update webui

diffsynth/models/dinov3_image_encoder.py

@@ -1,4 +1,4 @@
-from transformers import DINOv3ViTModel, DINOv3ViTImageProcessorFast
+from transformers import DINOv3ViTModel, DINOv3ViTImageProcessor
 from transformers.models.dinov3_vit.modeling_dinov3_vit import DINOv3ViTConfig
 import torch
 
@@ -40,7 +40,7 @@ class DINOv3ImageEncoder(DINOv3ViTModel):
             value_bias = False
         )
         super().__init__(config)
-        self.processor = DINOv3ViTImageProcessorFast(
+        self.processor = DINOv3ViTImageProcessor(
             crop_size = None,
             data_format = "channels_first",
             default_to_square = True,
@@ -56,7 +56,7 @@ class DINOv3ImageEncoder(DINOv3ViTModel):
                 0.456,
                 0.406
             ],
-            image_processor_type = "DINOv3ViTImageProcessorFast",
+            image_processor_type = "DINOv3ViTImageProcessor",
             image_std = [
                 0.229,
                 0.224,

diffsynth/models/siglip2_image_encoder.py

@@ -1,5 +1,5 @@
 from transformers.models.siglip.modeling_siglip import SiglipVisionTransformer, SiglipVisionConfig
-from transformers import SiglipImageProcessor, Siglip2VisionModel, Siglip2VisionConfig, Siglip2ImageProcessorFast
+from transformers import SiglipImageProcessor, Siglip2VisionModel, Siglip2VisionConfig, Siglip2ImageProcessor
 import torch
 
 from diffsynth.core.device.npu_compatible_device import get_device_type
@@ -90,7 +90,7 @@ class Siglip2ImageEncoder428M(Siglip2VisionModel):
             transformers_version = "4.57.1"
         )
         super().__init__(config)
-        self.processor = Siglip2ImageProcessorFast(
+        self.processor = Siglip2ImageProcessor(
             **{
                 "data_format": "channels_first",
                 "default_to_square": True,
@@ -106,7 +106,7 @@ class Siglip2ImageEncoder428M(Siglip2VisionModel):
                     0.5,
                     0.5
                 ],
-                "image_processor_type": "Siglip2ImageProcessorFast",
+                "image_processor_type": "Siglip2ImageProcessor",
                 "image_std": [
                     0.5,
                     0.5,

diffsynth/pipelines/anima_image.py

@@ -74,7 +74,7 @@ class AnimaImagePipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
+        prompt: str = "",
         negative_prompt: str = "",
         cfg_scale: float = 4.0,
         # Image

diffsynth/pipelines/flux2_image.py

@@ -75,7 +75,7 @@ class Flux2ImagePipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
+        prompt: str = "",
         negative_prompt: str = "",
         cfg_scale: float = 1.0,
         embedded_guidance: float = 4.0,
@@ -83,7 +83,7 @@ class Flux2ImagePipeline(BasePipeline):
         input_image: Image.Image = None,
         denoising_strength: float = 1.0,
         # Edit
-        edit_image: Union[Image.Image, List[Image.Image]] = None,
+        edit_image: List[Image.Image] = None,
         edit_image_auto_resize: bool = True,
         # Shape
         height: int = 1024,

diffsynth/pipelines/flux_image.py

@@ -181,7 +181,7 @@ class FluxImagePipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
+        prompt: str = "",
         negative_prompt: str = "",
         cfg_scale: float = 1.0,
         embedded_guidance: float = 3.5,
@@ -199,10 +199,6 @@ class FluxImagePipeline(BasePipeline):
         sigma_shift: float = None,
         # Steps
         num_inference_steps: int = 30,
-        # local prompts
-        multidiffusion_prompts=(),
-        multidiffusion_masks=(),
-        multidiffusion_scales=(),
         # Kontext
         kontext_images: Union[list[Image.Image], Image.Image] = None,
         # ControlNet
@@ -257,7 +253,6 @@ class FluxImagePipeline(BasePipeline):
             "height": height, "width": width,
             "seed": seed, "rand_device": rand_device,
             "sigma_shift": sigma_shift, "num_inference_steps": num_inference_steps,
-            "multidiffusion_prompts": multidiffusion_prompts, "multidiffusion_masks": multidiffusion_masks, "multidiffusion_scales": multidiffusion_scales,
             "kontext_images": kontext_images,
             "controlnet_inputs": controlnet_inputs,
             "ipadapter_images": ipadapter_images, "ipadapter_scale": ipadapter_scale,

