Update app.py

app.py

@@ -762,72 +762,6 @@ with gr.Blocks(title="PlagueKind · MiniMax-H3") as demo:
         lora_b_strength,
     ]
 
-    # Two blocks rather than one with empty keyframe cells: the positional order below matches `generate`'s
-    # signature, so each block fills a prefix of it and the defaults cover the rest.
-    gr.Examples(
-        label="Text to video",
-        examples=[
-            ["A red fox trotting through a snowy pine forest at dawn, snow crunching underfoot", DEFAULT_CANVAS],
-            ["A busy night market, neon signs reflecting in puddles, sizzling street food", "544x960 · 9:16 fast"],
-            ["A cellist playing a slow melody in an empty concert hall", "544x544 · 1:1 fast"],
-        ],
-        inputs=[prompt, canvas],
-        outputs=[video, report],
-        fn=generate,
-        cache_examples=True,
-        cache_mode="lazy",
-    )
-    gr.Examples(
-        label="First and last frame",
-        examples=[
-            [
-                "A slow seamless camera move from the first view to the last, wind in the pines",
-                "1152x640 · 16:9",
-                "examples/first.png",
-                "examples/last.png",
-            ],
-            [
-                "The fox looks around, then trots deeper into the forest",
-                "1152x640 · 16:9",
-                "examples/first.png",
-                "examples/first.png",
-            ],
-        ],
-        inputs=[prompt, canvas, first_frame, last_frame],
-        outputs=[video, report],
-        fn=generate,
-        cache_examples=True,
-        cache_mode="lazy",
-    )
-
-    with gr.Accordion("What this workflow changes, and what it cannot", open=False):
-        gr.Markdown(
-            """
-`Plaguekind/Minimax-H3` ships no weights — it is a ComfyUI graph over `Comfy-Org/MiniMax-H3`. Its nodes map onto
-this Space as:
-| ComfyUI node | widget | here |
-|---|---|---|
-| `KSamplerSelect` | `euler` | MiniMax-H3's only sampler; the checkpoint is CFG-distilled, so one forward per step and no negative prompt |
-| `BasicScheduler` | `linear_quadratic`, 15 steps | **Sigma schedule** / **Steps** |
-| `MiniMaxH3ImageToVideo` | prompt, first/last frame | **Prompt** / **First frame** / **Last frame** |
-| `UnifiedResizeImageMask` ("Target Dimension") | 1344x768 | **Target dimension** |
-| `ImageSharpenKJ` | `rcas`, 0.3 | **RCAS sharpening** |
-| `FrameInterpolate` + `film_net_fp16` | multiplier 2 | **FILM frame interpolation** |
-| `CreateVideo` | fps `24 * 2` | 48 fps output |
-| `RTXVideoSuperResolution` | 2x `ULTRA` | **not reproduced** |
-| `PathchSageAttentionKJ` | `sageattn_qk_int8_pv_fp8_cuda++` | cuDNN fused attention |
-Two deliberate deviations. `RTXVideoSuperResolution` is NVIDIA's NGX super-resolution, shipped as a driver-level
-Windows/RTX component with no Linux Python path, so the 2x upscale is missing — pick a larger **Target dimension**
-instead of upscaling a small one. And SageAttention's `qk_int8_pv_fp8_cuda++` kernel is not built for this pool's
-sm120 cards, so attention runs cuDNN's fused kernel, which is the fastest available here and is numerically the
-faithful one (SageAttention is a quantized approximation).
-One upgrade: the workflow loads `minimax_h3_fl2va_pruned_int8_convrot.safetensors` and a
-`qwen3vl_32b_..._int8_convrot` text encoder because that is what fits a consumer card. This Space runs both
-**unquantized bfloat16**, off `MiniMaxAI/MiniMax-H3`, with the 62 GiB text encoder in a
-[second Space](https://huggingface.co/spaces/multimodalart/qwen3vl-conditioner).
-"""
-        )
-
     run.click(generate, controls, [video, report], api_name="generate")