update

* Convert alphas for embedders for sd-scripts to ai toolkit conversion

* Add kohya embedders conversion test

* Apply style fixes

---------

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

examples/server-async/Pipelines.py

@@ -0,0 +1,91 @@
+import logging
+import os
+from dataclasses import dataclass, field
+from typing import List
+
+import torch
+from pydantic import BaseModel
+
+from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3 import StableDiffusion3Pipeline
+
+
+logger = logging.getLogger(__name__)
+
+
+class TextToImageInput(BaseModel):
+    model: str
+    prompt: str
+    size: str | None = None
+    n: int | None = None
+
+
+@dataclass
+class PresetModels:
+    SD3: List[str] = field(default_factory=lambda: ["stabilityai/stable-diffusion-3-medium"])
+    SD3_5: List[str] = field(
+        default_factory=lambda: [
+            "stabilityai/stable-diffusion-3.5-large",
+            "stabilityai/stable-diffusion-3.5-large-turbo",
+            "stabilityai/stable-diffusion-3.5-medium",
+        ]
+    )
+
+
+class TextToImagePipelineSD3:
+    def __init__(self, model_path: str | None = None):
+        self.model_path = model_path or os.getenv("MODEL_PATH")
+        self.pipeline: StableDiffusion3Pipeline | None = None
+        self.device: str | None = None
+
+    def start(self):
+        if torch.cuda.is_available():
+            model_path = self.model_path or "stabilityai/stable-diffusion-3.5-large"
+            logger.info("Loading CUDA")
+            self.device = "cuda"
+            self.pipeline = StableDiffusion3Pipeline.from_pretrained(
+                model_path,
+                torch_dtype=torch.float16,
+            ).to(device=self.device)
+        elif torch.backends.mps.is_available():
+            model_path = self.model_path or "stabilityai/stable-diffusion-3.5-medium"
+            logger.info("Loading MPS for Mac M Series")
+            self.device = "mps"
+            self.pipeline = StableDiffusion3Pipeline.from_pretrained(
+                model_path,
+                torch_dtype=torch.bfloat16,
+            ).to(device=self.device)
+        else:
+            raise Exception("No CUDA or MPS device available")
+
+
+class ModelPipelineInitializer:
+    def __init__(self, model: str = "", type_models: str = "t2im"):
+        self.model = model
+        self.type_models = type_models
+        self.pipeline = None
+        self.device = "cuda" if torch.cuda.is_available() else "mps"
+        self.model_type = None
+
+    def initialize_pipeline(self):
+        if not self.model:
+            raise ValueError("Model name not provided")
+
+        # Check if model exists in PresetModels
+        preset_models = PresetModels()
+
+        # Determine which model type we're dealing with
+        if self.model in preset_models.SD3:
+            self.model_type = "SD3"
+        elif self.model in preset_models.SD3_5:
+            self.model_type = "SD3_5"
+
+        # Create appropriate pipeline based on model type and type_models
+        if self.type_models == "t2im":
+            if self.model_type in ["SD3", "SD3_5"]:
+                self.pipeline = TextToImagePipelineSD3(self.model)
+            else:
+                raise ValueError(f"Model type {self.model_type} not supported for text-to-image")
+        elif self.type_models == "t2v":
+            raise ValueError(f"Unsupported type_models: {self.type_models}")
+
+        return self.pipeline

examples/server-async/README.md

@@ -0,0 +1,171 @@
+# Asynchronous server and parallel execution of models
+
+> Example/demo server that keeps a single model in memory while safely running parallel inference requests by creating per-request lightweight views and cloning only small, stateful components (schedulers, RNG state, small mutable attrs). Works with StableDiffusion3 pipelines.
+> We recommend running 10 to 50 inferences in parallel for optimal performance, averaging between 25 and 30 seconds to 1 minute and 1 minute and 30 seconds. (This is only recommended if you have a GPU with 35GB of VRAM or more; otherwise, keep it to one or two inferences in parallel to avoid decoding or saving errors due to memory shortages.)
+
+## ⚠️ IMPORTANT
+
+* The example demonstrates how to run pipelines like `StableDiffusion3-3.5` concurrently while keeping a single copy of the heavy model parameters on GPU.
+
+## Necessary components
+
+All the components needed to create the inference server are in the current directory:
+
+```
+server-async/
+├── utils/
+├─────── __init__.py
+├─────── scheduler.py              # BaseAsyncScheduler wrapper and async_retrieve_timesteps for secure inferences
+├─────── requestscopedpipeline.py  # RequestScoped Pipeline for inference with a single in-memory model
+├─────── utils.py                  # Image/video saving utilities and service configuration
+├── Pipelines.py                   # pipeline loader classes (SD3)
+├── serverasync.py                 # FastAPI app with lifespan management and async inference endpoints
+├── test.py                        # Client test script for inference requests
+├── requirements.txt               # Dependencies
+└── README.md                      # This documentation
+```
+
+## What `diffusers-async` adds / Why we needed it
+
+Core problem: a naive server that calls `pipe.__call__` concurrently can hit **race conditions** (e.g., `scheduler.set_timesteps` mutates shared state) or explode memory by deep-copying the whole pipeline per-request.
+
+`diffusers-async` / this example addresses that by:
+
+* **Request-scoped views**: `RequestScopedPipeline` creates a shallow copy of the pipeline per request so heavy weights (UNet, VAE, text encoder) remain shared and *are not duplicated*.
+* **Per-request mutable state**: stateful small objects (scheduler, RNG state, small lists/dicts, callbacks) are cloned per request. The system uses `BaseAsyncScheduler.clone_for_request(...)` for scheduler cloning, with fallback to safe `deepcopy` or other heuristics.
+* **Tokenizer concurrency safety**: `RequestScopedPipeline` now manages an internal tokenizer lock with automatic tokenizer detection and wrapping. This ensures that Rust tokenizers are safe to use under concurrency — race condition errors like `Already borrowed` no longer occur.
+* **`async_retrieve_timesteps(..., return_scheduler=True)`**: fully retro-compatible helper that returns `(timesteps, num_inference_steps, scheduler)` without mutating the shared scheduler. For users not using `return_scheduler=True`, the behavior is identical to the original API.
+* **Robust attribute handling**: wrapper avoids writing to read-only properties (e.g., `components`) and auto-detects small mutable attributes to clone while avoiding duplication of large tensors. Configurable tensor size threshold prevents cloning of large tensors.
+* **Enhanced scheduler wrapping**: `BaseAsyncScheduler` automatically wraps schedulers with improved `__getattr__`, `__setattr__`, and debugging methods (`__repr__`, `__str__`).
+
+## How the server works (high-level flow)
+
+1. **Single model instance** is loaded into memory (GPU/MPS) when the server starts.
+2. On each HTTP inference request:
+
+   * The server uses `RequestScopedPipeline.generate(...)` which:
+
+     * automatically wraps the base scheduler in `BaseAsyncScheduler` (if not already wrapped),
+     * obtains a *local scheduler* (via `clone_for_request()` or `deepcopy`),
+     * does `local_pipe = copy.copy(base_pipe)` (shallow copy),
+     * sets `local_pipe.scheduler = local_scheduler` (if possible),
+     * clones only small mutable attributes (callbacks, rng, small latents) with auto-detection,
+     * wraps tokenizers with thread-safe locks to prevent race conditions,
+     * optionally enters a `model_cpu_offload_context()` for memory offload hooks,
+     * calls the pipeline on the local view (`local_pipe(...)`).
+3. **Result**: inference completes, images are moved to CPU & saved (if requested), internal buffers freed (GC + `torch.cuda.empty_cache()`).
+4. Multiple requests can run in parallel while sharing heavy weights and isolating mutable state.
+
+## How to set up and run the server
+
+### 1) Install dependencies
+
+Recommended: create a virtualenv / conda environment.
+
+```bash
+pip install diffusers
+pip install -r requirements.txt
+```
+
+### 2) Start the server
+
+Using the `serverasync.py` file that already has everything you need:
+
+```bash
+python serverasync.py
+```
+
+The server will start on `http://localhost:8500` by default with the following features:
+- FastAPI application with async lifespan management
+- Automatic model loading and pipeline initialization
+- Request counting and active inference tracking
+- Memory cleanup after each inference
+- CORS middleware for cross-origin requests
+
+### 3) Test the server
+
+Use the included test script:
+
+```bash
+python test.py
+```
+
+Or send a manual request:
+
+`POST /api/diffusers/inference` with JSON body:
+
+```json
+{
+  "prompt": "A futuristic cityscape, vibrant colors",
+  "num_inference_steps": 30,
+  "num_images_per_prompt": 1
+}
+```
+
+Response example:
+
+```json
+{
+  "response": ["http://localhost:8500/images/img123.png"]
+}
+```
+
+### 4) Server endpoints
+
+- `GET /` - Welcome message
+- `POST /api/diffusers/inference` - Main inference endpoint
+- `GET /images/{filename}` - Serve generated images
+- `GET /api/status` - Server status and memory info
+
+## Advanced Configuration
+
+### RequestScopedPipeline Parameters
+
+```python
+RequestScopedPipeline(
+    pipeline,                        # Base pipeline to wrap
+    mutable_attrs=None,             # Custom list of attributes to clone
+    auto_detect_mutables=True,      # Enable automatic detection of mutable attributes
+    tensor_numel_threshold=1_000_000, # Tensor size threshold for cloning
+    tokenizer_lock=None,            # Custom threading lock for tokenizers
+    wrap_scheduler=True             # Auto-wrap scheduler in BaseAsyncScheduler
+)
+```
+
+### BaseAsyncScheduler Features
+
+* Transparent proxy to the original scheduler with `__getattr__` and `__setattr__`
+* `clone_for_request()` method for safe per-request scheduler cloning
+* Enhanced debugging with `__repr__` and `__str__` methods
+* Full compatibility with existing scheduler APIs
+
+### Server Configuration
+
+The server configuration can be modified in `serverasync.py` through the `ServerConfigModels` dataclass:
+
+```python
+@dataclass
+class ServerConfigModels:
+    model: str = 'stabilityai/stable-diffusion-3.5-medium'  
+    type_models: str = 't2im'  
+    host: str = '0.0.0.0' 
+    port: int = 8500
+```
+
+## Troubleshooting (quick)
+
+* `Already borrowed` — previously a Rust tokenizer concurrency error.
+  ✅ This is now fixed: `RequestScopedPipeline` automatically detects and wraps tokenizers with thread locks, so race conditions no longer happen.
+
+* `can't set attribute 'components'` — pipeline exposes read-only `components`.
+  ✅ The RequestScopedPipeline now detects read-only properties and skips setting them automatically.
+
+* Scheduler issues:
+  * If the scheduler doesn't implement `clone_for_request` and `deepcopy` fails, we log and fallback — but prefer `async_retrieve_timesteps(..., return_scheduler=True)` to avoid mutating the shared scheduler.
+  ✅ Note: `async_retrieve_timesteps` is fully retro-compatible — if you don't pass `return_scheduler=True`, the behavior is unchanged.
+
+* Memory issues with large tensors:
+  ✅ The system now has configurable `tensor_numel_threshold` to prevent cloning of large tensors while still cloning small mutable ones.
+
+* Automatic tokenizer detection:
+  ✅ The system automatically identifies tokenizer components by checking for tokenizer methods, class names, and attributes, then applies thread-safe wrappers.

