r/comfyui

I have been experimenting with ComfyUI locally with open models like Flux .1 dev etc. Whilst i was impressed with the results I then started using the same prompt with GPT2 images. And honestly these blew my local generation out of the water. Everything was better. My idea was to get good with cheaper hardware then invest in something like a 5090. However, after seeing the quality difference between some of the cloud models to what i can do locally. The question I have is: Are people able (even with intricate workflows) able to generate the quality locally up to the standard of the paid models? Because if not, i don't see the point in investing in my own hardware. I also get that people may have a hybrid setup and do certain things locally to save costs and export some things with credits. However I didn't want to have to use paid services at all to try and save money in the long run. Is this feasible or are these proprietary models too far ahead?

https://preview.redd.it/9cs08lz42gbh1.png?width=857&format=png&auto=webp&s=c9872fb3bb27336faca4c8ab0c444275f2b9d664

hehe... click bait title..

Disclaimer: I don't want to pass this over ChatGPT for correction, so bare with my rushed grammar and spelling.

Objective: Multi-character lora for 2 characters, ttprz and rgpz

Based on/Inspired by: https://www.youtube.com/watch?v=v6h_zbFW_XY <== This here explains a Flux 1 multichar LORA strategy, I basically took it with me and tried in Krea 2 as below.

1st test

Approach

• Hardware: RTX3090, Windows 10 (yes... I know), 64GB RAM
• AI-Toolkit (config file below). Model: Krea Raw
• Dataset, One unified datase, 15 photos of husband, 15 photos of wife, 5 photos together, Resolution 512/768
• Tokens: One for the Lora in general (cpnl), and then each character their own tokens (ttprz, rgpz)
• Descriptions: They all start with the Lora token (cpnl, ) then describe the character (Description instructions for AI-toolkit embedded Qwen3 VL). Example of descriptions below
• Training: Scheduler: Automagic2, LR: 0.0001 (Lower than my regular Automagic2 LR 0.001), 5K steps (due to lower LR), Lower VRAM Yes, Layer offloading Yes (15% and 15%)
• LORA: Linear 96 (I wanted to try a large LORA, ends ~660MB, I know, single chars I use network of 64 or 32, may reduce it in next test, large LORA comes with it's other set of downsides), Saved last 40 states (meaning all of them basically)
• Samples: 3, one ttprz, one rgpz , and one together
• Everything else pretty much unchanged

Training run:

• Training goes over 4hrs or so, but sampling, and 3 samplers each, and at every 250 steps, adds like 2.5 hrs in itself, sampling is painfully slow always
• Loss goes down slowly
• Samples are kind of messy, you start seeing good identity cloning around 2K, the samplers are way worse than the actual LORA once finished.

LORA performance in Comfy (latest version, overnight):

• Pretty solid, first time I'm able to actually call out two characters from a home-made LORA.
• Best LORA based on number of steps: Between 3.5 and 4.5 K steps.
• Other Loras: You can stack LORAS but you have to play with the strenght and also your sample and workflow
• Artifacts? A few, but I'd say 80% of images come out Ok
• Other comments: Not sure if it's Krea as I also experience this in single-char LORA, but passing from the Photo-based LORA to illustration absolutely requires other Style-LORAs, else there is no resemblance
• My workflow: Modified KREA 2 ComfyFlow, FlowMatch Euler Discrete Sigma (Dynamic Shifting, .5/1.15), SamplerEulerAncestralCFG++ 1/1, found it way better than default Comfy Flow
• Prompt strategy: Using Qwen 8B VL via llama.cpp on a separate RTX 3060 12GB to expand the prompt with node "LLM Chat"
• Sample below, Datasets had no photos of characters in formal attire, sillyness added to showcase GenAI role. Tokens : ncpl (general Lora trigger), ttprz and rgpz.

