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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Bernini-R Wan 2.2 R2V Lightning
+emoji: 🎬
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 6.15.0
+app_file: app.py
+pinned: false
+python_version: "3.10"
+---
+
+# Bernini-R Wan 2.2 R2V Lightning
+
+Reference to video generation with Bernini-R.
+
+- **Paper**: [arXiv 2605.22344](https://arxiv.org/abs/2605.22344)
+- **Model weights**: [Comfy-Org/Bernini-R](https://huggingface.co/Comfy-Org/Bernini-R)
+- **Code**: [github.com/bytedance/Bernini](https://github.com/bytedance/Bernini)

app.py

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+from __future__ import annotations
+
+import asyncio
+import json
+import os
+import pathlib
+import random
+import re
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+import uuid
+from typing import Any
+
+from PIL import Image as _PILImage
+
+_TARGET_MP = 848 * 480
+
+ASPECT_PRESETS: dict[str, tuple[int, int]] = {
+    "16:9": (848, 480),
+    "9:16": (480, 848),
+    "1:1":  (640, 640),
+    "4:3":  (736, 560),
+    "3:4":  (560, 736),
+}
+
+
+def _compute_dims(aspect_ratio: str, first_image_path: str | None, use_ref: bool) -> tuple[int, int]:
+    if use_ref and first_image_path and os.path.exists(first_image_path):
+        try:
+            with _PILImage.open(first_image_path) as im:
+                iw, ih = im.size
+            ratio = iw / ih
+            h = round((_TARGET_MP / ratio) ** 0.5 / 16) * 16
+            w = round((_TARGET_MP / h / 16)) * 16
+            h = max(16, h)
+            w = max(16, w)
+            return w, h
+        except Exception:
+            pass
+    return ASPECT_PRESETS.get(aspect_ratio, (848, 480))
+
+def _setup_cuda_lib_path() -> None:
+    candidates = [
+        "/cuda-image/usr/local/cuda-13.0/targets/x86_64-linux/lib",
+        "/cuda-image/usr/local/cuda-13.0/lib64",
+        "/usr/local/cuda-13.0/targets/x86_64-linux/lib",
+        "/usr/local/cuda-13.0/lib64",
+        "/usr/local/cuda/targets/x86_64-linux/lib",
+        "/usr/local/cuda/lib64",
+    ]
+    for base in ("/cuda-image/usr/local", "/usr/local"):
+        bp = pathlib.Path(base)
+        if bp.exists():
+            for found in bp.rglob("libcudart.so.13*"):
+                candidates.insert(0, str(found.parent))
+                break
+    for p in candidates:
+        if pathlib.Path(p).is_dir():
+            cur = os.environ.get("LD_LIBRARY_PATH", "")
+            os.environ["LD_LIBRARY_PATH"] = f"{p}:{cur}"
+            print(f"[cuda] LD_LIBRARY_PATH set to include {p}", flush=True)
+            return
+    print("[cuda] WARNING: could not find libcudart.so.13 directory", flush=True)
+
+_setup_cuda_lib_path()
+
+import gradio as gr
+import spaces
+import torch
+
+ROOT = pathlib.Path(__file__).resolve().parent
+COMFY = ROOT / "ComfyUI"
+MODELS = COMFY / "models"
+INPUT = COMFY / "input"
+OUTPUT = COMFY / "output"
+
+WORKFLOW_FILE = "bernini_r2v_base.json"
+
+COMFY_COMMIT = "4e1f7cb1db1c26bb9ee61cf1875776517e2abae8"
+
+CUSTOM_NODES = [
+    ("ComfyUI-WanVideoWrapper", "https://github.com/kijai/ComfyUI-WanVideoWrapper.git"),
+    ("ComfyUI-KJNodes", "https://github.com/kijai/ComfyUI-KJNodes.git"),
+    ("ComfyUI-RH-Bernini", "https://github.com/RH-RunningHub/ComfyUI-RH-Bernini.git"),
+    ("ComfyUI-VideoHelperSuite", "https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite.git"),
+    ("ComfyUI-Frame-Interpolation", "https://github.com/Fannovel16/ComfyUI-Frame-Interpolation.git"),
+]
+
+DOWNLOADS = [
+    {
+        "repo": "Comfy-Org/Bernini-R",
+        "file": "diffusion_models/wan2.2_bernini_r_high_noise_fp8_scaled.safetensors",
+        "dest": MODELS / "diffusion_models" / "Wan22_Bernini_HIGH_fp8_e4m3fn_scaled.safetensors",
+        "label": "bernini HIGH fp8",
+    },
+    {
+        "repo": "Comfy-Org/Bernini-R",
+        "file": "diffusion_models/wan2.2_bernini_r_low_noise_fp8_scaled.safetensors",
+        "dest": MODELS / "diffusion_models" / "Wan22_Bernini_LOW_fp8_e4m3fn_scaled.safetensors",
+        "label": "bernini LOW fp8",
+    },
+    {
+        "repo": "Osrivers/nsfw_wan_umt5-xxl_fp8_scaled.safetensors",
+        "file": "nsfw_wan_umt5-xxl_fp8_scaled.safetensors",
+        "dest": MODELS / "text_encoders" / "umt5_xxl_fp8_e4m3fn_scaled.safetensors",
+        "label": "t5 text encoder fp8",
+    },
+    {
+        "repo": "Comfy-Org/Wan_2.1_ComfyUI_repackaged",
+        "file": "split_files/vae/wan_2.1_vae.safetensors",
+        "dest": MODELS / "vae" / "wan_2.1_vae.safetensors",
+        "label": "wan vae",
+    },
+    {
+        "repo": "Kijai/WanVideo_comfy",
+        "file": "Lightx2v/lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16.safetensors",
+        "dest": MODELS / "loras" / "lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank64_bf16.safetensors",
+        "label": "lightx2v lora",
+    },
+    {
+        "repo": "Comfy-Org/Wan_2.1_ComfyUI_repackaged",
+        "file": "split_files/clip_vision/clip_vision_h.safetensors",
+        "dest": MODELS / "clip_vision" / "clip_vision_h.safetensors",
+        "label": "clip vision h",
+    },
+    {
+        "repo": "signsur4739379373/archive",
+        "file": "wan22/wamu_v3_lightning_lora_high_noise_r128.safetensors",
+        "dest": MODELS / "loras" / "wamu_v3_lora_high_noise_r128.safetensors",
+        "label": "wamu high lora",
+    },
+    {
+        "repo": "signsur4739379373/archive",
+        "file": "wan22/wamu_v3_lightning_lora_low_noise_r128.safetensors",
+        "dest": MODELS / "loras" / "wamu_v3_lora_low_noise_r128.safetensors",
+        "label": "wamu low lora",
+    },
+    {
+        "repo": "Kijai/WanVideo_comfy",
+        "file": "Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
+        "dest": MODELS / "loras" / "lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
+        "label": "lightx2v I2V lora",
+    },
+    {
+        "repo": "yeqiu168182/DR34ML4Y_I2V_14B_HIGH",
+        "file": "DR34ML4Y_I2V_14B_HIGH.safetensors",
+        "dest": MODELS / "loras" / "DR34ML4Y_I2V_14B_HIGH.safetensors",
+        "label": "dreamly high lora",
+    },
+    {
+        "repo": "yeqiu168182/DR34ML4Y_I2V_14B_LOW",
+        "file": "DR34ML4Y_I2V_14B_LOW.safetensors",
+        "dest": MODELS / "loras" / "DR34ML4Y_I2V_14B_LOW.safetensors",
+        "label": "dreamly low lora",
+    },
+    {
+        "repo": "signsur4739379373/ipnc_antiloras_archive",
+        "file": "Wan22_BerniniR_DR34ML4Y_HIGH_Rank1_IPNC.safetensors",
+        "dest": MODELS / "loras" / "Wan22_BerniniR_DR34ML4Y_HIGH_Rank1_IPNC.safetensors",
+        "label": "dreamly high IPNC",
+    },
+    {
+        "repo": "signsur4739379373/ipnc_antiloras_archive",
+        "file": "Wan22_BerniniR_DR34ML4Y_LOW_Rank1_IPNC.safetensors",
+        "dest": MODELS / "loras" / "Wan22_BerniniR_DR34ML4Y_LOW_Rank1_IPNC.safetensors",
+        "label": "dreamly low IPNC",
+    },
+    {
+        "repo": "signsur4739379373/ipnc_antiloras_archive",
+        "file": "Wan22_BerniniR_wamu_v3_HIGH_Rank1_IPNC.safetensors",
+        "dest": MODELS / "loras" / "Wan22_BerniniR_wamu_v3_HIGH_Rank1_IPNC.safetensors",
+        "label": "wamu high IPNC",
+    },
+    {
+        "repo": "signsur4739379373/ipnc_antiloras_archive",
+        "file": "Wan22_BerniniR_wamu_v3_LOW_Rank1_IPNC.safetensors",
+        "dest": MODELS / "loras" / "Wan22_BerniniR_wamu_v3_LOW_Rank1_IPNC.safetensors",
+        "label": "wamu low IPNC",
+    },
+    {
+        "repo": "signsur4739379373/archive",
