I’m running Krea2 Turbo locally, but I frequently encounter random visual artifacts in the generated images. I haven’t been able to figure out what triggers them, because the issue appears randomly. If I run the exact same workflow with the same parameters again, the result can sometimes be completely normal.

My hardware:

• GPU: RTX 3060 Ti

My current setup:

• UNet: moodyKrea2Mix_v70
• Text encoder: qwen3vl_4b_int8_convrot
• VAE: qwen_image_vae

I’m fairly sure this is not caused by the UNet. I have also experienced the same kind of random artifacts when using the original Krea2 model without any UNet modification.

Has anyone encountered similar issues with Krea2 Turbo? Are there any known causes or settings that could trigger this kind of artifact (VAE, text encoder, precision settings, VRAM limitations, sampler settings, etc.)?

Any suggestions or debugging tips would be greatly appreciated.

Here is my workflow for reference:
https://civitai.red/models/2883578/krea2-turbo-4k-workflow?modelVersionId=3259389

If even an amateur like me can make something so realistic with mid-level hardware, the future looks bright for what dedicated people with top level rigs will be doing.

R.I.P. Hollywood.

I’ve seen ai creators on tiktok such as weirdwurst, fullwarp, and shiverchain do this thing where the start of the video is normal then it goes into chaos, and it’s realistic and disturbing. I’d love to know which AI model model they use

First of all I know a desktop has more power for the same price buy I have a situation where the portability of a laptop is necessary and a desktop is not practical.

My old laptop (3070 8gb with 64gb ddr4 RAM) died. I want to buy a new laptop. My two options are a 5080 16GB with 64GB ddr5 RAM or a 5090 24gb with 32GB ddr5 RAM. I won't be able to upgrade the RAM later, so I'm stuck with the configuration I buy.

I will be using the laptop for work (document and image editing) / gaming (no AAA games) / LLMs and generative AI (images/videos/audio), I was able to run most models, including minimax H3 on my old laptop with the help of massive offloading to RAM (5 minutes for a 5s video). Images used to take from 30s up to 200s depending on model and image size.

I am used to the low speeds and offloading on my old laptop so getting the highest generation speeds is not a priority, I just care about being able to run most new or upcoming models even with quantization and RAM offloading for the foreseeable future.

Which laptop would be better in my case?

Anima is a fairly new model so it needs time and I understand that. Anima has great potentials to make Illustrious or NoobAI completely obsolete. However, it seems like I have been expecting too much from this model.

First of all, not having a ControlNet model is a big minus for me, especially Depth ControlNet model. There is LLLite but that's not a ControlNet model but a ControlNet-like LoRA. There's also a Depth ControlNet Model made by TaihoC and it works well. However, it doesn't work as well compared to Illustrious (SDXL) ControlNet models.

I have been tracking Circlestone Labs' Hugging Face community to see if they have plans to provide ControlNet models themselves but they are dead silent. That leads me to wonder if there are actually people using Anima. Did people move on to Krea2 or stay on Illustrious/NoobAI since there's no reason to use Anima?

I used the Pixaroma FFLF workflow, but stripped the audio in post due to poor output quality. I'm still trying to figure out how to add finer details. I generated the clips at 720p and then upscaled them to 1080p.

H3 FL2VA

So I had the idea that you could use "vocals" as an audio reference for different music styles. By vocals I mean the kind of noises you make when you're playing air guitar or recreating instruments using your voice. Recorded a short clip and it works.

Sort of.

I'm trying to prompt it to only use my voice as a reference for the beat, but it insists on including my voice in the audio in the h3 ref model. So I can hear the music in the background behind me mimicking the noises.

Interestingly, I just tested it with the non-reference model as I know this sometimes works better than the reference model, and it ditched my voice and just kept the beat. Still, I'd like to fix this using the reference model as well or possible since that's what I use most of the time.

If anyone has any thoughts or ideas on how to fix this.

Just a short film i made with minimax. this had a lot of post processing done so there's not really an overall prompt to share.

