Some shots I made based on the red headed league short story. Character reference sheets generated in Nano Banana + edited in Qwen image edit for spatial continuity.
12 steps +ref2va turbolora v0.1
10s generations 107s/it on a 3090 at 1 MP
Needs some editing and audio polishing.
Some shots I made based on the red headed league short story. Character reference sheets generated in Nano Banana + edited in Qwen image edit for spatial continuity.
12 steps +ref2va turbolora v0.1
10s generations 107s/it on a 3090 at 1 MP
Needs some editing and audio polishing.
Sharing a few sequences from a medieval battle attempt I’ve been working on. It’s still very much a draft, but the sequence has progressed enough that I thought it was worth sharing here and getting some feedback before I continue with the rest.
Most of the scenes were generated with MiniMax H3 using the default workflows with the Turbo LoRA at 4 steps. I used Nano Banana and Flux Klein to create the reference images, and LTX 2.5 for the opening crow sequence.
There’s still a lot of work to do. The cuts are rough, no proper sound work has been done yet, and there are plenty of shots I want to refine or replace. I’m planning to build out the entire sequence, so feedback at this stage would actually be really useful in deciding what to focus on next.
What’s interesting to me is that I genuinely don’t think I could have pulled off this level six months ago with the same amount of effort. It’s still far from perfect, but the progress in a relatively short time feels pretty significant.
Would love to hear what works, what breaks the illusion, and what you’d improve.
Qwen just released Qwen-AgentWorld-35B-A3B — a 35B-parameter MoE with only ~3B active parameters per token.
The interesting part: this is not positioned as a standard chat/instruction model or a full autonomous agent. It is a language world model trained to predict what an environment would return after an agent takes an action.
It covers seven agent interaction domains:
MCP / tool calling
Search
Terminal
Software engineering
Android
Web
Operating-system GUI interactions
The intended use seems to be simulating the environment side of an agent loop: given the action history and a new tool/GUI action, predict the next observation/state. That could be useful for agent training, offline evaluation, synthetic trajectories, testing tool-use workflows, or building sandbox-like environments without constantly running the real tools.
https://reddit.com/link/1u1x6bf/video/a9lqnqhibf6h1/player
Quick Bernini test on a low-res clip.
Still experimenting and learning the workflow, but I thought the results were interesting enough to share. This is a single source shot with a few different variations stitched together for comparison.
workflow link pastebin
https://reddit.com/link/1to3mkl/video/vl4df55irg3h1/player
I’ve been testing the new LTX director custom node alongside the transition lora, and it makes complex transitions incredibly clean.
Here is a segment from a project I'm working on
Links:
I made a small ComfyUI workflow for creating multi angle reference sheets from a single input image.
The main use case is character sheets. You give it one character image, and the workflow tries to generate multiple consistent views like front three quarter, side profile, rear view, rear three quarter, high angle, low angle, and a close detail view. The goal is to keep the same face, outfit, pose, expression, proportions, and general design while only changing the camera angle.
I built it mostly with native ComfyUI nodes. The only non native part, as far as I remember, is the GGUF loader. The prompts are written in a generic way, so it can also work for people, props, vehicles, creatures, or objects, but I mainly made it for character sheet generation.
I tested it with the Flux 2 Klein 4B Q4 GGUF model because I currently have access to only 4 GB VRAM. For such a small setup, it is giving acceptable results. It is not perfect, especially with difficult rear views or fine clothing continuity, but it is usable for blocking out reference angles and building rough character sheets.
I expect the 9B variant to give much better consistency and detail, especially for faces, costume continuity, proportions, and rear view inference.
This is not meant to be a final polished character turnaround solution. It is more of a practical workflow for quickly getting usable angle references from one image, especially when working with AI video, inpainting, first frame last frame generation, or character continuity.
Sharing it in case it is useful to anyone experimenting with Flux 2 Klein on low VRAM setups.
Built an offline audiobook reader called Auris.
https://github.com/nikhilprasanth/Auris
The idea started because I wanted a proper local audiobook workflow without depending on cloud TTS services or locked ecosystems. Auris reads EPUB, PDF, and TXT books using local OmniVoice TTS with character aware voices, narrator control, synced text highlighting, subtitle export, and voice cloning support.
You can import a book, assign voices to characters, preview them, upload reference WAV files, and export the final result as audio with subtitles. Everything runs locally after setup.
Currently experimenting with improving emotional delivery using lightweight local models through OpenAI compatible endpoints before TTS generation. Trying to make narration feel less flat without turning the pipeline into something huge and overengineered.
Still early and very experimental, so I’d genuinely appreciate feedback, criticism, or ideas from people working on local AI, audio tooling, TTS pipelines, inference optimization, or ebook workflows.
Also very open to collaborators if anyone wants to contribute or experiment with the codebase.
Have Qwen 3.6 27B and Qwen 3.6 35B basically made most of the older ~30B models irrelevant?
They seem to beat stuff like Qwen coder 30B, GPT OSS 20B, Gemma models, especially for coding and agent workflows.
At this point I’m not really finding a reason to keep the older ones around.
Anyone still using them for something specific?
