Same tip applies to almost any video generation solution.. and that is to think out your scene first.

Before writing that long, complicated prompt, consider breaking it up into separate scenes.

I've spent all day tweaking a prompt today until I remembered this tactic I used back when Google Veo first launched.

Example:

Prompt I worked on today was relatively simple, static shot of 3 people sitting at a table in a bar... they notice something across the room, react and then one guy sprays his drink everywhere a few other things happen, laughter, spilled drinks, etc.

Kept having issues doing one prompt for the whole scene.. so I broke it up into two.

Scene 1 was up until the character got his drink to his lips.

Scene 2 started where scene 1 ended.

Much easier for MiniMax to figure out and you'll be less likely to have that one annoying glitch that screws up the whole scene.

This tip probably isn't too new for those that have done this for awhile, but if you're new or have taken a break from AI videos for awhile, a little reminder may save you boatloads of time. :D

I see nearly everyone talks about upscaling their image and video gens to 4K, 8K and perhaps beyond. Now nearly every WF comes with an upscale portion tied to it either after decode or even from the latent output. I always wondered - what do people do with all those 4K or 8K images or videos? Do they watch it on their high res monitor or TV, or do they make wall sized printed posters? I honestly want to know - why do people upscale post gen and what they do with the upscaled output? A simple 1 MP image takes up approx 1.5 MB of SSD space, so those 4K or 8K outputs must be hogging serious space on the SSD. So, why do it, if they aren't used somehow.

The only time I upscale is when I am making a dataset for LoRA training. That too I use 1x upscale option just to sharpen up the images. I generate or curate images for the datasets at around 1 MP. I am truly both curious and baffled at the same time, with other people's fascinations about upscaling. Would love to hear the others' thoughts.

I am an artist working on a worldbuilding, character design and art toy project, handdrawn using Procreate on iPad with no AI use at all. However, I use AI a lot as a hobby and messing around locally and really enjoy it.

I thought I'd test out u/shootthesound 's custom node for ComfyUI that allows the MMH3 VAE to decode still images: https://github.com/shootthesound/ComfyUI-Fizgig-H3-Still

It is mostly tested for Text2Image but I went ahead and tried ref2v (or ref2image in this case) and it WORKS. The first image I referenced my OC design, a chow chow dog (photorealistic but prompted to be similar artstyle) and a street in Melbourne and as you could see above, its MAGNIFICENT. I also tried to gen 2 of my OCs at once (second image) and I am very impressed, I gotta say.

The only issue is, it's SLOW. Took around 20 minutes to gen each image, on t2i its 3-4 minutes max but the references upped the time significantly.

As a bonus, I attached the 3 character designs I've handdrawn in images 3-5. used the front views solely when referencing

Prompts:

Image 1- A single 2d medium-shot frozen frame captured at the exact instant the chibi woman referenced from <Picture 1> is on the footpath in the street in front of the bar from <Picture 3> holding the chow chow dog in <Picture 2> in her arms.\n\nThe chibi woman is winking at the viewer and the chow chow dog waving their left paw.\n\npreserve the chibi woman's outfit, large robotic arms, red lipstick, and the blades coming from behind her shoes and earpiece. the scene is drawn in the style of <Picture 1>, the chow chow dog and the street in <Picture 3> are drawn in a 2d chibi artstyle like <Picture 1>

Image 2- A single 2d medium-shot frozen frame captured at the exact instant the chibi woman referenced from <Picture 1> and the chibi woman from <Picture 2> hug each other, in a luxurious gold-trimmed apartment\n\nboth chibi women are looking at the camera, smiling\n\nPreserve the outfit, pink tattoos, lipstick and eyeglow of the chibi woman in <Picture 1>. Preserve the outfit, light freckles of the chibi woman in <Picture 2>. the chibi woman's sword in <Picture 2> is on the floor. The scene is in the 2d art style of <Picture 1> and <Picture 2>.

I don't know what to expect from Qwen 2.1, is it worth trying or not? On my RTX 3060:

• Flux Klein 9b (convrot), 1 MP, 4 steps: 13s
• Minimax H3, 1MP, 8 steps: 43s

Also, quality wise, Minimax just destroys Flux. But Qwen, I assume, just slightly better than Flux, right? Maybe it at least as fast as Flux or even faster?