diffsynth/pipelines/ltx2_audio_video.py

@@ -169,46 +169,46 @@ class LTX2AudioVideoPipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
-        negative_prompt: Optional[str] = "",
+        prompt: str = "",
+        negative_prompt: str = "",
         denoising_strength: float = 1.0,
         # Image-to-video
-        input_images: Optional[list[Image.Image]] = None,
-        input_images_indexes: Optional[list[int]] = [0],
-        input_images_strength: Optional[float] = 1.0,
+        input_images: list[Image.Image] = None,
+        input_images_indexes: list[int] = [0],
+        input_images_strength: float = 1.0,
         # In-Context Video Control
-        in_context_videos: Optional[list[list[Image.Image]]] = None,
-        in_context_downsample_factor: Optional[int] = 2,
+        in_context_videos: list[list[Image.Image]] = None,
+        in_context_downsample_factor: int = 2,
         # Video-to-video
-        retake_video: Optional[list[Image.Image]] = None,
-        retake_video_regions: Optional[list[tuple[float, float]]] = None,
+        retake_video: list[Image.Image] = None,
+        retake_video_regions: list[tuple[float, float]] = None,
         # Audio-to-video
-        retake_audio: Optional[torch.Tensor] = None,
-        audio_sample_rate: Optional[int] = 48000,
-        retake_audio_regions: Optional[list[tuple[float, float]]] = None,
+        retake_audio: torch.Tensor = None,
+        audio_sample_rate: int = 48000,
+        retake_audio_regions: list[tuple[float, float]] = None,
         # Randomness
-        seed: Optional[int] = None,
-        rand_device: Optional[str] = "cpu",
+        seed: int = None,
+        rand_device: str = "cpu",
         # Shape
-        height: Optional[int] = 512,
-        width: Optional[int] = 768,
-        num_frames: Optional[int] = 121,
-        frame_rate: Optional[int] = 24,
+        height: int = 512,
+        width: int = 768,
+        num_frames: int = 121,
+        frame_rate: int = 24,
         # Classifier-free guidance
-        cfg_scale: Optional[float] = 3.0,
+        cfg_scale: float = 3.0,
         # Scheduler
-        num_inference_steps: Optional[int] = 30,
+        num_inference_steps: int = 30,
         # VAE tiling
-        tiled: Optional[bool] = True,
-        tile_size_in_pixels: Optional[int] = 512,
-        tile_overlap_in_pixels: Optional[int] = 128,
-        tile_size_in_frames: Optional[int] = 128,
-        tile_overlap_in_frames: Optional[int] = 24,
+        tiled: bool = True,
+        tile_size_in_pixels: int = 512,
+        tile_overlap_in_pixels: int = 128,
+        tile_size_in_frames: int = 128,
+        tile_overlap_in_frames: int = 24,
         # Special Pipelines
-        use_two_stage_pipeline: Optional[bool] = False,
-        stage2_spatial_upsample_factor: Optional[int] = 2,
-        clear_lora_before_state_two: Optional[bool] = False,
-        use_distilled_pipeline: Optional[bool] = False,
+        use_two_stage_pipeline: bool = False,
+        stage2_spatial_upsample_factor: int = 2,
+        clear_lora_before_state_two: bool = False,
+        use_distilled_pipeline: bool = False,
         # progress_bar
         progress_bar_cmd=tqdm,
     ):