examples/server-async/requirements.txt

@@ -0,0 +1,10 @@
+torch 
+torchvision 
+transformers 
+sentencepiece 
+fastapi 
+uvicorn 
+ftfy
+accelerate
+xformers
+protobuf

examples/server-async/serverasync.py

@@ -0,0 +1,230 @@
+import asyncio
+import gc
+import logging
+import os
+import random
+import threading
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Type
+
+import torch
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.concurrency import run_in_threadpool
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import FileResponse
+from Pipelines import ModelPipelineInitializer
+from pydantic import BaseModel
+
+from utils import RequestScopedPipeline, Utils
+
+
+@dataclass
+class ServerConfigModels:
+    model: str = "stabilityai/stable-diffusion-3.5-medium"
+    type_models: str = "t2im"
+    constructor_pipeline: Optional[Type] = None
+    custom_pipeline: Optional[Type] = None
+    components: Optional[Dict[str, Any]] = None
+    torch_dtype: Optional[torch.dtype] = None
+    host: str = "0.0.0.0"
+    port: int = 8500
+
+
+server_config = ServerConfigModels()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    logging.basicConfig(level=logging.INFO)
+    app.state.logger = logging.getLogger("diffusers-server")
+    os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128,expandable_segments:True"
+    os.environ["CUDA_LAUNCH_BLOCKING"] = "0"
+
+    app.state.total_requests = 0
+    app.state.active_inferences = 0
+    app.state.metrics_lock = asyncio.Lock()
+    app.state.metrics_task = None
+
+    app.state.utils_app = Utils(
+        host=server_config.host,
+        port=server_config.port,
+    )
+
+    async def metrics_loop():
+        try:
+            while True:
+                async with app.state.metrics_lock:
+                    total = app.state.total_requests
+                    active = app.state.active_inferences
+                app.state.logger.info(f"[METRICS] total_requests={total} active_inferences={active}")
+                await asyncio.sleep(5)
+        except asyncio.CancelledError:
+            app.state.logger.info("Metrics loop cancelled")
+            raise
+
+    app.state.metrics_task = asyncio.create_task(metrics_loop())
+
+    try:
+        yield
+    finally:
+        task = app.state.metrics_task
+        if task:
+            task.cancel()
+            try:
+                await task
+            except asyncio.CancelledError:
+                pass
+
+        try:
+            stop_fn = getattr(model_pipeline, "stop", None) or getattr(model_pipeline, "close", None)
+            if callable(stop_fn):
+                await run_in_threadpool(stop_fn)
+        except Exception as e:
+            app.state.logger.warning(f"Error during pipeline shutdown: {e}")
+
+        app.state.logger.info("Lifespan shutdown complete")
+
+
+app = FastAPI(lifespan=lifespan)
+
+logger = logging.getLogger("DiffusersServer.Pipelines")
+
+
+initializer = ModelPipelineInitializer(
+    model=server_config.model,
+    type_models=server_config.type_models,
+)
+model_pipeline = initializer.initialize_pipeline()
+model_pipeline.start()
+
+request_pipe = RequestScopedPipeline(model_pipeline.pipeline)
+pipeline_lock = threading.Lock()
+
+logger.info(f"Pipeline initialized and ready to receive requests (model ={server_config.model})")
+
+app.state.MODEL_INITIALIZER = initializer
+app.state.MODEL_PIPELINE = model_pipeline
+app.state.REQUEST_PIPE = request_pipe
+app.state.PIPELINE_LOCK = pipeline_lock
+
+
+class JSONBodyQueryAPI(BaseModel):
+    model: str | None = None
+    prompt: str
+    negative_prompt: str | None = None
+    num_inference_steps: int = 28
+    num_images_per_prompt: int = 1
+
+
+@app.middleware("http")
+async def count_requests_middleware(request: Request, call_next):
+    async with app.state.metrics_lock:
+        app.state.total_requests += 1
+    response = await call_next(request)
+    return response
+
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Diffusers Server"}
+
+
+@app.post("/api/diffusers/inference")
+async def api(json: JSONBodyQueryAPI):
+    prompt = json.prompt
+    negative_prompt = json.negative_prompt or ""
+    num_steps = json.num_inference_steps
+    num_images_per_prompt = json.num_images_per_prompt
+
+    wrapper = app.state.MODEL_PIPELINE
+    initializer = app.state.MODEL_INITIALIZER
+
+    utils_app = app.state.utils_app
+
+    if not wrapper or not wrapper.pipeline:
+        raise HTTPException(500, "Model not initialized correctly")
+    if not prompt.strip():
+        raise HTTPException(400, "No prompt provided")
+
+    def make_generator():
+        g = torch.Generator(device=initializer.device)
+        return g.manual_seed(random.randint(0, 10_000_000))
+
+    req_pipe = app.state.REQUEST_PIPE
+
+    def infer():
+        gen = make_generator()
+        return req_pipe.generate(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            generator=gen,
+            num_inference_steps=num_steps,
+            num_images_per_prompt=num_images_per_prompt,
+            device=initializer.device,
+            output_type="pil",
+        )
+
+    try:
+        async with app.state.metrics_lock:
+            app.state.active_inferences += 1
+
+        output = await run_in_threadpool(infer)
+
+        async with app.state.metrics_lock:
+            app.state.active_inferences = max(0, app.state.active_inferences - 1)
+
+        urls = [utils_app.save_image(img) for img in output.images]
+        return {"response": urls}
+
+    except Exception as e:
+        async with app.state.metrics_lock:
+            app.state.active_inferences = max(0, app.state.active_inferences - 1)
+        logger.error(f"Error during inference: {e}")
+        raise HTTPException(500, f"Error in processing: {e}")
+
+    finally:
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
+            torch.cuda.empty_cache()
+            torch.cuda.reset_peak_memory_stats()
+            torch.cuda.ipc_collect()
+        gc.collect()
+
+
+@app.get("/images/{filename}")
+async def serve_image(filename: str):
+    utils_app = app.state.utils_app
+    file_path = os.path.join(utils_app.image_dir, filename)
+    if not os.path.isfile(file_path):
+        raise HTTPException(status_code=404, detail="Image not found")
+    return FileResponse(file_path, media_type="image/png")
+
+
+@app.get("/api/status")
+async def get_status():
+    memory_info = {}
+    if torch.cuda.is_available():
+        memory_allocated = torch.cuda.memory_allocated() / 1024**3  # GB
+        memory_reserved = torch.cuda.memory_reserved() / 1024**3  # GB
+        memory_info = {
+            "memory_allocated_gb": round(memory_allocated, 2),
+            "memory_reserved_gb": round(memory_reserved, 2),
+            "device": torch.cuda.get_device_name(0),
+        }
+
+    return {"current_model": server_config.model, "type_models": server_config.type_models, "memory": memory_info}
+
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host=server_config.host, port=server_config.port)

