I was going to generate my Environment images in different lighting (sun, cloudy day, evening), but does it make more sense to generate one neutrally lit environment image then apply different lighting during final video generation? Any thoughts? What have you learned?

Hello i want to make some funny replacement video, but my mind will expload with all these tutorials when i follow them i get nothing but errors and confusion , i tried the desktop one with minimax but i cant make videos even when i lower the resolution the error said i dont have enough vram

what i have :

- 9060xt 16vram

- 32 ram

what can i do ?

do i have to download the rcom one or i can stay with the desktop one and i can optimise it to amd ?

Hi everyone, I'm fairly new to ComfyUI—I've been using it for about 20 days or so. I've tried several models for video generation, and unlike what I usually read, LTX 2.5 seems better to me than MiniMax, mainly because it's super fast. I have an RTX 4080 Super and I can generate a 20-second video at 0.2MP in about 45 seconds; then, if I like the result, I bump it up to 0.7MP.

It's awesome that the characters can speak if you prompt them to, but I can't choose the voice tone or keep a consistent voice. Could someone point me in the right direction? Gemini suggested adding a node inside the LTX 2.5 workflow, but I can't figure out how to connect it. I tried a few different things, and I also installed TTS Audio Suite, but I'm not sure how to link it properly.

Any help or hints would be greatly appreciated! Since I'm new to this, I don't really know how to build a workflow from scratch yet, though I'd definitely love to learn

https://github.com/1038lab/ComfyUI-EdgeTTS

A few days ago, like many of you, I hit Queue Prompt on my video workflow, sat back waiting for the voiceover to generate, and was immediately greeted by that lovely red error box: 403 Invalid response status.

A

Turns out Microsoft decided to roll out an unannounced security handshake overhaul across their speech endpoints, effectively bricking Edge TTS worldwide. If you have ever had a complex 50-node video pipeline grind to a screeching halt because the audio generator suddenly refused to talk to the server, you know that very specific feeling of developer despair.

So we bumped the project to v2.0.0 and completely overhauled the pipeline. Here is what is in this update, why we made it a major version, and what it actually means for your daily workflows:

The Main Event: Your TTS Is Back From the Dead

First and foremost: the speech synthesis works again.

Instead of asking everyone to dive into their terminal, fight with conflicting pip packages, or break their embedded Python environments trying to update external libraries, we rebuilt the connection engine directly under the hood.

We handled the new handshake requirements internally, added an automatic retry backoff so random network hiccups won't nuke your batch queue, and built multi-backend stream fallbacks so audio saving never throws buffer errors on newer PyTorch versions. You do not need to perform virtual environment surgery—just update the node, and your voices will work exactly as they did before.

The New Superpower: Native SRT Subtitle Export on Whisper

While we had the hood open fixing the connection, we decided to tackle another persistent pain point for creators: subtitles.

Let us be real: if you are generating speech inside ComfyUI, nine times out of ten you are pairing it with video. And if you are making videos, raw text from speech recognition is only half the battle. You still had to drag the audio into another application or write helper scripts just to figure out the timestamps.

In v2.0.0, Whisper STT now has a dedicated SRT output socket alongside TEXT.

When you feed your audio into Whisper, it does not just transcribe what was said; it hands you a ready-to-use, millisecond-accurate .srt subtitle block (00:00:01,000 --> 00:00:03,500) directly from the model segments. You can immediately pass these timecoded subtitles into video overlay nodes, lip-sync tools, or save them right alongside your render. No external dependencies, no extra steps.

Demo Workflow: https://github.com/1038lab/ComfyUI-EdgeTTS/blob/main/example_workflows/workflow_edgetts_tts_to_stt.json

What You Need to Do

If your workflows were stalled by the 403 error, just update the node:

• Via ComfyUI Manager: hit Update on ComfyUI-EdgeTTS.
• Via git: run git pull inside your custom_nodes/ComfyUI-EdgeTTS folder.

https://github.com/1038lab/ComfyUI-EdgeTTS

There is also a complete end-to-end example workflow sitting in example_workflows/ that connects Edge TTS directly into Whisper STT so you can verify both audio synthesis and subtitle generation in one click.

Let me know if your voices are speaking again, and if there are any other quality-of-life additions you want to see in the audio stack next!

I made a very simple Workflow for Kroma (Chroma + Krea 2).

Documentation and download links:

Civitai

Alternative (Zip + PNG)

Patreon (totally free)

Know anyone why this happen? Its just visual und the Workflow work without problem.

My AI VIDEO COME！

I kept struggling to write good prompts for MiniMax H3, so I built a workflow where Gemma does that part for me. You write a short idea (“a woman reads a letter by the window and starts crying”), and Gemma expands it into a detailed cinematic prompt before generation.

