fix: convert diffusers from submodule to normal folder

diffusers/benchmarks/benchmarking_ltx.py

+from functools import partial
+
+import torch
+from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
+
+from diffusers import LTXVideoTransformer3DModel
+from diffusers.utils.testing_utils import torch_device
+
+
+CKPT_ID = "Lightricks/LTX-Video-0.9.7-dev"
+RESULT_FILENAME = "ltx.csv"
+
+
+def get_input_dict(**device_dtype_kwargs):
+    # 512x704 (161 frames)
+    # `max_sequence_length`: 256
+    hidden_states = torch.randn(1, 7392, 128, **device_dtype_kwargs)
+    encoder_hidden_states = torch.randn(1, 256, 4096, **device_dtype_kwargs)
+    encoder_attention_mask = torch.ones(1, 256, **device_dtype_kwargs)
+    timestep = torch.tensor([1.0], **device_dtype_kwargs)
+    video_coords = torch.randn(1, 3, 7392, **device_dtype_kwargs)
+
+    return {
+        "hidden_states": hidden_states,
+        "encoder_hidden_states": encoder_hidden_states,
+        "encoder_attention_mask": encoder_attention_mask,
+        "timestep": timestep,
+        "video_coords": video_coords,
+    }
+
+
+if __name__ == "__main__":
+    scenarios = [
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bf16",
+            model_cls=LTXVideoTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+            compile_kwargs={"fullgraph": True},
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-layerwise-upcasting",
+            model_cls=LTXVideoTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-group-offload-leaf",
+            model_cls=LTXVideoTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(
+                model_init_fn,
+                group_offload_kwargs={
+                    "onload_device": torch_device,
+                    "offload_device": torch.device("cpu"),
+                    "offload_type": "leaf_level",
+                    "use_stream": True,
+                    "non_blocking": True,
+                },
+            ),
+        ),
+    ]
+
+    runner = BenchmarkMixin()
+    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

diffusers/benchmarks/benchmarking_sdxl.py

+from functools import partial
+
+import torch
+from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
+
+from diffusers import UNet2DConditionModel
+from diffusers.utils.testing_utils import torch_device
+
+
+CKPT_ID = "stabilityai/stable-diffusion-xl-base-1.0"
+RESULT_FILENAME = "sdxl.csv"
+
+
+def get_input_dict(**device_dtype_kwargs):
+    # height: 1024
+    # width: 1024
+    # max_sequence_length: 77
+    hidden_states = torch.randn(1, 4, 128, 128, **device_dtype_kwargs)
+    encoder_hidden_states = torch.randn(1, 77, 2048, **device_dtype_kwargs)
+    timestep = torch.tensor([1.0], **device_dtype_kwargs)
+    added_cond_kwargs = {
+        "text_embeds": torch.randn(1, 1280, **device_dtype_kwargs),
+        "time_ids": torch.ones(1, 6, **device_dtype_kwargs),
+    }
+
+    return {
+        "sample": hidden_states,
+        "encoder_hidden_states": encoder_hidden_states,
+        "timestep": timestep,
+        "added_cond_kwargs": added_cond_kwargs,
+    }
+
+
+if __name__ == "__main__":
+    scenarios = [
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bf16",
+            model_cls=UNet2DConditionModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "unet",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+            compile_kwargs={"fullgraph": True},
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-layerwise-upcasting",
+            model_cls=UNet2DConditionModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "unet",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-group-offload-leaf",
+            model_cls=UNet2DConditionModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "unet",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(
+                model_init_fn,
+                group_offload_kwargs={
+                    "onload_device": torch_device,
+                    "offload_device": torch.device("cpu"),
+                    "offload_type": "leaf_level",
+                    "use_stream": True,
+                    "non_blocking": True,
+                },
+            ),
+        ),
+    ]
+
+    runner = BenchmarkMixin()
+    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