+        "file": "wan22/nsfwsvicamera_lora_high_r128.safetensors",
+        "dest": MODELS / "loras" / "nsfwsvicamera_lora_high_r128.safetensors",
+        "label": "svicamera high lora",
+    },
+    {
+        "repo": "signsur4739379373/archive",
+        "file": "wan22/nsfwsvicamera_lora_low_r128.safetensors",
+        "dest": MODELS / "loras" / "nsfwsvicamera_lora_low_r128.safetensors",
+        "label": "svicamera low lora",
+    },
+    {
+        "repo": "signsur4739379373/ipnc_antiloras_archive",
+        "file": "Wan22_BerniniR_nsfwsvicamera_HIGH_Rank1_IPNC.safetensors",
+        "dest": MODELS / "loras" / "Wan22_BerniniR_nsfwsvicamera_HIGH_Rank1_IPNC.safetensors",
+        "label": "svicamera high IPNC",
+    },
+    {
+        "repo": "signsur4739379373/ipnc_antiloras_archive",
+        "file": "Wan22_BerniniR_nsfwsvicamera_LOW_Rank1_IPNC.safetensors",
+        "dest": MODELS / "loras" / "Wan22_BerniniR_nsfwsvicamera_LOW_Rank1_IPNC.safetensors",
+        "label": "svicamera low IPNC",
+    },
+    {
+        "repo": "VMTamashii/rife49",
+        "file": "rife49.pth",
+        "dest": COMFY / "custom_nodes" / "ComfyUI-Frame-Interpolation" / "ckpts" / "rife" / "rife49.pth",
+        "label": "rife49 model",
+    },
+]
+
+SAVE_BASE = tempfile.mkdtemp(prefix="bernini_comfy_")
+os.makedirs(SAVE_BASE, exist_ok=True)
+
+MAX_SEED = 2_147_483_647
+
+_comfy_ready = False
+_nodes_ready = False
+_models_ready = False
+
+def _run(cmd: list[str], cwd: pathlib.Path | None = None, check: bool = True) -> subprocess.CompletedProcess:
+    print("[setup]", " ".join(str(x) for x in cmd), flush=True)
+    return subprocess.run(cmd, cwd=str(cwd) if cwd else None, check=check)
+
+def _pip_install(args: list[str], check: bool = True) -> None:
+    _run([sys.executable, "-m", "pip", "install", "--no-cache-dir", *args], check=check)
+
+def _install_filtered_requirements(req_path: pathlib.Path) -> None:
+    if not req_path.exists():
+        return
+    blocked = {"torch", "torchvision", "torchaudio", "transformers", "huggingface-hub", "accelerate"}
+    safe: list[str] = []
+    for line in req_path.read_text(encoding="utf-8", errors="ignore").splitlines():
+        item = line.strip()
+        if not item or item.startswith("#"):
+            continue
+        low = item.lower().replace("_", "-")
+        package = re.split(r"[<>=!~;\[\s]", low, maxsplit=1)[0]
+        if package in blocked:
+            continue
+        safe.append(item)
+    if safe:
+        _pip_install(safe, check=False)
+
+def _ensure_repo(path: pathlib.Path, url: str, commit: str | None = None) -> None:
+    if not path.exists():
+        _run(["git", "clone", "--depth", "1", url, str(path)])
+    if commit:
+        _run(["git", "fetch", "--depth", "1", "origin", commit], cwd=path, check=False)
+        _run(["git", "checkout", commit], cwd=path, check=False)
+
+def _apply_comfy_utils_namespace_fix() -> None:
+    utils_path = COMFY / "utils"
+    utilities_path = COMFY / "utilities"
+    if utils_path.exists() and not utilities_path.exists():
+        utils_path.rename(utilities_path)
+    replacements = [
+        (re.compile(r"(^|\n)(\s*)from utils(\s|\.)"), r"\1\2from utilities\3"),
+        (re.compile(r"(^|\n)(\s*)import utils(\s|\.|$)"), r"\1\2import utilities\3"),
+    ]
+    for path in COMFY.rglob("*.py"):
+        if "__pycache__" in path.parts:
+            continue
+        try:
+            text = path.read_text(encoding="utf-8")
+        except UnicodeDecodeError:
+            continue
+        updated = text
+        for pattern, repl in replacements:
+            updated = pattern.sub(repl, updated)
+        updated = updated.replace("from utils import", "from utilities import")
+        if updated != text:
+            path.write_text(updated, encoding="utf-8")
+
+def _download_to_dest(repo: str, file: str, dest: pathlib.Path, token: str | None = None) -> None:
+    from huggingface_hub import hf_hub_download
+    dest.parent.mkdir(parents=True, exist_ok=True)
+    downloaded = hf_hub_download(repo_id=repo, filename=file, token=token)
+    if not dest.exists():
+        shutil.copy2(downloaded, dest)
+
+def _ensure_comfy() -> None:
+    global _comfy_ready
+    if _comfy_ready:
+        return
+
+    _ensure_repo(COMFY, "https://github.com/Comfy-Org/ComfyUI.git", commit=COMFY_COMMIT)
+    _install_filtered_requirements(COMFY / "requirements.txt")
+
+    custom_root = COMFY / "custom_nodes"
+    custom_root.mkdir(parents=True, exist_ok=True)
+    for name, url in CUSTOM_NODES:
+        node_path = custom_root / name
+        _ensure_repo(node_path, url)
+        _install_filtered_requirements(node_path / "requirements.txt")
+
+    _apply_comfy_utils_namespace_fix()
+    _comfy_ready = True
+    print("[startup] ComfyUI + custom nodes ready.", flush=True)
+
+def _ensure_models() -> None:
+    global _models_ready
+    if _models_ready:
+        return
+
+    token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_HUB_TOKEN")
+    for item in DOWNLOADS:
+        dest = pathlib.Path(item["dest"])
+        if dest.exists():
+            continue
+        print(f"[startup] downloading {item['label']}...", flush=True)
+        _download_to_dest(item["repo"], item["file"], dest, token)
+
+    _models_ready = True
+    print("[startup] all models ready.", flush=True)
+
+def _init_comfy_nodes() -> None:
+    global _nodes_ready
+    if _nodes_ready:
+        return
+
+    comfy_path = str(COMFY)
+    sys.path = [p for p in sys.path if p != comfy_path]
+    sys.path.insert(0, comfy_path)
+    for module_name in list(sys.modules):
+        if module_name == "utils" or module_name.startswith("utils."):
+            del sys.modules[module_name]
+    os.chdir(COMFY)
+
+    import types as _types, importlib.util as _ilu
+    _ta = _types.ModuleType("torchaudio")
+    _ta.__spec__ = _ilu.spec_from_loader("torchaudio", loader=None)
+    _ta.__version__ = "0.0.0"
+    sys.modules.setdefault("torchaudio", _ta)
+    for _sub in ["torchaudio.functional", "torchaudio.transforms", "torchaudio._extension"]:
+        _m = _types.ModuleType(_sub)
+        _m.__spec__ = _ilu.spec_from_loader(_sub, loader=None)
+        sys.modules.setdefault(_sub, _m)
+
+    import execution
+    import nodes
+    import server
+
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    server_instance = server.PromptServer(loop)
+    execution.PromptQueue(server_instance)
+    loop.run_until_complete(nodes.init_extra_nodes())
+    _nodes_ready = True
+    print("[startup] ComfyUI nodes initialized.", flush=True)
+
+import numpy as _np
+
+def _compute_split(total_steps, flow_shift=3.0, boundary_ratio=0.9, num_train_timesteps=1000):
+    sigmas = _np.linspace(1, 0, total_steps + 1)[:-1]
+    sigma_shifted = flow_shift * sigmas / (1 + (flow_shift - 1) * sigmas)
+    timesteps = sigma_shifted * (num_train_timesteps - 1)
+    n_above = int(_np.sum(timesteps >= boundary_ratio * num_train_timesteps))
+    return total_steps - n_above
+
+
+DEFAULT_NEGATIVE = (
+    "色调艳丽，过曝，静态，场景切换,细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，"
+    "最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，"
+    "畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
+)
+
+
+R2V_TEMPLATE = """You are an expert at writing subject-driven video generation prompts. I'm providing you with:
+1. {image_num} reference image(s) of the subject(s) that will appear in the video (referred to as image0, image1, image2, ... in order).
