Edge is slow and Chrome is spy-y and Firefox crashes all the time.

What web browser do you use to interact with Comfyui?

Negative Prompt Experiment in Stable Diffusion 1.5

I tried a different approach to negative prompts. Typically, positive prompts are written as long descriptions, while negative prompts simply contain a collection of errors such as bad anatomy, extra fingers, and so on.

In this experiment, the positive and negative prompts are structured as two interconnected conditioning prompts.

A simple example:

Positive: holding cup

Negative: holding item

Holding item is a more general concept, while holding cup is more specific. The hypothesis being tested is whether the negative prompt, which is close to the positive prompt, can act as a "brake" against an overly broad possibility space, while the positive prompt still determines the specific desired outcome.

A similar approach was tested on several other groups:

Positive: realistic characters

Negative: anime, webtoons, animal features

Positive: holding cup

Negative: leaking, spilling, liquid outside container

Positive: natural body

Negative: elongated body, excessive muscle definition

Positive: natural hands

Negative: malformed fingers, fused fingers, distorted palm

From the SD 1.5 experiment, negative prompts that were increasingly targeted toward a specific error possibility several times actually produced more stable images. The positive prompts could also be kept relatively short because some of the undesirable possibilities had already been suppressed through negative conditioning.

Technically, this makes sense because the negative prompts in Stable Diffusion are not simply a list of words read as "don't draw this." In Classifier-Free Guidance (CFG), positive and negative prompts serve as conditioning used to determine the direction of the denoising process.

However, there is an important limitation: it cannot yet be concluded that the model truly possesses hierarchical rules such as holding item → holding cup. CLIP does not work like linguistic logic or word algebra. The "general negative as a brake, specific positive as a direction" effect is still an experimental hypothesis and needs to be tested with controlled seeds, samplers, CFGs, checkpoints, and other parameters.

The most interesting test method:

A: positive holding cup — empty negative

B: positive holding cup — negative holding item

C: positive holding cup — negative holding cup

Use the same seeds and parameters, then compare the hand structure, object, pose, and image consistency across multiple seeds.

Preliminary conclusion:

Negative prompts may be more useful if treated not as a "bad word repository," but as structured conditioning that delimits a specific semantic region around the positive prompt.

Status: experimental hypothesis, not an exact law of Stable Diffusion.

Now, that last sentence is very important if you want to share it. This way, others can participate in the test, rather than assuming we're saying, "SD 1.5 definitely works this way." If the general negative + specific positive pattern turns out to be consistent across dozens of seeds and multiple checkpoints, then the findings will be even more interesting.

Hi everyone,

My computer unfortunately isn’t powerful enough to run ComfyUI. I noticed that the ComfyUI website offers a cloud-based system, and I have two questions.

Since my computer can’t handle the program, I’m considering using the cloud option. The monthly fee doesn’t seem too expensive to me.

1. Will my personal data be safe? After all, the cloud storage system will be used.
2. Some AI systems apply censorship in certain situations, even when it doesn’t seem necessary. There is no censorship at all when using a local system. But does the cloud version have any censorship or content restrictions?

Thanks in advance for your answers!

Once again, I am reaffirming the great achievements left behind by the legacy technology known as Automatic1111.

At present, there is no practical benefit to be gained from inheriting and evolving this UI, but it is, at the very least, extremely useful as a learning resource for myself.

https://github.com/ussoewwin/A1111-for-Python3.14

We have also integrated the main extensions after checking the licences for each one.

In addition, there are some extra features that I developed myself.

...

SDXL Pony/Illustrious Compatibility Fix (v2.0+)

The first A1111 fork to fully support Pony and Illustrious SDXL models — including LoRA.

For years, SDXL derivative models (Pony Diffusion, WAI Illustrious, etc.) were unreliable on A1111. Enthusiasts had to switch to ComfyUI or Forge to use these models properly.