diffusers/benchmarks/benchmarking_utils.py

+import gc
+import inspect
+import logging
+import os
+import queue
+import threading
+from contextlib import nullcontext
+from dataclasses import dataclass
+from typing import Any, Callable
+
+import pandas as pd
+import torch
+import torch.utils.benchmark as benchmark
+
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils.testing_utils import require_torch_gpu, torch_device
+
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger(__name__)
+
+NUM_WARMUP_ROUNDS = 5
+
+
+def benchmark_fn(f, *args, **kwargs):
+    t0 = benchmark.Timer(
+        stmt="f(*args, **kwargs)",
+        globals={"args": args, "kwargs": kwargs, "f": f},
+        num_threads=1,
+    )
+    return float(f"{(t0.blocked_autorange().mean):.3f}")
+
+
+def flush():
+    gc.collect()
+    torch.cuda.empty_cache()
+    torch.cuda.reset_max_memory_allocated()
+    torch.cuda.reset_peak_memory_stats()
+
+
+# Adapted from https://github.com/lucasb-eyer/cnn_vit_benchmarks/blob/15b665ff758e8062131353076153905cae00a71f/main.py
+def calculate_flops(model, input_dict):
+    try:
+        from torchprofile import profile_macs
+    except ModuleNotFoundError:
+        raise
+
+    # This is a hacky way to convert the kwargs to args as `profile_macs` cries about kwargs.
+    sig = inspect.signature(model.forward)
+    param_names = [
+        p.name
+        for p in sig.parameters.values()
+        if p.kind
+        in (
+            inspect.Parameter.POSITIONAL_ONLY,
+            inspect.Parameter.POSITIONAL_OR_KEYWORD,
+        )
+        and p.name != "self"
+    ]
+    bound = sig.bind_partial(**input_dict)
+    bound.apply_defaults()
+    args = tuple(bound.arguments[name] for name in param_names)
+
+    model.eval()
+    with torch.no_grad():
+        macs = profile_macs(model, args)
+    flops = 2 * macs  # 1 MAC operation = 2 FLOPs (1 multiplication + 1 addition)
+    return flops
+
+
+def calculate_params(model):
+    return sum(p.numel() for p in model.parameters())
+
+
+# Users can define their own in case this doesn't suffice. For most cases,
+# it should be sufficient.
+def model_init_fn(model_cls, group_offload_kwargs=None, layerwise_upcasting=False, **init_kwargs):
+    model = model_cls.from_pretrained(**init_kwargs).eval()
+    if group_offload_kwargs and isinstance(group_offload_kwargs, dict):
+        model.enable_group_offload(**group_offload_kwargs)
+    else:
+        model.to(torch_device)
+    if layerwise_upcasting:
+        model.enable_layerwise_casting(
+            storage_dtype=torch.float8_e4m3fn, compute_dtype=init_kwargs.get("torch_dtype", torch.bfloat16)
+        )
+    return model
+
+
+@dataclass
+class BenchmarkScenario:
+    name: str
+    model_cls: ModelMixin
+    model_init_kwargs: dict[str, Any]
+    model_init_fn: Callable
+    get_model_input_dict: Callable
+    compile_kwargs: dict[str, Any] | None = None
+
+
+@require_torch_gpu
+class BenchmarkMixin:
+    def pre_benchmark(self):
+        flush()
+        torch.compiler.reset()
+
+    def post_benchmark(self, model):
+        model.cpu()
+        flush()
+        torch.compiler.reset()
+
+    @torch.no_grad()
+    def run_benchmark(self, scenario: BenchmarkScenario):
+        # 0) Basic stats
+        logger.info(f"Running scenario: {scenario.name}.")
+        try:
+            model = model_init_fn(scenario.model_cls, **scenario.model_init_kwargs)
+            num_params = round(calculate_params(model) / 1e9, 2)
+            try:
+                flops = round(calculate_flops(model, input_dict=scenario.get_model_input_dict()) / 1e9, 2)
+            except Exception as e:
+                logger.info(f"Problem in calculating FLOPs:\n{e}")
+                flops = None
+            model.cpu()
+            del model
+        except Exception as e:
+            logger.info(f"Error while initializing the model and calculating FLOPs:\n{e}")
+            return {}
+        self.pre_benchmark()
+
+        # 1) plain stats
+        results = {}
+        plain = None
+        try:
+            plain = self._run_phase(
+                model_cls=scenario.model_cls,
+                init_fn=scenario.model_init_fn,
+                init_kwargs=scenario.model_init_kwargs,
+                get_input_fn=scenario.get_model_input_dict,
+                compile_kwargs=None,
+            )
+        except Exception as e:
+            logger.info(f"Benchmark could not be run with the following error:\n{e}")
+            return results
+
+        # 2) compiled stats (if any)
+        compiled = {"time": None, "memory": None}
+        if scenario.compile_kwargs:
+            try:
+                compiled = self._run_phase(
+                    model_cls=scenario.model_cls,
+                    init_fn=scenario.model_init_fn,
+                    init_kwargs=scenario.model_init_kwargs,
+                    get_input_fn=scenario.get_model_input_dict,
+                    compile_kwargs=scenario.compile_kwargs,
+                )
+            except Exception as e:
+                logger.info(f"Compilation benchmark could not be run with the following error\n: {e}")
+                if plain is None:
+                    return results
+
+        # 3) merge
+        result = {
+            "scenario": scenario.name,
+            "model_cls": scenario.model_cls.__name__,
+            "num_params_B": num_params,
+            "flops_G": flops,
+            "time_plain_s": plain["time"],
+            "mem_plain_GB": plain["memory"],
+            "time_compile_s": compiled["time"],
+            "mem_compile_GB": compiled["memory"],
+        }
+        if scenario.compile_kwargs:
+            result["fullgraph"] = scenario.compile_kwargs.get("fullgraph", False)
+            result["mode"] = scenario.compile_kwargs.get("mode", "default")
+        else:
+            result["fullgraph"], result["mode"] = None, None
+        return result
+
+    def run_bencmarks_and_collate(self, scenarios: BenchmarkScenario | list[BenchmarkScenario], filename: str):
+        if not isinstance(scenarios, list):
+            scenarios = [scenarios]
+        record_queue = queue.Queue()
+        stop_signal = object()
+
+        def _writer_thread():
+            while True:
+                item = record_queue.get()
+                if item is stop_signal:
+                    break
+                df_row = pd.DataFrame([item])
+                write_header = not os.path.exists(filename)
+                df_row.to_csv(filename, mode="a", header=write_header, index=False)
+                record_queue.task_done()
+
+            record_queue.task_done()
+
+        writer = threading.Thread(target=_writer_thread, daemon=True)
+        writer.start()
+
+        for s in scenarios:
+            try:
+                record = self.run_benchmark(s)
+                if record:
+                    record_queue.put(record)
+                else:
+                    logger.info(f"Record empty from scenario: {s.name}.")
+            except Exception as e:
+                logger.info(f"Running scenario ({s.name}) led to error:\n{e}")
+        record_queue.put(stop_signal)
+        logger.info(f"Results serialized to {filename=}.")
+
+    def _run_phase(
+        self,
+        *,
+        model_cls: ModelMixin,
+        init_fn: Callable,
+        init_kwargs: dict[str, Any],
+        get_input_fn: Callable,
+        compile_kwargs: dict[str, Any] | None = None,
+    ) -> dict[str, float]:
+        # setup
+        self.pre_benchmark()
+
+        # init & (optional) compile
+        model = init_fn(model_cls, **init_kwargs)
+        if compile_kwargs:
+            model.compile(**compile_kwargs)
+
+        # build inputs
+        inp = get_input_fn()
+
+        # measure
+        run_ctx = torch._inductor.utils.fresh_inductor_cache() if compile_kwargs else nullcontext()
+        with run_ctx:
+            for _ in range(NUM_WARMUP_ROUNDS):
+                _ = model(**inp)
+            time_s = benchmark_fn(lambda m, d: m(**d), model, inp)
+        mem_gb = torch.cuda.max_memory_allocated() / (1024**3)
+        mem_gb = round(mem_gb, 2)
+
+        # teardown
+        self.post_benchmark(model)
+        del model
+        return {"time": time_s, "memory": mem_gb}

