Spaces:
Runtime error
Runtime error
File size: 10,542 Bytes
fb4fac3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
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)
|