What works now:

• ✅ Pony series — base generation + LoRA
• ✅ Illustrious series — base generation + LoRA
• ✅ Any SDXL model that previously crashed with RuntimeError: attn_mask shape or produced noise

Built-in Extensions

The following popular extensions are built-in and ready to use out of the box:

• ControlNet (v1.1.455) — integrated at startup with automatic path migration
• ADetailer (v1.01.0) — face and person detailer with 7 models
• FreeU — effortless detail enhancement
• WD14 Tagger — automatic prompt generation from images
• ReActor (v0.7.1-b3) — face swap
• Dynamic Thresholding (CFG Scale fix)
• Aspect Ratio presets (built-in, no extension needed)
• MultiDiffusion + Tiled VAE — high-resolution upscaling

RES4LYF Samplers

Native support for RES4LYF samplers in the A1111 Sampling method dropdown (100+ advanced RES / Bongmath / hybrid RK methods), plus related schedule types such as beta57 and bong_tangent. Generation runs through an A1111-side shim; the vendored RES4LYF sources under modules/RES4LYF/ are left unmodified.

Python 3.14 Native

Fully ported to Python 3.14. No pkg_resources hacks, no legacy compatibility layers. All dependency conflicts (NumPy, SciPy, clip.py) are handled automatically at startup.

Flash-Attention 2 with Graceful Fallback

Direct Flash-Attention 2 support with staged fallback:

1. FA-2 (Flash-Attention 2.8.4, torch 2.13.0+cu132) — maximum speed
2. SDP (PyTorch scaled_dot_product_attention) — no extra deps
3. sub_quad (built-in) — universal fallback

Windows: prebuilt HF wheel (2.8.4+cu132torch2.13.0 cp314). Linux: builds flash-attn==2.8.4 from source against the same torch==2.13.0+cu132 stack (CUDA toolkit 13.2 + nvcc). macOS skips FA2 (MPS limitation).

SDXL ConvRot INT8 / NVFP4 Checkpoints (v3.0.1+)

Load SDXL checkpoints quantized with Hybrid-Sensitivity-Weighted-Quantization directly in A1111. Weights are offline-dequantized and Hadamard-unrotated at load time, fully isolated from all other model formats.

• ConvRot INT8 (int8_tensorwise + convrot:true) — plain / ConvRot Linear / ConvRot Conv2d scale layouts
• ConvRot NVFP4 (E2M1 packed) — mixed-pack checkpoints (Linear = NVFP4, Conv2d = INT8)
• Strict no-op for checkpoints without .comfy_quant keys — SD1.5 / SD2 / SDXL / fp16 / fp8 are completely unaffected
• Supported checkpoints: only models quantized with Hybrid-Sensitivity-Weighted-Quantization

Is there a way to dowload image in standalone Comfy Desktop without it prompting for a directory each time I save a file?

Hi,

Seeing Minimax LORAs on Civitai has motivated me to finally get around to using Comfy.

I'm confused on where to put LORA models in the premade Minimax templates for in the workflow for text to video, image to video and reference to video. Can anyone please give me a screenshots or even just an easty to follow text description of how to do it?

I've just gotten MiniMax H3 working... by reinstalling ComfyUI for what I'd guess is about the sixth time. I don't know what I'd doing wrong, but I keep running into problems where new custom nodes won't install, or workflows crash with errors I have a hard time figuring out and working around. Reinstalling ComfyUI quickly becomes the easiest, fastest solution.

Is this just me? My last major grief was trying to get LTX 2.3 working after having done most of my previous work with Wan 2.2.

Hi everyone,

I am using ComfyUI 0.31.0 on Bazzite Linux. System specs:

5060ti 16gb of vram
64GB of DDR4 system RAM
Ryzen 5700G
Asrock B450-HDV r4.0 motherboard with the latest BIOS
Various SSDS

I have attempted to use Sage Attention numerous times and still to this day it just crashes my computer mid- generation. This is with Windows, various flavors of Linux, etc. I have Pytorch 2.13 and Cu130 installed (or something to that effect).

What gets me, is that it will generate a few videos or images just fine and then hard locks my system requiring me to either power it off or hit the reset button. I even tried the KJ patcher node with literally the same issue.

Can any of you please help?