diffsynth/pipelines/mova_audio_video.py

@@ -115,33 +115,33 @@ class MovaAudioVideoPipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
-        negative_prompt: Optional[str] = "",
+        prompt: str = "",
+        negative_prompt: str = "",
         # Image-to-video
-        input_image: Optional[Image.Image] = None,
+        input_image: Image.Image = None,
         # First-last-frame-to-video
-        end_image: Optional[Image.Image] = None,
+        end_image: Image.Image = None,
         # Video-to-video
-        denoising_strength: Optional[float] = 1.0,
+        denoising_strength: float = 1.0,
         # Randomness
-        seed: Optional[int] = None,
-        rand_device: Optional[str] = "cpu",
+        seed: int = None,
+        rand_device: str = "cpu",
         # Shape
-        height: Optional[int] = 352,
-        width: Optional[int] = 640,
-        num_frames: Optional[int] = 81,
-        frame_rate: Optional[int] = 24,
+        height: int = 352,
+        width: int = 640,
+        num_frames: int = 81,
+        frame_rate: int = 24,
         # Classifier-free guidance
-        cfg_scale: Optional[float] = 5.0,
+        cfg_scale: float = 5.0,
         # Boundary
-        switch_DiT_boundary: Optional[float] = 0.9,
+        switch_DiT_boundary: float = 0.9,
         # Scheduler
-        num_inference_steps: Optional[int] = 50,
-        sigma_shift: Optional[float] = 5.0,
+        num_inference_steps: int = 50,
+        sigma_shift: float = 5.0,
         # VAE tiling
-        tiled: Optional[bool] = True,
-        tile_size: Optional[tuple[int, int]] = (30, 52),
-        tile_stride: Optional[tuple[int, int]] = (15, 26),
+        tiled: bool = True,
+        tile_size: tuple[int, int] = (30, 52),
+        tile_stride: tuple[int, int] = (15, 26),
         # progress_bar
         progress_bar_cmd=tqdm,
     ):

diffsynth/pipelines/qwen_image.py

@@ -100,7 +100,7 @@ class QwenImagePipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
+        prompt: str = "",
         negative_prompt: str = "",
         cfg_scale: float = 4.0,
         # Image

