mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-24 01:48:13 +00:00
update UI
This commit is contained in:
Artiprocher
197
apps/streamlit/pages/2_Video_Creator.py
Normal file
197
apps/streamlit/pages/2_Video_Creator.py
Normal file
@@ -0,0 +1,197 @@
|
||||
import streamlit as st
|
||||
st.set_page_config(layout="wide")
|
||||
from diffsynth import SDVideoPipelineRunner
|
||||
import os
|
||||
import numpy as np
|
||||
|
||||
|
||||
def load_model_list(folder):
|
||||
file_list = os.listdir(folder)
|
||||
file_list = [i for i in file_list if i.endswith(".safetensors") or i.endswith(".pth") or i.endswith(".ckpt")]
|
||||
file_list = sorted(file_list)
|
||||
return file_list
|
||||
|
||||
|
||||
def match_processor_id(model_name, supported_processor_id_list):
|
||||
sorted_processor_id = [i[1] for i in sorted([(-len(i), i) for i in supported_processor_id_list])]
|
||||
for processor_id in sorted_processor_id:
|
||||
if processor_id in model_name:
|
||||
return supported_processor_id_list.index(processor_id) + 1
|
||||
return 0
|
||||
|
||||
|
||||
config = {
|
||||
"models": {
|
||||
"model_list": [],
|
||||
"textual_inversion_folder": "models/textual_inversion",
|
||||
"device": "cuda",
|
||||
"lora_alphas": [],
|
||||
"controlnet_units": []
|
||||
},
|
||||
"data": {
|
||||
"input_frames": None,
|
||||
"controlnet_frames": [],
|
||||
"output_folder": "output",
|
||||
"fps": 60
|
||||
},
|
||||
"pipeline": {
|
||||
"seed": 0,
|
||||
"pipeline_inputs": {}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
with st.expander("Model", expanded=True):
|
||||
stable_diffusion_ckpt = st.selectbox("Stable Diffusion", ["None"] + load_model_list("models/stable_diffusion"))
|
||||
if stable_diffusion_ckpt != "None":
|
||||
config["models"]["model_list"].append(os.path.join("models/stable_diffusion", stable_diffusion_ckpt))
|
||||
animatediff_ckpt = st.selectbox("AnimateDiff", ["None"] + load_model_list("models/AnimateDiff"))
|
||||
if animatediff_ckpt != "None":
|
||||
config["models"]["model_list"].append(os.path.join("models/AnimateDiff", animatediff_ckpt))
|
||||
column_lora, column_lora_alpha = st.columns([2, 1])
|
||||
with column_lora:
|
||||
sd_lora_ckpt = st.selectbox("LoRA", ["None"] + load_model_list("models/lora"))
|
||||
with column_lora_alpha:
|
||||
lora_alpha = st.slider("LoRA Alpha", min_value=-4.0, max_value=4.0, value=1.0, step=0.1)
|
||||
if sd_lora_ckpt != "None":
|
||||
config["models"]["model_list"].append(os.path.join("models/lora", sd_lora_ckpt))
|
||||
config["models"]["lora_alphas"].append(lora_alpha)
|
||||
|
||||
|
||||
with st.expander("Data", expanded=True):
|
||||
with st.container(border=True):
|
||||
input_video = st.text_input("Input Video File Path (e.g., data/your_video.mp4)", value="")
|
||||
column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1])
|
||||
with column_height:
|
||||
height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024)
|
||||
with column_width:
|
||||
width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024)
|
||||
with column_start_frame_index:
|
||||
start_frame_id = st.number_input("Start Frame id", value=0)
|
||||
with column_end_frame_index:
|
||||
end_frame_id = st.number_input("End Frame id", value=16)
|
||||
if input_video != "":
|
