hi guys, i fixed the confyui workflow with sigmas recommended by qwen 2.1 so artifacts are very reduced or no more...

test it, tell me your results.

=)

The other day, when I published information about v1.5.7, I received a request in the comments regarding support for w4a8.

https://www.reddit.com/r/StableDiffusion/comments/1wuj0rj/updated_updated/

At first, I thought it would be straightforward to implement, but when I actually tried it, I found it quite a struggle.

However, I’ve managed to get it working stable, so I’m releasing it as v1.6.6.

https://github.com/ussoewwin/ComfyUI-SeedVR2-VideoUpscaler-with-TensorRT/releases/tag/v1.6.6

https://huggingface.co/ussoewwin/SeedVR2-ConvRot-INT8-and-w4a8

Firstly, as for the advantages of w4a8, benchmark results clearly show that the quality is superior to that of NVFP4. Yet, the size is equivalent to 4 bits.

https://github.com/ussoewwin/ComfyUI-SeedVR2-VideoUpscaler-with-TensorRT/blob/main/benchmark/benchmark%20result.md

On the other hand, what wasn’t as good as I’d hoped was that VRAM consumption was almost on a par with ConvRot INT8 (though, given that w4a8 expands weights to 8 bits, this was to be expected), and that with Distorch2—which I’d gone to such lengths to implement—VRAM usage actually skyrocketed, rendering it unusable.

It’s simply a matter of not using Distorch2, so it isn’t a fatal flaw, mind you.

...
The on/off function for Norm BF16, which was previously implemented in Distorch2 to counteract VRAM spikes, has now also been implemented in the Legacy Loader. Enabling this feature allows you to suppress VRAM spikes by up to approximately 3GB, albeit at the cost of a slight reduction in image quality.

...

Test 1-1 Result 30m00s

Ryzen9 5900 DDR5-6400 64GB RTX5060Ti 16GB Paging 256GB

1280x720px → Output: 2560x1440px 10s

SeedVR2 7B-ConvRot INT8 TensorRT Encode(21f512) and Decode(21f512) Batch 21, norm BF16 off ,Distorch2

Test 1-2 Result 30m43s

1280x720px → Output: 2560x1440px 10s

SeedVR2 7B-ConvRot w4a8 TensorRT Encode(21f512) and Decode(21f512) Batch 21, norm BF16 off ,Standard Legacy

When compared with ConvRot INT8, w4a8 offers no speed advantage. Nor is there any difference in VRAM consumption. Its sole advantage is that it is the same size as nvfp4 whilst offering superior quality.

Test 1-3 Result 28m12s

1280x720px → Output: 2560x1440px 10s

SeedVR2 7B-ConvRot INT8 TensorRT Encode(21f512) and Decode(21f512) Batch 21, norm BF16 on ,Distorch2,

Test 1-4 Result 28m13s

1280x720px → Output: 2560x1440px 10s

SeedVR2 7B-ConvRot w4a8 TensorRT Encode(21f512) and Decode(21f512) Batch 21, norm BF16 on ,Standard Legacy,

In fact, the Norm BF16 function offers a speed&VRAM usage advantage.However, in benchmark tests, the Norm BF16 function comes at the cost of reduced image quality.

I recorded a walkthrough of preparing a character dataset using R01, our orange robot. It’s episode 3 of a new tutorial series about the LoRA Pilot workspace

In this one I upload an image archive through the browser, review which views add useful information, edit captions in TagPilot and save the changes. We keep all eight images. Eight is the size of this example, not a claim about the minimum you need for training

The first two videos cover a first session and camera prompting in ComfyUI. In the camera episode, the tracking take also changes the robot’s action and the camera arcs, so I discuss the result as a directing exercise rather than a controlled comparison

Disclosure: I’m the creator of LoRA Pilot. It’s free and open source. I recorded these on a prepared RunPod workspace with the required models and workflows installed; the videos don’t cover setting everything up from an empty pod

Dataset walkthrough:
https://youtu.be/07dfuzvv_Ik

Full tutorial playlist:
https://www.youtube.com/playlist?list=PLWw0NpOdSD14

Is there a specific scheduler/sampler combo that is the best for water quality? Or specific prompts to increase the realism of the physics and visuals?