+2. An original video description text.
+
+Your task is to rewrite the original description into a new format with TWO parts concatenated together:
+
+**Part 1 - Short instruction**: A concise sentence describing who the subject(s) from the reference image(s) are, what they look like briefly, where they are, and what key action/motion they perform. Reference the subject(s) using "image0", "image1", etc. to link them to the provided reference images.
+
+**Part 2 - Long instruction**: A detailed "Generate a video where..." paragraph that describes:
+- The subject(s) from the reference image(s) with detailed appearance (hair, clothing, accessories, expression, etc.), referencing them as "the person/man/woman from image0" etc.
+- The scene/environment in detail (background, lighting, objects, atmosphere).
+- The motion and actions in a step-by-step temporal sequence (at the start..., then..., after that...).
+- The motion should remain natural and realistic.
+
+Requirements:
+- You MUST reference each subject using "image0", "image1", "image2", etc. to correspond to the provided reference images in order.
+- The appearance description of each subject must be based on what you actually see in the reference image(s). Do NOT hallucinate details not visible in the images.
+- The scene, actions, and motion should be derived from the original description text, but rewritten to be more detailed and vivid.
+- The output must be entirely in English.
+- Return ONLY a JSON object with one key: "rewritten_text". The value should be the full rewritten text (short instruction + long instruction concatenated as one string). No extra text.
+
+Original description:
+{original_text}
+"""
+
+
+def _enhance_prompt_r2v(prompt: str, image_paths: list[str]) -> str | None:
+    """Call grok-4.3 via xAI API to enhance prompt for r2v. Returns None on failure."""
+    api_key = os.environ.get("XAI_API_KEY")
+    if not api_key:
+        print("[enhancer] XAI_API_KEY not set, skipping enhancement", flush=True)
+        return None
+    try:
+        import base64
+        import json as _json
+        from openai import OpenAI
+        client = OpenAI(api_key=api_key, base_url="https://api.x.ai/v1")
+        image_num = len(image_paths)
+        user_text = R2V_TEMPLATE.format(image_num=max(image_num, 1), original_text=prompt)
+        content: list = [{"type": "text", "text": user_text}]
+        for i, path in enumerate(image_paths):
+            if not path or not os.path.exists(path):
+                continue
+            with open(path, "rb") as f:
+                b64 = base64.b64encode(f.read()).decode("utf-8")
+            content.append({"type": "text", "text": f"\n[Image {i}]:"})
+            content.append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}})
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": content},
+        ]
+        resp = client.chat.completions.create(
+            model="grok-4.3",
+            messages=messages,
+            max_completion_tokens=8192,
+            response_format={"type": "json_object"},
+        )
+        text = resp.choices[0].message.content or ""
+        enhanced = _json.loads(text).get("rewritten_text", "").strip()
+        if enhanced:
+            print(f"[enhancer] enhanced prompt ({len(enhanced)} chars)", flush=True)
+            return enhanced
+        return None
+    except Exception as e:
+        print(f"[enhancer] failed: {e}", flush=True)
+        return None
+
+def _load_workflow() -> dict[str, Any]:
+    wf_path = ROOT / WORKFLOW_FILE
+    return json.loads(wf_path.read_text(encoding="utf-8"))
+
+NODE_OUTPUT = 67                            
+
+def _convert_visual_to_api(visual: dict) -> dict[str, Any]:
+    import nodes as comfy_nodes
+
+    link_map: dict[int, Any] = {}
+    for link in visual.get("links", []):
+        link_id, src_node, src_slot, *_ = link
+        link_map[int(link_id)] = [str(src_node), src_slot]
+
+    BYPASS_TYPES = {"PathchSageAttentionKJ"}
+    for node in visual.get("nodes", []):
+        if node.get("type") not in BYPASS_TYPES:
+            continue
+        src_ref = None
+        for inp in node.get("inputs") or []:
+            lid = inp.get("link")
+            if lid is not None and lid in link_map:
+                src_ref = link_map[lid]
+                break
+        if src_ref is None:
+            continue
+        for out in node.get("outputs") or []:
+            for out_lid in (out.get("links") or []):
+                if out_lid in link_map:
+                    link_map[out_lid] = list(src_ref)                                     
+
+    set_sources: dict[str, Any] = {}
+    set_node_sources: dict[int, Any] = {}
+    for node in visual.get("nodes", []):
+        if node.get("type") not in {"SetNode", "SetNodeAny"}:
+            continue
+        name = (node.get("widgets_values") or [""])[0]
+        for inp in node.get("inputs") or []:
+            lid = inp.get("link")
+            if lid in link_map:
+                set_sources[name] = link_map[lid]
+                set_node_sources[int(node["id"])] = link_map[lid]
+
+    changed = True
+    while changed:
+        changed = False
+        for lid, src in list(link_map.items()):
+            if isinstance(src, list):
+                try:
+                    sid = int(src[0])
+                except (ValueError, TypeError):
+                    continue
+                if sid in set_node_sources:
+                    rep = set_node_sources[sid]
+                    if link_map[lid] != rep:
+                        link_map[lid] = rep
+                        changed = True
+        for node in visual.get("nodes", []):
+            if node.get("type") not in {"GetNode", "GetNodeAny"}:
+                continue
+            name = (node.get("widgets_values") or [""])[0]
+            if name not in set_sources:
+                continue
+            for lid, src in list(link_map.items()):
+                if isinstance(src, list) and src[0] == str(node["id"]):
+                    rep = set_sources[name]
+                    if link_map[lid] != rep:
+                        link_map[lid] = rep
+                        changed = True
+
+    skip_types = {"Note", "NoteNode", "MarkdownNote", "GetNode", "GetNodeAny", "SetNode", "SetNodeAny",
+                  "PathchSageAttentionKJ"}
+    MUTED_MODE = 4
+    api: dict[str, Any] = {}
+
+    for node in visual.get("nodes", []):
+        node_id = int(node["id"])
+        class_type = node.get("type", "")
+        if class_type in skip_types:
+            continue
+        if node.get("mode") == MUTED_MODE:
+            print(f"[workflow] skipping muted node {class_type} id={node_id}", flush=True)
+            continue
+        if class_type not in comfy_nodes.NODE_CLASS_MAPPINGS:
+            print(f"[workflow] skipping unknown node {class_type} id={node_id}", flush=True)
+            continue
+
+        inputs: dict[str, Any] = {}
+        for inp in node.get("inputs") or []:
+            lid = inp.get("link")
+            if lid is not None and lid in link_map:
+                inputs[inp["name"]] = link_map[lid]