||||
config["data"]["input_frames"] = {
|
||||
"video_file": input_video,
|
||||
"image_folder": None,
|
||||
"height": height,
|
||||
"width": width,
|
||||
"start_frame_id": start_frame_id,
|
||||
"end_frame_id": end_frame_id
|
||||
}
|
||||
with st.container(border=True):
|
||||
output_video = st.text_input("Output Video File Path (e.g., data/a_folder_to_save_something)", value="output")
|
||||
fps = st.number_input("FPS", value=60)
|
||||
config["data"]["output_folder"] = output_video
|
||||
config["data"]["fps"] = fps
|
||||
|
||||
|
||||
with st.expander("ControlNet Units", expanded=True):
|
||||
supported_processor_id_list = ["canny", "depth", "softedge", "lineart", "lineart_anime", "openpose", "tile"]
|
||||
controlnet_units = st.tabs(["ControlNet Unit 0", "ControlNet Unit 1", "ControlNet Unit 2"])
|
||||
for controlnet_id in range(len(controlnet_units)):
|
||||
with controlnet_units[controlnet_id]:
|
||||
controlnet_ckpt = st.selectbox("ControlNet", ["None"] + load_model_list("models/ControlNet"),
|
||||
key=f"controlnet_ckpt_{controlnet_id}")
|
||||
processor_id = st.selectbox("Processor", ["None"] + supported_processor_id_list,
|
||||
index=match_processor_id(controlnet_ckpt, supported_processor_id_list),
|
||||
disabled=controlnet_ckpt == "None", key=f"processor_id_{controlnet_id}")
|
||||
controlnet_scale = st.slider("Scale", min_value=0.0, max_value=1.0, step=0.01, value=0.5,
|
||||
disabled=controlnet_ckpt == "None", key=f"controlnet_scale_{controlnet_id}")
|
||||
use_input_video_as_controlnet_input = st.checkbox("Use input video as ControlNet input", value=True,
|
||||
disabled=controlnet_ckpt == "None",
|
||||
key=f"use_input_video_as_controlnet_input_{controlnet_id}")
|
||||
if not use_input_video_as_controlnet_input:
|
||||
controlnet_input_video = st.text_input("ControlNet Input Video File Path", value="",
|
||||
disabled=controlnet_ckpt == "None", key=f"controlnet_input_video_{controlnet_id}")
|
||||
column_height, column_width, column_start_frame_index, column_end_frame_index = st.columns([2, 2, 1, 1])
|
||||
with column_height:
|
||||
height = st.select_slider("Height", options=[256, 512, 768, 1024, 1536, 2048], value=1024,
|
||||
disabled=controlnet_ckpt == "None", key=f"controlnet_height_{controlnet_id}")
|
||||
with column_width:
|
||||
width = st.select_slider("Width", options=[256, 512, 768, 1024, 1536, 2048], value=1024,
|
||||
disabled=controlnet_ckpt == "None", key=f"controlnet_width_{controlnet_id}")
|
||||
with column_start_frame_index:
|
||||
start_frame_id = st.number_input("Start Frame id", value=0,
|
||||
disabled=controlnet_ckpt == "None", key=f"controlnet_start_frame_id_{controlnet_id}")
|
||||
with column_end_frame_index:
|
||||
end_frame_id = st.number_input("End Frame id", value=16,
|
||||
disabled=controlnet_ckpt == "None", key=f"controlnet_end_frame_id_{controlnet_id}")
|
||||
if input_video != "":
|
||||
config["data"]["input_video"] = {
|
||||
"video_file": input_video,
|
||||
"image_folder": None,
|
||||
"height": height,
|
||||
"width": width,
|
||||
"start_frame_id": start_frame_id,
|
||||
"end_frame_id": end_frame_id
|
||||
}
|
||||
if controlnet_ckpt != "None":
|
||||
config["models"]["model_list"].append(os.path.join("models/ControlNet", controlnet_ckpt))