&#8203;

Prompt:  ncpl, Award-winning high-resolution photograph featuring a ttprz latina wearing a luxurious night gown seated elegantly next to an rpgz middle-aged man with a beard and glasses dressed in a formal tuxedo, sharing an intimate fine dining experience. The scene centers on a whimsical contrast: a large, vibrant bowl of colorful cereal is placed prominently on an elegant mirrored table, surrounded by sophisticated dining ware, soft golden ambient lighting, and blurred background details of an upscale restaurant interior to emphasize the call of luxury. The composition captures a moment of playful luxury with crisp details on the texture of the cereal and fabrics, using a shallow depth of field to keep the subjects and the colorful bowl in sharp focus while creating a dreamy, high-end atmosphere.

multi-character Image generation with LORA size 96, 4K steps, Prompt included in post.

2nd test: LOKR. Same as first training approach, same dataset, same captioning. Changes below.

• Learning rate lowered to .0005

• LOKR, left Size as for LORA network, 96 but AI-Tookit doesn't care as it calculates maximum size

• Training run: added 2 hours, like 2 seconds per iteration

• LOKR Safetensor size: 6 MB... no joke, carries.. 95% appearance of origin. This is the surprise that came out of it. I thought this was both the network and embeddings, need to understand more. I need to further test as I think that the internal consistency of the model is a bit impacted, but from say a Network of say 64~250MB/LORA (I know I'm testing first with 96) but down to 6MB.. some powerful stuff right there

  Dataset caption examples: Photo with both: ncpl, rgpz with glasses and a beard holding an umbrella, wearing a white shirt with a blue collar and a white scarf, smiling slightly. ttprz wearing a red shirt with Mickey Mouse designs and a white headscarf with polka dots, smiling broadly. rgpz is on the left, ttprz is on the right. Photo of individual char: ncpl, rgpz with a graying beard and mustache, smiling slightly, wearing a dark gray t-shirt, positioned in front of reflective spherical sculptures.

  AI-Toolkit config file for reference, LORA experiment

  job: "extension" config:   name: "cpl_v1"   process:     - type: "diffusion_trainer"       training_folder: "xxxxxxxxxxxxxxx"       sqlite_db_path: "./aitk_db.db"       device: "cuda"       trigger_word: null       performance_log_every: 10       network:         type: "lora"         linear: 96         linear_alpha: 96         lokr_full_rank: true         lokr_factor: -1         network_kwargs:           ignore_if_contains: []       save:         dtype: "bf16"         save_every: 250         max_step_saves_to_keep: 40         save_format: "diffusers"         push_to_hub: false       datasets:         - folder_path: "xxxxxxxxxxxxxxxxxx"           mask_path: null           mask_min_value: 0.1           default_caption: ""           caption_ext: "txt"           caption_dropout_rate: 0.05           cache_latents_to_disk: false           is_reg: false           network_weight: 1           resolution:             - 512             - 768           controls: []           shrink_video_to_frames: true           num_frames: 1           flip_x: false           flip_y: false           num_repeats: 1       train:         batch_size: 1         bypass_guidance_embedding: false         steps: 5000         gradient_accumulation: 1         train_unet: true         train_text_encoder: false         gradient_checkpointing: true         noise_scheduler: "flowmatch"         optimizer: "automagic2"         timestep_type: "linear"         content_or_style: "balanced"         optimizer_params:           weight_decay: 0.00005         unload_text_encoder: false         cache_text_embeddings: true         lr: 0.0001         ema_config:           use_ema: false           ema_decay: 0.99         skip_first_sample: false         force_first_sample: false         disable_sampling: false         dtype: "bf16"         diff_output_preservation: false         diff_output_preservation_multiplier: 1         diff_output_preservation_class: "person"         switch_boundary_every: 1         loss_type: "mse"       logging:         log_every: 1         use_ui_logger: true       model:         name_or_path: "krea/Krea-2-Raw"         quantize: true         qtype: "qfloat8"         quantize_te: true         qtype_te: "qfloat8"         arch: "krea2"         low_vram: true         model_kwargs: {}         compile: false         layer_offloading: true         layer_offloading_text_encoder_percent: 0.15         layer_offloading_transformer_percent: 0.15       sample:         sampler: "flowmatch"         sample_every: 250         width: 1024         height: 1024         samples:           - prompt: "ncpl, solo photo of ttprz latina with red hair"           - prompt: "ncpl,solo photo portrait of  rgpz holding a coffee cup, in a beanie, sitting at a cafe"           - prompt: "ncpl, photo portrait of ttprz and rgpz next to each other, smilling to the camera"         neg: ""         seed: 42         walk_seed: true         guidance_scale: 4         sample_steps: 30         num_frames: 1         fps: 1 meta:   name: "[name]"   version: "1.0"

I'm kind of a dummy when it comes to this, I mainly just use ComfyUI's templates and that's about the extent of my knowledge 😅.