diffsynth/pipelines/wan_video.py

@@ -190,82 +190,82 @@ class WanVideoPipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
-        negative_prompt: Optional[str] = "",
+        prompt: str = "",
+        negative_prompt: str = "",
         # Image-to-video
-        input_image: Optional[Image.Image] = None,
+        input_image: Image.Image = None,
         # First-last-frame-to-video
-        end_image: Optional[Image.Image] = None,
+        end_image: Image.Image = None,
         # Video-to-video
-        input_video: Optional[list[Image.Image]] = None,
-        denoising_strength: Optional[float] = 1.0,
+        input_video: list[Image.Image] = None,
+        denoising_strength: float = 1.0,
         # Speech-to-video
-        input_audio: Optional[np.array] = None,
-        audio_embeds: Optional[torch.Tensor] = None,
-        audio_sample_rate: Optional[int] = 16000,
-        s2v_pose_video: Optional[list[Image.Image]] = None,
-        s2v_pose_latents: Optional[torch.Tensor] = None,
-        motion_video: Optional[list[Image.Image]] = None,
+        input_audio: np.array = None,
+        audio_embeds: torch.Tensor = None,
+        audio_sample_rate: int = 16000,
+        s2v_pose_video: list[Image.Image] = None,
+        s2v_pose_latents: torch.Tensor = None,
+        motion_video: list[Image.Image] = None,
         # ControlNet
-        control_video: Optional[list[Image.Image]] = None,
-        reference_image: Optional[Image.Image] = None,
+        control_video: list[Image.Image] = None,
+        reference_image: Image.Image = None,
         # Camera control
-        camera_control_direction: Optional[Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"]] = None,
-        camera_control_speed: Optional[float] = 1/54,
-        camera_control_origin: Optional[tuple] = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0),
+        camera_control_direction: Literal["Left", "Right", "Up", "Down", "LeftUp", "LeftDown", "RightUp", "RightDown"] = None,
+        camera_control_speed: float = 1/54,
+        camera_control_origin: tuple = (0, 0.532139961, 0.946026558, 0.5, 0.5, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0),
         # VACE
-        vace_video: Optional[list[Image.Image]] = None,
-        vace_video_mask: Optional[Image.Image] = None,
-        vace_reference_image: Optional[Image.Image] = None,
-        vace_scale: Optional[float] = 1.0,
+        vace_video: list[Image.Image] = None,
+        vace_video_mask: Image.Image = None,
+        vace_reference_image: Image.Image = None,
+        vace_scale: float = 1.0,
         # Animate
-        animate_pose_video: Optional[list[Image.Image]] = None,
-        animate_face_video: Optional[list[Image.Image]] = None,
-        animate_inpaint_video: Optional[list[Image.Image]] = None,
-        animate_mask_video: Optional[list[Image.Image]] = None,
+        animate_pose_video: list[Image.Image] = None,
+        animate_face_video: list[Image.Image] = None,
+        animate_inpaint_video: list[Image.Image] = None,
+        animate_mask_video: list[Image.Image] = None,
         # VAP
-        vap_video: Optional[list[Image.Image]] = None,
-        vap_prompt: Optional[str] = " ",
-        negative_vap_prompt: Optional[str] = " ",
+        vap_video: list[Image.Image] = None,
+        vap_prompt: str = " ",
+        negative_vap_prompt: str = " ",
         # Randomness
-        seed: Optional[int] = None,
-        rand_device: Optional[str] = "cpu",
+        seed: int = None,
+        rand_device: str = "cpu",
         # Shape
-        height: Optional[int] = 480,
-        width: Optional[int] = 832,
-        num_frames=81,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
         # Classifier-free guidance
-        cfg_scale: Optional[float] = 5.0,
-        cfg_merge: Optional[bool] = False,
+        cfg_scale: float = 5.0,
+        cfg_merge: bool = False,
         # Boundary
-        switch_DiT_boundary: Optional[float] = 0.875,
+        switch_DiT_boundary: float = 0.875,
         # Scheduler
-        num_inference_steps: Optional[int] = 50,
-        sigma_shift: Optional[float] = 5.0,
+        num_inference_steps: int = 50,
+        sigma_shift: float = 5.0,
         # Speed control
-        motion_bucket_id: Optional[int] = None,
+        motion_bucket_id: int = None,
         # LongCat-Video
-        longcat_video: Optional[list[Image.Image]] = None,
+        longcat_video: list[Image.Image] = None,
         # VAE tiling
-        tiled: Optional[bool] = True,
-        tile_size: Optional[tuple[int, int]] = (30, 52),
-        tile_stride: Optional[tuple[int, int]] = (15, 26),
+        tiled: bool = True,
+        tile_size: tuple[int, int] = (30, 52),
+        tile_stride: tuple[int, int] = (15, 26),
         # Sliding window
-        sliding_window_size: Optional[int] = None,
-        sliding_window_stride: Optional[int] = None,
+        sliding_window_size: int = None,
+        sliding_window_stride: int = None,
         # Teacache
-        tea_cache_l1_thresh: Optional[float] = None,
-        tea_cache_model_id: Optional[str] = "",
+        tea_cache_l1_thresh: float = None,
+        tea_cache_model_id: str = "",
         # WanToDance
-        wantodance_music_path: Optional[str] = None,
-        wantodance_reference_image: Optional[Image.Image] = None,
-        wantodance_fps: Optional[float] = 30,
-        wantodance_keyframes: Optional[list[Image.Image]] = None,
-        wantodance_keyframes_mask: Optional[list[int]] = None,
+        wantodance_music_path: str = None,
+        wantodance_reference_image: Image.Image = None,
+        wantodance_fps: float = 30,
+        wantodance_keyframes: list[Image.Image] = None,
+        wantodance_keyframes_mask: list[int] = None,
         framewise_decoding: bool = False,
         # progress_bar
         progress_bar_cmd=tqdm,
-        output_type: Optional[Literal["quantized", "floatpoint"]] = "quantized",
+        output_type: Literal["quantized", "floatpoint"] = "quantized",
     ):
         # Scheduler
         self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)