diffusers/benchmarks/benchmarking_wan.py

+from functools import partial
+
+import torch
+from benchmarking_utils import BenchmarkMixin, BenchmarkScenario, model_init_fn
+
+from diffusers import WanTransformer3DModel
+from diffusers.utils.testing_utils import torch_device
+
+
+CKPT_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+RESULT_FILENAME = "wan.csv"
+
+
+def get_input_dict(**device_dtype_kwargs):
+    # height: 480
+    # width: 832
+    # num_frames: 81
+    # max_sequence_length: 512
+    hidden_states = torch.randn(1, 16, 21, 60, 104, **device_dtype_kwargs)
+    encoder_hidden_states = torch.randn(1, 512, 4096, **device_dtype_kwargs)
+    timestep = torch.tensor([1.0], **device_dtype_kwargs)
+
+    return {"hidden_states": hidden_states, "encoder_hidden_states": encoder_hidden_states, "timestep": timestep}
+
+
+if __name__ == "__main__":
+    scenarios = [
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-bf16",
+            model_cls=WanTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=model_init_fn,
+            compile_kwargs={"fullgraph": True},
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-layerwise-upcasting",
+            model_cls=WanTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(model_init_fn, layerwise_upcasting=True),
+        ),
+        BenchmarkScenario(
+            name=f"{CKPT_ID}-group-offload-leaf",
+            model_cls=WanTransformer3DModel,
+            model_init_kwargs={
+                "pretrained_model_name_or_path": CKPT_ID,
+                "torch_dtype": torch.bfloat16,
+                "subfolder": "transformer",
+            },
+            get_model_input_dict=partial(get_input_dict, device=torch_device, dtype=torch.bfloat16),
+            model_init_fn=partial(
+                model_init_fn,
+                group_offload_kwargs={
+                    "onload_device": torch_device,
+                    "offload_device": torch.device("cpu"),
+                    "offload_type": "leaf_level",
+                    "use_stream": True,
+                    "non_blocking": True,
+                },
+            ),
+        ),
+    ]
+
+    runner = BenchmarkMixin()
+    runner.run_bencmarks_and_collate(scenarios, filename=RESULT_FILENAME)

diffusers/benchmarks/push_results.py

+import os
+
+import pandas as pd
+from huggingface_hub import hf_hub_download, upload_file
+from huggingface_hub.utils import EntryNotFoundError
+
+
+REPO_ID = "diffusers/benchmarks"
+
+
+def has_previous_benchmark() -> str:
+    from run_all import FINAL_CSV_FILENAME
+
+    csv_path = None
+    try:
+        csv_path = hf_hub_download(repo_id=REPO_ID, repo_type="dataset", filename=FINAL_CSV_FILENAME)
+    except EntryNotFoundError:
+        csv_path = None
+    return csv_path
+
+
+def filter_float(value):
+    if isinstance(value, str):
+        return float(value.split()[0])
+    return value
+
+
+def push_to_hf_dataset():
+    from run_all import FINAL_CSV_FILENAME, GITHUB_SHA
+
+    csv_path = has_previous_benchmark()
+    if csv_path is not None:
+        current_results = pd.read_csv(FINAL_CSV_FILENAME)
+        previous_results = pd.read_csv(csv_path)
+
+        numeric_columns = current_results.select_dtypes(include=["float64", "int64"]).columns
+
+        for column in numeric_columns:
+            # get previous values as floats, aligned to current index
+            prev_vals = previous_results[column].map(filter_float).reindex(current_results.index)
+
+            # get current values as floats
+            curr_vals = current_results[column].astype(float)
+
+            # stringify the current values
+            curr_str = curr_vals.map(str)
+
+            # build an appendage only when prev exists and differs
+            append_str = prev_vals.where(prev_vals.notnull() & (prev_vals != curr_vals), other=pd.NA).map(
+                lambda x: f" ({x})" if pd.notnull(x) else ""
+            )
+
+            # combine
+            current_results[column] = curr_str + append_str
+        os.remove(FINAL_CSV_FILENAME)
+        current_results.to_csv(FINAL_CSV_FILENAME, index=False)
+
+    commit_message = f"upload from sha: {GITHUB_SHA}" if GITHUB_SHA is not None else "upload benchmark results"
+    upload_file(
+        repo_id=REPO_ID,
+        path_in_repo=FINAL_CSV_FILENAME,
+        path_or_fileobj=FINAL_CSV_FILENAME,
+        repo_type="dataset",
+        commit_message=commit_message,
+    )
+    upload_file(
+        repo_id="diffusers/benchmark-analyzer",
+        path_in_repo=FINAL_CSV_FILENAME,
+        path_or_fileobj=FINAL_CSV_FILENAME,
+        repo_type="space",
+        commit_message=commit_message,
+    )
+
+
+if __name__ == "__main__":
+    push_to_hf_dataset()

