Add Ref2VA storyboard studio and live TAE previews

app.py

@@ -4,10 +4,12 @@ from __future__ import annotations
 
 import hashlib
 import ipaddress
+import mimetypes
 import os
 import re
 import shutil
 import socket
+import subprocess
 import tempfile
 import time
 import traceback
@@ -44,6 +46,8 @@ TURBO_4_LORA_FILE = "minimax_h3_fl2v_turbo_4step_v0.1.safetensors"
 TURBO_4_LORA_SCALE = 8 / 128
 TURBO_8_LORA_REPO = "larryvrh/MiniMax-H3-Turbo-Lora"
 TURBO_8_LORA_FILE = "minimax_h3_turbo_4step_ema_ckpt850.safetensors"
+REF2VA_REPO = os.environ.get("H3_REF2VA_REPO", "lilcheaty/MiniMax-H3-NVFP4")
+REF2VA_FILE = os.environ.get("H3_REF2VA_FILE", "minimax_h3_ref2va_pruned_nvfp4.safetensors")
 LORA_MAX_BYTES = 2 * 1024**3
 EGRID_COMMIT = "a7624b4c00626a8ae7e78860769389d706565190"
 EGRID_SHA256 = "30eb3c2cc7fb6b470d9717ff840d359313ac27cd64b705e32da1baa10f72d6a8"
@@ -140,8 +144,10 @@ def lower_duration_floor(seconds: float = MIN_UI_DURATION) -> None:
 
 
 PIPE = None
+REF_PIPE = None
 MANAGER = None
 COND_PIPE = None
+REF_COND_PIPE = None
 COND_ERROR: str | None = None
 LOAD_ERROR: str | None = None
 LOADED_IN: float | None = None
@@ -177,7 +183,8 @@ def status() -> str:
         conditioner_status = f"remote `{CONDITIONER_SPACE}`" + (" (local fallback)" if COND_ERROR else "")
     return (
         f"Ready · **{engine_status}** · VAEs full precision · placement `{PLACEMENT}` · attention `{ATTENTION}` · "
-        f"loaded in {LOADED_IN:.0f}s · conditioner {conditioner_status}"
+        f"loaded in {LOADED_IN:.0f}s · conditioner {conditioner_status} · "
+        f"Ref2VA {'ready' if REF_PIPE is not None and REF_COND_PIPE is not None else 'unavailable'} · TAE live previews"
     )
 
 
@@ -189,7 +196,7 @@ def load_models() -> str | None:
     Both autoencoders carry `_keep_in_fp32_modules` over every module and stay float32: a bfloat16 audio VAE decodes
     the soundtrack roughly 20 dB too quiet.
     """
-    global PIPE, MANAGER, COND_PIPE, COND_ERROR, LOAD_ERROR, LOADED_IN
+    global PIPE, REF_PIPE, MANAGER, COND_PIPE, REF_COND_PIPE, COND_ERROR, LOAD_ERROR, LOADED_IN
 
     if PIPE is not None or LOAD_ERROR is not None:
         return LOAD_ERROR
@@ -199,7 +206,7 @@ def load_models() -> str | None:
         import torch
         from diffusers import ComponentsManager
 
-        from h3_split_blocks import MiniMaxH3GeneratorBlocks
+        from h3_split_blocks import MiniMaxH3GeneratorBlocks, MiniMaxH3Ref2VAGeneratorBlocks
 
         lower_duration_floor()
         manager = ComponentsManager()
@@ -209,6 +216,7 @@ def load_models() -> str | None:
         # This is consumed by the endpoint's Gradio Progress tracker and forwarded across the ZeroGPU worker RPC,
         # producing one real queue update per denoising step.
         pipe.set_progress_bar_config(desc="Denoising")
+        ref_pipe = None
         if ENGINE == "nvfp4":
             # Do not download the 61.7 GiB BF16 transformer. The schedulers and full-precision VAEs stay canonical;
             # only the repeatedly executed DiT is replaced with the pruned Blackwell-native checkpoint.
@@ -219,6 +227,19 @@ def load_models() -> str | None:
             from h3_nvfp4 import load_transformer
 
             pipe.update_components(transformer=load_transformer())
+            ref_blocks = MiniMaxH3Ref2VAGeneratorBlocks()
+            ref_pipe = ref_blocks.init_pipeline(MODEL_REPO, components_manager=manager, collection="h3-ref")
+            shared = {
+                component.name: getattr(pipe, component.name)
+                for component in ref_blocks.expected_components
+                if component.name != "transformer_ref" and getattr(pipe, component.name, None) is not None
+            }
+            ref_pipe.update_components(
+                **shared,
+                transformer_ref=load_transformer(REF2VA_REPO, REF2VA_FILE),
+            )
+            ref_pipe.transformer_ref.set_attention_backend(ATTENTION)
+            ref_pipe.set_progress_bar_config(desc="Denoising")
         elif ENGINE == "bf16":
             pipe.load_components(dtype=torch.bfloat16)
         else:
@@ -243,10 +264,11 @@ def load_models() -> str | None:
             _arm_decode_hooks(pipe)
 
         cond_pipe = None
+        ref_cond_pipe = None
         if CONDITIONER_MODE == "local":
             try:
                 from h3_local_conditioner import load_local_conditioner
-                from h3_split_blocks import MiniMaxH3ConditionerBlocks
+                from h3_split_blocks import MiniMaxH3ConditionerBlocks, MiniMaxH3Ref2VAConditionerBlocks
 
                 print("[cond] loading the local truncated NVFP4-AWQ conditioner ...", flush=True)
                 text_encoder, tokenizer, processor = load_local_conditioner()
@@ -256,6 +278,12 @@ def load_models() -> str | None:
                     tokenizer=tokenizer,
                     processor=processor,
                 )
+                ref_cond_pipe = MiniMaxH3Ref2VAConditionerBlocks().init_pipeline(MODEL_REPO)
+                ref_cond_pipe.update_components(
+                    text_encoder=text_encoder,
+                    tokenizer=tokenizer,
+                    processor=processor,
+                )
             except Exception as error:
                 traceback.print_exc()
                 COND_ERROR = f"{type(error).__name__}: {error}"
@@ -263,7 +291,10 @@ def load_models() -> str | None:
         elif CONDITIONER_MODE != "remote":
             raise ValueError(f"H3_CONDITIONER_MODE must be `local` or `remote`, got {CONDITIONER_MODE!r}")
 
-        PIPE, MANAGER, COND_PIPE = pipe, manager, cond_pipe
+        from h3_tae import load_preview_model
+
+        load_preview_model(OUTPUT_DIR)
+        PIPE, REF_PIPE, MANAGER, COND_PIPE, REF_COND_PIPE = pipe, ref_pipe, manager, cond_pipe, ref_cond_pipe
         LOADED_IN = time.time() - started
         print(f"[gen] ready in {LOADED_IN:.0f}s", flush=True)
     except Exception as error:
@@ -522,6 +553,15 @@ def get_duration(prompt, prompt_embeds, text_token_tags, image, last_image, heig
     latent_frames = (num_frames - LATENTS_PER_CHUNK) // FRAMES_PER_CHUNK * LATENTS_PER_CHUNK + 2
     patches = (height // 32) * (width // 32)
     rows = latent_frames * patches + (int(image is not None) + int(last_image is not None)) * patches
+    reference_specs = a[-1] if a and isinstance(a[-1], (list, tuple)) else []
+    if reference_specs:
+        # Ref2VA packs every reference beside the generated rows. Images are encoded at a 2048px short edge; videos
+        # occupy approximately one target-video block; audio adds two 40 Hz streams. This is deliberately conservative
+        # because under-booking a ZeroGPU request kills it after all earlier work has already been paid for.
+        kinds = [kind for _, kind in reference_specs]
+        rows += kinds.count("image") * 4096
+        rows += kinds.count("video") * latent_frames * patches
+        rows += kinds.count("audio") * int(num_frames / FPS * 80)
     denoise = steps * (_DUR_B * rows + _DUR_C * rows**2)
     decode = _DECODE_BASE + _DECODE_PER_DEFAULT_CANVAS * (height * width * num_frames) / _DEFAULT_CANVAS_PIXELS
     local_conditioning = 20 if prompt_embeds is None else 0
@@ -531,7 +571,7 @@ def get_duration(prompt, prompt_embeds, text_token_tags, image, last_image, heig
 @spaces.GPU(duration=get_duration, size=GPU_SIZE)
 def _generate(
     prompt, prompt_embeds, text_token_tags, image, last_image, height, width, num_frames, steps, seed, acceleration,
-    lora_path, egrid_path, lora_strength, conditioning_cache_key,
+    lora_path, egrid_path, lora_strength, conditioning_cache_key, reference_specs,
 ):
     """The only thing on GPU time: local conditioning, packed denoising and the two decoders.
 
@@ -540,19 +580,46 @@ def _generate(
     """
     import torch
 
-    if COND_PIPE is not None and prompt_embeds is None:
-        COND_PIPE.text_encoder.to("cuda")
-    if PLACEMENT == "lazy":
-        PIPE.to("cuda")
-    elif PLACEMENT == "pack":
-        PIPE.vae.to("cuda")
-        PIPE.audio_vae.to("cuda")
+    use_ref2va = bool(reference_specs)
+    active_pipe = REF_PIPE if use_ref2va else PIPE
+    active_conditioner = REF_COND_PIPE if use_ref2va else COND_PIPE
+    if active_pipe is None:
+        raise RuntimeError("The Ref2VA engine is unavailable on this deployment.")
+    references = None
+    if use_ref2va:
+        from diffusers.modular_pipelines.minimax_h3 import (
+            MiniMaxH3AudioReference,
+            MiniMaxH3ImageReference,
+            MiniMaxH3VideoReference,
+        )
+
+        classes = {
+            "image": MiniMaxH3ImageReference,
+            "video": MiniMaxH3VideoReference,
+            "audio": MiniMaxH3AudioReference,
+        }
+        references = [classes[kind].from_file(path) for path, kind in reference_specs]
+
+    if active_conditioner is not None and prompt_embeds is None:
+        active_conditioner.text_encoder.to("cuda")
+    if PLACEMENT in ("lazy", "pack"):
+        # Only one 12.5 GB transformer needs to be active. Shared VAEs stay on the card across workflow switches.
+        inactive = PIPE if use_ref2va else REF_PIPE
+        inactive_transformer = (
+            getattr(inactive, "transformer", None) or getattr(inactive, "transformer_ref", None)
+            if inactive is not None
+            else None
+        )
+        inactive_device = getattr(inactive_transformer, "device", None)
+        if inactive_transformer is not None and inactive_device is not None and torch.device(inactive_device).type == "cuda":
+            inactive_transformer.to("cpu")
+        active_pipe.to("cuda")
 
     condition_seconds = None
     num_text_tokens = None
     condition_cache_hit = False
     if prompt_embeds is None:
-        if COND_PIPE is None:
+        if active_conditioner is None:
             raise RuntimeError(f"The local conditioner is unavailable: {COND_ERROR or 'disabled'}")
         cached = CONDITION_CACHE.pop(conditioning_cache_key, None) if conditioning_cache_key else None
         if cached is not None:
@@ -564,12 +631,22 @@ def _generate(
             print(f"[cond] reused {num_text_tokens}-token embedding", flush=True)
         else:
             conditioned = time.time()
-            condition_state = COND_PIPE(
-                prompt=prompt,
-                image=image,
-                last_image=last_image,
-                height=int(height),
-                width=int(width),
+            condition_state = (
+                active_conditioner(
+                    prompt=prompt,
+                    references=references,
+                    height=int(height),
+                    width=int(width),
+                    num_frames=int(num_frames),
+                )
+                if use_ref2va
+                else active_conditioner(
+                    prompt=prompt,
+                    image=image,
+                    last_image=last_image,
+                    height=int(height),
+                    width=int(width),
+                )
             )
             prompt_embeds = condition_state.get("prompt_embeds")
             text_token_tags = condition_state.get("text_token_tags")
@@ -582,31 +659,35 @@ def _generate(
                 while len(CONDITION_CACHE) > CONDITION_CACHE_SIZE:
                     CONDITION_CACHE.popitem(last=False)
 