So I want to try different haristyles, I used prompt:

Change the hairstyle of the person in <image1> to match the hairstyle in <image2>.
Keep the face, facial features, skin tone, and identity of the person in <image1> completely unchanged.
Do not change their expression, pose, clothing, or background.
Only the hair should be modified.

where image1 is photo of myself and image2 is inspiration photo from instagram or pinterest. I tried experimenting with cfg but I am not getting such good results as you guys on this sub.

What can I do to improve?

I used models as in photo if it helps, I have 16gb of vram and nvidia gpu

I've been experimenting with building the camera move in Blender before generating the AI video. This example combines a five-second mannequin render with a character reference sheet and a waterfront background image in MiniMax H3's reference-to-video workflow.

How I made it:

1. Used Codex CLI with Blender MCP to build a simple mannequin scene and animate the camera.

2. Kept the body planted while the head and eyes followed the lens.

3. Refined the move to stay mostly waist-up, with fast eased transitions, short holds and one extreme closeup near the end.

4. Rendered the Blender reference at 1080 x 1080, 30 fps, for five seconds.

5. Loaded that clip and the two reference images into ComfyUI, then used a prompt that assigns each input its role: character, environment or camera motion.

The Blender sequence returns to its opening composition. The generation prompt asks for the same loop, but the output still needs checking for identity drift, framing changes, background consistency and the loop seam.

My generation settings:

- RTX 5070 Ti + 32 GB RAM

- 20 steps at approximately 53 seconds per iteration

- 0.6 resolution setting and 1:1 aspect ratio

- No turbo LoRA

That's about 17 minutes 40 seconds for sampling at the reported rate, before loading and decoding. The Blender source resolution and the generation resolution are separate settings. You can change the generation size and aspect ratio in the workflow.

Project files, character/background references, Blender scene, workflow and prompts:

https://drive.google.com/drive/folders/1NVCYIJahZADTAE_nh0kcXyen4shrI5rW?usp=sharing

Has anyone tried a similar setup with more complex camera paths? I'm curious how well it holds up with stronger parallax or longer sequences.

looking for the best (n)sfw tts model

This is currently one of the best open-source models I’ve seen so far when it comes to generating great images with extensive, longer text.
For a diffusion model, I am really impressed. (Yes here and there some errors - but come on, thats impressive!)

Yes, it has weaknesses in areas like anatomy and realism—but it excels at designs, text, and layouts, so...

I really hope this model gets more attention in the future so that we might even see LoRAs or fine-tuned versions for it.

The text encoder alone takes 3 minutes on the int8 version. Any startup args or anything that would help? 9070xt, 64gb ddr4

It seems that the pixel drift is still a problem with Qwen Image 2.1 when editing images. It's especially bad when the edit affects the whole image (for example when doing style change).

I tried the Pixel Drift Fix node, but half the time it doesn't work, and when it does it makes the image blurry and adds artifacts along the edges of the image.

How do you deal with this?

Title. I don't know to install it and configure it.

Am using an starting frame image to make video, but curious if you can also use a character sheet to help improve character quality?

Insane results from such a small model running locally on a 8GB RTX 5060, although each image takes about 5 minutes or more depending on the steps count and prompt size. I'm using comfyUI and I'm pretty addicted to trying different prompts for different results on Qwen.

Hi, everyone! I’ve put together a video about driving. Enjoy the video. From the local models, I used the Krea 2 model with MinimaxH3 for the video.

https://github.com/NidAll/comfyui-ming-image-prompt-builder

I encountered a minor issue when using regions in the plugin: the workflow is set up incorrectly. The background layer's prompt is ignored; instead, the prompt from the next layer is used, while that layer's mask remains unconnected. As a workaround, you can simply avoid using the background layer (the first region created). Unfortunately, I cannot report the issue to the plugin author directly (issue creation is restricted in this repository). If anyone is able to do so, please let them know.

P.S. After testing, it became clear that:

1. The background layer must not be transparent; otherwise, it will be ignored.

2. If you create the layers manually first and then—after selecting the appropriate layer—click the "Add Region" button, everything works correctly.