+
+        widgets = node.get("widgets_values") or []
+        if widgets:
+            if isinstance(widgets, dict):
+                                                                                
+                for k, v in widgets.items():
+                    if k != "videopreview":
+                        inputs.setdefault(k, v)
+            else:
+                                                                                
+                param_names = [
+                    inp["name"]
+                    for inp in node.get("inputs") or []
+                    if isinstance(inp.get("widget"), dict) and inp["widget"].get("name")
+                ]
+                if not param_names:
+                                               
+                    cls = comfy_nodes.NODE_CLASS_MAPPINGS[class_type]
+                    try:
+                        cls_inp = cls.INPUT_TYPES()
+                        for grp in ("required", "optional"):
+                            for pname, spec in cls_inp.get(grp, {}).items():
+                                typ = spec[0] if isinstance(spec, (tuple, list)) and spec else spec
+                                if isinstance(typ, (list, tuple)) or str(typ).upper() in {"FLOAT", "INT", "STRING", "BOOLEAN", "COMBO"}:
+                                    param_names.append(pname)
+                    except Exception:
+                        pass
+
+                CTRL = {'randomize', 'fixed', 'increment', 'decrement'}
+                wi = 0
+                for pname in param_names:
+                    if wi >= len(widgets):
+                        break
+                    inputs.setdefault(pname, widgets[wi])
+                    wi += 1
+                    if wi < len(widgets) and isinstance(widgets[wi], str) and widgets[wi] in CTRL:
+                        wi += 1                               
+
+        api[str(node_id)] = {"class_type": class_type, "inputs": inputs}
+
+    return api
+
+_LORA_CHAIN = [
+    {
+        "id": 2003,
+        "chain": "high",
+        "file": "DR34ML4Y_I2V_14B_HIGH.safetensors",
+        "default": 0.3,
+    },
+    {
+        "id": 2001,
+        "chain": "high",
+        "file": "wamu_v3_lora_high_noise_r128.safetensors",
+        "default": 1.0,
+    },
+    {
+        "id": 2002,
+        "chain": "high",
+        "file": "lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
+        "default": -1.0,
+    },
+    {
+        "id": 2004,
+        "chain": "low",
+        "file": "wamu_v3_lora_low_noise_r128.safetensors",
+        "default": 0.5,
+    },
+    {
+        "id": 2005,
+        "chain": "low",
+        "file": "lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
+        "default": -1.0,
+    },
+    {
+        "id": 2006,
+        "chain": "low",
+        "file": "DR34ML4Y_I2V_14B_LOW.safetensors",
+        "default": 0.5,
+    },
+]
+
+LORA_DEFAULTS = {l["id"]: l["default"] for l in _LORA_CHAIN}
+
+
+RIFE_MULTIPLIERS = {"no rife": 1, "2x rife": 2, "4x rife": 4}
+
+
+def _patch_api_workflow(
+    api: dict[str, Any],
+    prompt: str,
+    negative: str,
+    image_names: list[str],
+    seed: int,
+    width: int,
+    height: int,
+    num_steps: int = 6,
+    num_frames: int = 145,
+    sampler_name: str = "uni_pc",
+    base_fps: int = 15,
+    rife_mode: str = "no rife",
+    loras_enabled: bool = False,
+    lora_strengths: dict[int, float] | None = None,
+    ipnc_enabled: bool = False,
+    ipnc_strengths: dict[str, float] | None = None,
+) -> dict[str, Any]:
+    if ipnc_strengths is None:
+        ipnc_strengths = {"wamu_h": 100, "dreamly_h": 100, "wamu_l": 100, "dreamly_l": 100, "svicamera_h": 100, "svicamera_l": 100}
+
+    def _set(node_id: int, key: str, val: Any) -> None:
+        k = str(node_id)
+        if k in api:
+            api[k]["inputs"][key] = val
+
+    _set(6,   "text",       f"You are a helpful assistant specialized in subject-to-video generation. {prompt}")
+    _set(7,   "text",       negative)
+    _set(112, "value",      width)
+    _set(114, "value",      height)
+    _set(128, "value",      seed)
+    _set(57,  "noise_seed", seed)
+    _set(58,  "noise_seed", 0)
+    _set(132, "height",  height)
+    _set(132, "width",   width)
+    _set(132, "length",  num_frames)
+
+    _set(119, "image", image_names[0])
+
+    split = _compute_split(num_steps)
+    _set(57, "steps",        num_steps)
+    _set(57, "start_at_step", 0)
+    _set(57, "end_at_step",   split)
+    _set(57, "sampler_name",  sampler_name)
+    _set(58, "steps",        num_steps)
+    _set(58, "start_at_step", split)
+    _set(58, "end_at_step",   num_steps)
+    _set(58, "sampler_name",  sampler_name)
+
+    for i, name in enumerate(image_names[1:], 1):
+        nid = 5000 + i
+        api[str(nid)] = {
+            "class_type": "LoadImage",
+            "inputs": {"image": name, "upload": "image"},
+        }
+        api["136"]["inputs"][f"images.image{i}"] = [str(nid), 0]
+
+    if loras_enabled:
+        strengths = lora_strengths or LORA_DEFAULTS
+        high_src = ["71", 0]
+        low_src  = ["56", 0]
+
+        # IDs 2001-2006: main loras (existing)
+        # IDs 2011-2014: IPNC loras injected after specific mains
+        # Chain HIGH:
+        #   DR34ML4Y(2003) -> DR34ML4Y_IPNC(2011) -> wamu(2001) ->
+        #   lightx2v_I2V(2002) -> wamu_IPNC(2012) -> lightx2v_T2V(baked)
+        # Chain LOW:
+        #   wamu(2004) -> lightx2v_I2V(2005) -> wamu_IPNC(2013) ->
+        #   lightx2v_T2V(baked) -> DR34ML4Y(2006) -> DR34ML4Y_IPNC(2014)
+
+        s_wamu_h_val      = strengths.get(2001, 1.0)
+        s_dreamly_h_val   = strengths.get(2003, 0.3)
+        s_wamu_l_val      = strengths.get(2004, 0.5)
+        s_dreamly_l_val   = strengths.get(2006, 0.5)
+        s_svicamera_h_val = strengths.get(2021, 0.0)
+        s_svicamera_l_val = strengths.get(2023, 0.0)
+
+        def _add_lora(nid, src, fname, strength):
+            api[str(nid)] = {
+                "class_type": "LoraLoaderModelOnly",
+                "inputs": {"model": src, "lora_name": fname,
+                           "strength_model": strength},
+            }
+            return [str(nid), 0]
+
+        # ── HIGH chain ──────────────────────────────────────────────
+        # DR34ML4Y HIGH
+        high_src = _add_lora(2003, high_src,
+                              "DR34ML4Y_I2V_14B_HIGH.safetensors", s_dreamly_h_val)
+        # DR34ML4Y_IPNC HIGH
+        if ipnc_enabled and ipnc_strengths.get("dreamly_h", 0) > 0:
+            ipnc_s = s_dreamly_h_val * ipnc_strengths["dreamly_h"] / 100.0
+            high_src = _add_lora(2011, high_src,
+                                  "Wan22_BerniniR_DR34ML4Y_HIGH_Rank1_IPNC.safetensors", ipnc_s)
+        # wamu HIGH
+        high_src = _add_lora(2001, high_src,
+                              "wamu_v3_lora_high_noise_r128.safetensors", s_wamu_h_val)
+        # lightx2v_I2V HIGH (negation)
+        high_src = _add_lora(2002, high_src,
+                              "lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