-    activate_lora = getattr(PIPE.transformer, "activate_lora", None)
-    clear_lora = getattr(PIPE.transformer, "clear_lora", None)
+    active_transformer = active_pipe.transformer_ref if use_ref2va else active_pipe.transformer
+    activate_lora = getattr(active_transformer, "activate_lora", None)
+    clear_lora = getattr(active_transformer, "clear_lora", None)
     if lora_path and activate_lora is None:
         raise RuntimeError("LoRAs are supported only by the NVFP4 engine.")
     if activate_lora is not None:
         activate_lora(lora_path, egrid_path, float(lora_strength))
-    begin_request = getattr(PIPE.transformer, "begin_request", None)
-    end_request = getattr(PIPE.transformer, "end_request", None)
+    begin_request = getattr(active_transformer, "begin_request", None)
+    end_request = getattr(active_transformer, "end_request", None)
     if begin_request is not None:
         # MiniMaxH3Scheduler includes terminal sigma=0 in `num_inference_steps`, so N points execute N-1 forwards.
         begin_request(max(1, int(steps) - 1), acceleration)
     cache_stats = None
     try:
         with torch.inference_mode():
-            state = PIPE(
+            common = dict(
                 prompt_embeds=prompt_embeds.to("cuda", non_blocking=True),
                 text_token_tags=text_token_tags,
-                image=image,
-                last_image=last_image,
                 height=height,
                 width=width,
                 num_frames=num_frames,
                 num_inference_steps=int(steps),
                 generator=torch.Generator("cpu").manual_seed(int(seed)),
             )
+            state = (
+                active_pipe(references=references, **common)
+                if use_ref2va
+                else active_pipe(image=image, last_image=last_image, **common)
+            )
     finally:
         if end_request is not None:
             cache_stats = end_request()
@@ -641,7 +722,8 @@ def _generate_with_hardware_retry(*args):
 def generate(
     prompt, image_path=None, last_image_path=None, canvas=DEFAULT_CANVAS, duration=5, steps=28, seed=42,
     upsample=False, acceleration="Balanced", lora_preset="None", lora_repo="", lora_filename="",
-    lora_strength=1.0, generation_preset=CUSTOM_PRESET, ip_token=None, progress=gr.Progress(track_tqdm=True),
+    lora_strength=1.0, generation_preset=CUSTOM_PRESET, references=None, ip_token=None,
+    progress=gr.Progress(track_tqdm=True),
 ):
     """One request. The appended UI preset leaves older positional API parameters intact."""
     if LOAD_ERROR:
@@ -667,14 +749,58 @@ def generate(
             return value.get("path")
         return getattr(value, "path", value)
 
+    def reference_kind(value, path):
+        mime = ""
+        if isinstance(value, dict):
+            mime = value.get("mime_type") or ""
+        else:
+            mime = getattr(value, "mime_type", "") or ""
+        mime = mime or mimetypes.guess_type(str(path))[0] or ""
+        if mime.startswith("image/"):
+            return "image"
+        if mime.startswith("video/"):
+            return "video"
+        if mime.startswith("audio/"):
+            return "audio"
+        raise ValueError(f"Unsupported reference file {os.path.basename(str(path))!r}; use an image, video or audio file.")
+
     image_path, last_image_path = input_path(image_path), input_path(last_image_path)
+    reference_specs = []
+    for reference in references or []:
+        path = input_path(reference)
+        if path:
+            reference_specs.append((path, reference_kind(reference, path)))
+    use_ref2va = bool(reference_specs)
+    if use_ref2va:
+        if image_path or last_image_path:
+            raise ValueError("Choose keyframes or omni references for one shot, not both.")
+        if len(reference_specs) > 12:
+            raise ValueError("MiniMax-H3 accepts at most 12 ordered references.")
+        counts = {kind: sum(spec_kind == kind for _, spec_kind in reference_specs) for kind in ("image", "video", "audio")}
+        for kind, limit in (("image", 9), ("video", 3), ("audio", 3)):
+            if counts[kind] > limit:
+                raise ValueError(f"MiniMax-H3 accepts at most {limit} {kind} references.")
+        if counts["audio"] and not (counts["image"] or counts["video"]):
+            raise ValueError("Audio references need at least one image or video reference.")
+        if float(duration) < 5:
+            raise ValueError("Ref2VA clips must be at least 5 seconds long.")
+        if upsample:
+            raise ValueError("Prompt enhancement is not yet available for Ref2VA; turn Enhance off.")
+        if REF_PIPE is None or REF_COND_PIPE is None:
+            raise RuntimeError("The local Ref2VA engine or conditioner is unavailable.")
+        # FL2VA Turbo adapters do not target the reference checkpoint. Ref2VA retains the quality schedule and the
+        # conservative cache engine until a validated Ref2VA distillation is available.
+        steps, displayed_steps = 28, 28
+        acceleration = "Balanced"
+        lora_preset = "None"
+        generation_preset = "Ref2VA 28-step — ordered references"
     if image_path:
         image_path, canvas = _fit_keyframe(image_path, canvas)
     if last_image_path:
         last_image_path, canvas = _fit_keyframe(last_image_path, canvas)
 
     requested_steps = int(steps)
-    displayed_steps = requested_steps
+    displayed_steps = locals().get("displayed_steps", requested_steps)
     if lora_preset == "Turbo · 4 steps":
         # The native scheduler includes its terminal zero in this count; 5 points produce 4 exact Euler evaluations.
         steps, displayed_steps = 5, 4
@@ -712,7 +838,7 @@ def generate(
 
     # Prompt rewriting needs the discarded LM head and decoder tail, so it intentionally retains the remote path.
     # Normal generation—the default—keeps embeddings on this worker and never serializes them through another API.
-    if upsample or COND_PIPE is None:
+    if upsample or (COND_PIPE is None and not use_ref2va):
         progress(
             0.0,
             desc=f"Upsampling and conditioning on {CONDITIONER_SPACE} ..."
@@ -732,7 +858,7 @@ def generate(
 
     progress(
         0.1,
-        desc=("Local conditioning + " if prompt_embeds is None else "")
+        desc=("Local Ref2VA conditioning + " if use_ref2va else "Local conditioning + " if prompt_embeds is None else "")
         + f"denoising {displayed_steps} steps at {width}x{height}, {num_frames} frames ...",
     )
     started = time.time()
@@ -750,6 +876,7 @@ def generate(
                     str(height),
                     _sha256(image_path) if image_path else "",
                     _sha256(last_image_path) if last_image_path else "",
+                    *(_sha256(path) for path, _ in reference_specs),
                 ]
             ).encode()
         ).hexdigest()
@@ -769,6 +896,7 @@ def generate(
             egrid_path,
             float(lora_strength) * adapter_scale,
             conditioning_key,
+            reference_specs,
         )
     finally:
         LocalContext.progress.reset(progress_token)
@@ -892,6 +1020,7 @@ def generate_api(
     lora_filename: str = "",
     lora_strength: float = 1.0,
     generation_preset: str = DEFAULT_PRESET,
+    references: list[FileData] | None = None,
     request: Request = None,
 ) -> tuple[FileData, str, str]:
     """Queued generation endpoint used by both the React studio and ordinary gradio_client callers."""
@@ -911,10 +1040,54 @@ def generate_api(
         lora_filename,
         lora_strength,
         generation_preset,
+        references,
         ip_token=ip_token,
     )
 
 
+@app.api(name="stitch")
+def stitch_api(clips: list[FileData]) -> FileData:
+    """Join completed storyboard shots without another GPU allocation.
+
+    Every H3 clip already has the same frame and audio rates. Re-encoding keeps the endpoint resilient to different
+    canvas/pixel formats and trims one duplicated continuation frame from every shot after the first.
+    """
+    paths = [input.path if hasattr(input, "path") else input.get("path") for input in clips or []]
+    paths = [str(path) for path in paths if path]
+    if not 2 <= len(paths) <= 12:
+        raise ValueError("A storyboard must contain between 2 and 12 completed shots.")
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+    output = os.path.join(OUTPUT_DIR, f"h3-story-{int(time.time() * 1000)}.mp4")
+    command = ["ffmpeg", "-y", "-loglevel", "error"]
+    for path in paths:
+        command.extend(["-i", path])
+    chains = []
+    concat_inputs = []
+    seam = 1 / FPS
+    for index in range(len(paths)):
+        trim = "" if index == 0 else f"trim=start={seam},"
+        atrim = "" if index == 0 else f"atrim=start={seam},"
+        chains.append(
+            f"[{index}:v]{trim}setpts=PTS-STARTPTS,scale=trunc(iw/2)*2:trunc(ih/2)*2,"
+            f"fps={FPS},format=yuv420p[v{index}]"
+        )
+        chains.append(f"[{index}:a]{atrim}asetpts=PTS-STARTPTS,aresample=48000[a{index}]")
+        concat_inputs.append(f"[v{index}][a{index}]")
+    chains.append(f"{''.join(concat_inputs)}concat=n={len(paths)}:v=1:a=1[v][a]")
+    command.extend(
+        [
+            "-filter_complex", ";".join(chains), "-map", "[v]", "-map", "[a]",
+            "-c:v", "libx264", "-preset", "veryfast", "-crf", "18", "-c:a", "aac", "-b:a", "192k",
+            "-movflags", "+faststart", output,
+        ]
+    )
+    try:
+        subprocess.run(command, check=True, capture_output=True, text=True, timeout=300)
+    except subprocess.CalledProcessError as error:
+        raise RuntimeError(f"Could not assemble the storyboard: {error.stderr[-800:]}") from error
+    return FileData(path=output)
+
+
 @app.get("/status")
 def studio_status():
     return {"ready": PIPE is not None and LOAD_ERROR is None, "status": status()}
@@ -945,6 +1118,15 @@ def studio_config():
         "default_preset": DEFAULT_PRESET,
         "custom_preset": CUSTOM_PRESET,
         "examples": EXAMPLES,
+        "ref2va": {
+            "enabled": REF_PIPE is not None and REF_COND_PIPE is not None,
+            "max_total": 12,
+            "max_images": 9,
+            "max_videos": 3,
+            "max_audio": 3,
+            "minimum_duration": 5,
+        },
+        "tae_previews": True,
     }
 
 

frontend/dist/assets/__vite-browser-external-Cgmn0awE-17s0Hkgg.js

@@ -0,0 +1 @@
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frontend/dist/assets/__vite-browser-external-Cgmn0awE-BTUFketj.js

@@ -1 +0,0 @@
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frontend/dist/assets/{browser-GbxqfQNb.js → browser-C_c7vNYP.js}

