update webui

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)

examples/dev_tools/webui.py

@@ -1,6 +1,7 @@
-import importlib, inspect, pkgutil, traceback, torch, os, re
-from typing import Union, List, Optional, Tuple, Iterable, Dict
+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
@@ -140,6 +141,18 @@ def draw_multi_images(name="", value=None, disabled=False):
             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)
@@ -173,7 +186,7 @@ def draw_ui_element(name, dtype, value):
     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, disabled=not enable)
+            ui = draw_ui_element_safely(name, dtype, value=value, disabled=not enable)
         if enable:
             return ui
         else:
@@ -181,6 +194,13 @@ def draw_ui_element(name, dtype, value):
     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}
@@ -189,10 +209,10 @@ def draw_ui_element_safely(name, dtype, value, disabled=False):
         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, disabled=disabled)
+        ui = st.checkbox(name, value=value, disabled=disabled)
     elif dtype == ModelConfig:
-        ui = draw_single_model_config(name, value, disabled=disabled)
-    elif dtype == list[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"]
@@ -201,20 +221,39 @@ def draw_ui_element_safely(name, dtype, value, disabled=False):
             ui = draw_multi_model_config(name, disabled=disabled)
     elif dtype == str:
         if "prompt" in name:
-            ui = st.text_area(name, value, height=3, disabled=disabled)
+            ui = st.text_area(name, value=value, height=3, disabled=disabled)
         else:
-            ui = st.text_input(name, value, disabled=disabled)
+            ui = st.text_input(name, value=value, disabled=disabled)
     elif dtype == float:
-        ui = st.number_input(name, value, disabled=disabled)
+        ui = st.number_input(name, value=value, disabled=disabled)
     elif dtype == int:
-        ui = st.number_input(name, value, step=1, disabled=disabled)
+        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 == List[Image.Image]:
-        ui = draw_multi_images(name, value, disabled=disabled)
-    elif dtype == List[ControlNetInput]:
-        ui = draw_controlnet_inputs(name, value, disabled=disabled)
+    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
@@ -259,7 +298,7 @@ def launch_webui():
         with st.expander("Input", expanded=True):
             pipe = st.session_state["pipe"]
             input_params = {}
-            params = parse_params(pipe.__call__)
+            params = parse_params(pipeline_class.__call__)
             for param in params:
                 if param["name"] in ["self"]:
                     continue
@@ -280,4 +319,3 @@ def launch_webui():
                 print(f"unsupported result format: {result}")
 
 launch_webui()
-# streamlit run examples/dev_tools/webui.py --server.fileWatcherType none