Add the lightx2v Minimax-h3-Turbo LoRA as a per-request alternative

Default steps 4 -> 6

Switch to the recommended ckpt850 EMA LoRA; default steps back to 4

Default steps 6 -> 8 (better quality at the ckpt500 weights)

Default to the ckpt500 turbo weights, add a per-request LoRA toggle

Pass the caller's x-ip-token to the conditioner explicitly

Call the v2 named-endpoint API directly, unwrap the single-Api-output tuple

Rename /config to /studio-config: Gradio's own client-discovery route lives at /config, and shadowing it broke @gradio/client ('Could not get API info')

Rebuild the UI as a custom studio frontend on gradio.Server

Fold the 4-step Turbo LoRA into the transformer, default to 4 steps

Open badge links in a new tab

Price bookings against the AoTI blocks, generate from 2 s again, cross-link the demos

Start the duration slider at the 5 s the model actually generates

Restore encode_remote

Cache the conditioner client with functools.cache, tidy comments

Let gradio_client forward the caller's ZeroGPU token itself instead of pinning one by hand

Forward the caller's ZeroGPU token to the conditioner, plainly

Bill the conditioner call to the requesting user's own ZeroGPU token, never an org token

Pay for the conditioner call with the first ZeroGPU identity that can: the caller's token, this Space's HF_TOKEN, then none

Fall back to an anonymous conditioner call when the forwarded ZeroGPU token is refused

Forward the caller's ZeroGPU token to the conditioner so one request bills as one request

Load the AoTI packages from the public multimodalart/minimax-h3-aoti, move the upsample toggle under the prompt

Migrate to canonical diffusers PR 14371 + public MiniMaxAI/MiniMax-H3 weights, add prompt upsampling toggle

Duration slider from 2s

Repair and reorder the canvas table

Fast canvases for 4:3, 3:4 and 21:9

Use the public conditioner without a token

Fast default for generation, full quality examples

Default to full resolution, full quality examples

Idiomatic ZeroGPU startup packing for the transformer

Right size placement allowance

Dynamic GPU duration fitted from probes

Update app.py

Update app.py

Tighten description

Lazy cached examples, generate defaults

Model description and examples

Fix keyframe upload loop: use upload event, no op when no crop needed

Update app.py

Update app.py

Square fast canvas, keyframe auto fit, badge links

Clean demo UI, 28 step default, track_tqdm progress

Wire the AoTI load path (env-gated) and the sub-768p canvases

Update app.py

Sync the split MiniMax-H3 Spaces

Sync the split MiniMax-H3 Spaces

Sync the split MiniMax-H3 Spaces

Sync the split MiniMax-H3 Spaces