use gradio workflow (#6)

[Admin] Add NCII prompt guard for image-conditioned generations (#4)

[Admin] Modify name for distinction with previous demos (#1)

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

Mobile-friendly layout, all features kept

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)

Fix LoRA toggle (zip misuse made converted a tuple), add a queue/ETA progress bar

Drop __pycache__ from tracking, add .gitignore

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

Back to @gradio/client: ZeroGPU requires its HF iframe auth headers

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

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

Resolve returned FileData without a url through /gradio_api/file= (Server mode can return only path)

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

Update README.md

Duration slider from 2s

Lower minimum duration to 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

Demo short description

Rename to MiniMax H3

Tighten description

Lazy cached examples, generate defaults

Model description and examples

Upload examples/last.png with huggingface_hub