+                              -s_wamu_h_val)
+        # wamu_IPNC HIGH (after lightx2v_I2V negation)
+        if ipnc_enabled and ipnc_strengths.get("wamu_h", 0) > 0:
+            ipnc_s = s_wamu_h_val * ipnc_strengths["wamu_h"] / 100.0
+            high_src = _add_lora(2012, high_src,
+                                  "Wan22_BerniniR_wamu_v3_HIGH_Rank1_IPNC.safetensors", ipnc_s)
+        # svicamera HIGH
+        high_src = _add_lora(2021, high_src, "nsfwsvicamera_lora_high_r128.safetensors",
+                              s_svicamera_h_val)
+        # svicamera_IPNC HIGH
+        if ipnc_enabled and ipnc_strengths.get("svicamera_h", 0) > 0:
+            ipnc_s = s_svicamera_h_val * ipnc_strengths["svicamera_h"] / 100.0
+            high_src = _add_lora(2022, high_src,
+                                  "Wan22_BerniniR_nsfwsvicamera_HIGH_Rank1_IPNC.safetensors", ipnc_s)
+
+        # ── LOW chain ───────────────────────────────────────────────
+        # wamu LOW
+        low_src = _add_lora(2004, low_src,
+                             "wamu_v3_lora_low_noise_r128.safetensors", s_wamu_l_val)
+        # lightx2v_I2V LOW (negation)
+        low_src = _add_lora(2005, low_src,
+                             "lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors",
+                             -s_wamu_l_val)
+        # wamu_IPNC LOW (after lightx2v_I2V negation)
+        if ipnc_enabled and ipnc_strengths.get("wamu_l", 0) > 0:
+            ipnc_s = s_wamu_l_val * ipnc_strengths["wamu_l"] / 100.0
+            low_src = _add_lora(2013, low_src,
+                                 "Wan22_BerniniR_wamu_v3_LOW_Rank1_IPNC.safetensors", ipnc_s)
+        # DR34ML4Y LOW
+        low_src = _add_lora(2006, low_src,
+                             "DR34ML4Y_I2V_14B_LOW.safetensors", s_dreamly_l_val)
+        # DR34ML4Y_IPNC LOW
+        if ipnc_enabled and ipnc_strengths.get("dreamly_l", 0) > 0:
+            ipnc_s = s_dreamly_l_val * ipnc_strengths["dreamly_l"] / 100.0
+            low_src = _add_lora(2014, low_src,
+                                 "Wan22_BerniniR_DR34ML4Y_LOW_Rank1_IPNC.safetensors", ipnc_s)
+        # svicamera LOW
+        low_src = _add_lora(2023, low_src, "nsfwsvicamera_lora_low_r128.safetensors",
+                             s_svicamera_l_val)
+        # svicamera_IPNC LOW
+        if ipnc_enabled and ipnc_strengths.get("svicamera_l", 0) > 0:
+            ipnc_s = s_svicamera_l_val * ipnc_strengths["svicamera_l"] / 100.0
+            low_src = _add_lora(2024, low_src,
+                                 "Wan22_BerniniR_nsfwsvicamera_LOW_Rank1_IPNC.safetensors", ipnc_s)
+
+        api["63"]["inputs"]["model"] = high_src
+        api["64"]["inputs"]["model"] = low_src
+
+    multiplier = RIFE_MULTIPLIERS.get(rife_mode, 1)
+    output_fps = base_fps * multiplier
+
+    if multiplier > 1:
+        api["3001"] = {
+            "class_type": "RIFE VFI",
+            "inputs": {
+                "frames":                       ["8", 0],
+                "ckpt_name":                    "rife49.pth",
+                "clear_cache_after_n_frames":   10,
+                "multiplier":                   multiplier,
+                "fast_mode":                    True,
+                "ensemble":                     True,
+                "scale_factor":                 1,
+                "dtype":                        "float32",
+                "torch_compile":                False,
+                "batch_size":                   1,
+            },
+        }
+        api["67"]["inputs"]["images"] = ["3001", 0]
+
+    if "67" in api and isinstance(api["67"]["inputs"].get("frame_rate"), (int, float)):
+        api["67"]["inputs"]["frame_rate"] = output_fps
+
+    return api
+
+def _execute_workflow(api: dict[str, Any]) -> str:
+    import execution, server as comfy_server
+
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    srv = comfy_server.PromptServer(loop)
+
+    try:
+        executor = execution.PromptExecutor(
+            srv,
+            cache_type=execution.CacheType.RAM_PRESSURE,
+            cache_args={"lru": 0, "ram": 2.0, "ram_inactive": 8.0},
+        )
+    except Exception:
+        executor = execution.PromptExecutor(srv)
+
+    prompt_id = str(uuid.uuid4())
+    loop.run_until_complete(executor.execute_async(api, prompt_id, extra_data={}, execute_outputs=[str(NODE_OUTPUT)]))
+
+    if not getattr(executor, "success", True):
+        msgs = getattr(executor, "status_messages", [])
+        msg = msgs[-1] if msgs else "comfy execution failed"
+        raise RuntimeError(str(msg))
+
+    for output in (executor.history_result or {}).get("outputs", {}).values():
+        for items in output.values():
+            if not isinstance(items, list):
+                continue
+            for item in items:
+                fname = item.get("filename") if isinstance(item, dict) else None
+                if not fname:
+                    continue
+                subfolder = item.get("subfolder", "")
+                kind = item.get("type", "output")
+                base = OUTPUT if kind == "output" else COMFY / kind
+                candidate = base / subfolder / fname if subfolder else base / fname
+                if candidate.exists():
+                    return str(candidate)
+
+    raise RuntimeError("ComfyUI finished but no output video found")
+
+def _estimate_duration(num_steps, duration_secs, base_fps, aspect_ratio, use_ref_aspect, rife_mode):
+    BASE_FRAMES = 45
+    BASE_STEP_S = 5.3
+    STEP_EXP   = 1.2
+    n_frames = max(1, round(float(duration_secs) * int(base_fps)))
+    gen_w, gen_h = ASPECT_PRESETS.get(str(aspect_ratio), (848, 480))
+    frame_factor = (n_frames * gen_w * gen_h) / (BASE_FRAMES * 848 * 480)
+    step_time    = BASE_STEP_S * frame_factor ** STEP_EXP
+    sampler_t    = int(num_steps) * step_time
+    swap_t       = 12 + 6 * frame_factor ** 0.5
+    overhead_t   = 20 + 8 * max(0, frame_factor - 1)
+    rife_mult    = {"2x rife": 2, "4x rife": 4}.get(str(rife_mode), 1)
+    rife_t       = n_frames * (rife_mult - 1) * 0.05 if rife_mult > 1 else 0
+    total        = (sampler_t + swap_t + overhead_t + rife_t) * 1.05
+    return max(30, int(total))
+
+
+def _get_duration(
+    prompt, negative, image_input, seed, aspect_ratio, use_ref_aspect,
+    gpu_budget, num_steps, duration_secs, sampler_name, base_fps, rife_mode, *args, **kwargs,
+):
+    if gpu_budget and int(gpu_budget) > 0:
+        return int(gpu_budget)
+    return _estimate_duration(num_steps, duration_secs, base_fps, aspect_ratio, use_ref_aspect, rife_mode)
+
+@spaces.GPU(duration=_get_duration)
+def generate_handler(
+    prompt: str,
+    negative: str,
+    image_input: Any,
+    seed: int,
+    aspect_ratio: str = "16:9",
+    use_ref_aspect: bool = False,
+    gpu_budget: int = 0,  # 0 = auto-estimate
+    num_steps: int = 6,
+    duration_secs: float = 10.0,
+    sampler_name: str = "uni_pc",
+    base_fps: int = 15,
+    rife_mode: str = "no rife",
+    loras_enabled: bool = False,
+    s_wamu_h: float = 1.0,
+    s_dreamly_h: float = 0.3,
+    s_wamu_l: float = 0.5,
+    s_dreamly_l: float = 0.5,
