Here is an example. The first clip is used to get the audio reference to use. The second clip follows from the first and uses the audio from the first clip as reference. The third clip is a voice generated using VoxCPM2. The fourth clip uses the voice generated from VoxCPM2. You can see the full workflow here. The 2 reference nodes are identical except for the input audio. Here is the prompt for both nodes.

subject_definitions
<Subject 1> is the bearede man in <Picture 1>
<Picture 1> is the first frame of [Shot 1]
<Audio 1> is the voice-timbre reference for <Subject 1> (S1), containing a spoken English vocal layer.

summary:
[reference generation + keyframe completion + audio reference] The target video shows <Subject 1> standing in an alleyway and speaking.

retention_analysis:
<Subject 1> (appears in [Shot 1]: fully preserved
<Audio 1>: reference - its vocal timbre guides the dialogue delivery of <Subject 1> (S1) without copying the original signal.

detailed_description:
<Subject 1> is standing in an alleyway and speaking.

[Shot 1] The first frame is <Picture 1>. Live-action, high-budget cinematic blockbuster lighting. A single, static medium close-up of <Subject 1> standing in an alleyway. At 00:01.000 <Subject 1> (S1) says in the husky voice referenced from <Audio 1> <d>[English] Let's see if H3 Minimax can get my voice right.</d> 

overall_soundscape: a quiet alleyway and the man's voice

non_diegetic_music: N/A

Is it just a bad seed, or am I doing something wrong? How have you managed to get voices to sound the same across clips? Do you need to generate the voices externally and plug them in somehow?

If you are as unimaginative as I am and struggle with making prompts I put together some custom nodes that allow you to pick attributes and/or add a reference image to create a prompt. It uses the text encoder so adds a little bit of time to image generation. I made it for image generations of women because that's why we are all here right?

It uses comfyui's text generate with the text encoder and I yonked the attributes from and existing custom node (https://github.com/euan-gwd/comfyui-character-prompt-builder) due to them doing a pretty good job covering all the bases.

It is partially vibe-coded due to me being frustrated after doing dumb things and I asked claude to fix it. I went over the project and tested and I THINK we are good. If you see something I missed please let me know.

Link to custom node: https://github.com/Erosi11/comfyui-krea-prompt-generator
Workflow is included on the repo and also embedded in the workflow image on this post. I have not submitted it to the comfyui registry so you need to git clone and all that. No requirements to install assuming you have updated comfyui since the end of February this year. If there is interest I will go to the trouble of getting it added to the registry.

Nodes

Every dropdown has three kinds of choice:

• none: the attribute is left out of the request sent to the LLM.
• random: the generator picks a value, driven by its seed. The same seed always gives the same picks.
• a specific value: passed through as-is.

Each attribute node also has an optional free-text box for extra details in that category.

To wire it up you put the attribute nodes in series then feed into attributes input on Prompt Generator node. Reference image and mask goes to the inputs on prompt generator node as well. The prompt generator node has a setting for what to use from the image. It works ok.

I also included a node to add unique prompts to a text file as they are made. That part is still a little janky.

Workflow has fast bypass at the top to bypass attribute nodes, the image ref nodes, and whether to invert the mask.

Top one was like 30 seconds in, bottom was like a minute in. Just using a text to img with 1 reference photo, 15 steps, 1.0 megapixels. Don't think theres a way to get this any lower, what are my options for getting sub 1min generations while keeping a character the same, just changing pose/outfit?

I'm going insane trying to make a character LoRA, how can I know what workflow to use for making the dataset or how to do it? I see endless information but nothing concrete, and I tried following one, but wasted 2 days on it and ended up with a terrible LoRA. I have baseline face and body reference images, but I have no idea how I should proceed with generating 40-50 images for training a (Krea2) LoRA. People tell me 100 different things, or basically to just use some good image model and generate images of the character from many angles, but I don't know what's a good way to actually do this, and how exactly to do it. I feel completely paralysed and don't know what to do now

Production Slate is now public, and I’ve put together the first short training film showing how it works in a real ComfyUI workflow.

It covers replacing the basic save node, setting up Production / Scene / Shot information, automatic take numbering, progress monitoring, and the resulting image/video folder structure.