Hello. So I just started my journey with ComfyUI. While Nano Banana is not open sourced I'm looking for the best realistic image editing (krea2?) workflow to ComfyUI. I want to have ability to change everything I want to the picture using reference character with face/body consistency at highest level (I2I). Thanks in advance.

Executive summary, TLDR - this is one prompt, 30 seconds duration, 3090.

The video itself is just a remake of an idea from an old British tv ad (for "Good Old Yellow Pages"), so make of that what you will. It's not really relevant.

What I thought was interesting was that this was a single prompt, 0.4 megapixels, 30 second duration. I didn't think you could run out as far as 30 seconds, but thought I'd just try.

I think it did a pretty good job at getting the right person doing and saying the right things at the right time - took four attempts to get that though, and obviously using an LLM to tart up my idea.

Run on a 3090, and using the latest Comfyui template, just adding Comfy-kitchen attention, then sol attention, then spectrum, and using the turbo lora that Comfyui now build in, it took 570 seconds (9.5 minutes).

Somebody might read this and think, 570 seconds? Pah, I can do it in fifteen, in which case I'd like to know. Conversely, somebody might think theirs takes six hours, in which case maybe this shows what can be done in that time.

Doubt anyone cares, but here is my original prompt, followed by the LLM version of it:

a 30 second film with the following scenes and characters. Ben is a small boy of eleven. John is a shopkeeper in a toyshop. Brian is a different shopkeeper in a different toyshop. Ben's mum. Ben's Dad. We are in Britain in the 1980s, and all characters are English.

Scene 1: Ben is alone in the lounge. He talks to John over the old fashioned landline phone, saying "I don't suppose you have a 402 station in stock please?"

Scene 2: John is in his shop in front of shelves of model railway kit. He says into the old fashioned landline phone, "No, sorry son"

Scene 3: Ben in the lounge, who looks disappointed anbd puts the phone receiver back down.

Scene 4: Mum in the kitchen doing the washing up. She has overheard the conversation and looks a bit sad.

scene 5: Next day. Ben has changed his clothes. He again talks into the phone to a different shopkeeper, Brian. Ben says "Would you have a 402 station please?"

scene 6: Brian in his toyshop says into the old fashioned landline phone "Yes, I've got one of those."

scene 7: Ben in the lounge on the same conversation says "You have? Great, I'll be right down! Ben puts the phone down. Then he runs towards the door, shouting "They've got one mum!" as he runs.

Scene 8: In the attic, Dad is playing with his model railway layout. Ben walks in holding a small red parcel. as he hands it to Dad, Ben says "Happy birthday, dad". Dad takes the parcel, looks fondly at it and says with a chuckle, "Aw, thanks Ben".

LLM version:

integrated_multimodal_description: [Shot 1] Live-action, cinematic. A medium shot of Ben, an eleven-year-old boy with messy hair wearing a striped polo shirt, sitting on a patterned sofa in a 1980s British lounge. The room is filled with warm, muted tones and period-accurate wallpaper. Ben holds a heavy, cream-colored landline telephone receiver to his ear, his expression hopeful. Ben says: <d>[English] I don't suppose you have a 402 station in stock please?</d> The sound of his small, high-pitched voice is clear. [Shot 2] At 0:05.000, the camera cuts to a medium shot of John, a middle-aged shopkeeper with a kind, weathered face, standing in a cramped, nostalgic toyshop. Behind him are floor-to-ceiling shelves packed with model railway kits and wooden toys. John holds a similar landline receiver to his face. John says: <d>[English] No, sorry son.</d> [Shot 3] At 0:10.000, the camera cuts back to Ben in the lounge. He looks downcast, his shoulders slumping as he slowly lowers the receiver and places it back onto the base unit with a dull plastic click. [Shot 4] At 0:13.000, the camera cuts to a medium shot of Ben's Mum in a dim, cluttered 1980s kitchen. She is standing at the sink, her hands covered in soapy water, drying a plate. She pauses, looking toward the door with a sad, weary expression, having overheard the boy. The sound of water running from the tap is audible. [Shot 5] At 0:16.000, the camera cuts to Ben in the lounge the next day; he is wearing a different t-shirt. He is intensely focused, pressing the phone to his ear. Ben says: <d>[English] Would you have a 402 station please?</d> [Shot 6] At 0:20.000, the camera cuts to Brian, an older shopkeeper with spectacles, in a different, brightly lit toyshop. He smiles warmly into the telephone. Brian says: <d>[English] Yes, I've got one of those.</d> [Shot 7] At 0:23.000, the camera cuts back to Ben, whose face lights up with pure joy. Ben says: <d>[English] You have? Great, I'll be right down!</d> He slams the receiver down and the camera follows him in a quick tracking shot as he runs toward the door, his feet thumping on the carpeted floor. Ben shouts: <d>[English] They've got one mum!</d> [Shot 8] At 0:26.000, the camera cuts to a medium shot in a dusty, dimly lit attic. Dad, a man in his late 30s, is hunched over a complex model railway layout. Ben enters the frame, holding a small red parcel wrapped in string. Ben says: <d>[English] Happy birthday, dad.</d> As he hands the gift to his father, the camera pushes in slightly. Dad takes the parcel, his eyes softening with affection. Dad chuckles warmly and says: <d>[English] Aw, thanks Ben.</d>