examples/server-async/test.py

@@ -0,0 +1,65 @@
+import os
+import time
+import urllib.parse
+
+import requests
+
+
+SERVER_URL = "http://localhost:8500/api/diffusers/inference"
+BASE_URL = "http://localhost:8500"
+DOWNLOAD_FOLDER = "generated_images"
+WAIT_BEFORE_DOWNLOAD = 2  # seconds
+
+os.makedirs(DOWNLOAD_FOLDER, exist_ok=True)
+
+
+def save_from_url(url: str) -> str:
+    """Download the given URL (relative or absolute) and save it locally."""
+    if url.startswith("/"):
+        direct = BASE_URL.rstrip("/") + url
+    else:
+        direct = url
+    resp = requests.get(direct, timeout=60)
+    resp.raise_for_status()
+    filename = os.path.basename(urllib.parse.urlparse(direct).path) or f"img_{int(time.time())}.png"
+    path = os.path.join(DOWNLOAD_FOLDER, filename)
+    with open(path, "wb") as f:
+        f.write(resp.content)
+    return path
+
+
+def main():
+    payload = {
+        "prompt": "The T-800 Terminator Robot Returning From The Future, Anime Style",
+        "num_inference_steps": 30,
+        "num_images_per_prompt": 1,
+    }
+
+    print("Sending request...")
+    try:
+        r = requests.post(SERVER_URL, json=payload, timeout=480)
+        r.raise_for_status()
+    except Exception as e:
+        print(f"Request failed: {e}")
+        return
+
+    body = r.json().get("response", [])
+    # Normalize to a list
+    urls = body if isinstance(body, list) else [body] if body else []
+    if not urls:
+        print("No URLs found in the response. Check the server output.")
+        return
+
+    print(f"Received {len(urls)} URL(s). Waiting {WAIT_BEFORE_DOWNLOAD}s before downloading...")
+    time.sleep(WAIT_BEFORE_DOWNLOAD)
+
+    for u in urls:
+        try:
+            path = save_from_url(u)
+            print(f"Image saved to: {path}")
+        except Exception as e:
+            print(f"Error downloading {u}: {e}")
+
+
+if __name__ == "__main__":
+    main()

examples/server-async/utils/__init__.py

@@ -0,0 +1,2 @@
+from .requestscopedpipeline import RequestScopedPipeline
+from .utils import Utils

examples/server-async/utils/requestscopedpipeline.py

@@ -0,0 +1,296 @@
+import copy
+import threading
+from typing import Any, Iterable, List, Optional
+
+import torch
+
+from diffusers.utils import logging
+
+from .scheduler import BaseAsyncScheduler, async_retrieve_timesteps
+
+
+logger = logging.get_logger(__name__)
+
+
+def safe_tokenize(tokenizer, *args, lock, **kwargs):
+    with lock:
+        return tokenizer(*args, **kwargs)
+
+
+class RequestScopedPipeline:
+    DEFAULT_MUTABLE_ATTRS = [
+        "_all_hooks",
+        "_offload_device",
+        "_progress_bar_config",
+        "_progress_bar",
+        "_rng_state",
+        "_last_seed",
+        "latents",
+    ]
+
+    def __init__(
+        self,
+        pipeline: Any,
+        mutable_attrs: Optional[Iterable[str]] = None,
+        auto_detect_mutables: bool = True,
+        tensor_numel_threshold: int = 1_000_000,
+        tokenizer_lock: Optional[threading.Lock] = None,
+        wrap_scheduler: bool = True,
+    ):
+        self._base = pipeline
+        self.unet = getattr(pipeline, "unet", None)
+        self.vae = getattr(pipeline, "vae", None)
+        self.text_encoder = getattr(pipeline, "text_encoder", None)
+        self.components = getattr(pipeline, "components", None)
+
+        if wrap_scheduler and hasattr(pipeline, "scheduler") and pipeline.scheduler is not None:
+            if not isinstance(pipeline.scheduler, BaseAsyncScheduler):
+                pipeline.scheduler = BaseAsyncScheduler(pipeline.scheduler)
+
+        self._mutable_attrs = list(mutable_attrs) if mutable_attrs is not None else list(self.DEFAULT_MUTABLE_ATTRS)
+        self._tokenizer_lock = tokenizer_lock if tokenizer_lock is not None else threading.Lock()
+
+        self._auto_detect_mutables = bool(auto_detect_mutables)
+        self._tensor_numel_threshold = int(tensor_numel_threshold)
+
+        self._auto_detected_attrs: List[str] = []
+
+    def _make_local_scheduler(self, num_inference_steps: int, device: Optional[str] = None, **clone_kwargs):
+        base_sched = getattr(self._base, "scheduler", None)
+        if base_sched is None:
+            return None
+
+        if not isinstance(base_sched, BaseAsyncScheduler):
+            wrapped_scheduler = BaseAsyncScheduler(base_sched)
+        else:
+            wrapped_scheduler = base_sched
+
+        try:
+            return wrapped_scheduler.clone_for_request(
+                num_inference_steps=num_inference_steps, device=device, **clone_kwargs
+            )
+        except Exception as e:
+            logger.debug(f"clone_for_request failed: {e}; falling back to deepcopy()")
+            try:
+                return copy.deepcopy(wrapped_scheduler)
+            except Exception as e:
+                logger.warning(f"Deepcopy of scheduler failed: {e}. Returning original scheduler (*risky*).")
+                return wrapped_scheduler
+
+    def _autodetect_mutables(self, max_attrs: int = 40):
+        if not self._auto_detect_mutables:
+            return []
+
+        if self._auto_detected_attrs:
+            return self._auto_detected_attrs
+
+        candidates: List[str] = []
+        seen = set()
+        for name in dir(self._base):
+            if name.startswith("__"):
+                continue
+            if name in self._mutable_attrs:
+                continue
+            if name in ("to", "save_pretrained", "from_pretrained"):
+                continue
+            try:
+                val = getattr(self._base, name)
+            except Exception:
+                continue
+
+            import types
+
+            # skip callables and modules
+            if callable(val) or isinstance(val, (types.ModuleType, types.FunctionType, types.MethodType)):
+                continue
+
+            # containers -> candidate
+            if isinstance(val, (dict, list, set, tuple, bytearray)):
+                candidates.append(name)
+                seen.add(name)
+            else:
+                # try Tensor detection
+                try:
+                    if isinstance(val, torch.Tensor):
+                        if val.numel() <= self._tensor_numel_threshold:
+                            candidates.append(name)
+                            seen.add(name)
+                        else:
+                            logger.debug(f"Ignoring large tensor attr '{name}', numel={val.numel()}")
+                except Exception:
+                    continue
+
+            if len(candidates) >= max_attrs:
+                break
+
+        self._auto_detected_attrs = candidates
+        logger.debug(f"Autodetected mutable attrs to clone: {self._auto_detected_attrs}")
+        return self._auto_detected_attrs
+
+    def _is_readonly_property(self, base_obj, attr_name: str) -> bool:
+        try:
+            cls = type(base_obj)
+            descriptor = getattr(cls, attr_name, None)
+            if isinstance(descriptor, property):
+                return descriptor.fset is None
+            if hasattr(descriptor, "__set__") is False and descriptor is not None:
+                return False
+        except Exception:
+            pass
+        return False
+
+    def _clone_mutable_attrs(self, base, local):
+        attrs_to_clone = list(self._mutable_attrs)
+        attrs_to_clone.extend(self._autodetect_mutables())
+
+        EXCLUDE_ATTRS = {
+            "components",
+        }
+
+        for attr in attrs_to_clone:
+            if attr in EXCLUDE_ATTRS:
+                logger.debug(f"Skipping excluded attr '{attr}'")
+                continue
+            if not hasattr(base, attr):
+                continue
+            if self._is_readonly_property(base, attr):
+                logger.debug(f"Skipping read-only property '{attr}'")
+                continue
+
+            try:
+                val = getattr(base, attr)
+            except Exception as e:
+                logger.debug(f"Could not getattr('{attr}') on base pipeline: {e}")
+                continue
+
+            try:
+                if isinstance(val, dict):
+                    setattr(local, attr, dict(val))
+                elif isinstance(val, (list, tuple, set)):
+                    setattr(local, attr, list(val))
+                elif isinstance(val, bytearray):
+                    setattr(local, attr, bytearray(val))
+                else:
+                    # small tensors or atomic values
+                    if isinstance(val, torch.Tensor):
+                        if val.numel() <= self._tensor_numel_threshold:
+                            setattr(local, attr, val.clone())
+                        else:
+                            # don't clone big tensors, keep reference
+                            setattr(local, attr, val)
+                    else:
+                        try:
+                            setattr(local, attr, copy.copy(val))
+                        except Exception:
+                            setattr(local, attr, val)
+            except (AttributeError, TypeError) as e:
+                logger.debug(f"Skipping cloning attribute '{attr}' because it is not settable: {e}")
+                continue
+            except Exception as e:
+                logger.debug(f"Unexpected error cloning attribute '{attr}': {e}")
+                continue
+
+    def _is_tokenizer_component(self, component) -> bool:
+        if component is None:
+            return False
+
+        tokenizer_methods = ["encode", "decode", "tokenize", "__call__"]
+        has_tokenizer_methods = any(hasattr(component, method) for method in tokenizer_methods)
+
+        class_name = component.__class__.__name__.lower()
+        has_tokenizer_in_name = "tokenizer" in class_name
+
+        tokenizer_attrs = ["vocab_size", "pad_token", "eos_token", "bos_token"]
+        has_tokenizer_attrs = any(hasattr(component, attr) for attr in tokenizer_attrs)
+
+        return has_tokenizer_methods and (has_tokenizer_in_name or has_tokenizer_attrs)
+
+    def generate(self, *args, num_inference_steps: int = 50, device: Optional[str] = None, **kwargs):
+        local_scheduler = self._make_local_scheduler(num_inference_steps=num_inference_steps, device=device)
+
+        try:
+            local_pipe = copy.copy(self._base)
+        except Exception as e:
+            logger.warning(f"copy.copy(self._base) failed: {e}. Falling back to deepcopy (may increase memory).")
+            local_pipe = copy.deepcopy(self._base)
+
+        if local_scheduler is not None:
+            try:
+                timesteps, num_steps, configured_scheduler = async_retrieve_timesteps(
+                    local_scheduler.scheduler,
+                    num_inference_steps=num_inference_steps,
+                    device=device,
+                    return_scheduler=True,
+                    **{k: v for k, v in kwargs.items() if k in ["timesteps", "sigmas"]},
+                )
+
+                final_scheduler = BaseAsyncScheduler(configured_scheduler)
+                setattr(local_pipe, "scheduler", final_scheduler)
+            except Exception:
+                logger.warning("Could not set scheduler on local pipe; proceeding without replacing scheduler.")
+
+        self._clone_mutable_attrs(self._base, local_pipe)
+
+        # 4) wrap tokenizers on the local pipe with the lock wrapper
+        tokenizer_wrappers = {}  # name -> original_tokenizer
+        try:
+            # a) wrap direct tokenizer attributes (tokenizer, tokenizer_2, ...)
+            for name in dir(local_pipe):
+                if "tokenizer" in name and not name.startswith("_"):
+                    tok = getattr(local_pipe, name, None)
+                    if tok is not None and self._is_tokenizer_component(tok):
+                        tokenizer_wrappers[name] = tok
+                        setattr(
+                            local_pipe,
+                            name,
+                            lambda *args, tok=tok, **kwargs: safe_tokenize(
+                                tok, *args, lock=self._tokenizer_lock, **kwargs
+                            ),
+                        )
+
+            # b) wrap tokenizers in components dict
+            if hasattr(local_pipe, "components") and isinstance(local_pipe.components, dict):
+                for key, val in local_pipe.components.items():
+                    if val is None:
+                        continue
+
+                    if self._is_tokenizer_component(val):
+                        tokenizer_wrappers[f"components[{key}]"] = val
+                        local_pipe.components[key] = lambda *args, tokenizer=val, **kwargs: safe_tokenize(
+                            tokenizer, *args, lock=self._tokenizer_lock, **kwargs
+                        )
+
+        except Exception as e:
+            logger.debug(f"Tokenizer wrapping step encountered an error: {e}")
+
+        result = None
+        cm = getattr(local_pipe, "model_cpu_offload_context", None)
+        try:
+            if callable(cm):
+                try:
+                    with cm():
+                        result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
+                except TypeError:
+                    # cm might be a context manager instance rather than callable
+                    try:
+                        with cm:
+                            result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
+                    except Exception as e:
+                        logger.debug(f"model_cpu_offload_context usage failed: {e}. Proceeding without it.")
+                        result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
+            else:
+                # no offload context available — call directly
+                result = local_pipe(*args, num_inference_steps=num_inference_steps, **kwargs)
+
+            return result
+
+        finally:
+            try:
+                for name, tok in tokenizer_wrappers.items():
+                    if name.startswith("components["):
+                        key = name[len("components[") : -1]
+                        local_pipe.components[key] = tok
+                    else:
+                        setattr(local_pipe, name, tok)
+            except Exception as e:
+                logger.debug(f"Error restoring wrapped tokenizers: {e}")