+    enhance_prompt: bool = False,
+    ipnc_enabled: bool = False,
+    ipnc_wamu_h: float = 100.0,
+    ipnc_dreamly_h: float = 100.0,
+    ipnc_wamu_l: float = 100.0,
+    ipnc_dreamly_l: float = 100.0,
+    s_svicamera_h: float = 0.0,
+    s_svicamera_l: float = 0.0,
+    ipnc_svicamera_h: float = 100.0,
+    ipnc_svicamera_l: float = 100.0,
+    progress=gr.Progress(track_tqdm=True),
+):
+    import traceback as _tb
+    try:
+        if not (prompt or "").strip():
+            return None, "enter a prompt"
+
+        final_seed = int(seed) if seed else random.randint(0, MAX_SEED)
+        negative = negative or DEFAULT_NEGATIVE
+
+        def _coerce_gallery(g):
+            paths = []
+            for item in (g or []):
+                if isinstance(item, str):
+                    paths.append(item)
+                elif isinstance(item, dict):
+                    p = item.get("path") or item.get("name")
+                    if p: paths.append(p)
+                elif isinstance(item, (list, tuple)) and item:
+                    p = item[0]
+                    if isinstance(p, str): paths.append(p)
+                    elif isinstance(p, dict): paths.append(p.get("path",""))
+            return [p for p in paths if p and os.path.exists(p)][:5]
+
+        image_paths = _coerce_gallery(image_input)
+        if not image_paths:
+            return None, "upload at least one reference image"
+
+        INPUT.mkdir(parents=True, exist_ok=True)
+        dest_names = []
+        for p in image_paths:
+            dn = f"ref_{uuid.uuid4().hex[:8]}_{os.path.basename(p)}"
+            shutil.copy2(p, INPUT / dn)
+            dest_names.append(dn)
+
+        gen_w, gen_h = _compute_dims(str(aspect_ratio), image_paths[0], bool(use_ref_aspect))
+
+        enhanced_prompt_text = None
+        if enhance_prompt:
+            progress(0.05, desc="enhancing prompt...")
+            enhanced_prompt_text = _enhance_prompt_r2v(prompt, image_paths)
+            if enhanced_prompt_text:
+                prompt = enhanced_prompt_text
+
+        progress(0.1, desc="building workflow...")
+        visual_wf = _load_workflow()
+        api_wf = _convert_visual_to_api(visual_wf)
+        api_wf = _patch_api_workflow(
+            api_wf, prompt, negative,
+            dest_names,
+            final_seed, gen_w, gen_h,
+            num_steps=int(num_steps),
+            num_frames=max(1, round(float(duration_secs) * int(base_fps))),
+            sampler_name=str(sampler_name),
+            base_fps=int(base_fps),
+            rife_mode=str(rife_mode),
+            loras_enabled=bool(loras_enabled),
+            lora_strengths={
+                2001: float(s_wamu_h),
+                2002: -float(s_wamu_h),
+                2003: float(s_dreamly_h),
+                2004: float(s_wamu_l),
+                2005: -float(s_wamu_l),
+                2006: float(s_dreamly_l),
+                2021: float(s_svicamera_h),
+                2023: float(s_svicamera_l),
+            },
+            ipnc_enabled=bool(ipnc_enabled),
+            ipnc_strengths={
+                "wamu_h":       float(ipnc_wamu_h),
+                "dreamly_h":    float(ipnc_dreamly_h),
+                "wamu_l":       float(ipnc_wamu_l),
+                "dreamly_l":    float(ipnc_dreamly_l),
+                "svicamera_h":  float(ipnc_svicamera_h),
+                "svicamera_l":  float(ipnc_svicamera_l),
+            },
+        )
+
+        progress(0.3, desc="generating...")
+        output_video = _execute_workflow(api_wf)
+    except Exception as e:
+        tb = _tb.format_exc()
+        print(f"[generate] EXCEPTION: {tb}", flush=True)
+        return None, f"Generation failed: {type(e).__name__}: {e}"
+
+    ts = time.strftime("%Y%m%d_%H%M%S")
+    out_path = os.path.join(SAVE_BASE, f"r2v_{ts}.mp4")
+    shutil.copy2(output_video, out_path)
+
+    status = f"Done: {out_path}"
+    if enhanced_prompt_text:
+        status += f"\n\n--- Enhanced prompt ---\n{enhanced_prompt_text}"
+    return out_path, status
+
+with gr.Blocks(title="Bernini-R Wan 2.2 R2V Lightning") as demo:
+    gr.Markdown("# Bernini-R Wan 2.2 R2V Lightning")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Gallery(
+                label="Reference images (up to 5)",
+                columns=5,
+                type="filepath",
+                height=160,
+            )
+            prompt = gr.Textbox(
+                label="Prompt",
+                lines=3,
+                placeholder="Describe the subject's action in detail. Reference uploaded images by name (e.g. image0, image1). Example: the person in image0, wearing the outfit from image1, walks confidently through a busy train station.",
+                value="Keeping the exact identity and appearance the same as in image0, the person in image0 dances in a supermarket.",
+            )
+            with gr.Accordion("Negative prompt", open=False):
+                negative = gr.Textbox(
+                    label="",
+                    lines=2,
+                    value=DEFAULT_NEGATIVE,
+                )
+            with gr.Group():
+                aspect_ratio = gr.Radio(
+                    choices=list(ASPECT_PRESETS.keys()),
+                    value="16:9",
+                    label="Aspect ratio",
+                )
+                use_ref_aspect = gr.Checkbox(
+                    label="use first reference image aspect ratio",
+                    value=False,
+                )
+
+            with gr.Group(elem_id="loras_9999"):
+                loras_enabled = gr.Checkbox(label="optional loras", value=False)
+                with gr.Column(visible=False) as loras_section:
+                    with gr.Accordion("loras", open=True):
+                        with gr.Group():
+                            gr.Markdown("<div style='padding-left:8px'>High</div>")
+                            with gr.Row():
+                                s_wamu_h = gr.Slider(-2, 2, value=0.9, step=0.05, label="wamu")
+                                s_dreamly_h = gr.Slider(-2, 2, value=0.9, step=0.05, label="dreamly")
+                                s_svicamera_h = gr.Slider(-2, 2, value=0.0, step=0.05, label="svicamera")
+                        with gr.Group():
+                            gr.Markdown("<div style='padding-left:8px'>Low</div>")
+                            with gr.Row():
+                                s_wamu_l    = gr.Slider(-2, 2, value=0.5, step=0.05, label="wamu")
+                                s_dreamly_l = gr.Slider(-2, 2, value=0.7, step=0.05, label="dreamly")
+                                s_svicamera_l = gr.Slider(-2, 2, value=0.0, step=0.05, label="svicamera")
+                        gr.Markdown("<hr style='margin:8px 0'>")
+                        ipnc_enabled = gr.Checkbox(label="IPNC", value=False)
+                        with gr.Column(visible=False) as ipnc_section:
+                            with gr.Group():
+                                gr.Markdown("<div style='padding-left:8px'>High</div>")
+                                with gr.Row():