@@ -1,4 +1,4 @@
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t.page||(e=""),{...t,current_page:e,layout:t.page[e].layout,components:t.components.filter(s=>t.page[e].components.includes(s.id)),dependencies:this.config.dependencies.filter(s=>t.page[e].dependencies.includes(s.id))}}fetch(e,t){let s=new Headers(t?.headers||{});return this&&this.cookies&&s.append("Cookie",this.cookies),this&&this.options.headers&&new Headers(this.options.headers).forEach((n,i)=>{s.append(i,n)}),fetch(e,{...t,headers:s})}stream(e){let t=new Headers;return this&&this.cookies&&t.append("Cookie",this.cookies),this&&this.options.headers&&new Headers(this.options.headers).forEach((s,n)=>{t.append(n,s)}),this&&this.options.token&&t.append("Authorization",`Bearer ${this.options.token}`),this.abort_controller=new AbortController,this.stream_instance=rs(e.toString(),{credentials:this.options.credentials??"same-origin",headers:t,signal:this.abort_controller.signal}),this.stream_instance}view_api;upload_files;upload;handle_blob;post_data;submit;predict;open_stream;resolve_config;resolve_cookies;constructor(e,t={events:["data"]}){this.app_reference=e,this.deep_link=t.query_params?.deep_link||null,t.events||=["data"],ze(t),this.options=t,this.current_payload={},t.cookies&&(this.cookies=t.cookies),this.view_api=At.bind(this),this.upload_files=Ct.bind(this),this.handle_blob=Lt.bind(this),this.post_data=Ft.bind(this),this.submit=as.bind(this),this.predict=Rt.bind(this),this.open_stream=ts.bind(this),this.resolve_config=xt.bind(this),this.resolve_cookies=St.bind(this),this.upload=Nt.bind(this),this.fetch=this.fetch.bind(this),this.handle_space_success=this.handle_space_success.bind(this),this.stream=this.stream.bind(this)}async init(){Yt(),this.options.auth&&await this.resolve_cookies(),await this._resolve_config().then(e=>e?.config&&this._resolve_heartbeat(e.config));try{this.api_info=await this.view_api()}catch(e){console.error(e.message)}this.api_map=ye(this.config?.dependencies||[])}async _resolve_heartbeat(e){if(e&&(this.config=e,this.api_prefix=e.api_prefix||"",this.config&&this.config.connect_heartbeat&&this.config.space_id&&this.options.token&&(this.jwt=await we(this.config.space_id,this.options.token,this.cookies))),e.space_id&&this.options.token&&(this.jwt=await we(e.space_id,this.options.token)),this.config&&this.config.connect_heartbeat){let t=new URL(`${this.config.root}${this.api_prefix}/${pt}/${this.session_hash}`);this.jwt&&t.searchParams.set("__sign",this.jwt),this.heartbeat_event||=this.stream(t)}}static async connect(e,t={events:["data"]}){let s=new this(e,t);return t.session_hash&&(s.session_hash=t.session_hash),await s.init(),s}async reconnect(){let e=new URL(`${this.config.root}${this.api_prefix}/${ft}`),t;try{let s=await this.fetch(e);if(!s.ok)throw Error();t=(await s.json()).app_id}catch{return"broken"}return t===this.config.app_id?"connected":"changed"}close(){this.closed=!0,re(this.stream_status,this.abort_controller)}async refresh(){if(!this.config)throw Error(T);let e=await this.resolve_config(this.config.root,!1);if(!e)throw Error(T);this.config=e,this.api_prefix=e.api_prefix||"",this.api_map=ye(e.dependencies||[]);try{this.api_info=await this.view_api()}catch(t){console.error(me+t.message)}return this.get_url_config()}set_current_payload(e){this.current_payload=e}static async duplicate(e,t={events:["data"]}){return Jt(e,t)}async _resolve_config(){let{http_protocol:e,host:t,space_id:s}=await ne(this.app_reference,this.options.token),{status_callback:n}=this.options;s&&n&&await Fe(s,n);let i;try{let r=`${e}//${t}`;if(i=await this.resolve_config(r),!i)throw Error(T);return this.config_success(i)}catch(r){if(s&&n)M(s,se.test(s)?"space_name":"subdomain",this.handle_space_success);else throw n&&n({status:"error",message:"Could not load this space.",load_status:"error",detail:"NOT_FOUND"}),r instanceof Error?r:Error(String(r))}}async config_success(e){if(this.config=e,this.api_prefix=e.api_prefix||"",this.config.auth_required)return this.prepare_return_obj();try{this.api_info=await this.view_api()}catch(t){console.error(me+t.message)}return this.prepare_return_obj()}async handle_space_success(e){if(!this)throw Error(T);let{status_callback:t}=this.options;if(t&&t(e),e.status==="running")try{if(this.config=await this._resolve_config(),this.api_prefix=this?.config?.api_prefix||"",!this.config)throw Error(T);return await this.config_success(this.config)}catch(s){throw t&&t({status:"error",message:"Could not load this space.",load_status:"error",detail:"NOT_FOUND"}),s}}async component_server(e,t,s){if(!this.config)throw Error(T);let n={},{token:i}=this.options,{session_hash:r}=this;i&&(n.Authorization=`Bearer ${this.options.token}`);let a,o=this.config.components.find(c=>c.id===e);a=o?.props?.root_url?o.props.root_url:this.config.root;let l;if(typeof s=="object"&&s&&"binary"in s){let c=s;l=new FormData;for(let u in c.data)u!=="binary"&&l.append(u,c.data[u]);l.set("component_id",e.toString()),l.set("fn_name",t),l.set("session_hash",r)}else l=JSON.stringify({data:s,component_id:e,fn_name:t,session_hash:r}),n["Content-Type"]="application/json";i&&(n.Authorization=`Bearer ${i}`);try{let c=await this.fetch(`${a}${this.api_prefix}/${ut}/`,{method:"POST",body:l,headers:n,credentials:this.options.credentials??"same-origin"});if(!c.ok)throw Error("Could not connect to component server: "+c.statusText);return await c.json()}catch(c){console.warn(c)}}set_cookies(e){this.cookies=Le(e).join("; ")}prepare_return_obj(){return{config:this.config,predict:this.predict,submit:this.submit,view_api:this.view_api,component_server:this.component_server}}};export{Te as Client,ie as FileData,us as handle_file,Rt as predict,as as submit,ps as t,Nt as upload,Ct as upload_files};