diffusers/benchmarks/requirements.txt

+pandas 
+psutil
+gpustat
+torchprofile
+bitsandbytes
+psycopg2==2.9.9
\ No newline at end of file

diffusers/benchmarks/run_all.py

+import glob
+import logging
+import os
+import subprocess
+
+import pandas as pd
+
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+logger = logging.getLogger(__name__)
+
+PATTERN = "benchmarking_*.py"
+FINAL_CSV_FILENAME = "collated_results.csv"
+GITHUB_SHA = os.getenv("GITHUB_SHA", None)
+
+
+class SubprocessCallException(Exception):
+    pass
+
+
+def run_command(command: list[str], return_stdout=False):
+    try:
+        output = subprocess.check_output(command, stderr=subprocess.STDOUT)
+        if return_stdout and hasattr(output, "decode"):
+            return output.decode("utf-8")
+    except subprocess.CalledProcessError as e:
+        raise SubprocessCallException(f"Command `{' '.join(command)}` failed with:\n{e.output.decode()}") from e
+
+
+def merge_csvs(final_csv: str = "collated_results.csv"):
+    all_csvs = glob.glob("*.csv")
+    all_csvs = [f for f in all_csvs if f != final_csv]
+    if not all_csvs:
+        logger.info("No result CSVs found to merge.")
+        return
+
+    df_list = []
+    for f in all_csvs:
+        try:
+            d = pd.read_csv(f)
+        except pd.errors.EmptyDataError:
+            # If a file existed but was zero‐bytes or corrupted, skip it
+            continue
+        df_list.append(d)
+
+    if not df_list:
+        logger.info("All result CSVs were empty or invalid; nothing to merge.")
+        return
+
+    final_df = pd.concat(df_list, ignore_index=True)
+    if GITHUB_SHA is not None:
+        final_df["github_sha"] = GITHUB_SHA
+    final_df.to_csv(final_csv, index=False)
+    logger.info(f"Merged {len(all_csvs)} partial CSVs → {final_csv}.")
+
+
+def run_scripts():
+    python_files = sorted(glob.glob(PATTERN))
+    python_files = [f for f in python_files if f != "benchmarking_utils.py"]
+
+    for file in python_files:
+        script_name = file.split(".py")[0].split("_")[-1]  # example: benchmarking_foo.py -> foo
+        logger.info(f"\n****** Running file: {file} ******")
+
+        partial_csv = f"{script_name}.csv"
+        if os.path.exists(partial_csv):
+            logger.info(f"Found {partial_csv}. Removing for safer numbers and duplication.")
+            os.remove(partial_csv)
+
+        command = ["python", file]
+        try:
+            run_command(command)
+            logger.info(f"→ {file} finished normally.")
+        except SubprocessCallException as e:
+            logger.info(f"Error running {file}:\n{e}")
+        finally:
+            logger.info(f"→ Merging partial CSVs after {file} …")
+            merge_csvs(final_csv=FINAL_CSV_FILENAME)
+
+    logger.info(f"\nAll scripts attempted. Final collated CSV: {FINAL_CSV_FILENAME}")
+
+
+if __name__ == "__main__":
+    run_scripts()

diffusers/docker/diffusers-doc-builder/Dockerfile

+FROM python:3.10-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update && apt-get install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libglib2.0-0 \
+    libsndfile1-dev \
+    libgl1 \
+    zip \
+    wget
+
+ENV UV_PYTHON=/usr/local/bin/python
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN pip install uv
+RUN uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio \
+    --extra-index-url https://download.pytorch.org/whl/cpu
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    hf_xet \
+    setuptools==69.5.1 \
+    bitsandbytes \
+    torchao \
+    gguf \
+    optimum-quanto
+
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
+
+CMD ["/bin/bash"]

diffusers/docker/diffusers-onnxruntime-cpu/Dockerfile

+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
+
+RUN apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   libgl1 \
+                   python3.10 \
+                   python3-pip \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3 -m uv pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio\
+        onnxruntime \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers \
+        hf_xet
+
+CMD ["/bin/bash"]
\ No newline at end of file

diffusers/docker/diffusers-onnxruntime-cuda/Dockerfile

+FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
+
+RUN apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   libgl1 \
+                   python3.10 \
+                   python3-pip \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        "onnxruntime-gpu>=1.13.1" \
+        --extra-index-url https://download.pytorch.org/whl/cu117 && \
+    python3.10 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        hf_xet \
+        Jinja2 \
+        librosa \
+        numpy==1.26.4 \
+        scipy \
+        tensorboard \
+        transformers
+
+CMD ["/bin/bash"]
\ No newline at end of file

