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Update app.py

feat(app): add auto-canvas selection and update conditioner space

feat: add Turbo LoRA presets and ComfyUI support

feat(lora): add ComfyUI MiniMax-H3 LoRA conversion

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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 probe

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

Mirror the conditioner's canonical table, grouped by ratio

Mirror the conditioner's 16-canvas table (fast 4:3, 3:4 and 21:9 added)

Point at the public multimodalart/qwen3vl-conditioner, called without a token

Fast canvas stays the default; the examples carry full-resolution canvases

Full 16:9 as the default canvas; match the conditioner's renamed fast label

Dynamic GPU duration from the packed sequence, cached examples, torchaudio note

torchaudio for off-rate reference soundtracks; title, fillable width, slot height

Images/audio/video tab order, expandable image slots, native attention for the VAEs

Mirror the generator's title, theme and advanced-options layout

One tab per reference modality, in reading order

MiniMax-H3 ref2va, the denoising half of the split deployment