frontend/dist/assets/index-CCfkPg0W.css

@@ -1 +0,0 @@
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oklab,var(--color-accent)12%,transparent)}}.hover\:bg-bad\/20:hover{background-color:#ff636233}@supports (color:color-mix(in lab,red,red)){.hover\:bg-bad\/20:hover{background-color:color-mix(in oklab,var(--color-bad)20%,transparent)}}.hover\:bg-black\/85:hover{background-color:#000000d9}@supports (color:color-mix(in lab,red,red)){.hover\:bg-black\/85:hover{background-color:color-mix(in oklab,var(--color-black)85%,transparent)}}.hover\:bg-line:hover{background-color:var(--color-line)}.hover\:bg-raised:hover{background-color:var(--color-raised)}.hover\:bg-raised\/50:hover{background-color:#1b1d2380}@supports (color:color-mix(in lab,red,red)){.hover\:bg-raised\/50:hover{background-color:color-mix(in oklab,var(--color-raised)50%,transparent)}}.hover\:bg-raised\/60:hover{background-color:#1b1d2399}@supports (color:color-mix(in lab,red,red)){.hover\:bg-raised\/60:hover{background-color:color-mix(in oklab,var(--color-raised)60%,transparent)}}.hover\:text-accent:hover{color:var(--color-accent)}.hover\:text-bad:hover{color:var(--color-bad)}.hover\:text-ink:hover{color:var(--color-ink)}.hover\:ring-line-strong:hover{--tw-ring-color:var(--color-line-strong)}.hover\:brightness-\[1\.06\]:hover{--tw-brightness:brightness(1.06);filter:var(--tw-blur,)var(--tw-brightness,)var(--tw-contrast,)var(--tw-grayscale,)var(--tw-hue-rotate,)var(--tw-invert,)var(--tw-saturate,)var(--tw-sepia,)var(--tw-drop-shadow,)}}.focus\:bg-canvas:focus{background-color:var(--color-canvas)}.focus\:ring-accent:focus{--tw-ring-color:var(--color-accent)}.focus\:outline-none:focus{--tw-outline-style:none;outline-style:none}.focus-visible\:opacity-100:focus-visible{opacity:1}.disabled\:cursor-not-allowed:disabled{cursor:not-allowed}.disabled\:bg-raised:disabled{background-color:var(--color-raised)}.disabled\:bg-none:disabled{background-image:none}.disabled\:text-faint:disabled{color:var(--color-faint)}.disabled\:opacity-20:disabled{opacity:.2}.disabled\:opacity-25:disabled{opacity:.25}.disabled\:opacity-80:disabled{opacity:.8}.disabled\:shadow-none:disabled{--tw-shadow:0 0 #0000;box-shadow:var(--tw-inset-shadow),var(--tw-inset-ring-shadow),var(--tw-ring-offset-shadow),var(--tw-ring-shadow),var(--tw-shadow)}.disabled\:ring-line:disabled{--tw-ring-color:var(--color-line)}.data-checked\:ml-auto[data-checked]{margin-left:auto}.data-checked\:bg-accent[data-checked]{background-color:var(--color-accent)}.data-checked\:bg-accent-ink[data-checked]{background-color:var(--color-accent-ink)}.data-disabled\:cursor-not-allowed[data-disabled]{cursor:not-allowed}.data-disabled\:text-faint\/50[data-disabled]{color:#6c707980}@supports (color:color-mix(in lab,red,red)){.data-disabled\:text-faint\/50[data-disabled]{color:color-mix(in oklab,var(--color-faint)50%,transparent)}}.data-disabled\:opacity-40[data-disabled]{opacity:.4}@media(hover:hover){.data-disabled\:hover\:bg-transparent[data-disabled]:hover{background-color:#0000}}.data-ending-style\:h-0[data-ending-style]{height:calc(var(--spacing)*0)}.data-ending-style\:scale-95[data-ending-style]{--tw-scale-x:95%;--tw-scale-y:95%;--tw-scale-z:95%;scale:var(--tw-scale-x)var(--tw-scale-y)}.data-ending-style\:\[transform\:translateX\(calc\(100\%-3rem\+2px\)\)\][data-ending-style]{transform:translate(calc(100% - 3rem + 2px))}.data-ending-style\:\[transform\:translateY\(calc\(100\%-3rem\+2px\)\)\][data-ending-style]{transform:translateY(calc(100% - 3rem + 2px))}.data-ending-style\:opacity-0[data-ending-style]{opacity:0}.data-ending-style\:duration-\[calc\(var\(--drawer-swipe-strength\)\*400ms\)\][data-ending-style]{--tw-duration:calc(var(--drawer-swipe-strength)*.4s);transition-duration:calc(var(--drawer-swipe-strength)*.4s)}.data-pressed\:bg-accent\/12[data-pressed]{background-color:#ff963a1f}@supports (color:color-mix(in lab,red,red)){.data-pressed\:bg-accent\/12[data-pressed]{background-color:color-mix(in oklab,var(--color-accent)12%,transparent)}}.data-pressed\:bg-bad\/12[data-pressed]{background-color:#ff63621f}@supports (color:color-mix(in lab,red,red)){.data-pressed\:bg-bad\/12[data-pressed]{background-color:color-mix(in oklab,var(--color-bad)12%,transparent)}}.data-pressed\:bg-line[data-pressed]{background-color:var(--color-line)}.data-pressed\:bg-raised[data-pressed]{background-color:var(--color-raised)}.data-pressed\:bg-surface[data-pressed]{background-color:var(--color-surface)}.data-pressed\:text-accent[data-pressed]{color:var(--color-accent)}.data-pressed\:ring-accent\/45[data-pressed]{--tw-ring-color:#ff963a73}@supports (color:color-mix(in lab,red,red)){.data-pressed\:ring-accent\/45[data-pressed]{--tw-ring-color:color-mix(in oklab,var(--color-accent)45%,transparent)}}.data-pressed\:brightness-\[0\.96\][data-pressed]{--tw-brightness:brightness(.96);filter:var(--tw-blur,)var(--tw-brightness,)var(--tw-contrast,)var(--tw-grayscale,)var(--tw-hue-rotate,)var(--tw-invert,)var(--tw-saturate,)var(--tw-sepia,)var(--tw-drop-shadow,)}.data-starting-style\:h-0[data-starting-style]{height:calc(var(--spacing)*0)}.data-starting-style\:scale-95[data-starting-style]{--tw-scale-x:95%;--tw-scale-y:95%;--tw-scale-z:95%;scale:var(--tw-scale-x)var(--tw-scale-y)}.data-starting-style\:\[transform\:translateX\(calc\(100\%-3rem\+2px\)\)\][data-starting-style]{transform:translate(calc(100% - 3rem + 2px))}.data-starting-style\:\[transform\:translateY\(calc\(100\%-3rem\+2px\)\)\][data-starting-style]{transform:translateY(calc(100% - 3rem + 2px))}.data-starting-style\:opacity-0[data-starting-style]{opacity:0}.data-swiping\:duration-0[data-swiping]{--tw-duration:0s;transition-duration:0s}.data-swiping\:select-none[data-swiping]{-webkit-user-select:none;user-select:none}@media(min-width:40rem){.sm\:inline{display:inline}.sm\:w-\[calc\(36rem\+3rem\)\]{width:39rem}.sm\:max-w-\[calc\(100vw-2rem\+3rem\)\]{max-width:calc(100vw + 1rem)}.sm\:grid-cols-6{grid-template-columns:repeat(6,minmax(0,1fr))}.sm\:opacity-0{opacity:0}@media(hover:hover){.sm\:group-hover\/tile\:opacity-100:is(:where(.group\/tile):hover *){opacity:1}}}@media(min-width:64rem){.lg\:h-auto{height:auto}.lg\:h-dvh{height:100dvh}.lg\:h-full{height:100%}.lg\:min-h-0{min-height:calc(var(--spacing)*0)}.lg\:flex-1{flex:1}.lg\:grid-cols-3{grid-template-columns:repeat(3,minmax(0,1fr))}.lg\:grid-cols-\[minmax\(0\,22rem\)_minmax\(0\,1fr\)\]{grid-template-columns:minmax(0,22rem) minmax(0,1fr)}.lg\:overflow-hidden{overflow:hidden}.lg\:overflow-y-auto{overflow-y:auto}.lg\:overscroll-contain{overscroll-behavior:contain}.lg\:border-r{border-right-style:var(--tw-border-style);border-right-width:1px}.lg\:p-5{padding:calc(var(--spacing)*5)}}@media(min-width:80rem){.xl\:grid-cols-\[minmax\(0\,24rem\)_minmax\(0\,1fr\)\]{grid-template-columns:minmax(0,24rem) minmax(0,1fr)}}.\[\&_svg\]\:size-4 svg{width:calc(var(--spacing)*4);height:calc(var(--spacing)*4)}.\[\&_svg\]\:shrink-0 svg{flex-shrink:0}.\[\&\>svg\]\:size-4>svg{width:calc(var(--spacing)*4);height:calc(var(--spacing)*4)}.tabular{font-variant-numeric:tabular-nums}.scrollbar-slim{scrollbar-width:thin;scrollbar-color:var(--color-line-strong)transparent}.scrollbar-slim::-webkit-scrollbar{width:10px;height:10px}.scrollbar-slim::-webkit-scrollbar-thumb{background-clip:content-box;background-color:var(--color-line-strong);border:3px solid #0000;border-radius:999px}}@keyframes sweep{0%{transform:translate(-100%)}to{transform:translate(320%)}}.sweep{animation:1.5s cubic-bezier(.5,0,.5,1) infinite sweep}@media(prefers-reduced-motion:reduce){*,:before,:after{scroll-behavior:auto!important;transition-duration:.01ms!important;animation-duration:.01ms!important;animation-iteration-count:1!important}.sweep{opacity:.5;width:100%!important;animation:none!important}}@property --tw-translate-x{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-y{syntax:"*";inherits:false;initial-value:0}@property --tw-translate-z{syntax:"*";inherits:false;initial-value:0}@property --tw-rotate-x{syntax:"*";inherits:false}@property --tw-rotate-y{syntax:"*";inherits:false}@property --tw-rotate-z{syntax:"*";inherits:false}@property --tw-skew-x{syntax:"*";inherits:false}@property --tw-skew-y{syntax:"*";inherits:false}@property --tw-space-y-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-divide-y-reverse{syntax:"*";inherits:false;initial-value:0}@property --tw-border-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-gradient-position{syntax:"*";inherits:false}@property --tw-gradient-from{syntax:"<color>";inherits:false;initial-value:#0000}@property --tw-gradient-via{syntax:"<color>";inherits:false;initial-value:#0000}@property --tw-gradient-to{syntax:"<color>";inherits:false;initial-value:#0000}@property --tw-gradient-stops{syntax:"*";inherits:false}@property --tw-gradient-via-stops{syntax:"*";inherits:false}@property --tw-gradient-from-position{syntax:"<length-percentage>";inherits:false;initial-value:0%}@property --tw-gradient-via-position{syntax:"<length-percentage>";inherits:false;initial-value:50%}@property --tw-gradient-to-position{syntax:"<length-percentage>";inherits:false;initial-value:100%}@property --tw-leading{syntax:"*";inherits:false}@property --tw-font-weight{syntax:"*";inherits:false}@property --tw-tracking{syntax:"*";inherits:false}@property --tw-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-shadow-color{syntax:"*";inherits:false}@property --tw-shadow-alpha{syntax:"<percentage>";inherits:false;initial-value:100%}@property --tw-inset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-shadow-color{syntax:"*";inherits:false}@property --tw-inset-shadow-alpha{syntax:"<percentage>";inherits:false;initial-value:100%}@property --tw-ring-color{syntax:"*";inherits:false}@property --tw-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-inset-ring-color{syntax:"*";inherits:false}@property --tw-inset-ring-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-ring-inset{syntax:"*";inherits:false}@property --tw-ring-offset-width{syntax:"<length>";inherits:false;initial-value:0}@property --tw-ring-offset-color{syntax:"*";inherits:false;initial-value:#fff}@property --tw-ring-offset-shadow{syntax:"*";inherits:false;initial-value:0 0 #0000}@property --tw-outline-style{syntax:"*";inherits:false;initial-value:solid}@property --tw-blur{syntax:"*";inherits:false}@property --tw-brightness{syntax:"*";inherits:false}@property --tw-contrast{syntax:"*";inherits:false}@property --tw-grayscale{syntax:"*";inherits:false}@property --tw-hue-rotate{syntax:"*";inherits:false}@property --tw-invert{syntax:"*";inherits:false}@property --tw-opacity{syntax:"*";inherits:false}@property --tw-saturate{syntax:"*";inherits:false}@property --tw-sepia{syntax:"*";inherits:false}@property --tw-drop-shadow{syntax:"*";inherits:false}@property --tw-drop-shadow-color{syntax:"*";inherits:false}@property --tw-drop-shadow-alpha{syntax:"<percentage>";inherits:false;initial-value:100%}@property --tw-drop-shadow-size{syntax:"*";inherits:false}@property --tw-backdrop-blur{syntax:"*";inherits:false}@property --tw-backdrop-brightness{syntax:"*";inherits:false}@property --tw-backdrop-contrast{syntax:"*";inherits:false}@property --tw-backdrop-grayscale{syntax:"*";inherits:false}@property --tw-backdrop-hue-rotate{syntax:"*";inherits:false}@property --tw-backdrop-invert{syntax:"*";inherits:false}@property --tw-backdrop-opacity{syntax:"*";inherits:false}@property --tw-backdrop-saturate{syntax:"*";inherits:false}@property --tw-backdrop-sepia{syntax:"*";inherits:false}@property --tw-duration{syntax:"*";inherits:false}@property --tw-ease{syntax:"*";inherits:false}@property --tw-scale-x{syntax:"*";inherits:false;initial-value:1}@property --tw-scale-y{syntax:"*";inherits:false;initial-value:1}@property --tw-scale-z{syntax:"*";inherits:false;initial-value:1}@property --tw-content{syntax:"*";inherits:false;initial-value:""}@keyframes spin{to{transform:rotate(360deg)}}@keyframes pulse{50%{opacity:.5}}

frontend/dist/index.html

@@ -7,8 +7,8 @@
     <meta name="theme-color" content="#0b0c0f" />
     <meta name="description" content="Generate video with a synchronized soundtrack from a single prompt, on MiniMax-H3." />
     <title>MiniMax-H3 Ultra Fast</title>
-    <script type="module" crossorigin src="/studio-assets/assets/index-DoM51C4t.js"></script>
-    <link rel="stylesheet" crossorigin href="/studio-assets/assets/index-CCfkPg0W.css">
+    <script type="module" crossorigin src="/studio-assets/assets/index-Dxjs8B8O.js"></script>
+    <link rel="stylesheet" crossorigin href="/studio-assets/assets/index-DZbZsc56.css">
   </head>
   <body>
     <div id="root"></div>

frontend/src/App.tsx

@@ -1,5 +1,5 @@
 import { useCallback, useEffect, useMemo, useRef, useState } from "react";
-import { fetchModelStatus, fetchStudioConfig, runGeneration } from "./api";
+import { fetchModelStatus, fetchStudioConfig, runGeneration, stitchVideos } from "./api";
 import { AboutSheet } from "./components/AboutSheet";
 import { ComposeRail } from "./components/ComposeRail";
 import { Header } from "./components/Header";
@@ -11,17 +11,32 @@ import { deleteHistoryItem, restoreHistory, saveHistoryItem } from "./lib/histor
 import { estimateRuntime, loadRuntimeSamples, rememberRuntime, runtimeSampleFor } from "./lib/runtimeHistory";
 import { findCanvas, snapFrames, FPS } from "./lib/studio";
 import { draftValues, finalFrameFile, finalValues, recipeFrom, remixValues, valuesFromHistory } from "./lib/workflows";
-import type { GenerationValues, HistoryItem, ModelStatus, RunProgress, StudioConfig } from "./types";
+import type { GenerationValues, HistoryItem, ModelStatus, RunProgress, StoryboardShot, StudioConfig, StudioMode } from "./types";
 import { FALLBACK_CONFIG } from "./types";
 
 const IDLE: RunProgress = { stage: "idle", label: "Ready", progress: null };
 const STATUS_POLL_MS = 15_000;
 
+function storedMode(): StudioMode {
+  try { return localStorage.getItem("h3-studio-mode") === "storyboard" ? "storyboard" : "single"; }
+  catch { return "single"; }
+}
+
+function storedShots(): StoryboardShot[] {
+  const fresh = () => [{ id: crypto.randomUUID(), prompt: "" }, { id: crypto.randomUUID(), prompt: "" }];
+  try {
+    const parsed = JSON.parse(localStorage.getItem("h3-storyboard") || "null");
+    return Array.isArray(parsed) && parsed.length >= 2 ? parsed : fresh();
+  } catch { return fresh(); }
+}
+
 function initialValues(config: StudioConfig): GenerationValues {
   return {
     prompt: "",
     image: null,
     lastImage: null,
+    referenceMode: "keyframes",
+    references: [],
     canvas: config.default_canvas,
     duration: config.duration.default,
     seed: 42,
@@ -50,6 +65,9 @@ export default function App() {
   const [error, setError] = useState<string | null>(null);
   const [usageOpen, setUsageOpen] = useState(false);
   const [aboutOpen, setAboutOpen] = useState(false);
+  const [mode, setMode] = useState<StudioMode>(storedMode);
+  const [shots, setShots] = useState<StoryboardShot[]>(storedShots);
+  const [storyClips, setStoryClips] = useState<HistoryItem[]>([]);
   const viewerRef = useRef<HTMLDivElement>(null);
 
   const running =
@@ -122,6 +140,9 @@ export default function App() {
     values.loraStrength,
   ]);
 