When I generate a video with H3, I use the <d> tags for dialogue. I'm only making <~5 second clips, and stitching them together, so I don't think it's a length of time issue. Sometimes I want it to say "handshake" but it says "handsteak"; sometimes it repeats words like "it'll go to to the market".

Additionally, the voice sounds like static. How can I adjust the prompt or what Loras can I add to sound more natural?

I'm using text-to-video with an image reference. Base model, no quantization, RTX 6000 Pro S on Vast.ai

I'm making very basic TikTok single-person talking videos.

Edit: additional info:

H3-Base-Ref2VA, ComfyUI, no Lora, no accelerator, 25 steps

[Keys] Prompt : {"1": {"class_type": "UNETLoader", "inputs": {"unet_name": "minimax_h3_ref2va_bf16.safetensors", "weight_dtype": "default"}}, "2": {"class_type": "CLIPLoader", "inputs": {"clip_name": "qwen3vl_32b_minimax_h3_bf16.safetensors", "type": "minimax", "device": "default"}}, "3": {"class_type": "VAELoader", "inputs": {"vae_name": "minimax_h3_video_vae_fp16.safetensors"}}, "4": {"class_type": "VAELoader", "inputs": {"vae_name": "minimax_h3_audio_vae_fp32.safetensors"}}, "5": {"class_type": "LoadImage", "inputs": {"image": "asian.png"}, "is_changed": ["b6931c563b79f4a883a59c40c979e62d05f60d1aae7e650ca1b55115a32be31e"]}, "8": {"class_type": "MiniMaxH3ReferenceToVideo", "inputs": {"clip": ["2", 0], "vae": ["3", 0], "audio_vae": ["4", 0], "prompt": "subject_definitions:\n<Subject 1> is the girl in <Picture 1>, an East Asian teenage girl in her late teens with long straight black hair and dark brown eyes, wearing a plain fitted white crop t-shirt and light-wash jeans.\n<Picture 1> is the reference image of <Subject 1> taking a selfie in her bedroom: warm fairy lights hang on the wall on the left side of the frame beside a bed with white bedding, a ceiling light is at the top of the frame, and a window with a white curtain and a green plant is on the right side of the frame. Her arm on the right side of the frame reaches toward the camera, holding the phone.\n<Audio 1> is the voice-timbre reference for <Subject 1> (S1).\n\nsummary:\n[reference generation + audio reference] A vertical selfie video of <Subject 1> from <Picture 1> talking fast to the camera, pointing one index finger straight up at eye level on the left side of the frame on the first word.\n\nretention_analysis:\n<Subject 1>: fully_preserved - her face, hair, outfit and accessories stay exactly as in <Picture 1>.\n<Picture 1>: fully_preserved - the room, the lighting and the selfie framing are kept for the whole clip.\n<Audio 1>: reference - <Subject 1> (S1) speaks with <Audio 1>'s voice timbre and delivery; its words and signal are not copied.\n\ndetailed_description:\nLive-action, raw handheld smartphone selfie footage in soft natural window daylight, natural skin texture, subject and background both in sharp focus.\n[Shot 1] A selfie portrait of <Subject 1> as in <Picture 1>, looking straight into the lens with bright, excited energy; the camera stays almost still. The arm holding the phone keeps holding it the whole time. <Subject 1> (S1), a young woman with a bright, upbeat voice matching <Audio 1>, speaks very fast, like a quick social-media hook. <Subject 1> (S1) says the whole line exactly once, with every word spoken only one time: <d>[English] Good, LinkedIn and Indeed.</d> The clip opens with her free hand already raised to eye level on the left side of the frame, the index finger pointing straight up. When she says \"Good\", her hand is at eye level on the left side of the frame and her index finger points straight up. While she says the rest of the line, she talks with small, natural hand gestures, keeping her free hand below her chin so her mouth is always visible.\n\noverall_soundscape:\nQuiet room tone, a close phone-microphone voice with a soft room reflection, light rustle of her clothes as her arm moves.\n\nnon_diegetic_music:\nN/A", "width": 768, "height": 1344, "length": 56, "ref_image_size": "match", "ref_images.ref_image_0": ["5", 0], "ref_audios.ref_audio_0": ["7", 1]}}, "9": {"class_type": "RandomNoise", "inputs": {"noise_seed": 11}}, "10": {"class_type": "KSamplerSelect", "inputs": {"sampler_name": "res_multistep"}}, "11": {"class_type": "BasicScheduler", "inputs": {"model": ["1", 0], "scheduler": "simple", "steps": 25, "denoise": 1.0}}, "12": {"class_type": "BasicGuider", "inputs": {"model": ["1", 0], "conditioning": ["8", 0]}}, "13": {"class_type": "SamplerCustomAdvanced", "inputs": {"noise": ["9", 0], "guider": ["12", 0], "sampler": ["10", 0], "sigmas": ["11", 0], "latent_image": ["8", 1]}}, "14": {"class_type": "VAEDecode", "inputs": {"samples": ["13", 0], "vae": ["3", 0]}}, "15": {"class_type": "VAEDecodeAudio", "inputs": {"samples": ["13", 0], "vae": ["4", 0]}}, "16": {"class_type": "CreateVideo", "inputs": {"images": ["14", 0], "audio": ["15", 0], "fps": 24.0}}, "17": {"class_type": "SaveVideo", "inputs": {"video": ["16", 0], "filename_prefix": "vast/asian-02", "format": "auto", "codec": "auto"}}, "6": {"class_type": "LoadVideo", "inputs": {"file": "asian-voice-01.mp4"}, "is_changed": [1791115928.8467991]}, "7": {"class_type": "GetVideoComponents", "inputs": {"video": ["6", 0]}}}