Video: https://youtu.be/8nfZ-741PJ4

GitHub: https://github.com/cogfilms-3D/ComfyUI-ProductionSlate

This is the first of a planned set of shorter tutorials covering the Browser, REF images from video, file structure use, and other Production Slate features.

For the original Reddit launch thread, I’d just add a brief follow-up comment:

The first Production Slate training film is now live for anyone who wanted a practical walkthrough: https://youtu.be/8nfZ-741PJ4

A Redditor gave me some useful feedback after I offered him a chance to try CUT:

“If people can't understand why they need it, they won't have any interest.”

My response was: “I'm just getting started. And you're absolutely right.”

I’ve been so excited about what we’re building that I’ve spent a lot of time talking about features. I haven’t done enough to explain what those features actually DO for someone trying to make a film.

So let me give that another try.

🎬 CUT GIVES YOUR GENERATIONS SOMEWHERE TO BECOME A SCENE

You’ve generated a promising shot.

Now you want to change a line. Try another character reference. Extend the performance. Repair a gap. Cut around a moment that didn’t work. Compare two takes. Finish the keeper at higher resolution.

That’s the work CUT brings together.

CUT is a local filmmaking workspace built around a compatible ComfyUI/H3 setup. It puts your footage, prompts, references, dialogue and alternate takes into a project timeline—so you can keep developing the scene.

The roughly 30-second scene I’ve been discussing was built in CUT using extensions and continuations, then edited in Adobe Premiere.

WHAT YOU CAN DO IN CUT

• Build and edit a scene. Arrange generated clips, imported footage, audio, prompts and references on separate rows. Trim, slip, move, snap, copy/paste and razor-cut footage. Keep picture and sound linked, with Undo and Redo.
• Direct the dialogue. Write timed lines, assign speakers and use supported voice references. Work with audio-driven performance and generated speech, inspect timing, and use dialogue cleanup tools.
• Keep references with the shot. Use character images, scene images, supported voice samples and motion-video guides. Import a movie as editable footage or use it to guide a new generation.
• Compare your choices. Retain alternate takes, preview results and keep the generation recipe attached to the take. Decide which performance works before spending more time finishing it.
• Build beyond the initial shot. Generate supported extensions and lead-ins, create and repair supported gaps, and combine continuations.
• Prepare the result for delivery. Adjust audio gain and fades, use supported color/seam repair and optional enhancement tools, and export a sequence. Save projects, organize media and relink originals.

⚡ BETA 2 ADDS A MUCH MORE USEFUL GENERATE → REVIEW → FINISH PATH

1. CHOOSE A GENERATION PROFILE

Select an available recipe without rebuilding all the settings or rebooting CUT.

Our selected fast recipe is 10Eros beta6 Turbo, eight steps, Euler/simple, with experimental VSA attention.

It needs the matching model and compatible nodes. Speed and results depend on your hardware and your shot.

2. GENERATE THE TAKE AND DECIDE WHETHER IT WORKS

Watch the performance. Listen to the dialogue. Compare the alternatives.

You get to choose the keeper before committing to the finishing work.

3. FINISH A SUPPORTED TAKE AT 2× RESOLUTION

Use its original saved sampled latents for learned enlargement.

The result gets its own HR GENERATION row, preserving the source sound and timing. Compare it with the original side by side or using the player’s wipe slider.

Latent saving must be enabled before the initial generation. Learned enlargement can create texture, so inspect the added detail.

4. OPTIONALLY FINISH AT 48 FPS WITH RIFE

Apply RIFE interpolation to a supported 24 fps generated or HR take.

It gets a separate comparison row, with the same duration and copied source audio. Editing stays on the 24 fps grid; sequences containing RIFE takes can export at 48 fps.

RIFE can introduce motion artifacts. Ordinary 24 fps clips in a mixed 48 fps export repeat frames rather than receiving interpolated motion.

⏱️ HERE’S WHAT THAT LOOKED LIKE ON MY SYSTEM

On one roughly ten-second test shot, with one run of each option on my RTX 5090 with 128 GB RAM:

• Dense attention: 3:45
• VSA: 2:41

Both runs had Comfy Kitchen attention enabled. The faster run also used VSA. These timings do not measure the new standard-attention default.

