Name: EditAnything
Rating: 5 (448 reviews)
Author: NRDX

We're a Day-0 launch partner for MiniMax H3, a next-gen open-weights multimodal video model - and it's already in the generator. No waitlist. Open weights land in a few days.

MiniMax H3 is live: open-weights video, generate now

For each model, see the version details for more information. All the models here are experimental; they often won't work perfectly, the prompt will often need improvement, the input will often need alteration, or the parameters will need adjusting. Therefore, don't treat these models as something that should work perfectly right away for everything. They will only cease to be experimental when we discover a better way to do this. <a target="_blank" rel="ugc" href="https://huggingface.co/Alissonerdx/LTX-LoRAs">Alissonerdx/LTX-LoRAs</a> <a target="_blank" rel="ugc" href="https://huggingface.co/Alissonerdx/EditAnything">Alissonerdx/EditAnything</a> <a target="_blank" rel="ugc" href="https://buymeacoffee.com/nrdx">If this model was helpful to you in any way, please consider helping me continue creating more model for the price of a coffee.</a>

In this experimental version, it's possible to perform several operations together in a single prompt: Replace, Remove, Add, Restyle, and everything becomes easier with the help of the prompt enhancer, so you can write in natural language and it will convert it to the appropriate prompt.

LTX 2.5 - v2.0

This model was trained on 8,000 video pairs, and training is still ongoing for a few thousand more steps. It is still experimental, not trained with a fully professional production target, and the model may be updated unexpectedly as new checkpoints.The current goal is not final polished production quality, but to explore:<ul><li>edit-anything behavior</li><li>prompt-following</li><li>inference tradeoffs</li><li>synthetic dataset building, especially for style data</li></ul>The model was trained around four main prompt patterns:Add <code>Add a/an [subject/object] with [clear visual attributes], [precise location in the scene].</code>Remove <code>Remove the [subject/object] [location or identifying description].</code>Replace <code>Replace the [original subject/object] [location] with a/an [new subject/object] with [clear visual attributes].</code>Convert / Style <code>Convert the video into a [style name] style.</code>Workflow URL: <code>https://huggingface.co/Alissonerdx/LTX-LoRAs/blob/main/workflows/ltx23_edit_anything_v1.1.json</code>One important thing during inference is CFG.A good starting point is testing a distilled setup with CFG = 1. If the edit feels too weak or the model is not following the prompt well enough, increasing CFG can be the key. In some cases, increasing the LoRA strength to around 1.2 can also help.The workflow is also not fully optimized yet. It still needs more testing to find the best combination of:<ul><li>CFG</li><li>LoRA strength</li><li>number of steps</li><li>model combinations</li></ul>It may also be interesting to combine this model with other models and see what kinds of results emerge.If you can test it, please share your findings. Feedback on prompt behavior, edit strength, consistency, style transfer, and failure cases would be very helpful while training is still in progress.Another very important thing is that the Removal task should have a very clear direction indicating where you want to remove what you want to remove.Examples:Remove the black robot sitting at the table.Remove the person riding the electric scooter on the left.Remove the person with glasses and the microphone in the foreground.Remove the image of the green trees on the top left.Remove the woman and the smoking bottle.For example, if the object are in front, use foreground ... background, left, right, top, bottom.Another way to remove things that don't want to be removed is to simply add a mask, for example magenta, over the object you want to remove, and use this video as a guide. When writing the prompt, you write something like: "Remove object masked with the pink color." Sometimes this is much more precise than waiting for it to recognize what actually needs to be removed, because in this case the biggest indicator is the magenta-colored object.

v1.0

These are experimental versions; this is not v2, a continuation of v1. These two new versions are experiments I've been doing over the last month. The models are far from perfect, so please keep that in mind. In terms of motion transfer, it doesn't work well with fast-paced videos or scene cuts, so it's considered an experiment.Read the README on HuggingFace <a target="_blank" rel="ugc" href="https://huggingface.co/Alissonerdx/EditAnything">Alissonerdx/EditAnything · Hugging Face</a>

v0.1 - Edit + Motion T.

These are experimental versions; this is not v2, a continuation of v1. These two new versions are experiments I've been doing over the last month. The models are far from perfect, so please keep that in mind. Obs.: It works better with objects than with people.Read the README on HuggingFace <a target="_blank" rel="ugc" href="https://huggingface.co/Alissonerdx/EditAnything">Alissonerdx/EditAnything · Hugging Face</a>

v0.1 - RV2V

This is not a direct continuation of v1.0. It's a fresh training run with a different recipe, done in two stages:- Stage 1 — pretraining on image edit pairs. Training a video model on stills is admittedly not ideal, but it was a way to expand the editing vocabulary well beyond what a small video-only dataset could teach (broader Add / Remove / Replace / Change coverage than v1.0 had).- Stage 2 — video fine-tune with first-frame conditioning enabled. This brought the temporal prior back.In theory v0.1 can do everything v1.0 did. In practice, temporal consistency may be weaker on some edits because most of stage 1 happened on still images. Whether it actually beats v1.0 on a given task is something we genuinely don't know yet.This is still experimental

v0.1 - 2 Stages

EditAnything

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