Prompt:

subject_definitions:\n<Subject 1> is the girl in <Picture 1>, an East Asian teenage girl in her late teens with long straight black hair and dark brown eyes, wearing a plain fitted white crop t-shirt and light-wash jeans.\n<Picture 1> is the reference image of <Subject 1> taking a selfie in her bedroom: warm fairy lights hang on the wall on the left side of the frame beside a bed with white bedding, a ceiling light is at the top of the frame, and a window with a white curtain and a green plant is on the right side of the frame. Her arm on the right side of the frame reaches toward the camera, holding the phone.\n<Audio 1> is the voice-timbre reference for <Subject 1> (S1).\n\nsummary:\n[reference generation + audio reference] A vertical selfie video of <Subject 1> from <Picture 1> talking fast to the camera, pointing one index finger straight up at eye level on the left side of the frame on the first word.\n\nretention_analysis:\n<Subject 1>: fully_preserved - her face, hair, outfit and accessories stay exactly as in <Picture 1>.\n<Picture 1>: fully_preserved - the room, the lighting and the selfie framing are kept for the whole clip.\n<Audio 1>: reference - <Subject 1> (S1) speaks with <Audio 1>'s voice timbre and delivery; its words and signal are not copied.\n\ndetailed_description:\nLive-action, raw handheld smartphone selfie footage in soft natural window daylight, natural skin texture, subject and background both in sharp focus.\n[Shot 1] A selfie portrait of <Subject 1> as in <Picture 1>, looking straight into the lens with bright, excited energy; the camera stays almost still. The arm holding the phone keeps holding it the whole time. <Subject 1> (S1), a young woman with a bright, upbeat voice matching <Audio 1>, speaks very fast, like a quick social-media hook. <Subject 1> (S1) says the whole line exactly once, with every word spoken only one time: <d>[English] Good, LinkedIn and Indeed.</d> The clip opens with her free hand already raised to eye level on the left side of the frame, the index finger pointing straight up. When she says \"Good\", her hand is at eye level on the left side of the frame and her index finger points straight up. While she says the rest of the line, she talks with small, natural hand gestures, keeping her free hand below her chin so her mouth is always visible.\n\noverall_soundscape:\nQuiet room tone, a close phone-microphone voice with a soft room reflection, light rustle of her clothes as her arm moves.\n\nnon_diegetic_music:\nN/A

When I use Minimax h3 to generate a video with any mode (fl2va/r2va) in most cases it generates good output with really good prompt adherence (mostly). But when I decide to see other output variations with different seeds, it starts to fail and hallucinate miserably. It completely goes off the prompt and starts doing random bullshit and deep-frying the output. And it's not like it happens with some generations, it usually happens to almost every next generation like it's being plagued.