Is there a way to use Krea 2 without doing the cloud service with credits? I feel like running ComfyUI locally with your own hardware should beat the purpose of using credits for cloud services?

Anyway, if somebody can point me in the right direction, it would be much appreciated.

I have no idea how to describe it but I will do my best.
Basically some AI sites have "effects" such as cut to X. How it works, on a "cut to dance" example:

1. You upload an image
2. Output: For the first 1-2 seconds, the person idles (smiles, waves their hair, etc.)
3. Then there is transition (either fade to black or just a cut)
4. The person now performs the action (in this example, dances) with the same face, the same clothes, sometimes the same background, with different camera angle.

Does it have a name? If so, what is it called? And how can I do something that works in a simmilar way in comfyUI? I understand how to make the "regular" i2v video (for example the person just stands up and dances, there is no cut) but I have no idea how to try it with that transition.

New to stable diffusion, comfyui and AI in general.

Been messing with WAI-illustrious-SDXL on comfyui for the past couple of weeks after learning about it from the Krita Stable Diffusion plugin.

I was wondering if there were any good guides for starting off, and if there were any lists/databases/dictionaries or whatever you would call it for all the prompts / commands.

(I've been hearing a lot about the Danbooru tags, is that something that's built in to comfyui, or something I have to download to get it working?)

Having a good time so far, but I'm having a hell of a time getting one of my character's hair shorter, I've been using weighted prompts like (short hair:1.3), (messy hair:1.3) and variations of, but no luck. I've got weighted prompts to work on other stuff but on the hair, no dice. I've tired rearranging the prompt order and deleting stuff and that hasn't worked either.

I updated my graphics driver, but whenever I try to launch or update local host it just gives me this

&gt; C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\.venv\Scripts\python.exe -s ComfyUI\main.py --feature-flag show_signin_button=true --enable-manager --extra-model-paths-config "C:\Users\lyka\AppData\Roaming\Comfy Desktop\shared_model_paths.yaml" --input-directory C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\input --output-directory C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\output

[INFO] setup plugin alembic.autogenerate.schemas

[INFO] setup plugin alembic.autogenerate.tables

[INFO] setup plugin alembic.autogenerate.types

[INFO] setup plugin alembic.autogenerate.constraints

[INFO] setup plugin alembic.autogenerate.defaults

[INFO] setup plugin alembic.autogenerate.comments

[INFO] Adding extra search path checkpoints C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\checkpoints

[INFO] Adding extra search path classifiers C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\classifiers

[INFO] Adding extra search path clip_vision C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\clip_vision

[INFO] Adding extra search path configs C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\configs

[INFO] Adding extra search path controlnet C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\controlnet

[INFO] Adding extra search path controlnet C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\t2i_adapter

[INFO] Adding extra search path diffusers C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\diffusers

[INFO] Adding extra search path diffusion_models C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\diffusion_models

[INFO] Adding extra search path embeddings C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\embeddings

[INFO] Adding extra search path gligen C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\gligen

[INFO] Adding extra search path hypernetworks C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\hypernetworks

[INFO] Adding extra search path latent_upscale_models C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\latent_upscale_models

[INFO] Adding extra search path loras C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\loras

[INFO] Adding extra search path model_patches C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\model_patches

[INFO] Adding extra search path audio_encoders C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\audio_encoders

[INFO] Adding extra search path photomaker C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\photomaker

[INFO] Adding extra search path style_models C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\style_models

[INFO] Adding extra search path text_encoders C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\text_encoders

[INFO] Adding extra search path upscale_models C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\upscale_models

[INFO] Adding extra search path background_removal C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\background_removal

[INFO] Adding extra search path frame_interpolation C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\frame_interpolation

[INFO] Adding extra search path geometry_estimation C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\geometry_estimation