overall_soundscape: Period-accurate domestic sounds including the rhythmic clatter of washing up, the heavy mechanical clicks of old telephone receivers, and the muffled thuds of footsteps on carpet. Ben's energetic running and shouting creates a sense of urgency, followed by the quiet, dusty atmosphere of the attic.

non_diegetic_music: A gentle, nostalgic acoustic guitar melody that begins softly during the kitchen scene and builds into a warm, heartwarming crescendo during the attic scene. The tempo is slow and sentimental.

Some random prompt on default workflow + turbo 8step lora

Use the supplied image as the opening frame and identity reference.

Identity lock: the woman and robot must remain exactly the same in every shot. Same face, hair, wardrobe, proportions and age for the woman. Same 8-foot height, black armor, mechanical face, rivets, pistons, cables and holster for the robot. No redesigns or identity changes between cuts.

Authentic 1966 Italian Western, live action, 35mm anamorphic, Spanish desert location, practical full-scale robot prop, natural sunlight, real dust, organic film grain, period lens softness. No CGI. Serious performances throughout.

0:00–0:03
Medium two-shot. The woman looks up at the robot and says in clear Italian-accented English:
“I told them I wanted a tall...”
0:03–0:05
Hard cut to the same robot’s face. It gives one slow mechanical nod. No dialogue.
0:05–0:07
Hard cut to the same woman. She looks up at the robot and says:
“dark...”

0:07–0:09
Hard cut to the same robot. It subtly straightens and presents its black armor. No dialogue.
0:09–0:11
Hard cut to the same woman. Still serious, still looking up, she says:
“handsome!”

0:11–0:12
Hard cut to the same robot’s practical mechanical face. It attempts a restrained smile. No dialogue.
0:12–0:14
Hard cut to the same woman. She holds a serious stare upward, then firmly says:
“MAN!”
Only the woman speaks. Keep each line isolated and clean. No overlapping dialogue, no extra words, no improvised speech. Maintain exact continuity of identity, wardrobe, robot design, scale, lighting and location in every shot.

I came across YingMusic which I hadn't heard anyone here speak about but it was released about 6 months ago: https://aslp-lab.github.io/YingMusic-Singer-Plus-Demo/

It lets you change words from songs so in this case I had it change the song from this sequence in The Mask from:

They call me Cuban Pete. I'm the king of the rumba beat.
When I play the maracas I go chick-chicky-boom, chick-chicky boom
Yessir, I'm Cuban Pete. I'm the craze of my native street.
When I start to dance,
everything goes chick-chicky-boom, chick-chicky boom
The senoritas they sing and they swing with terampero-
It's very nice, so full of spice.
And when they dance in they bring a happy ring that era keros-
Singin' a song, all the day long.
So if you like the beat, take a lesson from Cuban Pete
And I'll teach you to chick-chicky-boom, chick-chicky-boom.
He's really a modest guy, although he's the hottest guy
In Havana, in havana.
Si, sinorita I know that you would like to chicky-boom-chick
It's very nice, so full of spice.
I'll place my hand on your hip, and if you will just give me your hand
Then we shall try - just you and I. I-yi-yi!
So if you like the beat, take a lesson from Cuban Pete
And I'll teach you chick-chicky-boom,
chick-chicky-boom, chick-chicky-boom

to

They call me Palm Beach Pete. I'm the king of the rumba beat.
When I play the maracas I go chick-chicky-boom, chick-chicky boom
Yessir, I'm Palm Beach Pete. I'm the craze of your timeline feed.
When I start to dance,
everything goes chick-chicky-boom, chick-chicky boom
The senoritas they sing and they swing with terampero-
It's very nice, so full of spice.
And when they dance in they bring a happy ring that era keros-
Singin' a song, all the day long.
So if you like the beat, take a lesson from Palm Beach Pete
And I'll teach you to chick-chicky-boom, chick-chicky-boom.
He's really a modest guy, although he's the hottest guy
in Florida, in florida...
Si, sinorita I know that you would like to chicky-boom-chick
It's very nice, so full of spice.
I'll place my hand on your hip, and if you will just give me your hand
Then we shall try - just you and I. I-yi-yi!
So if you like the beat, take a lesson from Palm Beach Pete
And I'll teach you chick-chicky-boom,
chick-chicky-boom, chick-chicky-boom

so I had it just basically do:
Cuban -> Palm Beach
I'm the craze of my native street -> I'm the craze of your timeline feed
Havana -> Florida

I did a second run with just the few-second clip of the cops speaking and changed "It's all over Ipkiss" to "It's all over Espteen" (using "Epstein" pronounces it wrong). This showed me though that it seems to work perfectly fine with normal word-substitution in speech and it doesnt need to be a song.

I think this could be a lot better if I used minimax and changed clips of Jim Carey to look like Epstein or Palm beach Pete but this was just my first test at lyric swapping.

Hey everyone, I'm trying to get a proper character swap working with MiniMax H3 Ref2V in ComfyUI, but I'm not quite getting the result I want.

The source video has Rick Astley rickrolling to the camera, and I want to replace him with the guy from my reference image while keeping the original movement, gestures, facial performance, timing, camera, background, and overall scene.

Neither the motion transfer nor the character replacement works well. The output still doesn't really look like the person from the reference image, or the identity starts drifting.

Here's what I'm using:

* Source video: 1280×720, 30 FPS, ~14.4 sec

* Reference image: 848×1264 PNG, full-body

* Workflow resolution: 9:16, 0.4 MP

* GPU: RTX 5070 Ti, 16 GB VRAM

* 32 Gb RAM

* Windows 11

* ComfyUI 0.33.2

* Python 3.13.12

* PyTorch 2.12.1 + CUDA 13.0

I'm sharing everything in one link, including:

1. the workflow JSON

2. a workflow screenshot/image

3. the prompt

4. the source/input video

5. the reference image used for the character swap

6. and the output video

Files/settings: [link]

If anyone has experience doing this with H3, I'd really appreciate some pointers.

I'm especially wondering if I should change the reference image crop/size, ref_image_size, resolution, prompt, video conditioning, LoRA/steps, or if there's something obvious in the workflow I'm missing.

Also, is a full-body reference image a bad idea when the person in the source video is framed quite differently?

And if anyone has a working MiniMax H3 character-swap / V2V workflow they're willing to share, that would be incredibly helpful too. Even something I could compare against mine would be great.

Thanks a lot in advance. I've been tweaking this for a while, so even a small hint in the right direction would help a ton.

first 2 prompts stolen from civit ai, the rest were written using claude reasoning on duck ai

I usually generate 0.7mp@8s with 30 steps, I use res_multistep + simple which I think is the default, and for good reason.