+                                    ipnc_wamu_h    = gr.Slider(0, 200, value=100, step=1, label="wamu")
+                                    ipnc_dreamly_h = gr.Slider(0, 200, value=100, step=1, label="dreamly")
+                                    ipnc_svicamera_h = gr.Slider(0, 200, value=100, step=1, label="svicamera")
+                            with gr.Group():
+                                gr.Markdown("<div style='padding-left:8px'>Low</div>")
+                                with gr.Row():
+                                    ipnc_wamu_l    = gr.Slider(0, 200, value=100, step=1, label="wamu")
+                                    ipnc_dreamly_l = gr.Slider(0, 200, value=100, step=1, label="dreamly")
+                                    ipnc_svicamera_l = gr.Slider(0, 200, value=100, step=1, label="svicamera")
+
+            loras_enabled.change(
+                fn=lambda x: gr.update(visible=x),
+                inputs=loras_enabled,
+                outputs=loras_section,
+            )
+
+            ipnc_enabled.change(
+                fn=lambda x: gr.update(visible=x),
+                inputs=ipnc_enabled,
+                outputs=ipnc_section,
+            )
+
+            with gr.Row():
+                seed = gr.Number(value=0, precision=0, label="Seed (0=random)")
+                gpu_budget = gr.Slider(0, 540, value=0, step=10, label="ZeroGPU budget (0=auto)")
+            with gr.Row():
+                num_steps = gr.Slider(4, 20, value=6, step=1, label="Steps")
+                duration_secs = gr.Slider(1, 15, value=5, step=0.5, label="Duration (s)")
+                sampler_name = gr.Dropdown(choices=["uni_pc", "lcm"], value="lcm", label="Sampler")
+                base_fps = gr.Number(value=15, precision=0, label="Combine FPS")
+                rife_mode = gr.Dropdown(
+                    choices=["no rife", "2x rife", "4x rife"],
+                    value="no rife",
+                    label="RIFE interpolation",
+                )
+
+            enhance_prompt = gr.Checkbox(label="enhance prompt", value=False)
+            generate_btn = gr.Button("Generate", variant="primary", size="lg")
+
+        with gr.Column(scale=1):
+            output_video = gr.Video(label="Generated video")
+            output_status = gr.Textbox(label="Status", interactive=False)
+
+    generate_btn.click(
+        fn=generate_handler,
+        inputs=[
+            prompt, negative, image_input, seed, aspect_ratio, use_ref_aspect, gpu_budget,
+            num_steps, duration_secs, sampler_name, base_fps, rife_mode,
+            loras_enabled,
+            s_wamu_h, s_dreamly_h,
+            s_wamu_l, s_dreamly_l,
+            enhance_prompt,
+            ipnc_enabled,
+            ipnc_wamu_h, ipnc_dreamly_h,
+            ipnc_wamu_l, ipnc_dreamly_l,
+            s_svicamera_h, s_svicamera_l,
+            ipnc_svicamera_h, ipnc_svicamera_l,
+        ],
+        outputs=[output_video, output_status],
+    )
+
+if __name__ == "__main__":
+    _ensure_comfy()
+    _ensure_models()
+    _init_comfy_nodes()
+    demo.queue().launch()

bernini_r2v_base.json

@@ -0,0 +1 @@
+{"id": "ec7da562-7e21-4dac-a0d2-f4441e1efd3b", "revision": 0, "last_node_id": 137, "last_link_id": 250, "nodes": [{"id": 70, "type": "CFGZeroStar", "pos": [91.64471435546875, -914.7239379882812], "size": [265.08551025390625, 26], "flags": {}, "order": 43, "mode": 0, "inputs": [{"label": "model", "localized_name": "model", "name": "model", "type": "MODEL", "link": 223}], "outputs": [{"label": "patched_model", "localized_name": "patched_model", "name": "patched_model", "type": "MODEL", "links": [155]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.48", "Node name for S&R": "CFGZeroStar", "widget_ue_connectable": {}}, "widgets_values": []}, {"id": 75, "type": "SetNode", "pos": [99.88265991210938, -827.1892700195312], "size": [254.6003875732422, 61.11500549316406], "flags": {}, "order": 51, "mode": 0, "inputs": [{"label": "MODEL", "name": "MODEL", "type": "MODEL", "link": 155}], "outputs": [{"label": "*", "name": "*", "type": "*"}], "title": "Set_high_model", "properties": {"Node name for S&R": "SetNode", "aux_id": "kijai/ComfyUI-KJNodes", "previousName": "high_model", "widget_ue_connectable": {}}, "widgets_values": ["high_model"], "color": "#223", "bgcolor": "#335"}, {"id": 38, "type": "CLIPLoader", "pos": [-366.0155944824219, -378.3727111816406], "size": [346.391845703125, 106], "flags": {}, "order": 0, "mode": 0, "inputs": [{"label": "clip_name", "localized_name": "clip_name", "name": "clip_name", "type": "COMBO", "widget": {"name": "clip_name"}}, {"label": "type", "localized_name": "type", "name": "type", "type": "COMBO", "widget": {"name": "type"}}, {"label": "device", "localized_name": "device", "name": "device", "shape": 7, "type": "COMBO", "widget": {"name": "device"}}], "outputs": [{"label": "CLIP", "localized_name": "CLIP", "name": "CLIP", "type": "CLIP", "slot_index": 0, "links": [156]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "CLIPLoader", "models": [{"directory_invalid": true, "name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors", "directory": "text_encoders", "url": "https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/resolve/main/split_files/text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors"}], "widget_ue_connectable": {}}, "widgets_values": ["umt5_xxl_fp8_e4m3fn_scaled.safetensors", "wan", "default"]}, {"id": 83, "type": "GetNode", "pos": [457.47674560546875, -280.5387878417969], "size": [210, 60], "flags": {}, "order": 1, "mode": 0, "inputs": [], "outputs": [{"label": "MODEL", "name": "MODEL", "type": "MODEL", "links": [163]}], "title": "Get_high_model", "properties": {"Node name for S&R": "GetNode", "aux_id": "kijai/ComfyUI-KJNodes", "widget_ue_connectable": {}}, "widgets_values": ["high_model"], "color": "#223", "bgcolor": "#335"}, {"id": 78, "type": "SetNode", "pos": [98.8753890991211, -195.62353515625], "size": [268.01397705078125, 58], "flags": {}, "order": 29, "mode": 0, "inputs": [{"label": "VAE", "name": "VAE", "type": "VAE", "link": 157}], "outputs": [{"label": "*", "name": "*", "type": "*"}], "title": "Set_vae", "properties": {"Node name for S&R": "SetNode", "aux_id": "kijai/ComfyUI-KJNodes", "previousName": "vae", "widget_ue_connectable": {}}, "widgets_values": ["vae"], "color": "#322", "bgcolor": "#533"}, {"id": 77, "type": "SetNode", "pos": [105.33979034423828, -374.97235107421875], "size": [263.9664611816406, 67.4441909790039], "flags": {}, "order": 25, "mode": 0, "inputs": [{"label": "CLIP", "name": "CLIP", "type": "CLIP", "link": 156}], "outputs": [{"label": "*", "name": "*", "type": "*"}], "title": "Set_clip", "properties": {"Node name for S&R": "SetNode", "aux_id": "kijai/ComfyUI-KJNodes", "previousName": "clip", "widget_ue_connectable": {}}, "widgets_values": ["clip"], "color": "#432", "bgcolor": "#653"}, {"id": 8, "type": "VAEDecode", "pos": [1972.1580810546875, -266.72650146484375], "size": [210, 46], "flags": {}, "order": 55, "mode": 0, "inputs": [{"label": "samples", "localized_name": "samples", "name": "samples", "type": "LATENT", "link": 