diffsynth/pipelines/z_image.py

@@ -95,7 +95,7 @@ class ZImagePipeline(BasePipeline):
     def __call__(
         self,
         # Prompt
-        prompt: str,
+        prompt: str = "",
         negative_prompt: str = "",
         cfg_scale: float = 1.0,
         # Image
@@ -109,7 +109,7 @@ class ZImagePipeline(BasePipeline):
         width: int = 1024,
         # Randomness
         seed: int = None,
-        rand_device: str = "cpu",
+        rand_device: Union[str, torch.device] = "cpu",
         # Steps
         num_inference_steps: int = 8,
         sigma_shift: float = None,

examples/dev_tools/webui.py

@@ -0,0 +1,321 @@
+import importlib, inspect, pkgutil, traceback, torch, os, re, typing
+from typing import Union, List, Optional, Tuple, Iterable, Dict, Literal
+from contextlib import contextmanager
+from diffsynth.utils.data import VideoData
+import streamlit as st
+from diffsynth import ModelConfig
+from diffsynth.diffusion.base_pipeline import ControlNetInput
+from PIL import Image
+from tqdm import tqdm
+st.set_page_config(layout="wide")
+
+class StreamlitTqdmWrapper:
+    """Wrapper class that combines tqdm and streamlit progress bar"""
+    def __init__(self, iterable, st_progress_bar=None):
+        self.iterable = iterable
+        self.st_progress_bar = st_progress_bar
+        self.tqdm_bar = tqdm(iterable)
+        self.total = len(iterable) if hasattr(iterable, '__len__') else None
+        self.current = 0
+
+    def __iter__(self):
+        for item in self.tqdm_bar:
+            if self.st_progress_bar is not None and self.total is not None:
+                self.current += 1
+                self.st_progress_bar.progress(self.current / self.total)
+            yield item
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args):
+        if hasattr(self.tqdm_bar, '__exit__'):
+            self.tqdm_bar.__exit__(*args)
+
+@contextmanager
+def catch_error(error_value):
+    try:
+        yield
+    except Exception as e:
+        error_message = traceback.format_exc()
+        print(f"Error {error_value}:\n{error_message}")
+
+def parse_model_configs_from_an_example(path):
+    model_configs = []
+    with open(path, "r") as f:
+        for code in f.readlines():
+            code = code.strip()
+            if not code.startswith("ModelConfig"):
+                continue
+            pairs = re.findall(r'(\w+)\s*=\s*["\']([^"\']+)["\']', code)
+            config_dict = {k: v for k, v in pairs}
+            model_configs.append(ModelConfig(model_id=config_dict["model_id"], origin_file_pattern=config_dict["origin_file_pattern"]))
+    return model_configs
+
+def list_examples(path, keyword=None):
+    examples = []
+    if os.path.isdir(path):
+        for file_name in os.listdir(path):
+            examples.extend(list_examples(os.path.join(path, file_name), keyword=keyword))
+    elif path.endswith(".py"):
+        with open(path, "r") as f:
+            code = f.read()
+        if keyword is None or keyword in code:
+            examples.extend([path])
+    return examples
+
+def parse_available_pipelines():
+    from diffsynth.diffusion.base_pipeline import BasePipeline
+    import diffsynth.pipelines as _pipelines_pkg
+    available_pipelines = {}
+    for _, name, _ in pkgutil.iter_modules(_pipelines_pkg.__path__):
+        with catch_error(f"Failed: import diffsynth.pipelines.{name}"):
+            mod = importlib.import_module(f"diffsynth.pipelines.{name}")
+            classes = {
+                cls_name: cls for cls_name, cls in inspect.getmembers(mod, inspect.isclass)
+                if issubclass(cls, BasePipeline) and cls is not BasePipeline and cls.__module__ == mod.__name__
+            }
+            available_pipelines.update(classes)
+    return available_pipelines
+
+def parse_available_examples(path, available_pipelines):
+    available_examples = {}
+    for pipeline_name in available_pipelines:
+        examples = ["None"] + list_examples(path, keyword=f"{pipeline_name}.from_pretrained")
+        available_examples[pipeline_name] = examples