|
||||
config["models"]["controlnet_units"].append({
|
||||
"processor_id": processor_id,
|
||||
"model_path": os.path.join("models/ControlNet", controlnet_ckpt),
|
||||
"scale": controlnet_scale,
|
||||
})
|
||||
if use_input_video_as_controlnet_input:
|
||||
config["data"]["controlnet_frames"].append(config["data"]["input_frames"])
|
||||
else:
|
||||
config["data"]["controlnet_frames"].append({
|
||||
"video_file": input_video,
|
||||
"image_folder": None,
|
||||
"height": height,
|
||||
"width": width,
|
||||
"start_frame_id": start_frame_id,
|
||||
"end_frame_id": end_frame_id
|
||||
})
|
||||
|
||||
|
||||
with st.container(border=True):
|
||||
with st.expander("Seed", expanded=True):
|
||||
use_fixed_seed = st.checkbox("Use fixed seed", value=False)
|
||||
if use_fixed_seed:
|
||||
seed = st.number_input("Random seed", min_value=0, max_value=10**9, step=1, value=0)
|
||||
else:
|
||||
seed = np.random.randint(0, 10**9)
|
||||
with st.expander("Textual Guidance", expanded=True):
|
||||
prompt = st.text_area("Positive prompt")
|
||||
negative_prompt = st.text_area("Negative prompt")
|
||||
column_cfg_scale, column_clip_skip = st.columns(2)
|
||||
with column_cfg_scale:
|
||||
cfg_scale = st.slider("Classifier-free guidance scale", min_value=1.0, max_value=10.0, value=7.0)
|
||||
with column_clip_skip:
|
||||
clip_skip = st.slider("Clip Skip", min_value=1, max_value=4, value=1)
|
||||
with st.expander("Denoising", expanded=True):
|
||||
column_num_inference_steps, column_denoising_strength = st.columns(2)
|
||||
with column_num_inference_steps:
|
||||
num_inference_steps = st.slider("Inference steps", min_value=1, max_value=100, value=10)
|
||||
with column_denoising_strength:
|
||||
denoising_strength = st.slider("Denoising strength", min_value=0.0, max_value=1.0, value=1.0)
|
||||
with st.expander("Efficiency", expanded=False):
|
||||
animatediff_batch_size = st.slider("Animatediff batch size (sliding window size)", min_value=1, max_value=32, value=16, step=1)
|
||||
animatediff_stride = st.slider("Animatediff stride",
|
||||
min_value=1,
|
||||
max_value=max(2, animatediff_batch_size),
|
||||
value=max(1, animatediff_batch_size // 2),
|
||||
step=1)
|
||||
unet_batch_size = st.slider("UNet batch size", min_value=1, max_value=32, value=1, step=1)
|
||||
controlnet_batch_size = st.slider("ControlNet batch size", min_value=1, max_value=32, value=1, step=1)
|
||||
cross_frame_attention = st.checkbox("Enable Cross-Frame Attention", value=False)
|
||||
config["pipeline"]["seed"] = seed
|
||||
config["pipeline"]["pipeline_inputs"] = {
|
||||
"prompt": prompt,
|
||||
"negative_prompt": negative_prompt,
|
||||
"cfg_scale": cfg_scale,
|
||||
"clip_skip": clip_skip,
|
||||
"denoising_strength": denoising_strength,
|
||||
"num_inference_steps": num_inference_steps,
|
||||
"animatediff_batch_size": animatediff_batch_size,
|
||||
"animatediff_stride": animatediff_stride,
|
||||
"unet_batch_size": unet_batch_size,
|
||||
"controlnet_batch_size": controlnet_batch_size,
|
||||
"cross_frame_attention": cross_frame_attention,
|
||||
}
|
||||
|
||||
run_button = st.button("☢️Run☢️", type="primary")
|
||||
if run_button:
|
||||
SDVideoPipelineRunner(in_streamlit=True).run(config)
|
||||
Reference in New Issue
Block a user