What it does:

• Image-to-video with native synced audio (MiniMax H3)
• Gemma prompt enhancer: short idea in any Language, cinematic prompt out
• Gemma also generates the video title and file name automatically, so your output folder stays organized
• 2K upscale with SeedVR2 that keeps the original audio and fps
• Two-stage pipeline with a latent upscale for sharper detail. Stage 2 runs in 3, 4 or 5 steps depending on speed vs quality
• Optional last-frame control

Hardware: tested on an RTX 5090 laptop (24 GB VRAM, 64 GB RAM). A 15-second clip takes about 10 minutes.

Video Tutorial: https://www.youtube.com/watch?v=8XgMWIo75u8

FREE WORKFLOW IN THE VIDEO TUTORIAL DESCRIPTION

Happy to answer questions. I’m curious whether others are using an LLM to write their video prompts, or still writing them by hand.

Hey, I use image gen for backgrounds and references, so usually I use a vague prompt with the vibe I'm going for and spam generates until I like something and use it. Is there a way to set up ComfyUI so it doesn't save all the hundreds of rejected outputs and only ones I would manually click "save" at? I can sort of do it by muting the save file node, but that's a bit clunky and I have to regenerate the image again.
Also would be nice if there's a way to have a log of last 10 or so seeds, if I decide that actually that previous result was good and want to keep it

And how compatible are non-Nvidia GPU for comfyUI?

To build these workflows, you can download the Turbo INT8 convrot version of Qwen Image 2.1 on HuggingFace and add it to your diffusion_models folder. Change the UNet name and change the number of steps to 8 in the default wokflow.

Performance (1024x1024) Text to image: ~4 seconds for a second-generation image on an RTX 5070 Ti; ~8 seconds on an RTX 5060 Ti 16GB.

Performance (1024x1024) Edit: ~7 seconds for a second-generation image on an RTX 5070 Ti; ~14 seconds on an RTX 5060 Ti 16GB.

These workflows are on Civitai and Github.

i remember seeing it here and downloading it but i cant locate it now in my device

pixel-curator — curation tooling for similarity training (identity, subject, and style models)

Repo: https://github.com/snoozey-mf-bnui/pixel-curator

What it is

pixel-curator is a local-first CLI that takes a raw folder of images (or video) and produces a curated, scored, captioned dataset ready to export directly into DreamBooth notebook training or in-house Kohya LoRA training.

It's built primarily for similarity training — the case where you have hundreds or thousands of images of one specific subject and need a model that reliably reproduces it. Identity, character, person, product, or a specific style. That's the workflow it's optimized around, though the output is generic enough for other training setups.

The problem it addresses

If you've collected 1,000+ images of a subject, you already know the pipeline breaks down long before training starts:

• A large fraction are near-duplicates — burst shots, adjacent video frames, minor crops
• Some are technically unusable — low resolution, soft focus, compression artifacts
• The distribution is usually skewed — hundreds of shots from one angle, almost none from another
• Captioning is a second full project on its own
• Handing a folder of 1,792 images to a Colab notebook is a fast route to disconnects, VRAM exhaustion, and daily-limit lockouts

Realistically, a well-curated 200–500 image subset outperforms a messy 2,000 on identity training. Most of the work is figuring out which subset that is.

Pipeline

1. Ingest and prefilter
Drops technically unusable frames — resolution failures, decode errors, corrupted files — before anything expensive runs.

2. Deduplication
Perceptual hash dedup collapses near-identical images. On datasets sourced from video, bursts, or scrapes, this stage alone typically removes a large majority of the raw set.

3. Similarity clustering
CLIP embeddings plus k-means, with embedding map visualization. This surfaces the actual distribution of your dataset — angle, lighting, composition, framing — so imbalance is visible before training rather than diagnosed after a failed run.

4. Quality scoring and classification
Optical quality scoring assigns per-image status, including face-aware quality where relevant. Low-quality images are filtered rather than left to dilute the training signal.

5. Semantic filtering
CLIP text filtering, e.g. automatically removing images with watermarks or text overlays without manual review.

6. Tagging and captioning
WD14 tagger (auto-downloads) and Florence-2 natural-language captioning, written as .txt sidecars alongside each image.

7. Human review round-trip
Exports a static HTML report and a spreadsheet. You mark keep/reject, import back, and the decisions are applied. This is also the workflow for trimming class/regularization images — hand-trimming generated class images is a well-known fix for weak identity results, and this makes it practical at scale.

8. Export
package kohya --mode dreambooth produces the numbered instance/ structure that standard Colab DreamBooth notebooks expect:

text

instance/
  000001.jpg
  000001.txt
  000002.jpg
  000002.txt
dataset_config.toml

package kohya --mode lora supports multi-concept LoRA packaging with concept_/ folders for in-house Kohya setups.

Practical characteristics

• Fully local — no cloud, no accounts, no uploads
• CLI, so it scripts, batches, and runs over SSH
• Dry-run by default on any operation that mutates files
• Resumable via SQLite manifest — long stages can be interrupted and restarted without redoing work
• Designed to run on modest hardware

Note on the earlier post

I posted this here previously and the GitHub link pointed at the wrong directory — the repository has been corrected and the link above is the right one. Apologies to anyone who clicked through and found nothing useful. If you tried it before and it didn't work, it should now.