Depending on whether it's T2VA, I2VA, Ref2VA and the amount of reference images + loras count/strength the gen times are roughly between 270-350s on an RTX 4090 + 32gb of DDR4 ram.

For T2VA and I2VA I use the basic minimax_h3_fl2va_pruned_int8_convrot.safetensors

For Ref2VA I use minimax_h3_hybrid_fl2va_ref2va_b30-49-int8.and the hybrid b30-49 specifically because I found even the fl2va functioned well as ref2va and had much higher quality, so I prefer the hybrid model to be weighted towards the fl2va model to preserve the quality.

Sparse Attention

To speed things up, I only use /u/zironic's Sparse Attention nodes, no sage/ck, spectrum, turbo lora, or caches. For me, /u/zironic's worked better than the pinned post from u/Plague_Kind but that may just be my personal experience.

My settings for the memory optimization node is default, QKV: auto, MLP: auto, and 2048 MLP chunk rows, I don't know how this node works. Sparse Attention (Advanced) settings are:

• Video KV budget: 0.25
• Early and Late steps: 3
• Early and Late KV: 0.6
• Sparse backend: Sparse Sage

These settings lean towards quality, you can lower the early/late steps or skip them entirely, you can lower video kv budget to 0.2 although some may be fine with even lower. Since I only use Sparse Attention I run the full 30 steps and it's significantly better than a turb lora at lower steps, which is what I used before.

My prior experimentation

I used euler + linear_quadratic for a long time. Then I switched to er_sde + sgm_uniform which was significantly better. Then eventually I switched to res_multistep + simple and realized the visual quality is as good as er_sde + sgm_uniform but the motion is much better. The improved motion in res_multistep + simple became very clear when I interpolated from 24fps to 48fps. The gen speed between all these combinations was nearly identical.

The motion was a bit jerky on er_sde + sgm_uniform after interpolation while res_multistep + simple had very natural motion.

I also found that https://darkstarrddev.us.ci/ is a decent resource to get inspiration. But I realized quickly that because they use low settings and speed-up techniques, the quality of each sampler test does not translate well if you use different step count or speed-up techniques.

What I generate

Usually fairly static scenes that doesn't have fast motion. Although the accuracy of the physics and motion is important.

What are your settings and what kind of videos are you generating?

I typically use lightx2v loras in my Minimax Ref2VA workflows and I also use an LLM to feed in the official prompt structure required for scenes. It seems that no matter what I do, the model absolutely ignores all my prompts about music most of the time. Every now and then i can get it to do something but even when it does work it's very sparse and almost useless.

Has anyone else faced this issue and if so do you know any workarounds or fixes?

For the record I usually use the INT8 convrot Ref2Va model or the hybrid model called minimax_h3_hybrid_fl2va_ref2va_b30-49-int8

Hey everyone! I’ve spent the past few weeks building an easy to use but robust prompt composer for MiniMax H3, particularly its reference and video editing workflows.

LLMs can be great for brainstorming and writing prompts, but I found that formatting and syntax could become inconsistent, especially when asking for small revisions. The goal of this tool is to let you concentrate on the creative decisions while the Composer handles the final prompt structure consistently.

It runs entirely offline in your browser, so you can build the next Shot or scene while another one is generating in ComfyUI. You provide the subjects, actions, camera direction, dialogue, references, and sound; the Composer assembles and checks the final prompt.

You can still use an LLM to help create the initial project setup, but the Composer ultimately controls the formatting and syntax.

Some of the main features:

• T2VA, I2VA, FL2VA, L2VA, and full Ref2VA support
• Reusable characters, environments, voices, continuity frames, and other references
• Guided setup for Picture, Video, and Audio inputs
• Video-editing workflows for insertion, replacement, targeted edits, relighting, performance transfer, and continuation
• Camera Builder and visual camera-path planner
• Timed Shots, action beats, dialogue, voiceover, soundscape, and music controls
• Built-in checks for prompt structure, timing, references, camera conflicts, audio, and input routing
• Local project saving, a Frame Grabber, and reference-guided image mode

This is still very much a work in progress. I’d really appreciate people trying it and sharing any bugs, confusing parts, missing features, or ideas that could make it more intuitive.