examples/server-async/utils/scheduler.py

@@ -0,0 +1,141 @@
+import copy
+import inspect
+from typing import Any, List, Optional, Union
+
+import torch
+
+
+class BaseAsyncScheduler:
+    def __init__(self, scheduler: Any):
+        self.scheduler = scheduler
+
+    def __getattr__(self, name: str):
+        if hasattr(self.scheduler, name):
+            return getattr(self.scheduler, name)
+        raise AttributeError(f"'{self.__class__.__name__}' object has no attribute '{name}'")
+
+    def __setattr__(self, name: str, value):
+        if name == "scheduler":
+            super().__setattr__(name, value)
+        else:
+            if hasattr(self, "scheduler") and hasattr(self.scheduler, name):
+                setattr(self.scheduler, name, value)
+            else:
+                super().__setattr__(name, value)
+
+    def clone_for_request(self, num_inference_steps: int, device: Union[str, torch.device, None] = None, **kwargs):
+        local = copy.deepcopy(self.scheduler)
+        local.set_timesteps(num_inference_steps=num_inference_steps, device=device, **kwargs)
+        cloned = self.__class__(local)
+        return cloned
+
+    def __repr__(self):
+        return f"BaseAsyncScheduler({repr(self.scheduler)})"
+
+    def __str__(self):
+        return f"BaseAsyncScheduler wrapping: {str(self.scheduler)}"
+
+
+def async_retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    r"""
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call.
+    Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+
+    Backwards compatible: by default the function behaves exactly as before and returns
+        (timesteps_tensor, num_inference_steps)
+
+    If the caller passes `return_scheduler=True` in kwargs, the function will **not** mutate the passed
+    scheduler. Instead it will use a cloned scheduler if available (via `scheduler.clone_for_request`)
+    or a deepcopy fallback, call `set_timesteps` on that cloned scheduler, and return:
+        (timesteps_tensor, num_inference_steps, scheduler_in_use)
+
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+
+    Optional kwargs:
+        return_scheduler (bool, default False): if True, return (timesteps, num_inference_steps, scheduler_in_use)
+            where `scheduler_in_use` is a scheduler instance that already has timesteps set.
+            This mode will prefer `scheduler.clone_for_request(...)` if available, to avoid mutating the original scheduler.
+
+    Returns:
+        `(timesteps_tensor, num_inference_steps)` by default (backwards compatible), or
+        `(timesteps_tensor, num_inference_steps, scheduler_in_use)` if `return_scheduler=True`.
+    """
+    # pop our optional control kwarg (keeps compatibility)
+    return_scheduler = bool(kwargs.pop("return_scheduler", False))
+
+    if timesteps is not None and sigmas is not None:
+        raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
+
+    # choose scheduler to call set_timesteps on
+    scheduler_in_use = scheduler
+    if return_scheduler:
+        # Do not mutate the provided scheduler: prefer to clone if possible
+        if hasattr(scheduler, "clone_for_request"):
+            try:
+                # clone_for_request may accept num_inference_steps or other kwargs; be permissive
+                scheduler_in_use = scheduler.clone_for_request(
+                    num_inference_steps=num_inference_steps or 0, device=device
+                )
+            except Exception:
+                scheduler_in_use = copy.deepcopy(scheduler)
+        else:
+            # fallback deepcopy (scheduler tends to be smallish - acceptable)
+            scheduler_in_use = copy.deepcopy(scheduler)
+
+    # helper to test if set_timesteps supports a particular kwarg
+    def _accepts(param_name: str) -> bool:
+        try:
+            return param_name in set(inspect.signature(scheduler_in_use.set_timesteps).parameters.keys())
+        except (ValueError, TypeError):
+            # if signature introspection fails, be permissive and attempt the call later
+            return False
+
+    # now call set_timesteps on the chosen scheduler_in_use (may be original or clone)
+    if timesteps is not None:
+        accepts_timesteps = _accepts("timesteps")
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler_in_use.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler_in_use.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps_out = scheduler_in_use.timesteps
+        num_inference_steps = len(timesteps_out)
+    elif sigmas is not None:
+        accept_sigmas = _accepts("sigmas")
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler_in_use.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler_in_use.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps_out = scheduler_in_use.timesteps
+        num_inference_steps = len(timesteps_out)
+    else:
+        # default path
+        scheduler_in_use.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps_out = scheduler_in_use.timesteps
+
+    if return_scheduler:
+        return timesteps_out, num_inference_steps, scheduler_in_use
+    return timesteps_out, num_inference_steps