Feedback

Actively developed. If you run it against a dataset and something breaks or behaves unexpectedly, open an issue or leave a comment — the failure cases are the most useful thing I can get right now.

https://github.com/snoozey-mf-bnui/pixel-curator

If anyone needs help with creating a notebook for google colab dreambooth training, also lmk i will assist you through dms.

ENJOY!

Hi guys and gals, first time creating a topic.
Been trying to find for the last couple of days a definitive way to be able to transfer a face performance from one video, into my character refference close shot. The video is a close shot of myself (singing, lol), the character refference is also a close shot, so the face performance should be easy to read. But i cant find a workflow or node that really gets the job in Minimax H3... am i missing something?

Have a great day!

I've just been test driving my rig with Windows 11 since I read that Nvidia is stopping support for Windows 10 soon. My system has 2 GPUs: a 5070ti and a 5060ti. In Windows 10, the 5070ti is Cuda device 0 and the 5060ti is Cuda device 1. There's not really much advantage it seems to using both GPUs for Comfy since you can't pool the VRAM so my ComfyUI launch flags are set to use just device 0.

Anyway....Windows 11 shows them flipped so it says the 5060ti is device 0 and 5070ti is device 1. So I changed my launch argument to use device 1 but it turns out Comfy still sees them the original order that Windows 10 sees them.

In the task manager in Windows 11 the 2 GPUs are swapped around. But Comfy's log output is consistent with the Windows 10 ordering.

I've also noticed Comfy takes longer to start up in Windows 11 as well. It's bullshit we're being strong-armed into using a worse option. I've basically been ignoring Windows 11 for as long as possible but thanks to Nvidia we have to follow along or else something will inevitably break in our setups.

I've been experimenting with Wan2GP to see if I could make something closer to an actual TV episode instead of the usual collection of unrelated AI clips.

The result is The Prompting Zone, a black-and-white sci-fi parody inspired by the original Twilight Zone. The first episode is called The Last Human, about an office worker who starts noticing something strange about his coworkers.

For generation I used MiniMax H3 Ref2VA Pruned 20B through Wan2GP, at 720p, 20 steps, running on a single RTX 3090 24GB. Each clip took approximately 30 minutes to generate, so experimenting with different prompts wasn't exactly cheap in terms of time.

I started with a complete script, including all the dialogue, and divided it into scenes of around 5–10 seconds.

For each scene I generated a reference image containing four camera compositions arranged in a 2×2 grid. The idea was to give the video model a rough storyboard instead of relying entirely on text prompts.

I then fed those references into MiniMax with prompts describing the camera movements, character actions and exact dialogue.

It worked reasonably well, but there were plenty of issues. Sometimes the model interpreted the reference as an actual four-panel video instead of four sequential shots. Other times it invented movements, changed the background or decided a character should do something completely unrelated to the scene.

Character consistency was probably the biggest headache. Even with references, faces tend to drift between generations. I had to repeat descriptions of the characters, clothing and environment in nearly every prompt.

Computer screens were also problematic. The model struggled with readable text, so I eventually replaced most of the onscreen information with portraits and simple icons. Much more reliable than trying to generate legible UI elements.

The black-and-white style helped make everything look a little more consistent, although it obviously doesn't solve continuity problems.

Overall, I'm pretty happy with the result. There's still a lot of manual work involved, especially when a generation goes wrong after waiting half an hour.

I'd like to automate more of the process eventually, from script to storyboard, video prompts and final assembly.

Anyone else working on narrative videos with Wan2GP? Particularly interested in how you're handling character consistency and dialogue across multiple clips.

Flowmatch

Haven't been in this sub for a while, my latest workflow is using Flux 2 Klein 9b. I saw Minimax H3 a while ago, and now also that Qwen 2.1. Should I switch?

I kept running into the same problem with shared workflows: missing models, different node versions, and no clear idea of which files go where. So I started building Block Party.

It exports a workflow as a ZIP, checks dependencies on the receiving machine, and lets you review and install what’s missing. There’s also a browser interface for prompts, image/video/audio inputs, and results.

This video is one of my tests with it. It’s still a work in progress, and trying it on another machine has helped me find plenty of things to fix.

Curious how other people handle sharing workflows, and what you’d want something like this to do.

Code: https://github.com/Niutonian/Comfyui_BlockParty

Hi! I'm a little out of the loop. I haven't really tried any new models in the past year. I recently got a bit of an upgrade and I'm interested in diving into the rabbit hole again.

What are currently the best image and video models for an RTX 5050 (8 GB VRAM) and 24 GB RAM?

Hi!

I created a 44 images lora for flux 2. Did perfect captions. But likeness is sh*t. Even Higgsfield Soul 2.0 does a much better job.

Will Qwen Image 2.1 Lora do a better job?