[INFO] Adding extra search path optical_flow C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\optical_flow

[INFO] Adding extra search path detection C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\detection

[INFO] Adding extra search path vae C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\vae

[INFO] Adding extra search path vae_approx C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\vae_approx

[INFO] Adding extra search path clip C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\clip

[INFO] Adding extra search path unet C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\models\unet

[INFO] Setting output directory to: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\output

[INFO] Setting input directory to: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Shared\input

[START] Security scan

[DONE] Security scan

** ComfyUI startup time: 2026-07-05 09:46:43.188

** Platform: Windows

** Python version: 3.13.12 (main, Feb 12 2026, 00:38:53) [MSC v.1944 64 bit (AMD64)]

** Python executable: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\.venv\Scripts\python.exe

** ComfyUI Path: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI

** ComfyUI Base Folder Path: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI

** User directory: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\user

** ComfyUI-Manager config path: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\user\__manager\config.ini

** Log path: C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\user\comfyui.log

[INFO] [PRE] ComfyUI-Manager

[ERROR] Failed to import comfy_kitchen, Error: cannot import name 'TensorWiseINT8Layout' from 'comfy_kitchen.tensor' (C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\.venv\Lib\site-packages\comfy_kitchen\tensor\__init__.py), fp8 and fp4 support will not be available.

[WARNING] comfy_kitchen does not support stochastic FP8 rounding, please update comfy_kitchen.

[INFO] Checkpoint files will always be loaded safely.

Traceback (most recent call last):

File "C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\main.py", line 227, in <module>

import execution

File "C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\execution.py", line 18, in <module>

import comfy.model_management

File "C:\Users\lyka\AppData\Local\Comfy-Desktop\ComfyUI-Installs\ComfyUI\ComfyUI\comfy\model_management.py", line 36, in <module>

import comfy_aimdo.vram_buffer

ModuleNotFoundError: No module named 'comfy_aimdo.vram_buffer'

So I'm looking to use a localised model on comfyui for fast visualisations and 3D renderings fed by my sketches, 3D models and notes. What would be the best model to download for technical accuracy and consistent realism? Especially when it comes to vehicles I have gotten results from paid, online AI generators that are mediocre at best. Thanks!

I just don’t believe that CIVITAI is the “only” place that has Loras stored in the whole internet. I know that there’s a secret website where all the Lora’s that are banned from Civitai go to flourish. I’m looking for the Weird Lora’s yeah judge me. I love horror movies, I love weird stuff and to me all the Lora’s available in Civitai are either too sexual or to cute.
So if anyone knows the name of the website please share it here. Don’t make me go dive in to the Dark Web. Thanks 🙏🏼

Hi guys what's the best images editing node or workflow you use right now

Hi,

I’m using WAN 2.2 to create realistic videos of human faces and bodies, along with a simple frame-by-frame workflow, in an attempt to keep the face and body from becoming too distorted during the video.
I want to create a face and body LoRA to achieve better consistency, but to create that LoRA, I first need to generate content using WAN.

Which workflows do you use that are best suited for consistency?

I’ve tried several workflows found on Civitai, but none of them work properly.

Thank you

Currently I’m preparing a dataset for training a ZIT LoRA and was wondering if it’s okay to use images with different resolutions instead of only/mostly 1024×1024

My dataset includes images like: 1024×1024, 1440×1920, 1080×1349, 1440x1693, 1200×1500 and more.

Will mixed aspect ratios and resolutions negatively affect the training, or is it fine as long as the images are high quality? Does the trainer crop/resize them automatically, or is it better to make everything the same resolution?

Please help the newbie out, appreciate any advice!

Every browser gets their own open tabs/workflows, right?

Is there no way to have a single shared session so that I can always resume what I'm doing no matter if I'm on my home desktop, in a coffeeshop on my laptop, or my office computer? They're all accessing the same Comfy instance running on the same server, my DGX Spark at home.

I know I can use git to sync workflows JSON, and I do save final workflows this way. But most of my time in Comfy is actually spent tinkering on work-in-progress stuff in a tab which isn't synchronized. I don't want to Save As + manual sync every time I move.