+  useEffect(() => { try { localStorage.setItem("h3-studio-mode", mode); } catch { /* optional */ } }, [mode]);
+  useEffect(() => { try { localStorage.setItem("h3-storyboard", JSON.stringify(shots)); } catch { /* optional */ } }, [shots]);
+
   const update = useCallback(
     <K extends keyof GenerationValues>(key: K, value: GenerationValues[K]) =>
       setValues((current) => ({ ...current, [key]: value })),
@@ -154,6 +175,7 @@ export default function App() {
         recipe: recipeFrom(requestValues),
         sourceImage: requestValues.image,
         sourceLastImage: requestValues.lastImage,
+        sourceReferences: requestValues.references.map((reference) => ({ name: reference.file.name, type: reference.file.type, blob: reference.file })),
       };
       setHistory((current) => [item, ...current]);
       // The UI can show the result immediately; durable browser storage continues without delaying completion.
@@ -168,6 +190,67 @@ export default function App() {
     }
   }
 
+  async function generateStoryboard(draft: boolean) {
+    const authored = shots.filter((shot) => shot.prompt.trim());
+    if (running || authored.length < 2) return;
+    setError(null); setStoryClips([]);
+    viewerRef.current?.scrollIntoView({ behavior: "smooth", block: "start" });
+    const completed: HistoryItem[] = [];
+    let continuation: File | null = null;
+    try {
+      for (let index = 0; index < authored.length; index++) {
+        let requestValues: GenerationValues = {
+          ...values,
+          prompt: authored[index].prompt,
+          seed: values.seed + index,
+          image: index === 0 ? values.image : continuation,
+          lastImage: index === authored.length - 1 ? values.lastImage : null,
+        };
+        if (draft) requestValues = draftValues(requestValues, config);
+        if (requestValues.referenceMode === "omni" && continuation) {
+          requestValues = {
+            ...requestValues, image: null, lastImage: null,
+            references: [{ id: `continuity-${index}`, file: continuation, kind: "image" }, ...requestValues.references],
+          };
+        }
+        const estimate = estimateRuntime(runtimeSamples, config, requestValues);
+        const result = await runGeneration(requestValues, (next) => setProgress({
+          ...next,
+          label: `Shot ${index + 1}/${authored.length} · ${next.label}`,
+          progress: next.progress == null ? index / authored.length : (index + next.progress) / authored.length,
+        }), estimate);
+        const item: HistoryItem = {
+          ...result,
+          id: crypto.randomUUID(), createdAt: Date.now() + index,
+          prompt: requestValues.prompt, canvas: findCanvas(config, requestValues.canvas),
+          seconds: snapFrames(requestValues.duration) / FPS, seed: requestValues.seed,
+          preset: `Shot ${index + 1} · ${requestValues.preset}`, recipe: recipeFrom(requestValues),
+          sourceImage: requestValues.image, sourceLastImage: requestValues.lastImage,
+          sourceReferences: requestValues.references.map((r) => ({ name: r.file.name, type: r.file.type, blob: r.file })),
+        };
+        completed.push(item); setStoryClips([...completed]);
+        setHistory((current) => [item, ...current]); void saveHistoryItem(item);
+        continuation = await finalFrameFile(result.url);
+      }
+      setProgress({ stage: "generating", phase: "finalizing", label: "Assembling storyboard", progress: .98 });
+      const url = await stitchVideos(completed.map((item) => item.url));
+      const film: HistoryItem = {
+        ...completed[0], id: crypto.randomUUID(), createdAt: Date.now(), url,
+        prompt: `Storyboard · ${authored.map((shot) => shot.prompt).join(" / ")}`,
+        seconds: completed.reduce((sum, item) => sum + item.seconds, 0),
+        preset: `Storyboard · ${draft ? "Draft" : "Final"}`,
+        report: `${completed.length} connected shots · ${completed.reduce((sum, item) => sum + item.seconds, 0).toFixed(1)} s · CPU assembled`,
+        runtimeSeconds: completed.reduce((sum, item) => sum + item.runtimeSeconds, 0),
+      };
+      setHistory((current) => [film, ...current]); setSelectedId(film.id); void saveHistoryItem(film);
+      setProgress({ stage: "complete", label: "Storyboard complete", progress: 1, exact: true });
+    } catch (caught) {
+      const message = caught instanceof Error ? caught.message : "Storyboard generation failed.";
+      setError(completed.length ? `${message} Your ${completed.length} completed shot${completed.length === 1 ? " is" : "s are"} saved below.` : message);
+      setProgress({ stage: "error", label: message, progress: null });
+    }
+  }
+
   function remix(item: HistoryItem) {
     setValues(remixValues(item, config));
     setError(null);
@@ -209,15 +292,19 @@ export default function App() {
     <div className="flex min-h-dvh flex-col lg:h-dvh lg:overflow-hidden">
       <Header model={model} onOpenUsage={() => setUsageOpen(true)} onOpenAbout={() => setAboutOpen(true)} />
 
-      <main className="grid min-h-0 flex-1 lg:grid-cols-[minmax(0,22rem)_minmax(0,1fr)] xl:grid-cols-[minmax(0,24rem)_minmax(0,1fr)]">
-        <div className="min-h-0 border-line lg:border-r">
+      <main className="grid min-h-0 min-w-0 flex-1 grid-cols-[minmax(0,1fr)] lg:grid-cols-[minmax(0,22rem)_minmax(0,1fr)] xl:grid-cols-[minmax(0,24rem)_minmax(0,1fr)]">
+        <div className="min-h-0 min-w-0 border-line lg:border-r">
           <ComposeRail
             config={config}
             values={values}
             update={update}
             onApplyExample={applyExample}
-            onGenerate={() => void generate()}
-            onGenerateDraft={() => void generate(draftValues(values, config))}
+            onGenerate={() => mode === "storyboard" ? void generateStoryboard(false) : void generate()}
+            onGenerateDraft={() => mode === "storyboard" ? void generateStoryboard(true) : void generate(draftValues(values, config))}
+            mode={mode}
+            onModeChange={setMode}
+            shots={shots}
+            onShotsChange={setShots}
             running={running}
             blockedReason={blockedReason}
             runtimeEstimate={runtimeEstimate}
@@ -242,6 +329,17 @@ export default function App() {
             onContinue={(item) => void continueFrom(item)}
             onRemix={remix}
           />
+          {mode === "storyboard" && storyClips.length > 0 && (
+            <section className="mt-3 shrink-0 rounded-xl bg-sunken p-3 ring-1 ring-inset ring-line">
+              <p className="mb-2 text-[10.5px] font-semibold uppercase tracking-[.1em] text-faint">Storyboard timeline</p>
+              <div className="flex gap-2 overflow-x-auto pb-1">
+                {storyClips.map((clip, index) => <button key={clip.id} onClick={() => setSelectedId(clip.id)} className="w-36 shrink-0 overflow-hidden rounded-lg bg-black ring-1 ring-line">
+                  <video src={`${clip.url}#t=.1`} muted preload="metadata" className="aspect-video w-full object-cover" />
+                  <span className="block truncate px-2 py-1.5 text-left text-[10.5px] text-muted">{index + 1}. {clip.prompt}</span>
+                </button>)}
+              </div>
+            </section>
+          )}
           <History
             items={history}
             selectedId={selectedId}

frontend/src/api.ts

@@ -71,10 +71,13 @@ function statusLabel(message: StatusMessage, startedAt: number, previous: RunPro
   }
   // Gradio 6 emits explicit gr.Progress packets with stage="pending" even after execution starts.
   if (message.stage === "generating" || message.stage === "streaming" || isProgressPacket) {
+    const previewParts = progressItem?.desc?.startsWith("TAE_PREVIEW|")
+      ? progressItem.desc.split("|", 3)
+      : null;
     const trackedStep = progressItem?.index != null && progressItem.length != null && progressItem.length > 0;
     const rawFraction = progressItem?.progress ?? (trackedStep ? progressItem.index! / progressItem.length! : null);
     const gpuInitialization = progressItem?.desc === "ZeroGPU init";
-    const denoising = trackedStep && !gpuInitialization;
+    const denoising = (trackedStep && !gpuInitialization) || Boolean(previewParts);
     const gpuPreflight = !trackedStep && progressItem?.desc?.toLowerCase().includes("denoising") === true;
     let phase: RunProgress["phase"] = denoising
       ? "denoising"
@@ -106,7 +109,7 @@ function statusLabel(message: StatusMessage, startedAt: number, previous: RunPro
     return {
       stage: "generating",
       label: denoising
-        ? progressItem?.desc || "Denoising video and audio"
+        ? previewParts?.[2] || progressItem?.desc || "Denoising video and audio"
         : phase === "finalizing"
           ? "Finalizing video and audio"
           : phase === "gpu"
@@ -121,6 +124,7 @@ function statusLabel(message: StatusMessage, startedAt: number, previous: RunPro
       unit: progressItem?.unit ?? undefined,
       exact,
       phase,
+      previewUrl: previewParts?.[1] || previous.previewUrl,
     };
   }
   if (message.stage === "complete") {
@@ -172,8 +176,8 @@ export async function runGeneration(
   const client = await getClient();
   const submission = client.submit("/generate", {
     prompt: values.prompt,
-    image_path: values.image ? handle_file(values.image) : null,
-    last_image_path: values.lastImage ? handle_file(values.lastImage) : null,
+    image_path: values.referenceMode === "keyframes" && values.image ? handle_file(values.image) : null,
+    last_image_path: values.referenceMode === "keyframes" && values.lastImage ? handle_file(values.lastImage) : null,
     canvas: values.canvas,
     duration: values.duration,
     steps: values.steps,
@@ -185,6 +189,8 @@ export async function runGeneration(
     lora_filename: values.loraFilename,
     lora_strength: values.loraStrength,
     generation_preset: values.preset,
+    references:
+      values.referenceMode === "omni" ? values.references.map((reference) => handle_file(reference.file)) : [],
   });
 
   let result: unknown[] | null = null;
@@ -232,3 +238,20 @@ export async function runGeneration(
     runtimeSeconds: (Date.now() - (runtimeStartedAt ?? startedAt)) / 1000,
   };
 }
+
+/** Assemble generated storyboard shots on the CPU endpoint, so editing never books another GPU. */
+export async function stitchVideos(urls: string[]): Promise<string> {
+  const { handle_file } = await import("@gradio/client");
+  const files = await Promise.all(
+    urls.map(async (url, index) => {
+      const response = await fetch(url);
+      if (!response.ok) throw new Error(`Could not read storyboard shot ${index + 1}.`);
+      return new File([await response.blob()], `shot-${index + 1}.mp4`, { type: "video/mp4" });
+    }),
+  );
+  const client = await getClient();
+  const result = await client.predict("/stitch", { clips: files.map(handle_file) });
+  const data = unwrapServerOutput(Array.isArray(result.data) ? result.data : [result.data]);
+  if (!data.length) throw new Error("The storyboard assembler returned no video.");
+  return outputUrl(data[0] as FilePayload | string);
+}

frontend/src/components/AboutSheet.tsx

@@ -11,6 +11,7 @@ const LINKS = [
   ["NVFP4 checkpoint", "https://huggingface.co/lilcheaty/MiniMax-H3-NVFP4"],
   ["4-step Turbo LoRA", "https://huggingface.co/lightx2v/Minimax-h3-Turbo"],
   ["Sana / Sol-Engine", "https://github.com/NVlabs/Sana/tree/sol-engine/models/minimax_h3/optimized"],
+  ["H3 TAE previews", "https://huggingface.co/Kijai/MiniMax-H3-TAE"],
 ];
 