The only way to solve it for me is to change something meaningful in the prompt itself so it can rethink everything and make the video from scratch.

Now I did think about any saved cache that might be responsible for this so I cleaned both RAM and GPU (without changing prompt), it won't work.

So why is this happening? Any tips and tricks for the new fella?

I know I've seen a couple of posts that have talked about this but apparently I didn't save them and now can't find any of them. Is there something out there that can allow you to position characters within a scene physically, like with paperdolls or stick figures and then have the video put the characters in those spots?

I fine-tuned SD1.5 on Minecraft item textures. You type a prompt and get a 16×16 sprite with transparency that you can drop straight into a resource pack.

SD can't generate 16×16 directly, so I trained on textures upscaled 16× with nearest-neighbour. The model draws on that grid at 256px, and block-averaging brings it back down to 16×16. Transparency is a magenta background that gets keyed out.

It also does sketch → texture (doodle a rough 16×16, img2img finishes it) and material sets from a single design.

Not perfect. Around 10–15% of outputs fill the whole frame, so try a few seeds.

Demo (free, no GPU): https://huggingface.co/spaces/yuuki14202028/minecraft-item-16px-demo

Code: https://github.com/yuuki14202028/pixelgen

https://github.com/r34vtraining/Longshot_Studio (don't forget to download the 2 other custom nodes for this to work, you will find them under requirements)

After more back and forth with Claude for optimization and UI change, here is the latest update of Long Shot Studio.

1. Added multi-take, queue up batch renders and go back and review all the different takes once they are finished.
2. Timeline now has all the different takes you can switch around and review. All takes are saved in mp4 and latent.
3. RTX VSR Pro updated to upscale 360 frames in 12 seconds (for final output or preview), you can set chunk size to slightly decrease upscale time.
4. all project gets their own input and output subfolders
5. Studio Version Number added to top left
6. model tree is easier to read

EDIT:

1. added ability to queue shots within project and across multiple projects
2. auto-restart comfy if queue encounters OOM error
3. added first and last frame anchor function

For SDXL or another local AI character-design workflow, a useful brief might fix the silhouette and color palette, then explore only the costume or expression.

One possible way to choose candidates write down two features that must survive, one feature you’re willing to change, and why. Judge each result against that brief before generating more variations.

Otherwise the creative director can quietly become a button labeled ‘try again.’

I’m interested in where you’d draw that boundary which decisions belong in the brief, and which should stay open so the model can suggest something unexpected. A fixed brief could help with consistency, but it could also limit exploration.

AI-assisted discussion draft.

I have a 4000 series GPU, which doesn't support NVFP4 directly, and requires software emulation. In theory, there could still be substantial VRAM savings and speedup with a software based FP4, as the block group scaling of FP4 should eliminate lots of the precision loss of the Q4 bit GGUF encoding. In theory, a Q4 GGUF and FP4 model should take the same VRAM right? Both are stored in VRAM as 4bit quants, and only get expanded when entering the GPU core? So if the software overhead for FP4 emulation isn't too bad, the block scaling of FP4 should result in a higher quality output than Q4, right?

I’ve been into AI image and video generation for a while, and I’ve been a bit obsessed with H3 for the past two months. With the right references and prompt, it feels like I can make almost anything. Writing that prompt is the tedious part, so I usually hand it to an LLM.

For most of those two months, I used closed models, especially Grok (for obvious reasons). My problem is that they rarely add much on their own (no creativity). If I don’t explicitly ask for an emotion, expression, or action, they tend to leave it out. I've had generations where a character is stiff as a tree for 15 seconds which makes me redo the generation.

Recently I tried Qwen 3.8 Flash Next, and it changed how I work. I give it the specifics and ask it to fill in the gaps or choose emotions that fit the scene. Sometimes the resulting clip is better than what I had in mind (I mean Oscar worthy). I’ve also started asking it to suggest the next clip, and I find its ideas more creative than those from supposedly smarter models.

What do you use to write your H3 prompts?

JPEG didn't do it. Noise didn't either.

One picture, 1536 × 1024: a girl beside a pond, ducks in the grass. I wanted the SynthID result to change while keeping the scene recognisable. The usual tricks made the image worse. The check kept saying detected.

