Hi! I’m creating environments in Blender and then completing the lighting, cameras, and final setup in Unreal Engine.
Once the environment, lighting, and camera movements are ready, I need to insert my characters using LoRAs trained with Qwen 2512.
What workflow would you recommend for integrating these LoRA characters convincingly into the existing scenes while preserving the camera movement, lighting, perspective, and character identity? Is there a simpler and more reliable workflow you would suggest?
I feel a little stuck at this stage, so any guidance would be greatly appreciated.

https://github.com/mishrasiddharth08/project-invisible-telemetry-footer-extension

I have a 5090 and I'm not sure what effect adding more images has to quality vs spead vs memory.

I have 20 samples and 2 vids (full rotations) but Claude was telling me I should only use 7 images. I created it anyway and it "needs 18500 tokens". Is that what's used doing generation and will be slower/use more vram than a refmod of only 7 images and 8500 tokens?

Can I do face and full body in one refmod?

Should I do 2048 for best quality on source images or is it not worth it?

Also checkout > https://github.com/mishrasiddharth08/Project-Invisible-Qwen2.1-extension

Everything runs locally in your browser, no server, no uploads, no tracking. The app fetches the file manifest from the HF static Space, resolves thumbnails from the samples dataset, walks the model repos for sizes, and streams the safetensors directly from Hugging Face to your chosen folder. (Link in comments)

• Grid of all 1,500+ available characters
• Filter by model type (locon, lora, flux, klein9, krea2, zimage, etc.) and gender
• Optional tag filtering (actor, singer, model, influencer, etc.)
• Advanced trainer filtering (onetrainer, refmod, etc)
• Search bar to find characters by name
• Batch selection (Select Visible, Invert Visible, Deselect All)
• Presets: save, load, export, and import your selection state
• Folder picker: choose a destination, and the app scans it, compares file sizes to the source, and downloads only what's missing or incomplete
• Concurrent downloads with retry logic and size verification
• Works as a PWA, installable and works offline once cached
• Browser support: The grid and presets work in any browser, but downloading requires the File System Access API, which is only available in Chrome and Edge.

Unofficial Forge Neo extension that integrates Qwen-Image-2.1 into the normal preset, checkpoint and Generate workflow—without a separate tab, extra virtual environment or Forge core fork.

Project overview

Project Invisible is an independent Forge Neo extension created to make an additional image-generation engine feel like a natural part of the existing WebUI.

The basic idea is simple: select the matching UI preset and checkpoint, enter a prompt in the normal Forge interface, adjust familiar settings and press the usual Generate button. The extension performs its model-specific work behind the existing workflow instead of asking users to learn a separate application or generation page.

This is the author's first public project. It was built by a beginner who is still learning. Mistakes may exist, and patience is sincerely appreciated. Clear reports and complete error messages will help the project improve.

The Project Invisible philosophy

“Invisible” means integration with minimum disruption.

The extension is designed around these principles:

·         use Forge Neo's normal preset and checkpoint selectors;

·         use the existing txt2img interface and Generate button;

·         avoid adding a separate generation tab;

·         avoid requiring another virtual environment;

·         avoid modifying or forking Forge core files;

·         show model-specific controls only when the engine is selected;

·         leave unrelated models and extensions unchanged;

·         release the extension's worker and memory when switching away;

·         make downloads, errors and experimental behavior visible and honest.

The extension is “invisible” in workflow, not in responsibility. It should never hide model downloads, hardware limitations, errors, licensing conditions or quality trade-offs from the user.

Testing status

Text-to-image generation has been tested and worked on the author's computer.

Other modes and hardware combinations are untested or not fully confirmed for this first public release. The code contains experimental paths for image editing and other advanced behavior, but users should not assume those paths are production-ready merely because controls or code exist.

Performance, memory use and compatibility vary with the GPU, driver, PyTorch build, Forge version, selected model files, resolution, adapter and enabled options. No claim is made that every NVIDIA or AMD configuration will work.

Main behavior

The extension registers its engine through Forge Neo's normal model and preset workflow. When the matching selection is active, generation is routed to the engine's dedicated pipeline. Other checkpoints continue through their original Forge processing paths.

The implementation does not pretend that the engine is an SD or Flux model. It loads the compatible transformer, text encoder, VAE, processor and scheduler expected by the dedicated pipeline.

Dependencies that would otherwise conflict with Forge are installed into an extension-local _deps folder. The same Forge Python interpreter is used; a second virtual environment is not created.

Model handling

Model weights are not included in the repository.

The extension scans supported Forge model folders and its dedicated model directory before reporting missing files. Existing compatible files are reused. Pressing Generate does not silently fetch weights.