 /**
@@ -104,6 +105,24 @@ export function AboutSheet({ open, onClose }: { open: boolean; onClose: () => vo
           </P>
         </Part>
 
+        <Part title="Creative workflows">
+          <P>
+            <Term>Reference studio</Term> runs MiniMax-H3’s Ref2VA checkpoint with up to 12 ordered references: nine
+            images, three videos and three audio files within that total. Reference order is preserved through both
+            the local Qwen conditioner and the joint video/audio denoiser. It stays on the full 28-step quality path.
+          </P>
+          <P>
+            <Term>Storyboard</Term> renders shots in order, captures each finished clip’s last frame locally, and uses
+            it to anchor the next shot. The clips are joined on CPU with a one-frame seam trim, so assembly does not
+            consume another ZeroGPU allocation. Shot recipes and source references remain in browser history.
+          </P>
+          <P>
+            <Term>TAE live preview</Term> decodes a tiny animated approximation at denoising milestones. It adds a
+            little work but gives useful visual feedback before the full video and audio VAEs finish; exported frames
+            always come from the original full-precision VAE.
+          </P>
+        </Part>
+
         <Part title="Things that will surprise you">
           <P>
             <Term>Lengths snap.</Term> The video VAE decodes 17n + 5 frames at 24 fps and nothing else, so a request for

frontend/src/components/ComposeRail.tsx

@@ -5,14 +5,16 @@ import { AnimatePresence, motion } from "framer-motion";
 import { cx } from "../lib/cx";
 import { FADE, POP } from "../lib/motion";
 import { findPreset, formatBudget } from "../lib/studio";
-import type { GenerationValues, RuntimeEstimate, StudioConfig } from "../types";
+import type { GenerationValues, RuntimeEstimate, StoryboardShot, StudioConfig, StudioMode } from "../types";
 import { Button } from "../ui/Button";
 import { Section } from "../ui/Section";
+import { Segmented } from "../ui/Segmented";
+import { ReferenceLibrary } from "./ReferenceLibrary";
+import { StoryboardEditor } from "./StoryboardEditor";
 import {
   budgetFor,
   fasterOption,
   FormatSettings,
-  KeyframeSettings,
   LengthSettings,
   SeedSettings,
   SpeedSettings,
@@ -25,6 +27,10 @@ type Props = {
   onApplyExample: (prompt: string, canvas: string) => void;
   onGenerate: () => void;
   onGenerateDraft: () => void;
+  mode: StudioMode;
+  onModeChange: (mode: StudioMode) => void;
+  shots: StoryboardShot[];
+  onShotsChange: (shots: StoryboardShot[]) => void;
   running: boolean;
   /** Non-null when generating is impossible right now — shown in place of the GPU estimate. */
   blockedReason: string | null;
@@ -50,6 +56,10 @@ export function ComposeRail({
   onApplyExample,
   onGenerate,
   onGenerateDraft,
+  mode,
+  onModeChange,
+  shots,
+  onShotsChange,
   running,
   blockedReason,
   runtimeEstimate,
@@ -59,7 +69,9 @@ export function ComposeRail({
   const steps = preset.custom ? values.steps : preset.steps;
   const budget = budgetFor(config, values, steps);
   const faster = fasterOption(config, values);
-  const ready = values.prompt.trim().length > 0 && !running && !blockedReason;
+  const hasPrompt = mode === "storyboard" ? shots.length >= 2 && shots.every((shot) => shot.prompt.trim()) : values.prompt.trim().length > 0;
+  const validReferences = values.referenceMode !== "omni" || (values.references.some((r) => r.kind !== "audio") && values.references.length > 0);
+  const ready = hasPrompt && validReferences && !running && !blockedReason;
 
   // Grow the prompt with its content instead of scrolling inside a fixed box, up to a point where the rail takes over.
   useEffect(() => {
@@ -72,6 +84,12 @@ export function ComposeRail({
   return (
     <div className="flex min-h-0 flex-col lg:h-full">
       <div className="scrollbar-slim divide-y divide-line lg:min-h-0 lg:flex-1 lg:overflow-y-auto lg:overscroll-contain">
+        <div className="p-4 pb-3">
+          <Segmented ariaLabel="Creation mode" value={mode} onChange={onModeChange} options={[
+            { value: "single", label: "Single shot" },
+            { value: "storyboard", label: "Storyboard", hint: `${shots.length} shots` },
+          ]} />
+        </div>
         <Section
           title="Speed"
           tone="accent"
@@ -103,6 +121,8 @@ export function ComposeRail({
           <SpeedSettings config={config} values={values} update={update} runtimeEstimate={runtimeEstimate} />
         </Section>
 
+        {mode === "storyboard" ? <Section title="Storyboard" defaultOpen><StoryboardEditor shots={shots} onChange={onShotsChange} /></Section> : <>
+
         <Section
           title="Prompt"
           action={
@@ -163,9 +183,10 @@ export function ComposeRail({
             ))}
           </div>
         </Section>
+        </>}
 
         <Section title="References" defaultOpen>
-          <KeyframeSettings values={values} update={update} />
+          <ReferenceLibrary config={config} values={values} update={update} />
         </Section>
 
         <Section title="Format" defaultOpen={false}>
@@ -202,13 +223,13 @@ export function ComposeRail({
           </AnimatePresence>
         </div>
 
-        <Button variant="outline" size="lg" disabled={!ready} onClick={onGenerateDraft}>
+        <Button variant="outline" size="lg" disabled={!ready || values.referenceMode === "omni"} onClick={onGenerateDraft} title={values.referenceMode === "omni" ? "Ref2VA uses the full quality schedule" : undefined}>
           {running ? <Loader2 className="animate-spin" /> : <Clapperboard />}
           Draft
         </Button>
         <Button variant="primary" size="lg" disabled={!ready} onClick={onGenerate}>
           {running ? <Loader2 className="animate-spin" /> : <Zap fill="currentColor" />}
-          {running ? "Generating…" : "Generate"}
+          {running ? "Generating…" : mode === "storyboard" ? `Render ${shots.length} shots` : "Generate"}
         </Button>
       </div>
     </div>

frontend/src/components/ReferenceLibrary.tsx

@@ -0,0 +1,108 @@
+import { useEffect, useMemo, useRef } from "react";
+import { ArrowDown, ArrowUp, FileAudio, Film, ImageIcon, Plus, Trash2 } from "lucide-react";
+import type { GenerationValues, ReferenceAsset, ReferenceKind, StudioConfig } from "../types";
+import { Button } from "../ui/Button";
+import { Segmented } from "../ui/Segmented";
+import { KeyframeSettings } from "./settings";
+
+type Props = {
+  config: StudioConfig;
+  values: GenerationValues;
+  update: <K extends keyof GenerationValues>(key: K, value: GenerationValues[K]) => void;
+};
+
+function kindOf(file: File): ReferenceKind | null {
+  if (file.type.startsWith("image/")) return "image";
+  if (file.type.startsWith("video/")) return "video";
+  if (file.type.startsWith("audio/")) return "audio";
+  return null;
+}
+
+function icon(kind: ReferenceKind) {
+  return kind === "image" ? <ImageIcon /> : kind === "video" ? <Film /> : <FileAudio />;
+}
+
+function ReferenceThumb({ asset }: { asset: ReferenceAsset }) {
+  const url = useMemo(() => URL.createObjectURL(asset.file), [asset.file]);
+  useEffect(() => () => URL.revokeObjectURL(url), [url]);
+  if (asset.kind === "image") return <img src={url} alt="" className="size-full object-cover" />;
+  if (asset.kind === "video") return <video src={`${url}#t=.1`} muted preload="metadata" className="size-full object-cover" />;
+  return icon(asset.kind);
+}
+
+export function ReferenceLibrary({ config, values, update }: Props) {
+  const input = useRef<HTMLInputElement>(null);
+  const counts = useMemo(
+    () => Object.fromEntries(["image", "video", "audio"].map((kind) => [kind, values.references.filter((r) => r.kind === kind).length])),
+    [values.references],
+  );
+  const limits = config.ref2va ?? { enabled: true, max_total: 12, max_images: 9, max_videos: 3, max_audio: 3, minimum_duration: 5 };
+
+  function add(files: FileList | null) {
+    const next = [...values.references];
+    for (const file of Array.from(files ?? [])) {
+      const kind = kindOf(file);
+      if (!kind || next.length >= limits.max_total) continue;
+      const limit = kind === "image" ? limits.max_images : kind === "video" ? limits.max_videos : limits.max_audio;
+      if (next.filter((asset) => asset.kind === kind).length >= limit) continue;
+      next.push({ id: crypto.randomUUID(), file, kind });
+    }
+    update("references", next);
+    if (values.duration < limits.minimum_duration) update("duration", limits.minimum_duration);
+  }
+
+  function move(index: number, delta: number) {
+    const target = index + delta;
+    if (target < 0 || target >= values.references.length) return;
+    const next = [...values.references];
+    [next[index], next[target]] = [next[target], next[index]];
+    update("references", next);
+  }
+
+  return (
+    <div>
+      <Segmented
+        ariaLabel="Reference workflow"
+        value={values.referenceMode}
+        onChange={(value) => update("referenceMode", value as GenerationValues["referenceMode"])}
+        options={[
+          { value: "keyframes", label: "Keyframes" },
+          { value: "omni", label: "Reference studio" },
+        ]}
+      />
+      {values.referenceMode === "keyframes" ? (
+        <div className="mt-3"><KeyframeSettings values={values} update={update} /></div>
+      ) : (
+        <div className="mt-3 space-y-3">
+          <p className="text-[11.5px] leading-relaxed text-muted">
+            Ordered image, video and audio references guide identity, motion, look and voice. Ref2VA uses the full 28-step quality path.
+          </p>
+          <div className="flex flex-wrap gap-1.5 text-[10.5px] text-faint">
+            <span>{counts.image}/{limits.max_images} images</span><span>·</span>
+            <span>{counts.video}/{limits.max_videos} videos</span><span>·</span>
+            <span>{counts.audio}/{limits.max_audio} audio</span>
+          </div>
+          <div className="space-y-1.5">
+            {values.references.map((asset, index) => (
+              <div key={asset.id} className="flex items-center gap-2 rounded-xl bg-sunken p-2 ring-1 ring-inset ring-line">
+                <span className="grid size-9 shrink-0 place-items-center overflow-hidden rounded-lg bg-raised text-accent [&>svg]:size-4"><ReferenceThumb asset={asset} /></span>
+                <div className="min-w-0 flex-1">
+                  <p className="truncate text-[12px] font-medium text-ink">{index + 1}. {asset.file.name}</p>
+                  <p className="text-[10.5px] capitalize text-faint">{asset.kind} reference</p>
+                </div>
+                <button aria-label="Move up" disabled={!index} onClick={() => move(index, -1)} className="text-muted disabled:opacity-25"><ArrowUp className="size-3.5" /></button>
+                <button aria-label="Move down" disabled={index === values.references.length - 1} onClick={() => move(index, 1)} className="text-muted disabled:opacity-25"><ArrowDown className="size-3.5" /></button>
+                <button aria-label="Remove" onClick={() => update("references", values.references.filter((item) => item.id !== asset.id))} className="text-muted hover:text-bad"><Trash2 className="size-3.5" /></button>
+              </div>
+            ))}
+          </div>
+          <input ref={input} hidden multiple type="file" accept="image/*,video/*,audio/*" onChange={(event) => add(event.target.files)} />
+          <Button variant="outline" size="sm" onClick={() => input.current?.click()} disabled={values.references.length >= limits.max_total} className="w-full">
+            <Plus /> Add references
+          </Button>
+          {counts.audio > 0 && counts.image + counts.video === 0 && <p className="text-[11px] text-warn">Add an image or video alongside audio.</p>}
+        </div>
+      )}
+    </div>
+  );
+}