124}, {"label": "vae", "localized_name": "vae", "name": "vae", "type": "VAE", "link": 160}], "outputs": [{"label": "IMAGE", "localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [182]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "VAEDecode", "widget_ue_connectable": {}}, "widgets_values": []}, {"id": 100, "type": "GetNode", "pos": [1225.3629150390625, -931.2969360351562], "size": [210, 60], "flags": {}, "order": 2, "mode": 0, "inputs": [], "outputs": [{"label": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "links": [232]}], "title": "Get_con+", "properties": {"Node name for S&R": "GetNode", "aux_id": "kijai/ComfyUI-KJNodes", "widget_ue_connectable": {}}, "widgets_values": ["con+"], "color": "#332922", "bgcolor": "#593930"}, {"id": 101, "type": "GetNode", "pos": [1226.2900390625, -828.277099609375], "size": [210, 58], "flags": {}, "order": 3, "mode": 0, "inputs": [], "outputs": [{"label": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "links": [233]}], "title": "Get_con-", "properties": {"Node name for S&R": "GetNode", "aux_id": "kijai/ComfyUI-KJNodes", "widget_ue_connectable": {}}, "widgets_values": ["con-"], "color": "#332922", "bgcolor": "#593930"}, {"id": 84, "type": "GetNode", "pos": [460.0235290527344, -173.7384490966797], "size": [210, 58], "flags": {}, "order": 4, "mode": 0, "inputs": [], "outputs": [{"label": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "links": [164, 168, 170]}], "title": "Get_con1-", "properties": {"Node name for S&R": "GetNode", "aux_id": "kijai/ComfyUI-KJNodes", "widget_ue_connectable": {}}, "widgets_values": ["con1-"], "color": "#332922", "bgcolor": "#593930"}, {"id": 80, "type": "GetNode", "pos": [467.8817138671875, 44.29520797729492], "size": [210, 58], "flags": {}, "order": 5, "mode": 0, "inputs": [], "outputs": [{"label": "VAE", "name": "VAE", "type": "VAE", "links": [160]}], "title": "Get_vae", "properties": {"Node name for S&R": "GetNode", "aux_id": "kijai/ComfyUI-KJNodes", "widget_ue_connectable": {}}, "widgets_values": ["vae"], "color": "#322", "bgcolor": "#533"}, {"id": 109, "type": "SetNode", "pos": [102.7603530883789, -91.38772583007812], "size": [210, 58], "flags": {}, "order": 30, "mode": 0, "inputs": [{"label": "CLIP_VISION", "name": "CLIP_VISION", "type": "CLIP_VISION", "link": 196}], "outputs": [{"label": "*", "name": "*", "type": "*"}], "title": "Set_clip_h", "properties": {"Node name for S&R": "SetNode", "aux_id": "kijai/ComfyUI-KJNodes", "previousName": "clip_h", "widget_ue_connectable": {}}, "widgets_values": ["clip_h"]}, {"id": 102, "type": "GetNode", "pos": [1224.45263671875, -730.7865600585938], "size": [210, 58], "flags": {}, "order": 6, "mode": 0, "inputs": [], "outputs": [{"label": "VAE", "name": "VAE", "type": "VAE", "links": [234]}], "title": "Get_vae", "properties": {"Node name for S&R": "GetNode", "aux_id": 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"widget_ue_connectable": {}}, "widgets_values": ["con-"], "color": "#332922", "bgcolor": "#593930"}, {"id": 81, "type": "SetNode", "pos": [948.0673217773438, -463.03466796875], "size": [210, 60], "flags": {}, "order": 41, "mode": 0, "inputs": [{"label": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "link": 161}], "outputs": [{"label": "*", "name": "*", "type": "*"}], "title": "Set_con+", "properties": {"Node name for S&R": "SetNode", "aux_id": "kijai/ComfyUI-KJNodes", "previousName": "con+", "widget_ue_connectable": {}}, "widgets_values": ["con+"], "color": "#332922", "bgcolor": "#593930"}, {"id": 7, "type": "CLIPTextEncode", "pos": [715.5911254882812, -705.5069580078125], "size": [425.27801513671875, 180.6060791015625], "flags": {}, "order": 28, "mode": 0, "inputs": [{"label": "clip", "localized_name": "clip", "name": "clip", "type": "CLIP", "link": 159}, {"label": "text", "localized_name": "text", "name": "text", "type": "STRING", "widget": {"name": "text"}}], "outputs": [{"label": "CONDITIONING", "localized_name": "CONDITIONING", "name": "CONDITIONING", "type": "CONDITIONING", "slot_index": 0, "links": [162]}], "title": "CLIP Text Encode (Negative Prompt)", "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "CLIPTextEncode", "widget_ue_connectable": {}}, "widgets_values": ["placeholder prompt"], "color": "#322", "bgcolor": "#533"}, {"id": 39, "type": "VAELoader", "pos": [-365.962158203125, -219.44821166992188], "size": [344.731689453125, 59.98149108886719], "flags": {}, "order": 8, "mode": 0, "inputs": [{"label": "vae_name", "localized_name": "vae_name", "name": "vae_name", "type": "COMBO", "widget": {"name": "vae_name"}}], "outputs": [{"label": "VAE", "localized_name": "VAE", "name": "VAE", "type": "VAE", "slot_index": 0, "links": [157]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "VAELoader", "models": [{"name": "wan_2.1_vae.safetensors", "directory": "vae", "url": "https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/resolve/main/split_files/vae/wan_2.1_vae.safetensors"}], "widget_ue_connectable": {}}, "widgets_values": ["wan_2.1_vae.safetensors"]}, {"id": 108, "type": "CLIPVisionLoader", "pos": [-330.003662109375, -99.01641845703125], "size": [300.462646484375, 58], "flags": {}, "order": 9, "mode": 0, "inputs": [{"label": "clip_name", "localized_name": "clip_name", "name": "clip_name", "type": "COMBO", "widget": {"name": "clip_name"}}], "outputs": [{"label": "CLIP_VISION", "localized_name": "CLIP_VISION", "name": "CLIP_VISION", "type": "CLIP_VISION", "links": [196]}], "properties": {"cnr_id": "comfy-core", "ver": "0.22.2", "Node name for S&R": "CLIPVisionLoader", "widget_ue_connectable": {}}, "widgets_values": ["clip_vision_h.safetensors"]}, {"id": 55, "type": "ModelSamplingSD3", "pos": [1034.045654296875, -126.17032623291016], "size": [249.24838256835938, 60.89899444580078], "flags": {}, "order": 33, "mode": 0, "inputs": [{"label": "model", "localized_name": "model", "name": "model", "type": "MODEL", "link": 166}, {"label": "shift", "localized_name": "shift", "name": "shift", "type": "FLOAT", "widget": {"name": "shift"}}], "outputs": [{"label": "MODEL", "localized_name": "MODEL", "name": "MODEL", "type": "MODEL", "slot_index": 0, "links": [123]}], "properties": {"cnr_id": "comfy-core", "ver": "0.3.45", "Node name for S&R": "ModelSamplingSD3", "widget_ue_connectable": {}}, "widgets_values": [8]}, {"id": 54, "type": "ModelSamplingSD3", "pos": [1022.064208984375, -292.55487060546875], "size": [248.53526306152344, 75.91676330566406], "flags": {}, "order": 40, "mode": 0, "inputs": [{"label": "model", "localized_name": "model", "name": "model", "type": "MODEL", "link": 165}, {"label": "shift", "localized_name": "shift", "name": "shift", "type": "FLOAT", "widget": {"name": "shift"}}], "outputs": [{"label": "MODEL", "localized_name": "MODEL", "name": "MODEL", "type": "MODEL", "slot_index": 0, "links": [125]}], 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requirements.txt

@@ -0,0 +1,20 @@
+torch==2.8.0
+torchvision
+diffusers>=0.31.0
+transformers
+accelerate
+safetensors
+einops
+numpy
+Pillow
+tqdm
+ftfy
+ninja
+decord
+imageio
+imageio-ffmpeg
+opencv-python-headless
+spaces
+gradio<7
+huggingface_hub
+openai