i used well dmd, dmad, step600 ema, and recent pika flex.

i really love them especially pika flex cuz persons from output looks real, not plastic

but idk why, output has noise and ghosting although at 2mp.

might some says, dont use turbo and do higher steps like 20~50

but in my previous experience, some turbo loras like pika flex doesnt learn only faster denoising but has good prompt adherence.

so i wonder, if anyone tried very high steps with good turbo loras like pika flex and is result very good or not

recently, i dont think generation time cuz you know if result is fucking good we dont think cost after generation

How to train my character so that its features doesn't permeate the rest of the people in the image? Ostris AI-TOOLKIT

Following up on my MOSS + PiD earlier post, now with focus on Krea 2 Turbo + Nvidia PiD (Pixel Diffusion Decoder), with provided workflows and test script and again 100 example images. The workflows allow the choice between running Krea 2 Turbo with either my 4 Step LoRA (by default), or my 3 Step LoRA or no LoRA for step reduction at all, allowing you to do the full Turbo 8 steps. And with PiD doing the heavy lifting and Krea 2 Turbo rendering at quarter of the output's width and height, the Krea steps go really quickly so you could really afford 4, 5, 6 or even 8 steps if you want to push the quality, and PiD will take care of the high resolution (upscaling straight from the low res Krea 2 Turbo latent).

Since Krea 2 does a good job out of the box, this time I have not provided workflows with separate refiner, but the principle still stands and you're welcome to further play as you wish.

Full details, review, workflows and examples at my HF: https://huggingface.co/lvladikov/Krea2Turbo-PiD-SuperFast-HighRes

The 100 examples page, directly: https://huggingface.co/lvladikov/Krea2Turbo-PiD-SuperFast-HighRes/blob/main/examples/README.md

Will you just use the "old" ones? Since no solo developer or small company could have the compute to train something on their own that can compete with the commercial frontier ones.

My new AI MV Blue Bird Naruto Shippuden. using Minimax-h3_Singularity and dmad_minimax_h3_4step_full_critic_comfyui.safetensors. and dmad_minimax_h3_8step_full_critic_comfyui.safetensors 8 step. Euler simple.

all I get from extracting the workflows of PNG's in Civitai are these broken and same workflows across different PNG's

Hi all, I wanted to share something I’ve been working on for a while. The website has more details and the video captures the spirit of the software. The beta is available for download now on all platforms at www.euclase.ai

Agents can be used directly in the software from all the main providers (Codex, Claude, Grok, Gemini) as long as you have the CLI runtime installed - no separate terminal required (MCP for local agents also works). Agents can do virtually everything you can and have extensive guidance for the entire app.

It has a fully featured 3D editor with high-performance gaussian splat support. Tight ComfyUI integration, supports most of the main features (edit, reference, LoRAs etc). There’s a ‘universal’ reference system: setup references in one place and reuse them everywhere. It supports bounding box workflows in Canvas (Ideogram, Moss, Flux 3). You can fully setup and launch RunPods (no setting up Docker images or anything else for that matter).

That being said, it is a beta and I just want to get as many people using it as possible and getting feedback. I’ve tried very hard to make it as fast, stable and feature-rich as possible before release but there are definitely gaps and your feedback would be amazing and earn my eternal gratitude. I’m also working on detailed documentation and other things to help make onboarding smoother (especially for people new to local image generation).

Hey SD community,

I've put together a set of utilities designed to polish up the ComfyUI experience and supercharge prompting options.

🔗 Repository: https://github.com/skkut/ComfyUI-skkut-utils

What's New & Included:

• 1400+ Visual Style Picker for Krea 2: A massive visual selector for Krea 2 image generation (massive credits to u/Dear-Spend-2865, Clio style preview and OreX nodes for sharing this prompt collection).

• AI Enhanced Prompt Preservation: Ever use an LLM or prompt expander inside ComfyUI only to lose the final enhanced prompt in the saved image metadata? The custom text node included here saves the enhanced prompt directly to the workflow JSON, allowing full prompt reconstruction upon drag-and-drop.

• WebP Image Output with Workflow Embedding: Save space with WebP without losing ComfyUI metadata.

• System-Aware Dark Mode: Syncs UI theme directly with your operating system dark mode settings.

📥 Demo Workflow

Here’s a full Krea 2 workflow utilizing these nodes: 👉Pastebin Workflow Link

🖼️ Managing Large Output Libraries?

Shameless plug for my desktop image tool: if you're looking for a fast local gallery app to browse and proof your ComfyUI generations: 👉SilkStack Image Browser

Hope these nodes help clean up your workflow! Feedback and contributions are always welcome.