examples/server-async/utils/utils.py

@@ -0,0 +1,48 @@
+import gc
+import logging
+import os
+import tempfile
+import uuid
+
+import torch
+
+
+logger = logging.getLogger(__name__)
+
+
+class Utils:
+    def __init__(self, host: str = "0.0.0.0", port: int = 8500):
+        self.service_url = f"http://{host}:{port}"
+        self.image_dir = os.path.join(tempfile.gettempdir(), "images")
+        if not os.path.exists(self.image_dir):
+            os.makedirs(self.image_dir)
+
+        self.video_dir = os.path.join(tempfile.gettempdir(), "videos")
+        if not os.path.exists(self.video_dir):
+            os.makedirs(self.video_dir)
+
+    def save_image(self, image):
+        if hasattr(image, "to"):
+            try:
+                image = image.to("cpu")
+            except Exception:
+                pass
+
+        if isinstance(image, torch.Tensor):
+            from torchvision import transforms
+
+            to_pil = transforms.ToPILImage()
+            image = to_pil(image.squeeze(0).clamp(0, 1))
+
+        filename = "img" + str(uuid.uuid4()).split("-")[0] + ".png"
+        image_path = os.path.join(self.image_dir, filename)
+        logger.info(f"Saving image to {image_path}")
+
+        image.save(image_path, format="PNG", optimize=True)
+
+        del image
+        gc.collect()
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        return os.path.join(self.service_url, "images", filename)

scripts/convert_wan_to_diffusers.py

@@ -278,6 +278,29 @@ def get_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]:
         }
         RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
         SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
+    elif model_type == "Wan2.2-VACE-Fun-14B":
+        config = {
+            "model_id": "alibaba-pai/Wan2.2-VACE-Fun-A14B",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 40,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "vace_layers": [0, 5, 10, 15, 20, 25, 30, 35],
+                "vace_in_channels": 96,
+            },
+        }
+        RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan2.2-I2V-14B-720p":
         config = {
             "model_id": "Wan-AI/Wan2.2-I2V-A14B",
@@ -975,7 +998,17 @@ if __name__ == "__main__":
             image_encoder=image_encoder,
             image_processor=image_processor,
         )
-    elif "VACE" in args.model_type:
+    elif "Wan2.2-VACE" in args.model_type:
+        pipe = WanVACEPipeline(
+            transformer=transformer,
+            transformer_2=transformer_2,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            vae=vae,
+            scheduler=scheduler,
+            boundary_ratio=0.875,
+        )
+    elif "Wan-VACE" in args.model_type:
         pipe = WanVACEPipeline(
             transformer=transformer,
             text_encoder=text_encoder,

src/diffusers/__init__.py

@@ -495,6 +495,7 @@ else:
             "LTXImageToVideoPipeline",
             "LTXLatentUpsamplePipeline",
             "LTXPipeline",
+            "LucyEditPipeline",
             "Lumina2Pipeline",
             "Lumina2Text2ImgPipeline",
             "LuminaPipeline",
@@ -1149,6 +1150,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LTXImageToVideoPipeline,
             LTXLatentUpsamplePipeline,
             LTXPipeline,
+            LucyEditPipeline,
             Lumina2Pipeline,
             Lumina2Text2ImgPipeline,
             LuminaPipeline,

src/diffusers/loaders/lora_conversion_utils.py

@@ -558,70 +558,62 @@ def _convert_kohya_flux_lora_to_diffusers(state_dict):
                     ait_sd[target_key] = value
 
         if any("guidance_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    (
-                        "time_text_embed.guidance_embedder.linear_1.{lora_key}.weight",
-                        "lora_unet_guidance_in_in_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                    (
-                        "time_text_embed.guidance_embedder.linear_2.{lora_key}.weight",
-                        "lora_unet_guidance_in_out_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_guidance_in_in_layer",
+                "time_text_embed.guidance_embedder.linear_1",
+            )
+
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                "lora_unet_guidance_in_out_layer",
+                "time_text_embed.guidance_embedder.linear_2",
             )
 
         if any("img_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    ("x_embedder.{lora_key}.weight", "lora_unet_img_in.{orig_lora_key}.weight", None),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_img_in",
+                "x_embedder",
             )
 
         if any("txt_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    ("context_embedder.{lora_key}.weight", "lora_unet_txt_in.{orig_lora_key}.weight", None),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_txt_in",
+                "context_embedder",
             )
 
         if any("time_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    (
-                        "time_text_embed.timestep_embedder.linear_1.{lora_key}.weight",
-                        "lora_unet_time_in_in_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                    (
-                        "time_text_embed.timestep_embedder.linear_2.{lora_key}.weight",
-                        "lora_unet_time_in_out_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_time_in_in_layer",
+                "time_text_embed.timestep_embedder.linear_1",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                "lora_unet_time_in_out_layer",
+                "time_text_embed.timestep_embedder.linear_2",
             )
 
         if any("vector_in" in k for k in sds_sd):
-            assign_remaining_weights(
-                [
-                    (
-                        "time_text_embed.text_embedder.linear_1.{lora_key}.weight",
-                        "lora_unet_vector_in_in_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                    (
-                        "time_text_embed.text_embedder.linear_2.{lora_key}.weight",
-                        "lora_unet_vector_in_out_layer.{orig_lora_key}.weight",
-                        None,
-                    ),
-                ],
+            _convert_to_ai_toolkit(
                 sds_sd,
+                ait_sd,
+                "lora_unet_vector_in_in_layer",
+                "time_text_embed.text_embedder.linear_1",
+            )
+            _convert_to_ai_toolkit(
+                sds_sd,
+                ait_sd,
+                "lora_unet_vector_in_out_layer",
+                "time_text_embed.text_embedder.linear_2",
             )
 
         if any("final_layer" in k for k in sds_sd):

src/diffusers/models/attention.py

@@ -674,7 +674,7 @@ class JointTransformerBlock(nn.Module):
         encoder_hidden_states: torch.FloatTensor,
         temb: torch.FloatTensor,
         joint_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         joint_attention_kwargs = joint_attention_kwargs or {}
         if self.use_dual_attention:
             norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 = self.norm1(

src/diffusers/models/autoencoders/autoencoder_kl_wan.py

@@ -1052,7 +1052,7 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             is_residual=is_residual,
         )
 
-        self.spatial_compression_ratio = 2 ** len(self.temperal_downsample)
+        self.spatial_compression_ratio = scale_factor_spatial
 
         # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
         # to perform decoding of a single video latent at a time.
@@ -1145,12 +1145,13 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     def _encode(self, x: torch.Tensor):
         _, _, num_frame, height, width = x.shape
 
-        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
-            return self.tiled_encode(x)
-
         self.clear_cache()
         if self.config.patch_size is not None:
             x = patchify(x, patch_size=self.config.patch_size)
+
+        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
+            return self.tiled_encode(x)
+
         iter_ = 1 + (num_frame - 1) // 4
         for i in range(iter_):
             self._enc_conv_idx = [0]

src/diffusers/models/transformers/auraflow_transformer_2d.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 
-from typing import Any, Dict, Optional, Union
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -92,7 +92,7 @@ class AuraFlowPatchEmbed(nn.Module):
 
         return selected_indices
 
-    def forward(self, latent):
+    def forward(self, latent) -> torch.Tensor:
         batch_size, num_channels, height, width = latent.size()
         latent = latent.view(
             batch_size,
@@ -173,7 +173,7 @@ class AuraFlowSingleTransformerBlock(nn.Module):
         hidden_states: torch.FloatTensor,
         temb: torch.FloatTensor,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> torch.Tensor:
         residual = hidden_states
         attention_kwargs = attention_kwargs or {}
 
@@ -242,7 +242,7 @@ class AuraFlowJointTransformerBlock(nn.Module):
         encoder_hidden_states: torch.FloatTensor,
         temb: torch.FloatTensor,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         residual = hidden_states
         residual_context = encoder_hidden_states
         attention_kwargs = attention_kwargs or {}
@@ -472,7 +472,7 @@ class AuraFlowTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, From
         timestep: torch.LongTensor = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/cogvideox_transformer_3d.py

@@ -122,7 +122,7 @@ class CogVideoXBlock(nn.Module):
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.size(1)
         attention_kwargs = attention_kwargs or {}
 
@@ -441,7 +441,7 @@ class CogVideoXTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cac
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/consisid_transformer_3d.py

@@ -315,7 +315,7 @@ class ConsisIDBlock(nn.Module):
         encoder_hidden_states: torch.Tensor,
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.size(1)
 
         # norm & modulate
@@ -691,7 +691,7 @@ class ConsisIDTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
         id_cond: Optional[torch.Tensor] = None,
         id_vit_hidden: Optional[torch.Tensor] = None,
         return_dict: bool = True,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/lumina_nextdit2d.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -124,7 +124,7 @@ class LuminaNextDiTBlock(nn.Module):
         encoder_mask: torch.Tensor,
         temb: torch.Tensor,
         cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-    ):
+    ) -> torch.Tensor:
         """
         Perform a forward pass through the LuminaNextDiTBlock.
 