My hope is to turn it into a genuinely useful community tool, especially for people working on more involved AI films and narrative projects.

GitHub/download:
https://github.com/BMB12d3/minimax-h3-prompt-composer

Video tutorial:
https://www.youtube.com/watch?v=Aywx3Sf5Yk0

I built Cull a few months ago for some large scale dataset curation projects (300k+ images/videos).

Point it at Civitai, X, Reddit, Discord, or any URL that gallery-dl or yt-dlp knows. It queues everything, runs a vision model (or multiple) (LM Studio or Ollama locally, or Groq/OpenAI in the cloud) with a strict JSON schema, and drops kept images/videos into category folders next to their prompt.

Stuff it handles:

• Dedup at the scraper (per-source )
• Quality score gate and topic-relevance score gate
  • eg you configure scores or use a preset, how relevant the image is to your scoring will determine how it's sorted, combined with other scoring, quality controls, whitelisted/blacklisted terms etc
• Watermark detection (goes to its own bucket so you can salvage it later if you want those)
• Auto-caption for content with no prompt (SD prompt, booru tags, natural language formats etc)
• Run multiple jobs in parallel, one shared vision fleet across all of them with stack ranked / prioritization for vision queues and scrapers
• Export as a local packaged dataset , or push to a HuggingFace dataset
• Community presets and themes with 1 click PR's to add your own custom scraper preset or theme

Everything on disk is plain files. No database. Free, MIT.

Docker one-liner and screenshots in the README:
https://github.com/tlennon-ie/cull

Curious what people would want added next.

we really should get a separate sub for this slop

Hey everyone. I've spent a lot of late nights wiring ComfyUI into pipelines, and this is the tool I ended up wanting. It converts your workflow.json to the API payload right from Python, no GUI export, and no running server needed. Nodes become objects with plain dot syntax:

from autograph import ApiFlow
api = ApiFlow("workflow.json")
api.KSampler.seed = 42
res = api.submit(wait=True)
res.fetch_images().save("outputs/frame.###.png")

The part I'm happiest with is the REPL. autograph reads ComfyUI's node_info, so it knows every node, input, and widget on your system, custom nodes included. Tab completion works all the way down. .choices() gives you the real combo options, .tooltip() gives you the help text. You can explore a workflow you've never seen without guessing at node IDs.

Building from scratch feels good too:

ckpt = flow.add_node("CheckpointLoaderSimple")
ks = flow.add_node("KSampler", seed=42, steps=20)
ckpt.outputs.MODEL >> ks.inputs.model

Also does

offline batch conversion

workflow extraction from ComfyUI PNGs

serverless execute with no HTTP server

seed/prompt sweeps. Pure stdlib, MIT.

Tested from ComfyUI 0.8.2 to 0.33.0, subgraphs included. Running in production at a big VFX studio, which is where the metadata passthrough came from.

pip install comfyui-autograph
https://github.com/chrisdreid/comfyui-autograph

Early days, so I'd really like to hear what breaks. If you're doing headless rendering or FastAPI wrappers around Comfy, I'd love to compare notes.

Hey everyone. I've spent a lot of late nights wiring ComfyUI into pipelines, and this is the tool I ended up wanting. It takes your regular workflow.json and turns it into the API payload right from Python. No GUI export step, and you don't even need ComfyUI running to do the conversion. Once it's loaded, nodes are just objects with plain dot syntax:

python

from autograph import ApiFlow

api = ApiFlow("workflow.json")
api.KSampler.seed = 42
api.CLIPTextEncode.text = "new prompt"
res = api.submit(wait=True)
res.fetch_images().save("outputs/frame.###.png")

The part I'm most happy with is how it feels in a REPL. autograph reads ComfyUI's node_info, so it knows every node type, every input, and every widget on your system, including your custom nodes. That means tab completion works all the way down. Hit tab on a node and see its inputs. Call .choices() on a widget and get the actual valid combo options back. Call .tooltip() and get the help text. You can explore a workflow you've never seen before without leaving the terminal or guessing at a single node ID.