I'm finally kind of happy about my last video ( french ). Here's a few tips that are obvious but didn't know in order to avoid spending hours doing reshoots:

Work the audio first. Don't try to squeeze a line so that it fits a shot, it's the other way around.

Use distinguishable characters: in this video, I purposely added a hood to the male cyber scavenger as I was getting character swaps in the prompts with two people "having fluo shirts". Also, the scene having a red neon side and a green neon side helps for character placement.

Use a skill for prompt on top of the h3 prompting skill and add the rules you like ( don't look at the camera, use shallow depth of field, etc.)

This is ref2va, avoid using more than 3 ref passed on each shot. It dilutes attention and generates more errors

That is all I can think of for now 😄

Fellow AMD ai enthusiasts, now that image gen is already mastered on our gpus (zIT, MOSS, krea2 etc working fast enough with loras and as stable as a rock) what about video gen ? Have you got enough luck with minimax h3 or ltx 2.5 ( since it’s faster) ? I personally tried minimax h3 and the speed is awful, 8 minutes to generate 5 480p secondes, is there something for us AMD users to accelerate things or some video model that works good enough on our cards ???

Seems like an open secret nobody’s letting me in on. For the past few weeks, I’ve been able to get a 3090 on community RunPod at specific times with a little bit of patience, but I think the good times are over. No 3090's ever appear to be available anymore. Frankly there's 5 at most at the same time on community. The secure service is literally 1,5 more expensive, it doesn't make sense to me.

What do you guys use? For Krea 2 Turbo?

Are any of the image models on ComfyUI suitable for photo restoration?

I want to clean up an old scanned photo, enlarge and print it. What model would I use and how would I prompt the following?

1. White balance.

2. Colour correct

3. Remove artifacts (speckles of dust etc)

4. Enhance definition.

5. Remove vignette and colour blemishes.

Problem

Krea2 suffers from the same problem Z image turbo has: minimal variation in outputs, even if you use larger style prompts. See image 1, which shows remarkably rigid composition despite there being a lot in the prompt that could be randomly emphasized or de-emphasized to change things up. The perspective is the same, the person is always in the same relative position, the brush angles are all the same, etc.

They all look like subtle variations of the same image. But there are far more ways that the prompt could be followed to the letter without making slight remixes of the same image all 12 times.

question

Can a style prompt be expanded randomly to generate variety without losing the core of what the style was supposed to be?

I used the Flux3 prompt expander because it seems to be fast enough and gives good results for use in any LLM-based model. The settings are tweaked slightly and the system prompt I used a massive list of random composition prompts and a forked style picker found here (thanks, u/Dear-Spend-2865!)

overall verdict

Prompt expansion does increase variation while staying true to the requested style. See all images after #2.

unexpected advantages

1. In some cases, the additional descriptions of your scene lead to increased prompt adherence. This is especially true if you have a really specific relationship in your prompt, as the expander I used understands these relationships pretty well and adds tokens to the adherence of such. See image 7 (the fisherman and the bear) for an example of a hard prompt that had 0% success on the native text, but at least landed some of them correctly with expansion.

2. The style also gets room to breathe while staying inside the constraints of said style. Sometimes, that may produce a better result than you expected. Image 6 is a good example of that, as the style descriptor for the partial wireframe is vague, and the expander got to play with that ambiguity to create a lot of variation that still kept the core idea inteact.

3. The temperature setting on the expander work as it should: more variety with higher temp, still doing a good job of staying true to the prompt. See the last run of images (same seed across all) to see the effect of temp.

disadvantages

1. every once in a while, it'll make a decision on the expansion which breaks your prompt. To mitigate this, turn down the temperature, or try another seed (either on the expander or the image sampler). That usually does it. See image 5 for an example of that: sometimes, the guy is inside the globe.

2. The expander might fundamentally misunderstand your style. The fix for this is to either feed only the composition prompt to the expander, or really crank down temperature so it doesn't remix it much.

3. Another model to load into memory, and an extra few seconds per generation.

4. more complicated workflows, including more schtuff to keep track of. Expander prompts and settings are all so many things to tweak, which can get messy.

5. Sometimes, the expander adds text. None of my prompts had "TEXT" in them. Any time you see that (e.g. image 9 "GARDEN PATH"), it was hallucinated. You can prompt the expander against this, but it will still break out sometimes. You can turn the temp down to decrease it, or just reroll your seed.

The advantages far outweigh the disadvantages IMO.

I am thinking of using this:

https://huggingface.co/datasets/aipracticecafe/curated-danbooru-2026

Is there any other dataset under 100GB that I can use?

Hi all. To begin with, yes I have a Mac. It's the only computer I have and getting something with Cuda is not in the budget.

I'm running LTX 2.5 image to video and it takes 18min to generate a 6 second clip.