diffusers/docker/diffusers-pytorch-cpu/Dockerfile

+FROM python:3.10-slim
+ENV PYTHONDONTWRITEBYTECODE=1
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update && apt-get install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libglib2.0-0 \
+    libsndfile1-dev \
+    libgl1
+
+ENV UV_PYTHON=/usr/local/bin/python
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN pip install uv
+RUN uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio \
+    --extra-index-url https://download.pytorch.org/whl/cpu
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    hf_xet
+
+RUN apt-get clean && rm -rf /var/lib/apt/lists/* && apt-get autoremove && apt-get autoclean
+
+CMD ["/bin/bash"]

diffusers/docker/diffusers-pytorch-cuda/Dockerfile

+FROM nvidia/cuda:12.9.1-runtime-ubuntu24.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ARG PYTHON_VERSION=3.10
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
+
+RUN apt install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libglib2.0-0 \
+    libsndfile1-dev \
+    libgl1 \
+    python3 \
+    python3-pip \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# Install torch, torchvision, and torchaudio together to ensure compatibility
+RUN uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio \
+    --index-url https://download.pytorch.org/whl/cu129
+
+# Install compatible versions of numba/llvmlite for Python 3.10+
+RUN uv pip install --no-cache-dir \
+    "llvmlite>=0.40.0" \
+    "numba>=0.57.0"
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    pytorch-lightning \
+    hf_xet
+
+CMD ["/bin/bash"]

diffusers/docker/diffusers-pytorch-minimum-cuda/Dockerfile

+FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ARG PYTHON_VERSION=3.10
+ENV DEBIAN_FRONTEND=noninteractive
+ENV MINIMUM_SUPPORTED_TORCH_VERSION="2.1.0"
+ENV MINIMUM_SUPPORTED_TORCHVISION_VERSION="0.16.0"
+ENV MINIMUM_SUPPORTED_TORCHAUDIO_VERSION="2.1.0"
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
+
+RUN apt install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libglib2.0-0 \
+    libsndfile1-dev \
+    libgl1 \
+    python3 \
+    python3-pip \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN uv pip install --no-cache-dir \
+    torch==$MINIMUM_SUPPORTED_TORCH_VERSION \
+    torchvision==$MINIMUM_SUPPORTED_TORCHVISION_VERSION \
+    torchaudio==$MINIMUM_SUPPORTED_TORCHAUDIO_VERSION
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    pytorch-lightning \
+    hf_xet
+
+CMD ["/bin/bash"]

diffusers/docker/diffusers-pytorch-xformers-cuda/Dockerfile

+FROM nvidia/cuda:12.9.1-runtime-ubuntu24.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ARG PYTHON_VERSION=3.10
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa && \
+    apt-get update
+
+RUN apt install -y bash \
+    build-essential \
+    git \
+    git-lfs \
+    curl \
+    ca-certificates \
+    libglib2.0-0 \
+    libsndfile1-dev \
+    libgl1 \
+    python3 \
+    python3-pip \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+ENV VIRTUAL_ENV="/opt/venv"
+ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+# Install torch, torchvision, and torchaudio together to ensure compatibility
+RUN uv pip install --no-cache-dir \
+    torch \
+    torchvision \
+    torchaudio \
+    --index-url https://download.pytorch.org/whl/cu129
+
+# Install compatible versions of numba/llvmlite for Python 3.10+
+RUN uv pip install --no-cache-dir \
+    "llvmlite>=0.40.0" \
+    "numba>=0.57.0"
+
+RUN uv pip install --no-cache-dir "git+https://github.com/huggingface/diffusers.git@main#egg=diffusers[test]"
+
+# Extra dependencies
+RUN uv pip install --no-cache-dir \
+    accelerate \
+    numpy==1.26.4 \
+    pytorch-lightning \
+    hf_xet \
+    xformers
+
+CMD ["/bin/bash"]