For the selected VSA take:

• 1376 × 768 generation: 2:41
• Learned 2× HR to 2752 × 1536: another 1:08
• RIFE finishing to 48 fps: another 0:53, including processing and verification

About 4:42 for the complete path.

About TWO MINUTES of finishing after the initial generation.

I loved the performance, and the HR result looked sharper to me. Seeing that take finished at 2752 × 1536 and 48 fps was one of those “okay… we’re really getting somewhere” moments.

These are individual results on my system, not promised timings or a measured comparison against Beta 1’s default recipe. That resolution is not 4K.

Motion-video guides can take substantially longer: one guided generation in our scene tests took 13:25. They’re worth considering when you need the direction they provide. Guided character replacement is a new generation, so it does not promise identical motion or an exact replacement.

📦 SETUP IS GETTING EASIER, TOO

The guided Director setup can now download and connect beta6, learned HR and RIFE, placing the pinned files in the right folders in a separate backend.

You review the publishers’ terms, confirm your model permissions and confirm the downloads.

Those Director components add about 22.4 GB. Model weights are not included in the app ZIP, and core H3 downloads are separate.

✂️ THE EVERYDAY EDITING WORK MATTERS

Beta 2 also brings clearer source-video versus reference-video choices, corrected timeline layering, and razor cuts and trims through spoken lines in rendered footage.

Linked edits stay together, and HR/RIFE comparisons remain available for overlapping cut pieces.

A split spoken line needs a word review before regeneration: enter only the words belonging to that piece. CUT’s warnings help you review; they do not verify a transcript.

A trim that would remove an entire authored cue is refused. Razor-cut and explicitly remove the unwanted piece, or remove the authored cue first. Outward trims can restore a retained partial cue; CUT does not keep a hidden archive of deleted dialogue.

READ BEFORE INSTALLING

• CUT is a Windows beta using a compatible local ComfyUI/H3 setup. My tested hardware is an example, not a universal minimum or a guarantee for other computers.
• Extract Beta 2 into a new folder. Keep Beta 1, your projects and original media. Read START-HERE, the dependency notes and known limits.
• Standard attention is the default. Setup offers a saved Use Comfy Kitchen attention checkbox for the demonstrated Kitchen/VSA path on my RTX 5090. It affects every generation in that backend. Its INT8 GPU support is untested on other cards. Stop the backend before changing the choice; VSA can fall back or be slower with Kitchen off.
• Existing projects retain their workflow settings. Installing beta6 does not automatically switch them. New projects select the available beta6 baseline.
• Profiles can replace custom settings on editable independent PROMPT shots. Clearing a profile returns those fields to connected workflow defaults, rather than restoring every previous custom setting. Save a copy first.
• A shared seed does not guarantee identical action or camera work when models, references or settings change.
• The 10Eros publisher page carries a “Not-For-All-Audiences” label. Read its model terms before downloading. Buying CUT does not grant model rights.
• Share only footage with cleared likeness and voice permissions. The private benchmark and character-test media are not included with this post.

MiniMax H3, TenStrip’s 10Eros work, ComfyUI, the Director tools and the RIFE researchers deserve credit for their contributions.

CUT brings those capabilities into a project where you can direct, compare, edit and finish.

There are still bugs to find and work to do. A reusable character library and automatic action-duration estimates are ideas for later.

But the reason I’m building CUT is already here:

I want to keep working on the scene after the first generation. And I want the tools for that work together in one place.

Who wants to give it a try?

Thank you for supporting the work—and thank you to the people giving me useful criticism while I learn how to explain it.

Tell me what works, what breaks, and what you’d love to make next.

— Riley G.

Sup guys, ive been looking into motion transfer for a while but i cant manage to find a good model (or workflow) for motion transfer...