frontend/src/components/StoryboardEditor.tsx

@@ -0,0 +1,31 @@
+import { ArrowDown, ArrowUp, Copy, Plus, Trash2 } from "lucide-react";
+import type { StoryboardShot } from "../types";
+import { Button } from "../ui/Button";
+
+export function StoryboardEditor({ shots, onChange }: { shots: StoryboardShot[]; onChange: (shots: StoryboardShot[]) => void }) {
+  const patch = (id: string, prompt: string) => onChange(shots.map((shot) => shot.id === id ? { ...shot, prompt } : shot));
+  const move = (index: number, delta: number) => {
+    const target = index + delta;
+    if (target < 0 || target >= shots.length) return;
+    const next = [...shots]; [next[index], next[target]] = [next[target], next[index]]; onChange(next);
+  };
+  return (
+    <div className="space-y-2.5">
+      {shots.map((shot, index) => (
+        <article key={shot.id} className="rounded-xl bg-sunken p-2.5 ring-1 ring-inset ring-line">
+          <div className="mb-2 flex items-center gap-2">
+            <span className="grid size-5 place-items-center rounded-md bg-accent/15 text-[10px] font-semibold text-accent">{index + 1}</span>
+            <span className="text-[11px] font-medium text-muted">Shot {index + 1}</span><span className="flex-1" />
+            <button disabled={!index} onClick={() => move(index, -1)} className="text-muted disabled:opacity-20"><ArrowUp className="size-3.5" /></button>
+            <button disabled={index === shots.length - 1} onClick={() => move(index, 1)} className="text-muted disabled:opacity-20"><ArrowDown className="size-3.5" /></button>
+            <button onClick={() => onChange([...shots.slice(0, index + 1), { ...shot, id: crypto.randomUUID() }, ...shots.slice(index + 1)])} className="text-muted hover:text-ink"><Copy className="size-3.5" /></button>
+            <button disabled={shots.length <= 2} onClick={() => onChange(shots.filter((item) => item.id !== shot.id))} className="text-muted hover:text-bad disabled:opacity-20"><Trash2 className="size-3.5" /></button>
+          </div>
+          <textarea value={shot.prompt} onChange={(event) => patch(shot.id, event.target.value)} rows={3} placeholder="Action, camera, dialogue and sound for this shot…" className="w-full resize-none rounded-lg bg-canvas p-2.5 text-[12.5px] leading-relaxed text-ink ring-1 ring-inset ring-line placeholder:text-faint focus:ring-accent focus:outline-none" />
+        </article>
+      ))}
+      <Button variant="outline" size="sm" onClick={() => onChange([...shots, { id: crypto.randomUUID(), prompt: "" }])} disabled={shots.length >= 8} className="w-full"><Plus /> Add shot</Button>
+      <p className="text-[10.5px] leading-relaxed text-faint">Each shot continues from the previous final frame, then the editor joins them into one film.</p>
+    </div>
+  );
+}

frontend/src/components/Viewer.tsx

@@ -186,8 +186,16 @@ function RunState({ progress }: { progress: RunProgress }) {
   return (
     <section
       aria-label="Generation progress"
-      className="flex size-full flex-col items-center justify-center gap-6 bg-sunken px-6"
+      className="flex size-full flex-col items-center justify-center gap-5 bg-sunken px-6"
     >
+      {progress.previewUrl && (
+        <div className="relative w-full max-w-sm overflow-hidden rounded-xl bg-black ring-1 ring-accent/35">
+          <img src={progress.previewUrl} alt="Approximate live generation preview" className="aspect-video w-full object-contain" />
+          <span className="absolute bottom-2 left-2 rounded-md bg-black/75 px-2 py-1 text-[10px] font-medium text-white backdrop-blur-sm">
+            Live TAE preview · final uses full VAE
+          </span>
+        </div>
+      )}
       <div className="w-full max-w-sm">
         {/* A finished phase fills left-to-right rather than switching colour, so the row reads as a track being
             covered — which is what the five phases actually are. */}

frontend/src/lib/history.ts

@@ -70,8 +70,9 @@ async function deleteIds(database: IDBDatabase, ids: string[]): Promise<void> {
   await transactionDone(transaction);
 }
 
-function clipBytes(clip: Pick<StoredClip, "video" | "sourceImage" | "sourceLastImage">): number {
-  return clip.video.size + (clip.sourceImage?.size ?? 0) + (clip.sourceLastImage?.size ?? 0);
+function clipBytes(clip: Pick<StoredClip, "video" | "sourceImage" | "sourceLastImage" | "sourceReferences">): number {
+  return clip.video.size + (clip.sourceImage?.size ?? 0) + (clip.sourceLastImage?.size ?? 0)
+    + (clip.sourceReferences ?? []).reduce((sum, reference) => sum + reference.blob.size, 0);
 }
 
 /** Save the actual MP4, pruning before the write so one large clip cannot grow storage without bound. */
@@ -84,7 +85,8 @@ export async function saveHistoryItem(item: HistoryItem): Promise<void> {
   const database = await openHistory();
   try {
     const existing = (await storedClips(database)).filter((clip) => clip.id !== item.id);
-    let bytes = video.size + (item.sourceImage?.size ?? 0) + (item.sourceLastImage?.size ?? 0);
+    let bytes = video.size + (item.sourceImage?.size ?? 0) + (item.sourceLastImage?.size ?? 0)
+      + (item.sourceReferences ?? []).reduce((sum, reference) => sum + reference.blob.size, 0);
     const keep: StoredClip[] = [];
     for (const clip of existing) {
       if (keep.length + 1 >= MAX_CLIPS || bytes + clipBytes(clip) > MAX_BYTES) continue;

frontend/src/lib/runtimeHistory.ts

@@ -70,7 +70,9 @@ export function runtimeSampleFor(
     height: canvas.height,
     frames: snapFrames(values.duration),
     steps,
-    keyframes: Number(values.image != null) + Number(values.lastImage != null),
+    keyframes: values.referenceMode === "omni"
+      ? values.references.reduce((sum, reference) => sum + (reference.kind === "video" ? 5 : 1), 0)
+      : Number(values.image != null) + Number(values.lastImage != null),
     upsample: values.upsample,
     seconds,
     createdAt: Date.now(),
@@ -115,7 +117,9 @@ export function estimateRuntime(
     height: canvas.height,
     frames: snapFrames(values.duration),
     steps,
-    keyframes: Number(values.image != null) + Number(values.lastImage != null),
+    keyframes: values.referenceMode === "omni"
+      ? values.references.reduce((sum, reference) => sum + (reference.kind === "video" ? 5 : 1), 0)
+      : Number(values.image != null) + Number(values.lastImage != null),
   };
   const targetWork = work(target);
   const matches = samples

frontend/src/lib/workflows.ts

@@ -11,7 +11,7 @@ function fileFromBlob(blob: Blob | null | undefined, name: string): File | null
 
 /** Store every authored setting so a clip is a reusable project, not merely an MP4. */
 export function recipeFrom(values: GenerationValues): GenerationRecipe {
-  const { image: _image, lastImage: _lastImage, ...recipe } = values;
+  const { image: _image, lastImage: _lastImage, references: _references, ...recipe } = values;
   return recipe;
 }
 
@@ -29,6 +29,7 @@ function legacyRecipe(item: HistoryItem, config: StudioConfig): GenerationRecipe
     loraRepo: "",
     loraFilename: "",
     loraStrength: 1,
+    referenceMode: "keyframes",
   };
 }
 
@@ -39,6 +40,11 @@ export function valuesFromHistory(item: HistoryItem, config: StudioConfig): Gene
     canvas: findCanvas(config, recipe.canvas).label,
     image: fileFromBlob(item.sourceImage, "first-frame.png"),
     lastImage: fileFromBlob(item.sourceLastImage, "last-frame.png"),
+    references: (item.sourceReferences ?? []).map((reference, index) => ({
+      id: `${item.id}-ref-${index}`,
+      file: new File([reference.blob], reference.name, { type: reference.type }),
+      kind: reference.type.startsWith("video/") ? "video" : reference.type.startsWith("audio/") ? "audio" : "image",
+    })),
   };
 }
 

frontend/src/types.ts

@@ -29,15 +29,37 @@ export type StudioConfig = {
   default_preset: string;
   custom_preset: string;
   examples: PromptExample[];
+  ref2va?: {
+    enabled: boolean;
+    max_total: number;
+    max_images: number;
+    max_videos: number;
+    max_audio: number;
+    minimum_duration: number;
+  };
+  tae_previews?: boolean;
 };
 
 export type Acceleration = "Balanced" | "Ultra Fast" | "Exact";
 export type LoraPreset = "None" | "Turbo · 4 steps" | "Turbo · 8 steps" | "Custom";
+export type ReferenceKind = "image" | "video" | "audio";
+export type ReferenceMode = "keyframes" | "omni";
+export type StudioMode = "single" | "storyboard";
+
+export type ReferenceAsset = {
+  id: string;
+  file: File;
+  kind: ReferenceKind;
+};
+
+export type StoryboardShot = { id: string; prompt: string };
 
 export type GenerationValues = {
   prompt: string;
   image: File | null;
   lastImage: File | null;
+  referenceMode: ReferenceMode;
+  references: ReferenceAsset[];
   canvas: string;
   duration: number;
   seed: number;
@@ -80,9 +102,11 @@ export type HistoryItem = GeneratedVideo & {
   /** Original FL2VA anchors, cloned by IndexedDB so Draft -> Final remains faithful after a reload. */
   sourceImage?: Blob | null;
   sourceLastImage?: Blob | null;
+  /** Ordered Ref2VA media, retained locally; never leaves the browser except for a requested generation. */
+  sourceReferences?: { name: string; type: string; blob: Blob }[];
 };
 
-export type GenerationRecipe = Omit<GenerationValues, "image" | "lastImage">;
+export type GenerationRecipe = Omit<GenerationValues, "image" | "lastImage" | "references">;
 
 export type RunPhase = "queue" | "conditioning" | "gpu" | "denoising" | "finalizing";
 
@@ -101,6 +125,8 @@ export type RunProgress = {
   unit?: string;
   exact?: boolean;
   phase?: RunPhase;
+  /** Approximate low-resolution TAE animation emitted while the full decoder is still pending. */
+  previewUrl?: string;
 };
 
 export type ModelStatus = {

h3_nvfp4.py

@@ -1188,15 +1188,17 @@ class H3NVFP4Transformer(nn.Module):
         return MiniMaxH3TransformerOutput(sample=video_output, audio_sample=audio_output)
 
 
-def load_transformer() -> H3NVFP4Transformer:
+def load_transformer(repo_id: str | None = None, filename: str | None = None) -> H3NVFP4Transformer:
     if torch.version.cuda is None or int(torch.version.cuda.split(".")[0]) < 13:
         raise RuntimeError("NVFP4 requires the CUDA 13 PyTorch build.")
     from huggingface_hub import hf_hub_download
 
-    path = hf_hub_download(repo_id=NVFP4_REPO, filename=NVFP4_FILE)
+    repo_id = repo_id or NVFP4_REPO
+    filename = filename or NVFP4_FILE
+    path = hf_hub_download(repo_id=repo_id, filename=filename)
     transformer = H3NVFP4Transformer()
     transformer.load(path)
-    print(f"[h3-nvfp4] loaded {NVFP4_REPO}/{NVFP4_FILE}", flush=True)
+    print(f"[h3-nvfp4] loaded {repo_id}/{filename}", flush=True)
     return transformer
 