+    return available_examples
+
+def draw_selectbox(label, options, option_map, value=None, disabled=False):
+    default_index = 0 if value is None else tuple(options).index([option for option in option_map if option_map[option]==value][0])
+    option = st.selectbox(label=label, options=tuple(options), index=default_index, disabled=disabled)
+    return option_map.get(option)
+
+def parse_params(fn):
+    params = []
+    for name, param in inspect.signature(fn).parameters.items():
+        annotation = param.annotation if param.annotation is not inspect.Parameter.empty else None
+        default = param.default if param.default is not inspect.Parameter.empty else None
+        params.append({"name": name, "dtype": annotation, "value": default})
+    return params
+
+def draw_model_config(model_config=None, key_suffix="", disabled=False):
+    with st.container(border=True):
+        if model_config is None:
+            model_config = ModelConfig()
+        path = st.text_input(label="path", key="path" + key_suffix, value=model_config.path, disabled=disabled)
+        col1, col2 = st.columns(2)
+        with col1:
+            model_id = st.text_input(label="model_id", key="model_id" + key_suffix, value=model_config.model_id, disabled=disabled)
+        with col2:
+            origin_file_pattern = st.text_input(label="origin_file_pattern", key="origin_file_pattern" + key_suffix, value=model_config.origin_file_pattern, disabled=disabled)
+        model_config = ModelConfig(
+            path=None if path == "" else path,
+            model_id=model_id,
+            origin_file_pattern=origin_file_pattern,
+        )
+    return model_config
+
+def draw_multi_model_config(name="", value=None, disabled=False):
+    model_configs = []
+    with st.container(border=True):
+        st.markdown(name)
+        num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
+        for i in range(num):
+            model_config = draw_model_config(key_suffix=f"_{name}_{i}", model_config=None if value is None else value[i], disabled=disabled)
+            model_configs.append(model_config)
+    return model_configs
+
+def draw_single_model_config(name="", value=None, disabled=False):
+    with st.container(border=True):
+        st.markdown(name)
+        model_config = draw_model_config(value, key_suffix=f"_{name}", disabled=disabled)
+    return model_config
+
+def draw_multi_images(name="", value=None, disabled=False):
+    images = []
+    with st.container(border=True):
+        st.markdown(name)
+        num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
+        for i in range(num):
+            image = st.file_uploader(name, type=["png", "jpg", "jpeg", "webp"], key=f"{name}_{i}", disabled=disabled)
+            if image is not None: images.append(Image.open(image))
+    return images
+
+def draw_multi_elements(st_element, name="", value=None, disabled=False, kwargs=None):
+    if kwargs is None:
+        kwargs = {}
+    elements = []
+    with st.container(border=True):
+        st.markdown(name)
+        num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
+        for i in range(num):
+            element = st_element(name, key=f"{name}_{i}", disabled=disabled, value=None if value is None else value[i], **kwargs)
+            elements.append(element)
+    return elements
+
+def draw_controlnet_input(name="", value=None, disabled=False):
+    with st.container(border=True):
+        st.markdown(name)
+        controlnet_id = st.number_input("controlnet_id", value=0, min_value=0, max_value=20, step=1, key=f"{name}_controlnet_id")
+        scale = st.number_input("scale", value=1.0, min_value=0.0, max_value=10.0, key=f"{name}_scale")
+        image = st.file_uploader("image", type=["png", "jpg", "jpeg", "webp"], disabled=disabled, key=f"{name}_image")
+        if image is not None: image = Image.open(image)
+        inpaint_image = st.file_uploader("inpaint_image", type=["png", "jpg", "jpeg", "webp"], disabled=disabled, key=f"{name}_inpaint_image")
+        if inpaint_image is not None: inpaint_image = Image.open(inpaint_image)