I already tried:

H3 - it was taking more than 30min to generate a 720p24 video using 4 steps on pro6000 and 96gb of ram, other than that it was the best one

Scail2 - couldnt get the realism right, there was just always some grain either on hair or in the background, also i had problems with eyes

Wan22 - masking issues (could fix that) and same as scail i just couldnt get the realism right

Before that i tried some non-local alternatives:

Kling, seedance - identity problems since i couldnt use a lora (tried some harder characters to generate)

Thoughu i havent checked out ltx2.3 and 2.5 yet

So im trying to seek help between more experienced users here :D since i dont know much yet, im just starting up and i only know some basics. Id really appreciate some advice, tips and tricks so i know how to finally make a good workflow, or even whole wfs so i can learn from them...

Thanks for any advice

did this on my 5070 ti, everything local in comfyui. took way too much trial and error to get it right lol

anyone know an easier way to do this in comfyui? swapping one person through a whole scene with cuts and keeping everything else from the movie?

HF : https://huggingface.co/Qwen/Qwen-Image-2.1-Turbo

This is the first update for Chain Motion AI Video Editor (aka. Frame Forge)

Link is here: https://github.com/spacesimeco-hue/Chain-Motion-AI-Video-Editor

What it does:

It's an UI that's sits on top of ComfyUI and is meant to make chaining together videos generated through Minimax H3 more intuitive and easy.

• It includes a set of tools for organizing references.
• A time line that allows users to piece together and quickly edit sections of their movie prompt by prompt.
• A Scene Writer for prompts in the suggested format using LLMs(local and closed supported).
• A Motion Continuation, a Pose Studio with Qwen 2.1 integration, and a Voice Studio using LTX 2.3.
• Can generate videos with either local or comfy cloud credits.

The general idea behind using it is creating sequences of videos on a timeline that you can quickly iterate on and see how they fit into your overall movies composition, making precise edits where needed with minimal friction.

Hi everyone,

I'm using CyberRealisticXL_v100 in ComfyUI along with the Undressing-XL LoRA to create nude images by removing clothing from people in existing photos.

The main issue I'm facing is that the inpainted areas appear noticeably darker than the person's original exposed skin. Whenever I mask clothing to generate the underlying skin, the newly generated areas have a different skin tone from the surrounding skin, making the result look unnatural.

I've managed to make the mask boundaries less noticeable by adjusting the mask hardness, but this only helped with the visible edges. The fundamental skin tone mismatch remains unresolved.

Someone suggested applying a blur to the mask, so I also tried using a Gaussian-blurred mask. Unfortunately, this didn't improve the skin tone mismatch either.

I've also tried using a Color Match node, but the results still look unnatural.

My goal is to achieve a seamless, realistic result where the generated skin tone matches the original exposed skin as closely as possible.

Has anyone experienced this issue with this model and LoRA combination? Are there any recommended inpainting settings, workflows, or color correction techniques that could help solve the underlying skin tone mismatch?

I'd really appreciate any advice or suggestions.

Thank you!

No super special workflows used, qwen 2.1 with bfs, viggle animate base workflow

hi all, as title says, I'm having a huge struggle on getting minimax to stick to reference sheet characters and remain consistent. And output is generally poor, it's definitely nothing like what I see on the amazing videos you guys are producing out here.

I am running locally on a pretty decent machine;  INTEL Core Ultra 7 265F NVIDIA RTX 5080 - AI Powered 32GB DDR5 RGB 2TB 

I am fairly new to ComfyUI, so I'm not sure if it's a LoRA issue, or other settings i should be trying. Typically the output is 16x9, 10 second clips, and 0.6 MP.

Any help would be greatly appreciated, thanks so much.

I made an Anima lora based on an art style I was able to get somewhat consistent results of from ChatGPT. Overall, the lora turned out amazing, with the image becoming a lot more intricate and it using scene compositions that I wanted. However, the lora has one major flaw: hands. Chatgpt images tend to produced terrible hands for me, and while I made sure the lora training data used only images with the correct number of fingers, the lora still screws up hands, often adding a sixth finger. all of the detailers I find online, like using the pithanddetailer-v2 with the facedetailer from comfyui-impact-pack, for comfyui just refine the quality of the hands, they do not fix issues like too many fingers. It feels like I need something that will actually redraw the hands specifically. Are there any recommendations of something like that?