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

README.md

@@ -38,7 +38,7 @@ exported at all.
 ## AoTI-compiled blocks
 
 With `H3_AOTI=1` the 50 repeated transformer blocks run from a compiled package,
-`diffusers-internal-dev/minimax-h3-aoti:bf16/torch2.11/sm120/dynamic` — a single dynamic-sequence artifact that serves
+[`multimodalart/minimax-h3-aoti`](https://huggingface.co/multimodalart/minimax-h3-aoti)`:bf16/torch2.11/sm120/dynamic` — a single dynamic-sequence artifact that serves
 every canvas, duration and prompt length. It carries no weights (it reads each block's live ones), so patching it in
 is startup CPU work and costs no GPU time.
 
@@ -160,8 +160,11 @@ one-time `PIPE.to("cuda")` is inside the first row's 339 s and does not reappear
 
 ## Secrets
 
-The weights and the conditioner Space are both public, so neither needs a token. `HF_TOKEN` is still read by
-`h3_aoti`, whose compiled-package repository is private — without it, set `H3_AOTI=0`.
+Nothing this Space loads is private any more: the weights are the public
+[`MiniMaxAI/MiniMax-H3`](https://huggingface.co/MiniMaxAI/MiniMax-H3) checkpoint, the compiled AoTI packages are the
+public [`multimodalart/minimax-h3-aoti`](https://huggingface.co/multimodalart/minimax-h3-aoti) model repo, and the
+conditioner is a public Space called without a token — so that round trip runs on the caller's own quota rather than
+this org's. No `HF_TOKEN` is required.
 
 ## Where diffusers comes from
 

app.py

@@ -117,8 +117,8 @@ def load_models() -> str | None:
         blocks = MiniMaxH3GeneratorBlocks()
         print(f"[gen] loading {[c.name for c in blocks.expected_components]} from {MODEL_REPO} ...", flush=True)
         pipe = blocks.init_pipeline(MODEL_REPO, components_manager=manager, collection="h3")
-        # `MiniMaxAI/MiniMax-H3` is public, so the weights are fetched without a token. `HF_TOKEN` is still read by
-        # `h3_aoti`, whose compiled-package repository is not.
+        # Every repository this Space reads is public — the checkpoint, the AoTI packages and the conditioner
+        # Space — so no token is passed anywhere.
         pipe.load_components(dtype=torch.bfloat16)
         pipe.transformer.set_attention_backend(ATTENTION)
 
@@ -312,7 +312,7 @@ def generate(prompt, image_path=None, last_image_path=None, canvas=DEFAULT_CANVA
         f"denoise + decode {generate_seconds:.0f}s ({generate_seconds / int(steps):.1f} s/step) · seed {int(seed)}"
     )
     print(f"[gen] {report}", flush=True)
-    return path, report, refined
+    return path, report, refined, gr.update(visible=bool(refined))
 
 
 
@@ -383,6 +383,7 @@ with gr.Blocks(title="MiniMax-H3") as demo:
                 lines=3,
                 value="A red fox trotting through a snowy pine forest at dawn, snow crunching underfoot",
             )
+            upsample = gr.Checkbox(label="Upsample prompt", value=False)
             with gr.Row():
                 image = gr.Image(label="First frame (optional)", type="filepath")
                 last_image = gr.Image(label="Last frame (optional)", type="filepath")
@@ -392,22 +393,14 @@ with gr.Blocks(title="MiniMax-H3") as demo:
                 duration = gr.Slider(label="Duration (s)", minimum=2, maximum=MAX_UI_DURATION, step=1, value=5)
                 steps = gr.Slider(label="Steps", minimum=10, maximum=40, step=1, value=28)
                 seed = gr.Number(label="Seed", value=42, precision=0)
-                upsample = gr.Checkbox(
-                    label="Upsample prompt",
-                    value=False,
-                    info="Rewrites the prompt into the model's trained format with the conditioner's Qwen3-VL before encoding.",
-                )
 
         with gr.Column():
             video = gr.Video(label="Video + soundtrack")
             report = gr.Markdown(visible=False)
-            with gr.Accordion("Upsampled prompt", open=False):
-                upsampled = gr.Textbox(
-                    show_label=False,
-                    lines=8,
-                    interactive=False,
-                    placeholder="Turn on “Upsample prompt” to see the rewrite that was encoded.",
-                )
+            # Only shown for a request that actually asked for a rewrite, so a plain request is not left with an
+            # empty panel. The accordion is an output for that reason: its visibility is part of the answer.
+            with gr.Accordion("Upsampled prompt", open=False, visible=False) as upsampled_panel:
+                upsampled = gr.Textbox(show_label=False, lines=8, interactive=False)
 
     image.upload(_fit_keyframe, [image, canvas], [image, canvas])
 