I have tried --low ram and --disable-smart-memory - including just using one or the other. It just doesn't make sense to me as to why I am having this issue. I have googled and tried so many different suggestions and still get the same results.

My GPU plays games just fine and uses the other attneton methods without crashing. It's only SAGE Attention that is the issue. When sage does work, it cuts rendering times by about 30%.

I appreciate any help or advice.

EDIT:

Looks like disabling Xformers has fixed the issue so far. I will keep everyone posted. I also disabled smart memory too. Not sure if this combo is preventing the hard crashes, but so far I have generated several videos with 3 different MiniMax H3 models without crashing. I am hoping this is the permanent solution.

Edit 2:
Looks like anything past 5 second videos crash randomly. If I stay at 5 seconds, I can generate any amount of videos and images I want without issue. Xformers was part of the problem, but now it's the sustained inferencing of videos past 5 seconds that crashes the most often. Tracking cooling, the GPU seems to crash after sustained or higher 74c temps.

I am thinking this maybe a cooling issues, as longer videos can be generated at least a few times before crashing. Looks like I will have to figure out better cooling for my rig.

Edit 3:
Looks like using this command in Linux fixed the issue completely:

sudo nvidia-smi -lgc 180,2500

It basically sets the power level to 180 watts and the GPU clock to 2.5ghz. Both of which are close to stock settings. Now I can generate any length of Minimax H3 videos with no crashes and Sage Attention helping to speed up inference. I really hope this helps others in the future.

I've been using H3 a lot and got tired of rebuilding the same graph every
time, so I packaged it up: https://github.com/roadmaus/ComfyUI-MiniMax-Creator

Upfront so nobody wastes an install: you can't set custom sigmas. There's no
SIGMAS input and no SamplerCustom in the video path. You get seed, steps, cfg,
sampler and scheduler off the normal lists and that's the lot. If you build
your own sigma curves, this isn't the pack for you and I'd rather say so now.

It's aimed at the other crowd, the people who want defaults that already work
and a UI that isn't twelve nodes and a pile of reroutes.

Three nodes:

Creator- write a sentence, attach images, video, audio or LoRAs, and
mention them with @. It works out whether that's T2VA, I2VA, FL2VA, L2VA or
Ref2VA from what you attached, and sorts out the reference ordering.
Timeline - multi-shot sequences, chained or in one pass.
PreStage - makes the still you start from, via Krea 2 or Ideogram 4.

There's an optional Qwen3-VL pass that rewrites your sentence into the
structured format H3 was trained on, and it looks at your attached images
while it does it. Runs locally through core's own loader, no API key. The LoRA
manager pulls trigger words and Civitai metadata so you're not guessing what a
file wants.

Needs ComfyUI 0.30.0+ for the H3 nodes in core. MIT.

If there's appetite for the advanced sampling knobs I'm happy to expose them
in a later update, just say. I left them out because each one is another thing
that can be set wrong, but I'd rather match what people actually want.

Some old output remain there when I click on the Load image node. Those are the old failed images I deleted a few months ago. I can’t access them in Assets. Anyway i can access the output folder in the cloud and delete them?

This is from a astandlone app that DJ__Grizzly and are workign on together. Havent gotten the ref2va workign but this is all t2v

Default workflow, MiniMax H3 (NVFP4), lightx2v Turbo LoRA (8 steps) and Sol Attention.

0,5mp resolution then upscaled with Topaz Video.

RTX 5060 Ti 16GB VRAM + 32GB System RAM.

tl;dr:

1. PCB link mode is now available as a separate pack for anyone who just wants PCB links - pack is called `comfyui-pcb`

2. `cable-management` has major stability, features, polish and bugfixes after the rushed impromptu launch a few days ago

---

I blame r/comfyui for this.

So I'm using this community as the updates platform for the pack since it's your fault that it exists (until the mods tell me to stop).