diffusers/docs/README.md

+<!---
+Copyright 2024- The HuggingFace 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.
+-->
+
+# Generating the documentation
+
+To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
+you can install them with the following command, at the root of the code repository:
+
+```bash
+pip install -e ".[docs]"
+```
+
+Then you need to install our open source documentation builder tool:
+
+```bash
+pip install git+https://github.com/huggingface/doc-builder
+```
+
+---
+**NOTE**
+
+You only need to generate the documentation to inspect it locally (if you're planning changes and want to
+check how they look before committing for instance). You don't have to commit the built documentation.
+
+---
+
+## Previewing the documentation
+
+To preview the docs, first install the `watchdog` module with:
+
+```bash
+pip install watchdog
+```
+
+Then run the following command:
+
+```bash
+doc-builder preview {package_name} {path_to_docs}
+```
+
+For example:
+
+```bash
+doc-builder preview diffusers docs/source/en
+```
+
+The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
+
+---
+**NOTE**
+
+The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
+
+---
+
+## Adding a new element to the navigation bar
+
+Accepted files are Markdown (.md).
+
+Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
+the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml) file.
+
+## Renaming section headers and moving sections
+
+It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
+
+Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
+
+So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
+
+```md
+Sections that were moved:
+
+[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
+```
+and of course, if you moved it to another file, then:
+
+```md
+Sections that were moved:
+
+[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
+```
+
+Use the relative style to link to the new file so that the versioned docs continue to work.
+
+For an example of a rich moved section set please see the very end of [the transformers Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.md).
+
+
+## Writing Documentation - Specification
+
+The `huggingface/diffusers` documentation follows the
+[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
+although we can write them directly in Markdown.
+
+### Adding a new tutorial
+
+Adding a new tutorial or section is done in two steps:
+
+- Add a new Markdown (.md) file under `docs/source/<languageCode>`.
+- Link that file in `docs/source/<languageCode>/_toctree.yml` on the correct toc-tree.
+
+Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
+depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or four.
+
+### Adding a new pipeline/scheduler
+
+When adding a new pipeline:
+
+- Create a file `xxx.md` under `docs/source/<languageCode>/api/pipelines` (don't hesitate to copy an existing file as template).
+- Link that file in (*Diffusers Summary*) section in `docs/source/api/pipelines/overview.md`, along with the link to the paper, and a colab notebook (if available).
+- Write a short overview of the diffusion model:
+    - Overview with paper & authors
+    - Paper abstract
+    - Tips and tricks and how to use it best
+    - Possible an end-to-end example of how to use it
+- Add all the pipeline classes that should be linked in the diffusion model. These classes should be added using our Markdown syntax. By default as follows:
+
+```
+[[autodoc]] XXXPipeline
+    - all
+	- __call__
+```
+
+This will include every public method of the pipeline that is documented, as well as the  `__call__` method that is not documented by default. If you just want to add additional methods that are not documented, you can put the list of all methods to add in a list that contains `all`.
+
+```
+[[autodoc]] XXXPipeline
+    - all
+	- __call__
+	- enable_attention_slicing
+	- disable_attention_slicing
+    - enable_xformers_memory_efficient_attention
+    - disable_xformers_memory_efficient_attention
+```
+
+You can follow the same process to create a new scheduler under the `docs/source/<languageCode>/api/schedulers` folder.
+
+### Writing source documentation
+
+Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
+and objects like True, None, or any strings should usually be put in `code`.
+
+When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
+adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or
+function to be in the main package.
+
+If you want to create a link to some internal class or function, you need to
+provide its path. For instance: \[\`pipelines.ImagePipelineOutput\`\]. This will be converted into a link with
+`pipelines.ImagePipelineOutput` in the description. To get rid of the path and only keep the name of the object you are
+linking to in the description, add a ~: \[\`~pipelines.ImagePipelineOutput\`\] will generate a link with `ImagePipelineOutput` in the description.
+
+The same works for methods so you can either use \[\`XXXClass.method\`\] or \[\`~XXXClass.method\`\].
+
+#### Defining arguments in a method
+
+Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
+an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
+description:
+
+```
+    Args:
+        n_layers (`int`): The number of layers of the model.
+```
+
+If the description is too long to fit in one line, another indentation is necessary before writing the description
+after the argument.
+
+Here's an example showcasing everything so far:
+
+```
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AlbertTokenizer`]. See [`~PreTrainedTokenizer.encode`] and
+            [`~PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+```
+
+For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
+following signature:
+
+```py
+def my_function(x: str=None, a: float=3.14):
+```
+
+then its documentation should look like this:
+
+```
+    Args:
+        x (`str`, *optional*):
+            This argument controls ...
+        a (`float`, *optional*, defaults to `3.14`):
+            This argument is used to ...
+```
+
+Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
+if the first line describing your argument type and its default gets long, you can't break it on several lines. You can
+however write as many lines as you want in the indented description (see the example above with `input_ids`).
+
+#### Writing a multi-line code block
+
+Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
+
+
+````
+```
+# first line of code
+# second line
+# etc
+```
+````
+
+#### Writing a return block
+
+The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
+The first line should be the type of the return, followed by a line return. No need to indent further for the elements
+building the return.
+
+Here's an example of a single value return:
+
+```
+    Returns:
+        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
+```
+
+Here's an example of a tuple return, comprising several objects:
+
+```
+    Returns:
+        `tuple(torch.Tensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
+        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.Tensor` of shape `(1,)` --
+          Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
+        - **prediction_scores** (`torch.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
+          Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+```
+
+#### Adding an image
+
+Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
+the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
+them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
+If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
+to this dataset.
+
+## Styling the docstring
+
+We have an automatic script running with the `make style` command that will make sure that:
+- the docstrings fully take advantage of the line width
+- all code examples are formatted using black, like the code of the Transformers library
+
+This script may have some weird failures if you made a syntax mistake or if you uncover a bug. Therefore, it's
+recommended to commit your changes before running `make style`, so you can revert the changes done by that script
+easily.