@@ -420,7 +413,7 @@ with gr.Blocks(title="MiniMax-H3") as demo:
             ["A slow seamless camera move from the first view to the last", "examples/first.png", "examples/last.png", "1344x768 · 16:9 full"],
         ],
         inputs=[prompt, image, last_image, canvas],
-        outputs=[video, report, upsampled],
+        outputs=[video, report, upsampled, upsampled_panel],
         fn=generate,
         cache_examples=True,
         cache_mode="lazy",
@@ -432,7 +425,7 @@ with gr.Blocks(title="MiniMax-H3") as demo:
     run.click(
         generate,
         [prompt, image, last_image, canvas, duration, steps, seed, upsample],
-        [video, report, upsampled],
+        [video, report, upsampled, upsampled_panel],
         api_name="generate",
     )
 

h3_aoti.py

@@ -2,7 +2,7 @@
 
 Shared byte-identically by every MiniMax-H3 Space. A Space only ever calls `maybe_load()`; the compile path runs from
 the debug Space's "Compile (AoTI)" tab, or off-Space from `job_bf16_aoti.py` on an `rtx-pro-6000` Job, and pushes its
-artifacts to `diffusers-internal-dev/minimax-h3-aoti` under `<width>/torch<X.Y>/sm<cc>/<shape>`.
+artifacts to `multimodalart/minimax-h3-aoti` under `<width>/torch<X.Y>/sm<cc>/<shape>`.
 
 What is measured, so nobody has to guess whether this is worth turning on. Unquantized bfloat16, 124 frames,
 everything resident, one dynamic-sequence package serving every row — on an RTX PRO 6000 Blackwell, torch 2.11,
@@ -62,8 +62,11 @@ import os
 from pathlib import Path
 
 AOTI = os.environ.get("H3_AOTI", "0") == "1"
-AOTI_REPO = os.environ.get("H3_AOTI_REPO", "diffusers-internal-dev/minimax-h3-aoti")
-AOTI_REPO_TYPE = os.environ.get("H3_AOTI_REPO_TYPE", "dataset")
+# A public **model** repo. It used to be a private dataset, which is why the repo type is still a variable: the
+# artifacts are keyed by quant/torch/arch under `<width>/torch<X.Y>/sm<cc>/<shape>` rather than laid out the way
+# `spaces.aoti_load` expects, so the download is done by hand either way (see `maybe_load`).
+AOTI_REPO = os.environ.get("H3_AOTI_REPO", "multimodalart/minimax-h3-aoti")
+AOTI_REPO_TYPE = os.environ.get("H3_AOTI_REPO_TYPE", "model")
 # `dynamic` is the one package that serves every canvas, duration *and prompt*, and for bfloat16 it is what gets built:
 # a dynamic sequence dimension exports and compiles cleanly (measured on an rtx-pro-6000 Job, torch 2.11). It has to be
 # dynamic to be useful at all — `build_packed_sequence` pads nothing, so
@@ -211,8 +214,8 @@ def maybe_load(transformer) -> None:
     """Patch the block stack with its compiled package. Once, and safe to call at **startup**.
 
     Nothing here touches a GPU: the download is CPU work and the `.pt2` archive is not opened until the first forward,
-    which happens inside the `@spaces.GPU` call. Proven on the pool — `diffusers-internal-dev/minimax-h3-generator-aoti`
-    loads `bf16/torch2.11/sm120/dynamic` at startup, patches all 50 blocks, and generates.
+    which happens inside the `@spaces.GPU` call. Proven on the pool: `bf16/torch2.11/sm120/dynamic` loads at startup,
+    patches all 50 blocks, and generates. The repo is public, so no token is passed for it.
     """
     if not AOTI or id(transformer) in _LOADED:
         return
@@ -226,7 +229,6 @@ def maybe_load(transformer) -> None:
         repo_id=AOTI_REPO,
         repo_type=AOTI_REPO_TYPE,
         allow_patterns=f"{key}/package/*",
-        token=os.environ.get("HF_TOKEN"),
     )
     package_dir = Path(local) / key / "package"
     if not package_dir.is_dir():
@@ -420,7 +422,7 @@ def upload(package_dir: str | os.PathLike[str], key: str) -> str:
     if not token:
         raise RuntimeError("`HF_TOKEN` is needed to push the AoTI package.")
     api = HfApi(token=token)
-    api.create_repo(repo_id=AOTI_REPO, repo_type=AOTI_REPO_TYPE, private=True, exist_ok=True)
+    api.create_repo(repo_id=AOTI_REPO, repo_type=AOTI_REPO_TYPE, private=False, exist_ok=True)
     api.upload_folder(
         folder_path=str(package_dir),
         path_in_repo=f"{key}/package",