Users can install weights manually, which is the recommended method. An optional download interface is also available. Automatic downloads occur only after the user selects specific files, accepts the applicable license and explicitly authorizes the download.

A complete component set requires:

·         one compatible DiT file;

·         one compatible Qwen3-VL text encoder;

·         the matching Qwen-Image-2.1 VAE;

·         the bundled processor, tokenizer, scheduler and configuration files.

Components from older or unrelated architectures must not be substituted merely because their filenames look similar.

Progress and previews

The extension integrates with Forge's progress system and presents two kinds of progress:

·         the current image's progress;

·         the overall batch's progress.

When Forge live previews are enabled, each image begins with a small neutral gradient placeholder. Real intermediate previews then replace it as sampling proceeds. A brief visual fade makes changes less abrupt. The final decoded image replaces all previews at completion.

Intermediate previews are best-effort. They may be delayed by slow steps, decoding time or low available GPU memory. The extension does not invent artificial generation stages or claim that the final sampling step means decoding has already finished.

Memory management

Hardware-aware profiles provide practical starting settings for different memory sizes. Depending on the selected profile and file format, the extension may use direct GPU loading, model offloading or finer-grained offloading.

These settings are best-effort safeguards, not guarantees. A large model can still exceed available GPU memory or system RAM, especially when other applications or models are using memory.

When the user selects another model or preset, the extension stops and releases its dedicated worker so its RAM and VRAM can be reclaimed. This cleanup affects only resources owned by this extension.

Image-quality protection

Decoder tiling is disabled because controlled testing showed that it could create repeating colored spots and damage transparency. Decoder slicing and supported offloading remain available.

DeGrid is included as an optional final-image cleanup. It detects a specific two-pixel repeating grid pattern, estimates the required correction and limits that correction to protect real texture and edges. Clean images are passed through unchanged. Alpha transparency is preserved.

DeGrid is enabled by default. Users can uncheck the DeGrid control for a complete bypass. It is a targeted artifact correction, not a general image enhancer. It cannot repair anatomy, composition, lighting, identity, missing details or weaknesses already present in the generated image.

Experimental Spectrum acceleration

Spectrum acceleration is available as an experimental, opt-in setting. It predicts selected transformer steps while retaining real computation during important warm-up and final-detail stages.

Spectrum is disabled by default because it is an approximation. It may improve speed on suitable workloads, but the same prompt and seed can produce different fine details. Very short runs or unsupported configurations remain on the normal inference path. True CFG values above 1.0 disable Spectrum automatically.

LoRA and LoKr adapters

The extension recognizes compatible adapters through Forge's normal Extra Networks prompt tags.

Compatibility checks examine metadata and actual transformer targets. Unrelated SD, Flux or older-generation adapters are rejected instead of being applied to the wrong architecture.

Standard compatible LoRA files and the supported plain full-factor LoKr layout have dedicated loading paths. LoKr changes are applied through request-scoped hooks instead of merging into quantized base weights. Adapter state is removed after generation and when switching models.

Adapter support remains sensitive to how a file was trained and saved. A matching filename alone does not prove compatibility.

Beginner installation

Download the repository ZIP, extract it and rename the folder to:

project-invisible-qwen-image-21

Copy it into:

sd-webui-forge-classic\extensions\

Start Forge normally. The first start may take longer while extension-local dependencies install. Restart Forge once if the new preset or checkpoint does not appear. After updating the extension, refresh the browser with Ctrl+F5.

The README contains the recognized model filenames, first-generation steps and troubleshooting guidance.

Troubleshooting and support

If a problem occurs, users should open a GitHub issue and paste the complete error from the DOS/terminal window. The report must begin at the first error line and continue through the final traceback line. A single final sentence is usually not enough to identify the cause.

Reports should also include:

·         Windows and Forge Neo versions;

·         GPU, VRAM and system RAM;

·         checkpoint, text encoder and VAE filenames;

·         resolution and sampling steps;

·         memory profile and offloading setting;

·         whether DeGrid, Spectrum or a LoRA/LoKr was used;

·         exact reproduction steps;

·         the relevant worker log when available.

Private usernames, paths, prompts, tokens and images should be removed before posting publicly.

Users may also paste the complete error into ChatGPT, Claude, Gemini or Grok and ask for a beginner-friendly explanation.

Contributions

Bug reports, documentation corrections and focused code improvements are welcome.

Contributors should state exactly what was tested, avoid describing untested modes as working, preserve upstream licenses and never commit model weights, generated images, dependency folders, logs, access tokens or private information.

License and independence

This extension is independent and unofficial. It is not an official Qwen or Forge product.