 

h3_split_blocks.py

@@ -13,7 +13,14 @@ frame count is aligned to `17 * n + 5` before the call, since that arithmetic li
 """
 
 from diffusers.modular_pipelines.minimax_h3.before_encoder import MiniMaxH3Ref2VASetupStep
+from diffusers.modular_pipelines.minimax_h3.before_denoise import (
+    MiniMaxH3PrepareLatentsStep,
+    MiniMaxH3PrepareLayoutStep,
+    MiniMaxH3Ref2VAPrepareLayoutStep,
+    MiniMaxH3SetTimestepsStep,
+)
 from diffusers.modular_pipelines.minimax_h3.decoders import MiniMaxH3AfterDenoiseStep
+from diffusers.modular_pipelines.minimax_h3.denoise import MiniMaxH3DenoiseStep, MiniMaxH3Ref2VADenoiseStep
 from diffusers.modular_pipelines.minimax_h3.encoders import (
     MiniMaxH3Ref2VAReferenceEncoderStep,
     MiniMaxH3Ref2VATextEncoderStep,
@@ -22,15 +29,60 @@ from diffusers.modular_pipelines.minimax_h3.encoders import (
 from diffusers.modular_pipelines.minimax_h3.modular_blocks_minimax_h3 import (
     MiniMaxH3AutoKeyframeVaeEncoderStep,
     MiniMaxH3AutoResizeStep,
-    MiniMaxH3CoreDenoiseStep,
     MiniMaxH3DecodeStep,
-    MiniMaxH3Ref2VACoreDenoiseStep,
     _generation_outputs,
 )
 from diffusers.modular_pipelines.modular_pipeline import SequentialPipelineBlocks
 from diffusers.modular_pipelines.modular_pipeline_utils import OutputParam
 
 
+class _PreviewLoopMixin:
+    """The pinned Diffusers loop plus four non-authoritative TAE preview emissions."""
+
+    def __call__(self, components, state):
+        from h3_tae import maybe_emit_preview
+
+        block_state = self.get_block_state(state)
+        total = len(block_state.timesteps)
+        with self.progress_bar(total=total) as progress_bar:
+            for index, timestep in enumerate(block_state.timesteps):
+                components, block_state = self.loop_step(components, block_state, i=index, t=timestep)
+                maybe_emit_preview(components, block_state, index, total)
+                progress_bar.update()
+        self.set_block_state(state, block_state)
+        return components, state
+
+
+class MiniMaxH3PreviewDenoiseStep(_PreviewLoopMixin, MiniMaxH3DenoiseStep):
+    pass
+
+
+class MiniMaxH3Ref2VAPreviewDenoiseStep(_PreviewLoopMixin, MiniMaxH3Ref2VADenoiseStep):
+    pass
+
+
+class MiniMaxH3PreviewCoreDenoiseStep(SequentialPipelineBlocks):
+    model_name = "minimax-h3"
+    block_classes = [
+        MiniMaxH3PrepareLayoutStep,
+        MiniMaxH3PrepareLatentsStep,
+        MiniMaxH3SetTimestepsStep,
+        MiniMaxH3PreviewDenoiseStep,
+    ]
+    block_names = ["prepare_layout", "prepare_latents", "set_timesteps", "denoise"]
+
+
+class MiniMaxH3Ref2VAPreviewCoreDenoiseStep(SequentialPipelineBlocks):
+    model_name = "minimax-h3"
+    block_classes = [
+        MiniMaxH3Ref2VAPrepareLayoutStep,
+        MiniMaxH3PrepareLatentsStep,
+        MiniMaxH3SetTimestepsStep,
+        MiniMaxH3Ref2VAPreviewDenoiseStep,
+    ]
+    block_names = ["prepare_layout", "prepare_latents", "set_timesteps", "denoise"]
+
+
 def _wire_outputs(num_frames: bool = True) -> list[OutputParam]:
     """The wire format of the split. `num_frames` is declared by the `ref2va` half alone, whose setup resolves one."""
     return [
@@ -73,7 +125,7 @@ class MiniMaxH3GeneratorBlocks(SequentialPipelineBlocks):
     block_classes = [
         MiniMaxH3AutoResizeStep,
         MiniMaxH3AutoKeyframeVaeEncoderStep,
-        MiniMaxH3CoreDenoiseStep,
+        MiniMaxH3PreviewCoreDenoiseStep,
         MiniMaxH3AfterDenoiseStep,
         MiniMaxH3DecodeStep,
     ]
@@ -128,7 +180,7 @@ class MiniMaxH3Ref2VAGeneratorBlocks(SequentialPipelineBlocks):
     block_classes = [
         MiniMaxH3Ref2VASetupStep,
         MiniMaxH3Ref2VAReferenceEncoderStep,
-        MiniMaxH3Ref2VACoreDenoiseStep,
+        MiniMaxH3Ref2VAPreviewCoreDenoiseStep,
         MiniMaxH3AfterDenoiseStep,
         MiniMaxH3DecodeStep,
     ]

h3_tae.py

@@ -0,0 +1,153 @@
+"""Tiny MiniMax-H3 latent previews streamed through Gradio progress packets.
+
+The 2D decoder architecture follows ComfyUI's MIT-licensed TAESD blocks and Kijai's H3 checkpoint layout. It is
+preview-only: final frames still come exclusively from MiniMax-H3's full video VAE.
+"""
+
+from __future__ import annotations
+
+import os
+import time
+from urllib.parse import quote
+
+import torch
+import torch.nn as nn
+
+
+TAE_REPO = "Kijai/MiniMax-H3-TAE"
+TAE_FILE = "vae_approx/taeh3.safetensors"
+PREVIEW_MAX_EDGE = 384
+PREVIEW_POINTS = 4
+
+_DECODER = None
+_OUTPUT_DIR = None
+_FAILED = False
+
+
+def _conv(n_in: int, n_out: int, **kwargs):
+    return nn.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+
+class _Clamp(nn.Module):
+    def forward(self, value):
+        return torch.tanh(value / 3) * 3
+
+
+class _Block(nn.Module):
+    def __init__(self, n_in: int, n_out: int):
+        super().__init__()
+        self.conv = nn.Sequential(
+            _conv(n_in, n_out), nn.ReLU(), _conv(n_out, n_out), nn.ReLU(), _conv(n_out, n_out)
+        )
+        self.skip = nn.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+        self.fuse = nn.ReLU()
+
+    def forward(self, value):
+        return self.fuse(self.conv(value) + self.skip(value))
+
+
+def _build_decoder(state):
+    by_index = {}
+    for key, value in state.items():
+        head, _, tail = key.partition(".")
+        by_index.setdefault(int(head), {})[tail] = value
+    modules = []
+    for index in range(max(by_index) + 1):
+        entry = by_index.get(index)
+        if entry is None:
+            modules.append(_Clamp() if index == 0 else nn.ReLU() if index == 2 else nn.Upsample(scale_factor=2))
+        elif "conv.0.weight" in entry:
+            weight = entry["conv.0.weight"]
+            modules.append(_Block(weight.shape[1], weight.shape[0]))
+        elif "weight" in entry:
+            weight = entry["weight"]
+            modules.append(_conv(weight.shape[1], weight.shape[0], bias="bias" in entry))
+        else:
+            raise ValueError(f"Unrecognized H3 TAE module {index}: {sorted(entry)}")
+    decoder = nn.Sequential(*modules)
+    decoder.load_state_dict(state)
+    return decoder.eval()
+
+
+def load_preview_model(output_dir: str):
+    """Load the 9.8 MB decoder on CPU at startup; CUDA placement happens only inside a booked request."""
+    global _DECODER, _OUTPUT_DIR, _FAILED
+    if _DECODER is not None or _FAILED:
+        return
+    try:
+        from huggingface_hub import hf_hub_download
+        from safetensors.torch import load_file
+
+        path = hf_hub_download(TAE_REPO, TAE_FILE)
+        _DECODER = _build_decoder(load_file(path, device="cpu"))
+        _OUTPUT_DIR = os.path.join(output_dir, "previews")
+        os.makedirs(_OUTPUT_DIR, exist_ok=True)
+        print(f"[tae] loaded {TAE_REPO}/{TAE_FILE}", flush=True)
+    except Exception as error:
+        _FAILED = True
+        print(f"[tae] disabled ({type(error).__name__}: {error})", flush=True)
+
+
+def _unpatchify(components, state):
+    patch_t, patch_h, patch_w = components.patch_size
+    channels = components.vae_latent_channels
+    rows = state.latents[state.num_condition_video_rows :]
+    rows = rows.reshape(
+        -1,
+        state.num_latent_frames // patch_t,
+        state.latent_height // patch_h,
+        state.latent_width // patch_w,
+        channels,
+        patch_t,
+        patch_h,
+        patch_w,
+    )
+    rows = rows.permute(0, 4, 1, 5, 2, 6, 3, 7)
+    return rows.reshape(-1, channels, state.num_latent_frames, state.latent_height, state.latent_width)
+
+
+@torch.inference_mode()
+def maybe_emit_preview(components, state, step: int, total: int) -> None:
+    """Decode three representative latent frames at four milestones and publish a tiny animated WebP."""
+    if _DECODER is None or _OUTPUT_DIR is None or total < 2:
+        return
+    milestones = {max(0, round((total - 1) * fraction)) for fraction in (0.12, 0.38, 0.66, 0.9)}
+    if step not in milestones:
+        return
+    try:
+        from gradio.context import LocalContext
+        from PIL import Image
+
+        progress = LocalContext.progress.get()
+        if progress is None:
+            return
+        latents = _unpatchify(components, state)
+        picks = torch.linspace(0, latents.shape[2] - 1, min(3, latents.shape[2])).round().long().tolist()
+        decoder = _DECODER.to(device=latents.device, dtype=torch.bfloat16)
+        frames = []
+        for index in picks:
+            rgb = decoder(latents[:1, :, index].to(torch.bfloat16))[0].float().clamp(0, 1)
+            array = rgb.mul(255).to(torch.uint8).movedim(0, -1).cpu().numpy()
+            image = Image.fromarray(array)
+            image.thumbnail((PREVIEW_MAX_EDGE, PREVIEW_MAX_EDGE), Image.Resampling.LANCZOS)
+            frames.append(image)
+        name = f"tae-{os.getpid()}-{int(time.time() * 1000)}.webp"
+        path = os.path.join(_OUTPUT_DIR, name)
+        frames[0].save(
+            path,
+            format="WEBP",
+            save_all=len(frames) > 1,
+            append_images=frames[1:],
+            duration=420,
+            loop=0,
+            quality=72,
+            method=3,
+        )
+        url = "/gradio_api/file=" + quote(path, safe="")
+        progress((step + 1) / total, desc=f"TAE_PREVIEW|{url}|Preview {step + 1}/{total}")
+    except Exception as error:
+        print(f"[tae] preview skipped ({type(error).__name__}: {error})", flush=True)
+
+
+def status() -> str:
+    return "TAE live previews" if _DECODER is not None else "TAE unavailable"

requirements.txt

@@ -9,6 +9,8 @@
 diffusers @ git+https://github.com/huggingface/diffusers.git@665f578278365ea4a3318cb8c9b66ce6c01204b9
 torch==2.11.0
 torchvision==0.26.0
+# Ref2VA preserves each audio/video reference's native sample rate, then resamples it to the audio VAE rate.
+torchaudio==2.11.0
 # The Qwen3-VL processor decides the vision patch count, so a different minor changes the conditioning.
 transformers==5.8.0
 accelerate==1.14.0