+        inpaint_mask = st.file_uploader("inpaint_mask", type=["png", "jpg", "jpeg", "webp"], disabled=disabled, key=f"{name}_inpaint_mask")
+        if inpaint_mask is not None: inpaint_mask = Image.open(inpaint_mask)
+    return ControlNetInput(controlnet_id=controlnet_id, scale=scale, image=image, inpaint_image=inpaint_image, inpaint_mask=inpaint_mask)
+
+def draw_controlnet_inputs(name, value=None, disabled=False):
+    controlnet_inputs = []
+    with st.container(border=True):
+        st.markdown(name)
+        num = st.number_input(f"num_{name}", min_value=0, max_value=20, value=0 if value is None else len(value), disabled=disabled)
+        for i in range(num):
+            controlnet_input = draw_controlnet_input(name=f"{name}_{i}", value=None, disabled=disabled)
+            controlnet_inputs.append(controlnet_input)
+    return controlnet_inputs
+
+def draw_ui_element(name, dtype, value):
+    unsupported_dtype = [
+        Dict[str, torch.Tensor],
+        torch.Tensor,
+    ]
+    if dtype in unsupported_dtype:
+        return
+    if value is None:
+        with st.container(border=True):
+            enable = st.checkbox(f"Enable {name}", value=False)
+            ui = draw_ui_element_safely(name, dtype, value=value, disabled=not enable)
+        if enable:
+            return ui
+        else:
+            return None
+    else:
+        return draw_ui_element_safely(name, dtype, value)
+
+def draw_video(name, value=None, disabled=False):
+    ui = st.file_uploader(name, type=["mp4"], disabled=disabled)
+    if ui is not None:
+        ui = VideoData(ui)
+        ui = [ui[i] for i in range(len(ui))]
+    return ui
+
+def draw_ui_element_safely(name, dtype, value, disabled=False):
+    if dtype == torch.dtype:
+        option_map = {"bfloat16": torch.bfloat16, "float32": torch.float32, "float16": torch.float16}
+        ui = draw_selectbox(name, option_map.keys(), option_map, value=value, disabled=disabled)
+    elif dtype == Union[str, torch.device]:
+        option_map = {"cuda": "cuda", "cpu": "cpu"}
+        ui = draw_selectbox(name, option_map.keys(), option_map, value=value, disabled=disabled)
+    elif dtype == bool:
+        ui = st.checkbox(name, value=value, disabled=disabled)
+    elif dtype == ModelConfig:
+        ui = draw_single_model_config(name, value=value, disabled=disabled)
+    elif dtype in [list[ModelConfig], List[ModelConfig], Union[list[ModelConfig], ModelConfig, str]]:
+        if name == "model_configs" and "model_configs_from_example" in st.session_state:
+            model_configs = st.session_state["model_configs_from_example"]
+            del st.session_state["model_configs_from_example"]
+            ui = draw_multi_model_config(name, model_configs, disabled=disabled)
+        else:
+            ui = draw_multi_model_config(name, disabled=disabled)
+    elif dtype == str:
+        if "prompt" in name:
+            ui = st.text_area(name, value=value, height=3, disabled=disabled)
+        else:
+            ui = st.text_input(name, value=value, disabled=disabled)
+    elif dtype == float:
+        ui = st.number_input(name, value=value, disabled=disabled)
+    elif dtype == int:
+        ui = st.number_input(name, value=value, step=1, disabled=disabled)
+    elif dtype == Image.Image:
+        ui = st.file_uploader(name, type=["png", "jpg", "jpeg", "webp"], disabled=disabled)
+        if ui is not None: ui = Image.open(ui)
+    elif dtype in [List[Image.Image], list[Image.Image], Union[list[Image.Image], Image.Image], Union[List[Image.Image], Image.Image]]:
+        if "video" in name:
+            ui = draw_video(name, value=value, disabled=disabled)
+        else:
+            ui = draw_multi_images(name, value=value, disabled=disabled)
+    elif dtype in [List[ControlNetInput], list[ControlNetInput]]:
+        ui = draw_controlnet_inputs(name, value=value, disabled=disabled)
+    elif dtype in [List[str], list[str]]:
+        ui = draw_multi_elements(st.text_input, name, value=value, disabled=disabled)