@@ -297,7 +297,7 @@ class LuminaNextDiT2DModel(ModelMixin, ConfigMixin):
         image_rotary_emb: torch.Tensor,
         cross_attention_kwargs: Dict[str, Any] = None,
         return_dict=True,
-    ) -> torch.Tensor:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         """
         Forward pass of LuminaNextDiT.
 

src/diffusers/models/transformers/transformer_bria.py

@@ -472,7 +472,7 @@ class BriaSingleTransformerBlock(nn.Module):
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_len = encoder_hidden_states.shape[1]
         hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
 
@@ -588,7 +588,7 @@ class BriaTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOrig
         return_dict: bool = True,
         controlnet_block_samples=None,
         controlnet_single_block_samples=None,
-    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         """
         The [`BriaTransformer2DModel`] forward method.
 

src/diffusers/models/transformers/transformer_cogview3plus.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 
-from typing import Dict, Union
+from typing import Dict, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -79,7 +79,7 @@ class CogView3PlusTransformerBlock(nn.Module):
         hidden_states: torch.Tensor,
         encoder_hidden_states: torch.Tensor,
         emb: torch.Tensor,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.size(1)
 
         # norm & modulate
@@ -293,7 +293,7 @@ class CogView3PlusTransformer2DModel(ModelMixin, ConfigMixin):
         target_size: torch.Tensor,
         crop_coords: torch.Tensor,
         return_dict: bool = True,
-    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         """
         The [`CogView3PlusTransformer2DModel`] forward method.
 

src/diffusers/models/transformers/transformer_cogview4.py

@@ -494,7 +494,7 @@ class CogView4TransformerBlock(nn.Module):
         ] = None,
         attention_mask: Optional[Dict[str, torch.Tensor]] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         # 1. Timestep conditioning
         (
             norm_hidden_states,
@@ -717,7 +717,7 @@ class CogView4Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, Cach
         image_rotary_emb: Optional[
             Union[Tuple[torch.Tensor, torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]]
         ] = None,
-    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/transformer_hidream_image.py

@@ -55,7 +55,7 @@ class HiDreamImageTimestepEmbed(nn.Module):
         self.time_proj = Timesteps(num_channels=frequency_embedding_size, flip_sin_to_cos=True, downscale_freq_shift=0)
         self.timestep_embedder = TimestepEmbedding(in_channels=frequency_embedding_size, time_embed_dim=hidden_size)
 
-    def forward(self, timesteps: torch.Tensor, wdtype: Optional[torch.dtype] = None):
+    def forward(self, timesteps: torch.Tensor, wdtype: Optional[torch.dtype] = None) -> torch.Tensor:
         t_emb = self.time_proj(timesteps).to(dtype=wdtype)
         t_emb = self.timestep_embedder(t_emb)
         return t_emb
@@ -87,7 +87,7 @@ class HiDreamImagePatchEmbed(nn.Module):
         self.out_channels = out_channels
         self.proj = nn.Linear(in_channels * patch_size * patch_size, out_channels, bias=True)
 
-    def forward(self, latent):
+    def forward(self, latent) -> torch.Tensor:
         latent = self.proj(latent)
         return latent
 
@@ -534,7 +534,7 @@ class HiDreamImageTransformerBlock(nn.Module):
         encoder_hidden_states: Optional[torch.Tensor] = None,
         temb: Optional[torch.Tensor] = None,
         image_rotary_emb: torch.Tensor = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         wtype = hidden_states.dtype
         (
             shift_msa_i,
@@ -592,7 +592,7 @@ class HiDreamBlock(nn.Module):
         encoder_hidden_states: Optional[torch.Tensor] = None,
         temb: Optional[torch.Tensor] = None,
         image_rotary_emb: torch.Tensor = None,
-    ) -> torch.Tensor:
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         return self.block(
             hidden_states=hidden_states,
             hidden_states_masks=hidden_states_masks,
@@ -786,7 +786,7 @@ class HiDreamImageTransformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
         **kwargs,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         encoder_hidden_states = kwargs.get("encoder_hidden_states", None)
 
         if encoder_hidden_states is not None:

src/diffusers/models/transformers/transformer_hunyuan_video.py

@@ -529,7 +529,7 @@ class HunyuanVideoSingleTransformerBlock(nn.Module):
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         *args,
         **kwargs,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.shape[1]
         hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
 
@@ -684,7 +684,7 @@ class HunyuanVideoTokenReplaceSingleTransformerBlock(nn.Module):
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
         token_replace_emb: torch.Tensor = None,
         num_tokens: int = None,
-    ) -> torch.Tensor:
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
         text_seq_length = encoder_hidden_states.shape[1]
         hidden_states = torch.cat([hidden_states, encoder_hidden_states], dim=1)
 
@@ -1038,7 +1038,7 @@ class HunyuanVideoTransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin,
         guidance: torch.Tensor = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/transformers/transformer_hunyuan_video_framepack.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
@@ -216,7 +216,7 @@ class HunyuanVideoFramepackTransformer3DModel(
         indices_latents_history_4x: Optional[torch.Tensor] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
         return_dict: bool = True,
-    ):
+    ) -> Union[Tuple[torch.Tensor], Transformer2DModelOutput]:
         if attention_kwargs is not None:
             attention_kwargs = attention_kwargs.copy()
             lora_scale = attention_kwargs.pop("scale", 1.0)

src/diffusers/models/unets/unet_1d.py

@@ -82,6 +82,7 @@ class UNet1DModel(ModelMixin, ConfigMixin):
         out_channels: int = 2,
         extra_in_channels: int = 0,
         time_embedding_type: str = "fourier",
+        time_embedding_dim: Optional[int] = None,
         flip_sin_to_cos: bool = True,
         use_timestep_embedding: bool = False,
         freq_shift: float = 0.0,
@@ -100,15 +101,23 @@ class UNet1DModel(ModelMixin, ConfigMixin):
 
         # time
         if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
             self.time_proj = GaussianFourierProjection(
-                embedding_size=8, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+                embedding_size=time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
             )
-            timestep_input_dim = 2 * block_out_channels[0]
+            timestep_input_dim = time_embed_dim
         elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
             self.time_proj = Timesteps(
                 block_out_channels[0], flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=freq_shift
             )
             timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
 
         if use_timestep_embedding:
             time_embed_dim = block_out_channels[0] * 4

src/diffusers/pipelines/__init__.py

@@ -285,6 +285,7 @@ else:
     ]
     _import_structure["lumina"] = ["LuminaPipeline", "LuminaText2ImgPipeline"]
     _import_structure["lumina2"] = ["Lumina2Pipeline", "Lumina2Text2ImgPipeline"]
+    _import_structure["lucy"] = ["LucyEditPipeline"]
     _import_structure["marigold"].extend(
         [
             "MarigoldDepthPipeline",
@@ -682,6 +683,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LEditsPPPipelineStableDiffusionXL,
         )
         from .ltx import LTXConditionPipeline, LTXImageToVideoPipeline, LTXLatentUpsamplePipeline, LTXPipeline
+        from .lucy import LucyEditPipeline
         from .lumina import LuminaPipeline, LuminaText2ImgPipeline
         from .lumina2 import Lumina2Pipeline, Lumina2Text2ImgPipeline
         from .marigold import (

src/diffusers/pipelines/lucy/__init__.py

@@ -0,0 +1,47 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_lucy_edit"] = ["LucyEditPipeline"]
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_lucy_edit import LucyEditPipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/lucy/pipeline_lucy_edit.py