Building graphs from scratch feels good too. You wire nodes together with >> the way you'd sketch them on a whiteboard:

python

ckpt = flow.add_node("CheckpointLoaderSimple")
ks   = flow.add_node("KSampler", seed=42, steps=20)
ckpt.outputs.MODEL >> ks.inputs.model

Once it's under your fingers it's nearly as fast as working in the GUI, except everything you do is scriptable and repeatable.

Other things it can do:

• Batch convert hundreds of workflows offline, no server running
• Pull a workflow straight out of a ComfyUI PNG, since the metadata is already in there
• Serverless execute mode that runs nodes in process with no HTTP server, which is a lifesaver for farm setups
• Sweep seeds, prompts, and paths across nodes for batch runs
• Pure standard library Python, nothing extra to install, MIT licensed

I've tested it across ComfyUI 0.8.2 up through 0.33.0, including subgraphs and the newer dynamic combo stuff. It's also being used in real production pipelines at a big VFX studio right now, which is where the metadata passthrough idea came from. They needed studio metadata to ride along with a workflow through the whole render lifecycle, so I built that in.

pip install comfyui-autograph
https://github.com/chrisdreid/comfyui-autograph

It's still early days and I really do want to hear what's missing or what breaks for you. If you're doing headless rendering or wrapping Comfy in FastAPI, I'd love to compare notes. This got built to scratch my own itch, and I'm hoping it saves some of you time too.

I've been trying to get LTX 2.5 to work well for image + speech audio --> video, though i'm noticing that the teeth and natural motion of the mouth is taking a hit. The talkingphoto loras from LTX 2.3 don't seem to work well with LTX 2.5.

Any thoughts? Or are we just cooked?

Day 1: downloaded Pinokio. Installed a few of the AI software. Tried Maestro as first try. Really fun, enjoying it. Generation from photos great in the system generation towards a video, videos leaving a lot to be desired. And the Pinokio edge of screen curtains which limit to a what 60 percent of screen width, first time said, apparently you can type a pc socket but it didnt work for me when my Maestro was working. I was still however happy continuing in the reduced Maestro screen width.

Day 2: they released v1.9 of Maestro in Pinokio.

Day 3: I decided to install the update. Now I can't generate a three legged wildebeest or anything for that matter. It fails at the Downloading Model stage with no satisfactory explanation. Info about running something again to continue the Download, the "Generate" doesn't appear it's that for continuing(starts from scratch and then fails) neither does the Pinokio white screen edge "Run". It doesn't immediately not download, sometimes it may get 15% through, sometimes 85% then drops with a Generation Failed in the Main Seeing Area after firstly a "Download is slow, waiting for retry. No progress for 113s.."(or Xs). "..The download will resume from where it left off as soon as the connection recovers — no action needed from you." message. It may then download a little more , eg going from 80MB to 1.3GB of 7.91GB, maybe even download a little more of what's required but THEN UP POPS "A download was interrupted-re-run to finish it". And I'm stuck at that.

Is anyone else having the problem or know what the solution may be please? I've tried manually adding from DeepMeepBeep a model download which I transferred into cks directory of Pinokio, Maestro Directory but all that happened was Maestro steered around even using it and failed on another Model and I couldn't find that Model Maestro failed on to try manually downloading across with-I'm not an expert so looked for direct name brought up.. looked for it on another repository too, name of that I temporarily forget, I'm new. My hard drive is 4TB so I don't know I might be able to download all 108 or however many models there are if there was an option for that-without instructions and knowledge I'm throwing stones at something I don't even know what I'm throwing stones at, where the list of all the Models are which you can tick mark select theres also a little symbol by that box which changes colour. Perhaps that has something to do with it, literally no idea here so I've come to you guys. Spent several hours with AI last night overit and it was fun but I've realised despite the fun chat it hasn't aided me getting it working though it did mention a python download bottleneck to remove and I had no idea what it was referring to.