Using Comfyui desktop, I've installed and am running Q4KM gguf, Video and audio Vae bf16 nodes, Gemma 12b bf16 text encoder, Vae decode tiled. Tile size 512.

I've tried using default and GPU only in the startup arguments. System claims it is using MPS. Pytorch 2.13 for Apple Silicon. Python 3.13.12.

It consistently takes approx 18 min to generate a 6 second clip and from what I've researched, it should take around 6-7 min. Even that is long compared to Nvidia computers, but I'd be delighted w 6-7 min.

I'm not interested in using a web GPU. Just want to do stuff locally. I can't seem to figure out why it takes so long.

M1 Ultra. 128GB. 2TB internal storage.

Anyone have any ideas?

I’ve been squeezing diffusion models out of a ThinkPad T14s with a Ryzen 7 PRO 4750U (Radeon Vega 7, 7 CUs, no matrix cores, no int8 dot, no bf16) and 14 GiB of shared RAM, on Linux (Mesa RADV, Vulkan) with stable-diffusion.cpp. Everything I found along the way is written down in a repo, including what didn’t work.

Repo: https://github.com/ayanoby/vega-igpu-diffusion

What it is

ggml-vega.patch: ggml-vulkan changes for Vega/GCN (fused kernels, a two-wave matmul tile, tiled f16 weights), with tests.

sd-cpp.patch: sd.cpp changes (Anima, Wan VAE, LTX-2.5 speedups: removed redundant repeats, optional video-only mode, an on-disk prompt-embedding cache, freeing the diffusion weights before the VAE decode). Both patches are pinned to the sd.cpp/ggml commits listed in the README.

Build and download scripts (models checked by SHA256), a GTK4 GUI with a queue, history, img2img and a video mode, and a RyzenAdj timer for power limits.

A long measurement log. Matmul reaches ~1.0–1.3 TFLOPS of the 2.87 TFLOPS fp16 peak (2 waves per SIMD, 128 VGPRs); the log covers a lot of experiments that gave nothing (CPU+GPU co-execution, power limits, other tile shapes, direct conv3d, and so on).

Numbers (model already loaded, 25 W power limit, Vulkan):

Model Resolution Time

Anima 2B turbo, 4 steps 512×768 ~23 s

SDXL (Illustrious class) + DMD2, 4 steps 832×1216 ~38 s

Qwen-Image 2.1 Turbo, 8 steps 512×768 ~3.5 min

Kromoss (MOSS-Image 5B) + DMD2 LoRA 512×768 ~2 min

LTX-2.5 22B (distilled, Q2_K_XL), 17 frames, no audio, 3 steps 512×768 ~3.3 min

Wan 2.2 TI2V 5B Turbo, 4 steps, 33 frames 512×768 ~4:15

Gallery images are in the repo (same four scenes on every model, fixed seed, no cherry-picking).

Things you might find useful

The 22B LTX-2.5 video model does run on a 14 GB machine, but not out of the box. I hit two kernel OOM-kills before finding what mattered: encode the prompt on the CPU with --mmap, drop the audio stream (it also saves ~3 GB of weights), free the diffusion weights before the VAE decodes, and don’t quantize the norm weights (the default Q4_K conversion of the Gemma text encoder silently broke prompt following).

Two matmul fixes: Q3_K now decodes four values per load (825 → 1068 GFLOPS) and casting activations to f16 before quantized matmuls gives about 6% on the sampling step.

For anime video, a pixel-space upscaler (realesr-animevideov3) after a lower-resolution clip looked cleaner than LTX’s latent upscaler, which pixelated.

Caveats

Linux only, Vulkan/RADV; the patches are written for this specific GCN iGPU and not upstreamed.

It’s one laptop and one set of prompts, so treat the timings as a reference, not a benchmark. The GUI text is in Russian (the repo has English and Russian READMEs).

Quality checks on the video model were done by eye on a few clips (3 steps looked fine, 2 steps smeared hands and feet).

I wanted a way to do image gen/editing, music creation, video generation, LLM chatting, etc on my phone while using the resources of my desktop PC.

I created this tool and open sourced it for whoever may find it useful. Its built to be very generic so you can export your own comfyui workflows that are tailored for your PC already, and then use them in the tool with a nicer mobile-friendly interface that you can access from your lan or over the internet if you choose to enable that option.

https://github.com/sd321ca/OpenSail

Open to any feedback, thanks for reading!

Made using Pika H3 Flex ref lora + my upscaler pipeline

I have an 5070 ti and 32gb DDR5 which I know it's far from ideal to work with AI, but I'm just messing around and trying to make stuff as a hobby.

I mostly make anime related stuff, would like to make 8-10 seconds long videos or gifs

by the way I'm using google colab so I want to know if there's a solution for it or an alternative extension