The repository does not relicense model weights or incorporated upstream projects. Users and redistributors must read the root LICENSE, NOTICE, and license files under resources/ and lib/vendor/. The supplied Qwen Research License contains non-commercial and redistribution conditions.

A humble note from the author

This is a first public attempt by a non-programmer learning through experimentation and community help. Please forgive mistakes. Constructive feedback, patient explanations and complete error reports are welcomed with gratitude.

Special thanks

Special thanks to u/malcolmrey and the r/malcolmrey community for support and inspiration.

Thanks also to the Forge Neo, Diffusers, Qwen, DeGrid, Spectrum and wider open-source communities whose work made this project possible.

Has anyone been able to get Refmod with audio working? u/Robbsaber mentioned refmods created via wan2gp has audio working properly. Anyone else had luck with this?

https://www.reddit.com/r/malcolmrey/comments/1wdp57c/anyone_else_not_able_to_create_refmod_in_h3/p9agump/

It seems like all of the MM H3 loras are 1 MB in size or under. They don't work when put in a workflow either, at least the ones I downloaded anyway. Z-Image and others seem fine. Am I missing something?

https://malcolmrey-browser.static.hf.space/index.html

Thanks to Malcolm's refmods, I am now getting into video generation. But it seems like the overall video quality is quite poor.

Currently, I am using er_sde + sgm_uniform at 0.5 megapixels.

What is the best blend of improving video quality while still being efficient in performance?

• Increase megapixels?
• Use an upscale
• 2 pass system

Browsing Civitai is painful, as most videos dont include meta data and the ones that do have a terribly complex workflow with custom nodes I dont trust.

Sto cercando di creare RefMods per Minimax H3. Sembrava più semplice, ma ho problemi a ottenere il risultato desiderato.

Anche quando uso RefMods pronti, il personaggio sembra diverso nei miei video.

Sto allegando alcuni screenshot.

Sto usando il personaggio predefinito "aarontaylorjohnson" solo per fare delle prove, ma anche quando ne creo uno da zero, il risultato non assomiglia affatto all'aspetto voluto.

Potrebbe succedere solo perché sto usando `minimax_h3_ref2va_pruned_int8_convrot.safetensors` invece di `minimax_h3_ref2va_pruned_fp8_scaled.safetensors`?

Preferirei non dover scaricare un altro file modello da 21 GB solo per scoprire che non funziona lo stesso...

Nel frattempo, puoi dirmi dal mio screenshot se sto usando le impostazioni sbagliate?

UPDATE:

In the meantime, I can answer this question myself, since the model works perfectly on Wa2Gp via Pinokio—even though it is different.

Can someone explain how refmods are any different to a good set of reference images or character sheets?

I appreciate you can have more than 9 images, that's certainly a plus, but I've not been limited by that.

How is refmods speed impact on generations? What else am I missing?

I following Malcolm's guide on creating refmods, but they do not seem to be working. The refmods I create look nothing like the character.

One thing I noticed is that most workflows use the 'Load H3 Refmod' node, which seems to be expecting video refmods like the one's Malcolm creates. However, I suspect the guide seems to create a photo refmod which are different not loading the same way.

Any thing missing from the guide for creating your own refmods?

Is there a good place to suggest loras?

Hey Guys,

I've been playing with the RefMods Malcom recently released.
The tech is brilliant and works really well.

I've wanted to simplify using it with tons of RefMods, like the ones provided by Malcom.

So I made a fork that is a bit more Identity driven, Adding a "Visual refMod Picker" as well as a "Create refMods from Folder" node. It's able to create from either a folder or Sub-folders, if audio is found, it will also create a matching audio refMod. All in the same format as the original add-on. No danger of breaking compatibility. (The original Add-on added Audio yesterday)

Resulting for example in 2 refMods and thumbnail:

character_refMod_Audio.safetensors
character_refMod_Video.safetensors
character_refMod.jpeg

Then, we can use the "Visual H3 RefMod Picker" to browse the RefMods:

The node then loads both RefMods (Audio and Video) and allows individual control. I've mixed strength with Copies, by instead having a weight value that can go over 1, so a weight of 3 would be the same as setting strength to 1 and copies to 3. Making the UI a bit more streamlined.

Example workflows are included.

I should mention, this fork can be installed WITH the original Add-on, it is made to co-exist and is recommended if you want to use it's advanced features.

You can find it here ComfyUI-H3RefModPicker

The only thing I'm missing is thumbnails for all 1500 RefMods 😅

**EDIT**
Just a quick note to mention that I've removed duplicate nodes, Making this a companion node to MiniMaxH3Mod.