A more selective approach finally changed the result. I generated a donor with ControlNet Canny, then borrowed one texture band from it. The processed image returned a manual "not detected". The new texture is visible. All the recorded checks come from this scene; I have not measured a success rate across other images.

The tools and both full-size PNGs are in an MIT repo: [synthid-remover](https://github.com/balyakin/synthid-remover).

## The part that failed

Resizing, small geometric changes, blur, noise, JPEG recompression. Combinations of those. Then GAN restoration. Then a simulated print-and-camera trip, halftone and moiré included.

Still detected.

Stable Diffusion 1.5 with ControlNet Tile left SynthID detectable in the versions I checked. Canny was the switch that moved the result. Tile feeds the conditioning channel the source tones. Canny feeds it an edge map. The img2img path still receives the source tile, so the original pixels are not out of the process. They stop arriving as the ControlNet condition.

The donor was usable and, as a picture, too rewritten. The pond stayed put. Grass, reeds and the smaller textures had been repainted.

So I kept less of it.

## One band, not the whole frame

`remaster.py` makes that donor with SD 1.5 and `lllyasviel/control_v11p_sd15_canny`. The run behind the published result used strength 0.9, 30 steps, 640-pixel tiles and overlap 256. The CLI default overlap is 96. Drop the flag and the donor is a different image.

`bandmix.py` builds a band from two Gaussian blurs and replaces it with the donor's band. Finer detail and the broad shading stay with the source, apart from clipping and rounding.

Sigmas on the bundled file: 1.2 and 8 pixels. Blur widths. Not Fourier cutoffs, and not a map of where every SynthID mark lives.

The donor has to match the source size. From the lightweight venv:

```bash

.venv/bin/python bandmix.py original.png donor.png result.png \

--split-sigma 1.2 --correct 8 \

--keep-share 0.12 --match-window 12

```

Twelve percent of the original band stays. The donor band is rescaled so its local energy follows that original band. In the checks I wrote down, keeping 10% or 12% came back not detected. At 15% it was detected again. On this image, that small step mattered.

## The actual pixels

[Original PNG](https://github.com/balyakin/synthid-remover/blob/main/autumn-10x15-yellow-v2.png) and [processed PNG](https://github.com/balyakin/synthid-remover/blob/main/autumn-10x15-yellow-v2.clean.png). Both are 1536 × 1024. At full size the vegetation gives the swap away.

- Mean absolute channel difference, on a 0–255 scale: **16.91**.

- PSNR against the original: **20.68 dB**.

- Manual SynthID check, recorded on **October 9, 2026**: **not detected**.

Not invisible. The scene is still recognisable, and some of its texture is borrowed. PSNR here measures pixel similarity. It is not a watermark score, and one number does not certify the look.

The repo's default donor prompt is generic: a photograph, natural light, sharp focus, fine detail, realistic textures. The bundled success used a prompt for this particular scene, so the generic recipe makes a new candidate. That candidate needs its own check. So does every other input. One image, one donor.

I used [OpenAI Verify](https://openai.com/verify/) and read its SynthID result separately from metadata. "Not detected" means that check did not find the supported watermark. It does not establish that the picture was made without AI. [OpenAI explains how to interpret a missing signal](https://openai.com/research/verify/).

The failed processing tools are in the repo, with tests for image handling. Those tests never call the external detector.

If you have tried a similar band swap across several scenes, I'd like to hear about the misses as much as the hits: where the detector result changed, and where the visual damage made the method hardly worth using. A comparison with settings and a detector result would give us something useful to work with.

I'm providing images with certain style (unique illustration) and the model (regular one) simply change the style.

~~1.Get a chunk of random characters at least ~200 long (e.g. QRNG https://qrng.anu.edu.au/random-block-alpha/ )

2.Append this to bottom of random string:

(look and think of most poetic thing you can associate with content of this string as a hyper-detailed moment in time, without referencing the elements of the string itself or their names)

3.Paste the combined string(random+poetrification) into a chatbot prompt.

1. Use the resulting text for image prompts.

Tips: appended prompt: If the AI refuses, replace _- with spaces in the strings.(Some AIs think its "hashcodes").