diffusers/docs/TRANSLATING.md

+<!--Copyright 2025 The HuggingFace 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.
+-->
+
+### Translating the Diffusers documentation into your language
+
+As part of our mission to democratize machine learning, we'd love to make the Diffusers library available in many more languages! Follow the steps below if you want to help translate the documentation into your language 🙏.
+
+**🗞️ Open an issue**
+
+To get started, navigate to the [Issues](https://github.com/huggingface/diffusers/issues) page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the "🌐 Translating a New Language?" from the "New issue" button.
+
+Once an issue exists, post a comment to indicate which chapters you'd like to work on, and we'll add your name to the list.
+
+
+**🍴 Fork the repository**
+
+First, you'll need to [fork the Diffusers repo](https://docs.github.com/en/get-started/quickstart/fork-a-repo). You can do this by clicking on the **Fork** button on the top-right corner of this repo's page.
+
+Once you've forked the repo, you'll want to get the files on your local machine for editing. You can do that by cloning the fork with Git as follows:
+
+```bash
+git clone https://github.com/<YOUR-USERNAME>/diffusers.git
+```
+
+**📋 Copy-paste the English version with a new language code**
+
+The documentation files are in one leading directory:
+
+- [`docs/source`](https://github.com/huggingface/diffusers/tree/main/docs/source): All the documentation materials are organized here by language.
+
+You'll only need to copy the files in the [`docs/source/en`](https://github.com/huggingface/diffusers/tree/main/docs/source/en) directory, so first navigate to your fork of the repo and run the following:
+
+```bash
+cd ~/path/to/diffusers/docs
+cp -r source/en source/<LANG-ID>
+```
+
+Here, `<LANG-ID>` should be one of the ISO 639-1 or ISO 639-2 language codes -- see [here](https://www.loc.gov/standards/iso639-2/php/code_list.php) for a handy table.
+
+**✍️ Start translating**
+
+The fun part comes - translating the text!
+
+The first thing we recommend is translating the part of the `_toctree.yml` file that corresponds to your doc chapter. This file is used to render the table of contents on the website.
+
+> 🙋 If the `_toctree.yml` file doesn't yet exist for your language, you can create one by copy-pasting from the English version and deleting the sections unrelated to your chapter. Just make sure it exists in the `docs/source/<LANG-ID>/` directory!
+
+The fields you should add are `local` (with the name of the file containing the translation; e.g. `autoclass_tutorial`), and `title` (with the title of the doc in your language; e.g. `Load pretrained instances with an AutoClass`) -- as a reference, here is the `_toctree.yml` for [English](https://github.com/huggingface/diffusers/blob/main/docs/source/en/_toctree.yml):
+
+```yaml
+- sections:
+  - local: pipeline_tutorial # Do not change this! Use the same name for your .md file
+    title: Pipelines for inference # Translate this!
+    ...
+  title: Tutorials # Translate this!
+```
+
+Once you have translated the `_toctree.yml` file, you can start translating the [MDX](https://mdxjs.com/) files associated with your docs chapter.
+
+> 🙋 If you'd like others to help you with the translation, you should [open an issue](https://github.com/huggingface/diffusers/issues) and tag @patrickvonplaten.

diffusers/docs/source/_config.py

+# docstyle-ignore
+INSTALL_CONTENT = """
+# Diffusers installation
+! pip install diffusers transformers datasets accelerate
+# To install from source instead of the last release, comment the command above and uncomment the following one.
+# ! pip install git+https://github.com/huggingface/diffusers.git
+"""
+
+notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}]