@@ -0,0 +1,735 @@
+# Copyright 2025 The Wan Team and The HuggingFace Team. All rights reserved.
+# Copyright 2025 The Decart AI Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Modifications by Decart AI Team:
+# - Based on pipeline_wan.py, but with supports recieving a condition video appended to the channel dimension.
+
+import html
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import regex as re
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, UMT5EncoderModel
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...loaders import WanLoraLoaderMixin
+from ...models import AutoencoderKLWan, WanTransformer3DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import LucyPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+if is_ftfy_available():
+    import ftfy
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> from typing import List
+
+        >>> import torch
+        >>> from PIL import Image
+
+        >>> from diffusers import AutoencoderKLWan, LucyEditPipeline
+        >>> from diffusers.utils import export_to_video, load_video
+
+        >>> # Arguments
+        >>> url = "https://d2drjpuinn46lb.cloudfront.net/painter_original_edit.mp4"
+        >>> prompt = "Change the apron and blouse to a classic clown costume: satin polka-dot jumpsuit in bright primary colors, ruffled white collar, oversized pom-pom buttons, white gloves, oversized red shoes, red foam nose; soft window light from left, eye-level medium shot, natural folds and fabric highlights."
+        >>> negative_prompt = ""
+        >>> num_frames = 81
+        >>> height = 480
+        >>> width = 832
+
+
+        >>> # Load video
+        >>> def convert_video(video: List[Image.Image]) -> List[Image.Image]:
+        ...     video = load_video(url)[:num_frames]
+        ...     video = [video[i].resize((width, height)) for i in range(num_frames)]
+        ...     return video
+
+
+        >>> video = load_video(url, convert_method=convert_video)
+
+        >>> # Load model
+        >>> model_id = "decart-ai/Lucy-Edit-Dev"
+        >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+        >>> pipe = LucyEditPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> # Generate video
+        >>> output = pipe(
+        ...     prompt=prompt,
+        ...     video=video,
+        ...     negative_prompt=negative_prompt,
+        ...     height=480,
+        ...     width=832,
+        ...     num_frames=81,
+        ...     guidance_scale=5.0,
+        ... ).frames[0]
+
+        >>> # Export video
+        >>> export_to_video(output, "output.mp4", fps=24)
+        ```
+"""
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+def prompt_clean(text):
+    text = whitespace_clean(basic_clean(text))
+    return text
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class LucyEditPipeline(DiffusionPipeline, WanLoraLoaderMixin):
+    r"""
+    Pipeline for video-to-video generation using Lucy Edit.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Args:
+        tokenizer ([`T5Tokenizer`]):
+            Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer),
+            specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        text_encoder ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        transformer ([`WanTransformer3DModel`]):
+            Conditional Transformer to denoise the input latents.
+        scheduler ([`UniPCMultistepScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLWan`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        transformer_2 ([`WanTransformer3DModel`], *optional*):
+            Conditional Transformer to denoise the input latents during the low-noise stage. If provided, enables
+            two-stage denoising where `transformer` handles high-noise stages and `transformer_2` handles low-noise
+            stages. If not provided, only `transformer` is used.
+        boundary_ratio (`float`, *optional*, defaults to `None`):
+            Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising.
+            The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided,
+            `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps <
+            boundary_timestep. If `None`, only `transformer` is used for the entire denoising process.
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->transformer_2->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer", "transformer_2"]
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        text_encoder: UMT5EncoderModel,
+        vae: AutoencoderKLWan,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        transformer: Optional[WanTransformer3DModel] = None,
+        transformer_2: Optional[WanTransformer3DModel] = None,
+        boundary_ratio: Optional[float] = None,
+        expand_timesteps: bool = False,  # Wan2.2 ti2v
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+            transformer_2=transformer_2,
+        )
+        self.register_to_config(boundary_ratio=boundary_ratio)
+        self.register_to_config(expand_timesteps=expand_timesteps)
+        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial if getattr(self, "vae", None) else 8
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline._get_t5_prompt_embeds
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [prompt_clean(u) for u in prompt]
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
+        seq_lens = mask.gt(0).sum(dim=1).long()
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
+        )
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+
+            negative_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def check_inputs(
+        self,
+        video,
+        prompt,
+        negative_prompt,
+        height,
+        width,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        guidance_scale_2=None,
+    ):
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+        elif negative_prompt is not None and (
+            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
+        ):
+            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+
+        if self.config.boundary_ratio is None and guidance_scale_2 is not None:
+            raise ValueError("`guidance_scale_2` is only supported when the pipeline's `boundary_ratio` is not None.")
+
+        if video is None:
+            raise ValueError("`video` is required, received None.")
+
+    def prepare_latents(
+        self,
+        video: Optional[torch.Tensor] = None,
+        batch_size: int = 1,
+        num_channels_latents: int = 16,
+        height: int = 480,
+        width: int = 832,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        num_latent_frames = (
+            (video.size(2) - 1) // self.vae_scale_factor_temporal + 1 if latents is None else latents.size(1)
+        )
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_latent_frames,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+        # Prepare noise latents
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # Prepare condition latents
+        condition_latents = [
+            retrieve_latents(self.vae.encode(vid.unsqueeze(0)), sample_mode="argmax") for vid in video
+        ]
+
+        condition_latents = torch.cat(condition_latents, dim=0).to(dtype)
+
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean).view(1, self.vae.config.z_dim, 1, 1, 1).to(device, dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+            device, dtype
+        )
+
+        condition_latents = (condition_latents - latents_mean) * latents_std
+
+        # Check shapes
+        assert latents.shape == condition_latents.shape, (
+            f"Latents shape {latents.shape} does not match expected shape {condition_latents.shape}. Please check the input."
+        )
+
+        return latents, condition_latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1.0
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        video: List[Image.Image],
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        guidance_scale_2: Optional[float] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "np",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ):
+        r"""
+        The call function to the pipeline for generation.
+
+        Args:
+            video (`List[Image.Image]`):
+                The video to use as the condition for the video generation.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds`
+                instead. Ignored when not using guidance (`guidance_scale` < `1`).
+            height (`int`, defaults to `480`):
+                The height in pixels of the generated image.
+            width (`int`, defaults to `832`):
+                The width in pixels of the generated image.
+            num_frames (`int`, defaults to `81`):
+                The number of frames in the generated video.
+            num_inference_steps (`int`, defaults to `50`):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, defaults to `5.0`):
+                Guidance scale as defined in [Classifier-Free Diffusion
+                Guidance](https://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
+                `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
+                the text `prompt`, usually at the expense of lower image quality.
+            guidance_scale_2 (`float`, *optional*, defaults to `None`):
+                Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
+                `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
+                and the pipeline's `boundary_ratio` are not None.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.Tensor`, *optional*):
+                Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`LucyPipelineOutput`] instead of a plain tuple.
+            attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
+                `self.processor` in
+                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int`, defaults to `512`):
+                The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
+                truncated. If the prompt is shorter, it will be padded to this length.
+
+        Examples:
+
+        Returns:
+            [`~LucyPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`LucyPipelineOutput`] is returned, otherwise a `tuple` is returned where
+                the first element is a list with the generated images and the second element is a list of `bool`s
+                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            video,
+            prompt,
+            negative_prompt,
+            height,
+            width,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+            guidance_scale_2,
+        )
+
+        if num_frames % self.vae_scale_factor_temporal != 1:
+            logger.warning(
+                f"`num_frames - 1` has to be divisible by {self.vae_scale_factor_temporal}. Rounding to the nearest number."
+            )
+            num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
+        num_frames = max(num_frames, 1)
+
+        if self.config.boundary_ratio is not None and guidance_scale_2 is None:
+            guidance_scale_2 = guidance_scale
+
+        self._guidance_scale = guidance_scale
+        self._guidance_scale_2 = guidance_scale_2
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        device = self._execution_device
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+
+        transformer_dtype = self.transformer.dtype if self.transformer is not None else self.transformer_2.dtype
+        prompt_embeds = prompt_embeds.to(transformer_dtype)
+        if negative_prompt_embeds is not None:
+            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        num_channels_latents = (
+            self.transformer.config.out_channels
+            if self.transformer is not None
+            else self.transformer_2.config.out_channels
+        )
+        video = self.video_processor.preprocess_video(video, height=height, width=width).to(
+            device, dtype=torch.float32
+        )
+        latents, condition_latents = self.prepare_latents(
+            video,
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+
+        mask = torch.ones(latents.shape, dtype=torch.float32, device=device)
+
+        # 6. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
+        if self.config.boundary_ratio is not None:
+            boundary_timestep = self.config.boundary_ratio * self.scheduler.config.num_train_timesteps
+        else:
+            boundary_timestep = None
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+
+                if boundary_timestep is None or t >= boundary_timestep:
+                    # wan2.1 or high-noise stage in wan2.2
+                    current_model = self.transformer
+                    current_guidance_scale = guidance_scale
+                else:
+                    # low-noise stage in wan2.2
+                    current_model = self.transformer_2
+                    current_guidance_scale = guidance_scale_2
+
+                # latent_model_input = latents.to(transformer_dtype)
+                latent_model_input = torch.cat([latents, condition_latents], dim=1).to(transformer_dtype)
+                # latent_model_input = torch.cat([latents, latents], dim=1).to(transformer_dtype)
+                if self.config.expand_timesteps:
+                    # seq_len: num_latent_frames * latent_height//2 * latent_width//2
+                    temp_ts = (mask[0][0][:, ::2, ::2] * t).flatten()
+                    # batch_size, seq_len
+                    timestep = temp_ts.unsqueeze(0).expand(latents.shape[0], -1)
+                else:
+                    timestep = t.expand(latents.shape[0])
+
+                with current_model.cache_context("cond"):
+                    noise_pred = current_model(
+                        hidden_states=latent_model_input,
+                        timestep=timestep,
+                        encoder_hidden_states=prompt_embeds,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0]
+
+                if self.do_classifier_free_guidance:
+                    with current_model.cache_context("uncond"):
+                        noise_uncond = current_model(
+                            hidden_states=latent_model_input,
+                            timestep=timestep,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )[0]
+                    noise_pred = noise_uncond + current_guidance_scale * (noise_pred - noise_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        self._current_timestep = None
+
+        if not output_type == "latent":
+            latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
+            video = self.vae.decode(latents, return_dict=False)[0]
+            video = self.video_processor.postprocess_video(video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return LucyPipelineOutput(frames=video)

src/diffusers/pipelines/lucy/pipeline_output.py

@@ -0,0 +1,20 @@
+from dataclasses import dataclass
+
+import torch
+
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class LucyPipelineOutput(BaseOutput):
+    r"""
+    Output class for Lucy pipelines.
+
+    Args:
+        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    frames: torch.Tensor

src/diffusers/pipelines/pipeline_loading_utils.py

@@ -48,7 +48,6 @@ from .transformers_loading_utils import _load_tokenizer_from_dduf, _load_transfo
 if is_transformers_available():
     import transformers
     from transformers import PreTrainedModel, PreTrainedTokenizerBase
-    from transformers.utils import FLAX_WEIGHTS_NAME as TRANSFORMERS_FLAX_WEIGHTS_NAME
     from transformers.utils import SAFE_WEIGHTS_NAME as TRANSFORMERS_SAFE_WEIGHTS_NAME
     from transformers.utils import WEIGHTS_NAME as TRANSFORMERS_WEIGHTS_NAME
 
@@ -112,7 +111,7 @@ def is_safetensors_compatible(filenames, passed_components=None, folder_names=No
     ]
 
     if is_transformers_available():
-        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]
+        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME]
 
     # model_pytorch, diffusion_model_pytorch, ...
     weight_prefixes = [w.split(".")[0] for w in weight_names]
@@ -191,7 +190,7 @@ def filter_model_files(filenames):
     ]
 
     if is_transformers_available():
-        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]
+        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME]
 
     allowed_extensions = [wn.split(".")[-1] for wn in weight_names]
 
@@ -212,7 +211,7 @@ def variant_compatible_siblings(filenames, variant=None, ignore_patterns=None) -
     ]
 
     if is_transformers_available():
-        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME, TRANSFORMERS_FLAX_WEIGHTS_NAME]
+        weight_names += [TRANSFORMERS_WEIGHTS_NAME, TRANSFORMERS_SAFE_WEIGHTS_NAME]
 
     # model_pytorch, diffusion_model_pytorch, ...
     weight_prefixes = [w.split(".")[0] for w in weight_names]

src/diffusers/pipelines/wan/pipeline_wan_vace.py

@@ -152,16 +152,26 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         text_encoder ([`T5EncoderModel`]):
             [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
             the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
-        transformer ([`WanTransformer3DModel`]):
+        transformer ([`WanVACETransformer3DModel`]):
             Conditional Transformer to denoise the input latents.
+        transformer_2 ([`WanVACETransformer3DModel`], *optional*):
+            Conditional Transformer to denoise the input latents during the low-noise stage. In two-stage denoising,
+            `transformer` handles high-noise stages and `transformer_2` handles low-noise stages. If not provided, only
+            `transformer` is used.
         scheduler ([`UniPCMultistepScheduler`]):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
         vae ([`AutoencoderKLWan`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        boundary_ratio (`float`, *optional*, defaults to `None`):
+            Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising.
+            The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided,
+            `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps <
+            boundary_timestep. If `None`, only `transformer` is used for the entire denoising process.
     """
 
     model_cpu_offload_seq = "text_encoder->transformer->vae"
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer_2"]
 
     def __init__(
         self,
@@ -170,6 +180,8 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         transformer: WanVACETransformer3DModel,
         vae: AutoencoderKLWan,
         scheduler: FlowMatchEulerDiscreteScheduler,
+        transformer_2: WanVACETransformer3DModel = None,
+        boundary_ratio: Optional[float] = None,
     ):
         super().__init__()
 
@@ -178,9 +190,10 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             text_encoder=text_encoder,
             tokenizer=tokenizer,
             transformer=transformer,
+            transformer_2=transformer_2,
             scheduler=scheduler,
         )
-
+        self.register_to_config(boundary_ratio=boundary_ratio)
         self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
         self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
         self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
@@ -321,6 +334,7 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         video=None,
         mask=None,
         reference_images=None,
+        guidance_scale_2=None,
     ):
         base = self.vae_scale_factor_spatial * self.transformer.config.patch_size[1]
         if height % base != 0 or width % base != 0:
@@ -332,6 +346,8 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             raise ValueError(
                 f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
             )
+        if self.config.boundary_ratio is None and guidance_scale_2 is not None:
+            raise ValueError("`guidance_scale_2` is only supported when the pipeline's `boundary_ratio` is not None.")
 
         if prompt is not None and prompt_embeds is not None:
             raise ValueError(
@@ -667,6 +683,7 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         num_frames: int = 81,
         num_inference_steps: int = 50,
         guidance_scale: float = 5.0,
+        guidance_scale_2: Optional[float] = None,
         num_videos_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
@@ -728,6 +745,10 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
                 Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                 usually at the expense of lower image quality.
+            guidance_scale_2 (`float`, *optional*, defaults to `None`):
+                Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
+                `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
+                and the pipeline's `boundary_ratio` are not None.
             num_videos_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
@@ -793,6 +814,7 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             video,
             mask,
             reference_images,
+            guidance_scale_2,
         )
 
         if num_frames % self.vae_scale_factor_temporal != 1:
@@ -802,7 +824,11 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
         num_frames = max(num_frames, 1)
 
+        if self.config.boundary_ratio is not None and guidance_scale_2 is None:
+            guidance_scale_2 = guidance_scale
+
         self._guidance_scale = guidance_scale
+        self._guidance_scale_2 = guidance_scale_2
         self._attention_kwargs = attention_kwargs
         self._current_timestep = None
         self._interrupt = False
@@ -896,36 +922,53 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)
 
+        if self.config.boundary_ratio is not None:
+            boundary_timestep = self.config.boundary_ratio * self.scheduler.config.num_train_timesteps
+        else:
+            boundary_timestep = None
+
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 if self.interrupt:
                     continue
 
                 self._current_timestep = t
+
+                if boundary_timestep is None or t >= boundary_timestep:
+                    # wan2.1 or high-noise stage in wan2.2
+                    current_model = self.transformer
+                    current_guidance_scale = guidance_scale
+                else:
+                    # low-noise stage in wan2.2
+                    current_model = self.transformer_2
+                    current_guidance_scale = guidance_scale_2
+
                 latent_model_input = latents.to(transformer_dtype)
                 timestep = t.expand(latents.shape[0])
 
-                noise_pred = self.transformer(
-                    hidden_states=latent_model_input,
-                    timestep=timestep,
-                    encoder_hidden_states=prompt_embeds,
-                    control_hidden_states=conditioning_latents,
-                    control_hidden_states_scale=conditioning_scale,
-                    attention_kwargs=attention_kwargs,
-                    return_dict=False,
-                )[0]
-
-                if self.do_classifier_free_guidance:
-                    noise_uncond = self.transformer(
+                with current_model.cache_context("cond"):
+                    noise_pred = current_model(
                         hidden_states=latent_model_input,
                         timestep=timestep,
-                        encoder_hidden_states=negative_prompt_embeds,
+                        encoder_hidden_states=prompt_embeds,
                         control_hidden_states=conditioning_latents,
                         control_hidden_states_scale=conditioning_scale,
                         attention_kwargs=attention_kwargs,
                         return_dict=False,
                     )[0]
-                    noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+
+                if self.do_classifier_free_guidance:
+                    with current_model.cache_context("uncond"):
+                        noise_uncond = current_model(
+                            hidden_states=latent_model_input,
+                            timestep=timestep,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            control_hidden_states=conditioning_latents,
+                            control_hidden_states_scale=conditioning_scale,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )[0]
+                        noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1592,6 +1592,21 @@ class LTXPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class LucyEditPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class Lumina2Pipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/lora/test_lora_layers_flux.py

@@ -907,6 +907,13 @@ class FluxLoRAIntegrationTests(unittest.TestCase):
 
         assert max_diff < 1e-3
 
+    def test_flux_kohya_embedders_conversion(self):
+        """Test that embedders load without throwing errors"""
+        self.pipeline.load_lora_weights("rockerBOO/flux-bpo-po-lora")
+        self.pipeline.unload_lora_weights()
+
+        assert True
+
     def test_flux_xlabs(self):
         self.pipeline.load_lora_weights("XLabs-AI/flux-lora-collection", weight_name="disney_lora.safetensors")
         self.pipeline.fuse_lora()

tests/pipelines/wan/test_wan_vace.py

@@ -87,6 +87,7 @@ class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "scheduler": scheduler,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
+            "transformer_2": None,
         }
         return components