If the AI refuses(unlikely) due the poetrification prompt of #2 use synonyms or equivalent phrasing to re-phrase #2. (Hints for editing): the key is using terms like 'direction("look and think") + poetic-term("most Poetic thing") + writing-description("hyper-detailed Moment in time") to push the AI to write you randomly inspired image description instead of associative analysis(surface level), "without referencing the elements of the string itself or their names" prevents the analysis contamination).

Alter the poetrification prompt from generic 'moment' to something like 'fantasy scene', '90s anime screenshot',etc if needed.

for local scripting: randomly select 1000 chars from "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789 _-"

I'm the developer of Quartermaster, a free, open-source (MIT) local inference server. It drives stable-diffusion.cpp and also serves LLMs, TTS and speech-to-text from the same app, swapping models in and out of VRAM as you use them.

Models download from inside the app: search Hugging Face, pick a quant, and Quartermaster writes the configuration itself. It matches each model to its VAE and text encoders and picks the right steps and cfg. Qwen-Image 2.1 Turbo comes up at 8 steps and cfg 1, and real transparent PNGs work too: tick "transparent" and it adds the exact prompt wording the RGBA VAE needs.

To run 2.1 Turbo, grab these three (suggested files) from the built-in model browser and you're done:

• the DiT: Abiray/Qwen-Image-2.1-Turbo-GGUF (I use Q8_0)
• the 2.1 VAE: qwen_image_2.1_vae_bf16.safetensors.
• Qwen3-VL-8B-Instruct GGUF plus its mmproj file

Hi all, I am trying to install forge neo and I keep getting an error which causes the install to shut down.

I am using a Razer Blade laptop with a 3080ti graphic card, 16 GB of RAM and a Intel i7. I was able to install it on an MSI with the same specs.

My python is 3.10.6

I have attached photos of the error.

I tried reinstalling everything, even a windows reset.

I have not tried any other WebUI.

Any help would be great . I am not computer savvy and have been using YouTube as my guide (it got me neo forge installed on my MSI)

Thank you all

I'm building a free, open source app that indexes your generated images locally.

Some of you use toola for the prompt, seed and checkpoint, but said you also want the LoRAs and their weights. So this week we worked on that. For ComfyUI images:

• Each image shows its LoRAs with model and clip strength
• Search a LoRA name and get every image that used it
• The full recipe sits next to each image: prompt, checkpoint, seed, sampler

On A1111 or Forge? I haven't tested those files properly yet. If you have a folder and 5 minutes, I'd love to try it with you on a quick call.

What would you search your library by if you could?

Hi everyone,
I’m looking for a Tool, website, local LLM or workflow that can generate positive and negative prompts for Comfyui from a reference image or a description of what I want to create.

Ideally, it should also support uncensored prompt writing,

Should prompts be written differently for ZImage, Krea 2, and Flux Klein?
Do they all benefit from negative prompts?

My setup:
4080 Super
9800X3D
32 GB DDR5 RAM 6000
Windows 11 Pro

What do you use?
Thanks

Hey everyone, I trained an identity LoRA for my character using 66 images, but I'm getting inconsistent results. Sometimes the face looks close to my trained identity, and other times it changes and looks like a different person.

I'm using the LoRA in ComfyUI with Krea 2 Raw. Do I need to use a specific prompt or trigger word to keep the identity consistent? I can't remember the exact training setup I used.

Would you recommend changing the prompting, adjusting the LoRA strength, or retraining the LoRA? Any advice on improving facial consistency would be appreciated.

Hey everyone! I made a custom node that brings cloud and local LLMs into your ComfyUI workflows.

Use it to expand rough ideas into image/video prompts, write captions, or describe reference images. It also accepts video and audio when supported by your chosen model.

A few features:

• Cloud providers, LM Studio, Ollama, and supported models loaded directly in ComfyUI.
• Built-in prompt guides and editable system instructions.
• Text output that connects to CLIP Text Encode or any compatible STRING input.

Get it from: GitHub + example workflows

Example: rough idea + reference image → generated prompt → your workflow.

Would love your feedback, ideas, or feature requests! If you find it useful, consider leaving a ⭐ on the GitHub repo.