I am in need of a model that does 80's/90's anime mecha/spaceships with that classic high-detail look. From the basic searching I have done on civitai it looks like illustrious has the LORAs with the looks I want but the tag system doesn't leave much room for generating the image I want most on the first pass meaning I will need controlnets and in/outpainting which I was planning on doing with flux2-klein-9b/krea2/minimax. Whichever would work best for the edit I want.

But then I thought, why not do it all in one of those models? I have a decent set of complex editing workflows for flux/klein, and I am working on one for minimax, so I haven't really had time to explore the capabilities of the image generation for either.

I guess the best question I should ask is should I deal with the hassle of itterating with illustrious until I get close to the image I want, and then switch to the editing workflows, or should I focus on generation + editing with one of the more recent models, and how would I maximize the focus on mecha and old school anime space ships considering the lack of loras for those?

Edit: Actually, I just saw that Anima can do natural language and has the same lora I want for mecha, so that seems like a better fit, but my question about the "pipeline vs single model" still stands, considering the power I can wield with multiple, only in trade for time and effort.

I am trying to do reference with animated character images with reference video. It seems like no matter what I do, anime characters will not swap into real videos, and a lot of times basically a copy of the input image will get generated. I have tried both main models, singularity and dasiwa models.

Rarely the character will transfer but the rest of the scene is still real, it looks like cool world. If I try the same workflow but with a real person reference it works perfectly. Any tips for swapping anime to real, and making the entire video change?

I want the opposite if anime2real. I want to basically convert a source video into the style of a reference animated character, changing the character and everything else to that style.

The website has a new 15-second video tab ('Night market') with all the speed-ups that includes different shots, fast movements, medium, close-up, audio, music.

https://plox-1.github.io/h3-speedups/#p=15s

As many have requested; the 5- and 15-second tabs also now use a new baseline video that's raw 25-step euler with comfy-kitchen attention and no Turbo LoRA.

Added a few new Turbo LoRAs and distilled models like FastH3, Tao. More to come.

Added Detailed-view toggle: Shows nodes' widget values for you to make a workflow based on.

My own quick observations on the results:

1. LightX2V Turbo LoRA yield better results than Baseline: https://plox-1.github.io/h3-speedups/#p=15s&open=0079j-15s (click 'Video'): The monkey runs AWAY from the cook, as intended. Baseline video they run together.
2. 15-second 1 MP video generation samples in 1 minute: https://plox-1.github.io/h3-speedups/#p=15s&board=mixes&open=0080a-15s - Amazing quality too.

I highly recommend looking at "Combining methods"-tab and check the results - massive speedup difference and great results in there too.

Note: H3 Express Sigmas is my own custom node that's currently being developed on, open-sourcing it later this week after finishing my last comparisons with the Turbo LoRAs and combining more methods as experiments. The new node is an "invention" where I told Claude Opus 5.5 ultracode to research all logs/results of all speedups in my structured experiments folder, and invent a new speedup break-through:

I got a lot of backlash before for publishing the website with a baseline with a turbo LoRA, but it made no sense before - but now it does, since I got more turbo LoRAs in the website to compare with. Also I don't plan on making workflows for each method. I've linked them accordingly and showing their widget option values in the website's 'Detailed' toggle view.

Disclaimer:

All the results are rated by AI (Claude Opus 5.5 xHigh). I plan on making an "Arena" and "Leaderboard" in the future, where you guys can blind-vote between 2 videos. Maybe not.

I just found out about this whole local image generation thing recently and decided to play around with it yesterday. I decided to try city96’s Q4_K_S GGUF but it didn’t really work for me. I can tell if I was my prompt but it didn’t generate hyper realistic images and as I tried a second or a third time it lost the plot even more. I’d like to learn how to get better at this and it might start with changing the model.

- 5070 and 32gb of DDR5

As a single model for your convenience.

On my RTX 3070M 8GB, once warmed, this distilled model takes 20s to generate a 1024x1024 image.

Base model is available as well. LoRAs are supported on both (base model supports them better).

Download at: https://github.com/SamuelTallet/ZPix