+    elif dtype in [List[float], list[float], Union[list[float], float], Union[List[float], float]]:
+        ui = draw_multi_elements(st.number_input, name, value=value, disabled=disabled)
+    elif dtype in [List[int], list[int]]:
+        ui = draw_multi_elements(st.number_input, name, value=value, disabled=disabled, kwargs={"step": 1})
+    elif dtype in [List[List[Image.Image]], list[list[Image.Image]]]:
+        ui = draw_multi_elements(draw_video, name, value=value, disabled=disabled)
+    elif dtype in [tuple[int, int], Tuple[int, int]]:
+        with st.container(border=True):
+            st.markdown(name)
+            ui = (st.text_input(f"{name}_0", value=value[0], disabled=disabled), st.text_input(f"{name}_1", value=value[1], disabled=disabled))
+    elif isinstance(dtype, typing._LiteralGenericAlias):
+        with st.container(border=True):
+            st.markdown(f"{name} ({dtype})")
+            ui = st.text_input(name, value=value, disabled=disabled, label_visibility="hidden")
+    elif dtype is None:
+        if name == "progress_bar_cmd":
+            ui = value
+    else:
+        st.markdown(f"(`{name}` is not not configurable in WebUI). dtype: `{dtype}`.")
+        ui = value
+    return ui
+
+
+def launch_webui():
+    input_col, output_col = st.columns(2)
+    with input_col:
+        if "available_pipelines" not in st.session_state:
+            st.session_state["available_pipelines"] = parse_available_pipelines()
+        if "available_examples" not in st.session_state:
+            st.session_state["available_examples"] = parse_available_examples("./examples", st.session_state["available_pipelines"])
+
+        with st.expander("Pipeline", expanded=True):
+            pipeline_class = draw_selectbox("Pipeline Class", st.session_state["available_pipelines"].keys(), st.session_state["available_pipelines"], value=st.session_state["available_pipelines"]["ZImagePipeline"])
+            example = st.selectbox("Parse model configs from an example (optional)", st.session_state["available_examples"][pipeline_class.__name__])
+            if example != "None":
+                st.session_state["model_configs_from_example"] = parse_model_configs_from_an_example(example)
+        if st.button("Step 1: Parse Pipeline", type="primary"):
+            st.session_state["pipeline_class"] = pipeline_class
+
+        if "pipeline_class" not in st.session_state:
+            return
+        with st.expander("Model", expanded=True):
+            input_params = {}
+            params = parse_params(pipeline_class.from_pretrained)
+            for param in params:
+                input_params[param["name"]] = draw_ui_element(**param)
+        if st.button("Step 2: Load Models", type="primary"):
+            with st.spinner("Loading models", show_time=True):
+                if "pipe" in st.session_state:
+                    del st.session_state["pipe"]
+                    torch.cuda.empty_cache()
+                st.session_state["pipe"] = pipeline_class.from_pretrained(**input_params)
+
+        if "pipe" not in st.session_state:
+            return
+        with st.expander("Input", expanded=True):
+            pipe = st.session_state["pipe"]
+            input_params = {}
+            params = parse_params(pipeline_class.__call__)
+            for param in params:
+                if param["name"] in ["self"]:
+                    continue
+                input_params[param["name"]] = draw_ui_element(**param)
+    
+    with output_col:
+        if st.button("Step 3: Generate", type="primary"):
+            if "progress_bar_cmd" in input_params:
+                input_params["progress_bar_cmd"] = lambda iterable: StreamlitTqdmWrapper(iterable, st.progress(0))
+            result = pipe(**input_params)
+            st.session_state["result"] = result
+        
+        if "result" in st.session_state:
+            result = st.session_state["result"]
+            if isinstance(result, Image.Image):
+                st.image(result)
+            else:
+                print(f"unsupported result format: {result}")
+
+launch_webui()