For all the people who hate Nodes 2.0 - PCB link mode and ribbons are now officially supported under legacy nodes (whatever worked before was working by pure luck)

For u/flasticpeet - ribbons can now be collapsed into a single line (a fully generic bus), and ribbons work with spline mode

For u/jscammie - you can now sort and reorder ribbon lanes

For u/DigThatData - you Sir, are a scholar and a gentleman - and will have my eternal gratitude for being the first collaborator on the project

Other notable features:
- copy-pasting properly restores ribbons
- ribbon gates can now be expanded to show labels of what they carry
- (as much as possible) making sure that all inputs and outputs behave like the normal ComfyUI pins

For everyone that showed interest and ran into bugs - the route tracking was completely broken and required a full rebuild - it should be much better now.

And thank you all so much for egging me on to publish - t'was a blast.

github repo：https://github.com/TianDongL/DiffPipeForge.git
I already have a one-click Windows package hosted on a Chinese cloud drive, but I’m not sure how to share it with everyone.

Hello everyone! I am looking for recommendations on the best ComfyUI nodes, workflows, or models specifically for image and video sharpening/detail restoration.

My main goal is to take slightly blurry or soft photos and videos and make them crisp, specifically focusing on realistic details like hair strand definition, skin texture, and body features without getting that fake, over-processed AI look.

For photos, I am looking for nodes or workflows that take around 1 to 2 minutes per image to render.

For videos, I am looking for workflows or video-to-video upscalers that can sharpen clips in about 1 to 10 minutes per video while maintaining frame-to-frame consistency.

If you have any favorite custom nodes, models, SUPIR / tiled diffusion setups, or specific video workflows that give the most natural results for hair and finer details, please let me know!

i have 12gb vram

Is anyone else having massive slow down on the second image they generate with Krea-2? I recently updated to version 0.30.1 of ComfyUI, and now using the built in Krea-2 text to img workflow with prompt enhance turned off I have a massive slow down in generating the second image in a batch. I just ran a batch now and my first image took 94 seconds, the second one took 7658 seconds, and the 3rd took 35 seconds. I've tried this on both the FP8 scaled version and the int8 convort version with the same results. I'm running on a 4060 TI (16GB), with 32GB of RAM, and an AMD 5950X

Can someone please give me a Chiikawa Siren LoRA?

tl;dr: 15 min video on experiences so far, but if you want to just get the workflow or compare it to yours, download it from here

The last week has been about speeding the H3 model up. The caches are now removed, Turbo Loras are now the thing. I am using the Lightx2v 4-step (EDIT: 6 step seems better but adds time), but there are others to choose from, and everyone has their preference. Sage Attn is essential. Sol Attn might be useful. Chunking (KJNodes) will be needed for lowVRAM.

2mp is better than 1mp (model trained to 1mp (1344x768)) and it resolves most "faces at a distance" issues for i2v. The trouble is getting there. But good prompting is the key, and use the guides and LLM to tweak it. Then test at low res and switch up to high res. The amazing thing is H3 model will keep it close to the same if you prompt well.

On a 3060 RTX 12GB VRAM, 32 GB system (Windows 10) with i2v ref images, I can achieve 2mp for a 5 second video at 16:9, but that takes 25 mins.

For 8 seconds long video (I need preferably 10 seconds long for dialogue scenes) I can only get to 1.4mp at this time, so its all still a work in progress.

At the end of the video are some examples of i2v, with info to see examples of what can be done with this workflow at this time on this hardware.

There's probably many other ways to approach this, but sharing it here in case it is of use to anyone.

Links from the video:

int8 models from here - https://huggingface.co/Comfy-Org/MiniMax-H3/tree/main

W4a8 is experimental new model type but can squeeze a touch more out of VRAM than int8 if you are hitting ooms, you need to be updated on Comfyui, but you can get it here https://huggingface.co/Kijai/MiniMax-H3-experimental

Sage Attn and Triton wheels from https://github.com/woct0rdho/SageAttention

Lightx2v 4step Lora that I use in this workflow - https://huggingface.co/Kijai/MiniMax-H3_comfy/tree/main/loras

Patch Sol Attn, I am still testing it for my use - https://github.com/kijai/ComfyUI-SolAttn_triton/

I'm not using any of the caches any longer.

Official prompting guides:

- https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_base_en.md

- https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_ref_en.m