diffusers/docs/source/en/advanced_inference/outpaint.md

+<!--Copyright 2025 The HuggingFace 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.
+-->
+
+# Outpainting
+
+Outpainting extends an image beyond its original boundaries, allowing you to add, replace, or modify visual elements in an image while preserving the original image. Like [inpainting](../using-diffusers/inpaint), you want to fill the white area (in this case, the area outside of the original image) with new visual elements while keeping the original image (represented by a mask of black pixels). There are a couple of ways to outpaint, such as with a [ControlNet](https://hf.co/blog/OzzyGT/outpainting-controlnet) or with [Differential Diffusion](https://hf.co/blog/OzzyGT/outpainting-differential-diffusion).
+
+This guide will show you how to outpaint with an inpainting model, ControlNet, and a ZoeDepth estimator.
+
+Before you begin, make sure you have the [controlnet_aux](https://github.com/huggingface/controlnet_aux) library installed so you can use the ZoeDepth estimator.
+
+```py
+!pip install -q controlnet_aux
+```
+
+## Image preparation
+
+Start by picking an image to outpaint with and remove the background with a Space like [BRIA-RMBG-1.4](https://hf.co/spaces/briaai/BRIA-RMBG-1.4).
+
+<iframe
+	src="https://briaai-bria-rmbg-1-4.hf.space"
+	frameborder="0"
+	width="850"
+	height="450"
+></iframe>
+
+For example, remove the background from this image of a pair of shoes.
+
+<div class="flex flex-row gap-4">
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/original-jordan.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
+  </div>
+  <div class="flex-1">
+    <img class="rounded-xl" src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/no-background-jordan.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">background removed</figcaption>
+  </div>
+</div>
+
+[Stable Diffusion XL (SDXL)](../using-diffusers/sdxl) models work best with 1024x1024 images, but you can resize the image to any size as long as your hardware has enough memory to support it. The transparent background in the image should also be replaced with a white background. Create a function (like the one below) that scales and pastes the image onto a white background.
+
+```py
+import random
+
+import requests
+import torch
+from controlnet_aux import ZoeDetector
+from PIL import Image, ImageOps
+
+from diffusers import (
+    AutoencoderKL,
+    ControlNetModel,
+    StableDiffusionXLControlNetPipeline,
+    StableDiffusionXLInpaintPipeline,
+)
+
+def scale_and_paste(original_image):
+    aspect_ratio = original_image.width / original_image.height
+
+    if original_image.width > original_image.height:
+        new_width = 1024
+        new_height = round(new_width / aspect_ratio)
+    else:
+        new_height = 1024
+        new_width = round(new_height * aspect_ratio)
+
+    resized_original = original_image.resize((new_width, new_height), Image.LANCZOS)
+    white_background = Image.new("RGBA", (1024, 1024), "white")
+    x = (1024 - new_width) // 2
+    y = (1024 - new_height) // 2
+    white_background.paste(resized_original, (x, y), resized_original)
+
+    return resized_original, white_background
+
+original_image = Image.open(
+    requests.get(
+        "https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/no-background-jordan.png",
+        stream=True,
+    ).raw
+).convert("RGBA")
+resized_img, white_bg_image = scale_and_paste(original_image)
+```
+
+To avoid adding unwanted extra details, use the ZoeDepth estimator to provide additional guidance during generation and to ensure the shoes remain consistent with the original image.
+
+```py
+zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
+image_zoe = zoe(white_bg_image, detect_resolution=512, image_resolution=1024)
+image_zoe
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/zoedepth-jordan.png"/>
+</div>
+
+## Outpaint
+
+Once your image is ready, you can generate content in the white area around the shoes with [controlnet-inpaint-dreamer-sdxl](https://hf.co/destitech/controlnet-inpaint-dreamer-sdxl), a SDXL ControlNet trained for inpainting.
+
+Load the inpainting ControlNet, ZoeDepth model, VAE and pass them to the [`StableDiffusionXLControlNetPipeline`]. Then you can create an optional `generate_image` function (for convenience) to outpaint an initial image.
+
+```py
+controlnets = [
+    ControlNetModel.from_pretrained(
+        "destitech/controlnet-inpaint-dreamer-sdxl", torch_dtype=torch.float16, variant="fp16"
+    ),
+    ControlNetModel.from_pretrained(
+        "diffusers/controlnet-zoe-depth-sdxl-1.0", torch_dtype=torch.float16
+    ),
+]
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16).to("cuda")
+pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
+    "SG161222/RealVisXL_V4.0", torch_dtype=torch.float16, variant="fp16", controlnet=controlnets, vae=vae
+).to("cuda")
+
+def generate_image(prompt, negative_prompt, inpaint_image, zoe_image, seed: int = None):
+    if seed is None:
+        seed = random.randint(0, 2**32 - 1)
+
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+
+    image = pipeline(
+        prompt,
+        negative_prompt=negative_prompt,
+        image=[inpaint_image, zoe_image],
+        guidance_scale=6.5,
+        num_inference_steps=25,
+        generator=generator,
+        controlnet_conditioning_scale=[0.5, 0.8],
+        control_guidance_end=[0.9, 0.6],
+    ).images[0]
+
+    return image
+
+prompt = "nike air jordans on a basketball court"
+negative_prompt = ""
+
+temp_image = generate_image(prompt, negative_prompt, white_bg_image, image_zoe, 908097)
+```
+
+Paste the original image over the initial outpainted image. You'll improve the outpainted background in a later step.
+
+```py
+x = (1024 - resized_img.width) // 2
+y = (1024 - resized_img.height) // 2
+temp_image.paste(resized_img, (x, y), resized_img)
+temp_image
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/initial-outpaint.png"/>
+</div>
+
+> [!TIP]
+> Now is a good time to free up some memory if you're running low!
+>
+> ```py
+> pipeline=None
+> torch.cuda.empty_cache()
+> ```
+
+Now that you have an initial outpainted image, load the [`StableDiffusionXLInpaintPipeline`] with the [RealVisXL](https://hf.co/SG161222/RealVisXL_V4.0) model to generate the final outpainted image with better quality.
+
+```py
+pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
+    "OzzyGT/RealVisXL_V4.0_inpainting",
+    torch_dtype=torch.float16,
+    variant="fp16",
+    vae=vae,
+).to("cuda")
+```
+
+Prepare a mask for the final outpainted image. To create a more natural transition between the original image and the outpainted background, blur the mask to help it blend better.
+
+```py
+mask = Image.new("L", temp_image.size)
+mask.paste(resized_img.split()[3], (x, y))
+mask = ImageOps.invert(mask)
+final_mask = mask.point(lambda p: p > 128 and 255)
+mask_blurred = pipeline.mask_processor.blur(final_mask, blur_factor=20)
+mask_blurred
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/blurred-mask.png"/>
+</div>
+
+Create a better prompt and pass it to the `generate_outpaint` function to generate the final outpainted image. Again, paste the original image over the final outpainted background.
+
+```py
+def generate_outpaint(prompt, negative_prompt, image, mask, seed: int = None):
+    if seed is None:
+        seed = random.randint(0, 2**32 - 1)
+
+    generator = torch.Generator(device="cpu").manual_seed(seed)
+
+    image = pipeline(
+        prompt,
+        negative_prompt=negative_prompt,
+        image=image,
+        mask_image=mask,
+        guidance_scale=10.0,
+        strength=0.8,
+        num_inference_steps=30,
+        generator=generator,
+    ).images[0]
+
+    return image
+
+prompt = "high quality photo of nike air jordans on a basketball court, highly detailed"
+negative_prompt = ""
+
+final_image = generate_outpaint(prompt, negative_prompt, temp_image, mask_blurred, 7688778)
+x = (1024 - resized_img.width) // 2
+y = (1024 - resized_img.height) // 2
+final_image.paste(resized_img, (x, y), resized_img)
+final_image
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/stevhliu/testing-images/resolve/main/final-outpaint.png"/>
+</div>

diffusers/docs/source/en/api/activations.md

+<!--Copyright 2025 The HuggingFace 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.
+-->
+
+# Activation functions
+
+Customized activation functions for supporting various models in 🤗 Diffusers.
+
+## GELU
+
+[[autodoc]] models.activations.GELU
+
+## GEGLU
+
+[[autodoc]] models.activations.GEGLU
+
+## ApproximateGELU
+
+[[autodoc]] models.activations.ApproximateGELU
+
+
+## SwiGLU
+
+[[autodoc]] models.activations.SwiGLU
+
+## FP32SiLU
+
+[[autodoc]] models.activations.FP32SiLU
+
+## LinearActivation
+
+[[autodoc]] models.activations.LinearActivation