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Initial attempt at tabbed UI, still isn't correctly selecting loras from defaults

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This commit is contained in:
Chris Malone 2025-03-22 09:42:49 +11:00
parent 51a3e4a702
commit 9fd7df371e
1 changed files with 467 additions and 469 deletions
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732
gradio_server.py
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@ -256,18 +256,19 @@ else:
text = reader.read() text = reader.read()
server_config = json.loads(text) server_config = json.loads(text)
ui_defaults_filename = "ui_defaults.json" def get_defaults():
global use_image2video, ui_defaults
if not Path(ui_defaults_filename).is_file(): defaults_filename = "i2v_defaults.json" if use_image2video else "t2v_defaults.json"
default_ui_defaults = { if not Path(defaults_filename).is_file():
"prompts": get_default_prompt(use_image2video), ui_defaults = {
"prompts": "",
"resolution": "832x480", "resolution": "832x480",
"video_length": 81, "video_length": 81,
"num_inference_steps": 30, "num_inference_steps": 30,
"seed": -1, "seed": -1,
"repeat_generation": 1, "repeat_generation": 1,
"guidance_scale": 5.0, "guidance_scale": 5.0,
"flow_shift": get_default_flow(transformer_filename_i2v if use_image2video else transformer_filename_t2v), "flow_shift": 5.0,
"negative_prompt": "", "negative_prompt": "",
"activated_loras": [], "activated_loras": [],
"loras_multipliers": "", "loras_multipliers": "",
@ -279,11 +280,10 @@ if not Path(ui_defaults_filename).is_file():
"slg_start_perc": 10, "slg_start_perc": 10,
"slg_end_perc": 90 "slg_end_perc": 90
} }
with open(ui_defaults_filename, "w", encoding="utf-8") as f: with open(defaults_filename, "w", encoding="utf-8") as f:
json.dump(default_ui_defaults, f, indent=4) json.dump(ui_defaults, f, indent=4)
ui_defaults = default_ui_defaults
else: else:
with open(ui_defaults_filename, "r", encoding="utf-8") as f: with open(defaults_filename, "r", encoding="utf-8") as f:
ui_defaults = json.load(f) ui_defaults = json.load(f)
transformer_filename_t2v = server_config["transformer_filename"] transformer_filename_t2v = server_config["transformer_filename"]
@ -324,8 +324,8 @@ if args.t2v_1_3B:
use_image2video = False use_image2video = False
lock_ui_transformer = False lock_ui_transformer = False
get_defaults()
only_allow_edit_in_advanced = False only_allow_edit_in_advanced = False
lora_dir =args.lora_dir lora_dir =args.lora_dir
if use_image2video and len(lora_dir)==0: if use_image2video and len(lora_dir)==0:
lora_dir =args.lora_dir_i2v lora_dir =args.lora_dir_i2v
@ -586,21 +586,18 @@ def load_i2v_model(model_filename, value):
return wan_model, pipe return wan_model, pipe
def load_models(i2v, lora_dir, lora_preselected_preset): def load_models(i2v, lora_dir, lora_preselected_preset):
download_models(transformer_filename_i2v if i2v else transformer_filename_t2v, text_encoder_filename) model_filename = transformer_filename_i2v if i2v else transformer_filename_t2v
download_models(model_filename, text_encoder_filename)
if i2v: if i2v:
res720P= "720p" in transformer_filename_i2v res720P = "720p" in model_filename
wan_model, pipe = load_i2v_model(transformer_filename_i2v,"720P" if res720P else "480P") wan_model, pipe = load_i2v_model(model_filename, "720P" if res720P else "480P")
else: else:
wan_model, pipe = load_t2v_model(transformer_filename_t2v,"") wan_model, pipe = load_t2v_model(model_filename, "")
kwargs = { "extraModelsToQuantize": None} kwargs = { "extraModelsToQuantize": None}
if profile == 2 or profile == 4: if profile == 2 or profile == 4:
kwargs["budgets"] = { "transformer" : 100 if preload == 0 else preload, "text_encoder" : 100, "*" : 1000 } kwargs["budgets"] = { "transformer" : 100 if preload == 0 else preload, "text_encoder" : 100, "*" : 1000 }
elif profile == 3: elif profile == 3:
kwargs["budgets"] = { "*" : "70%" } kwargs["budgets"] = { "*" : "70%" }
offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", **kwargs) offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", **kwargs)
loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets = setup_loras(pipe["transformer"], lora_dir, lora_preselected_preset, None) loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets = setup_loras(pipe["transformer"], lora_dir, lora_preselected_preset, None)
if "activated_loras" in ui_defaults: if "activated_loras" in ui_defaults:
@ -613,7 +610,6 @@ def load_models(i2v, lora_dir, lora_preselected_preset ):
except ValueError: except ValueError:
print(f"Warning: Lora file {lora_file} from config not found in loras directory") print(f"Warning: Lora file {lora_file} from config not found in loras directory")
ui_defaults["activated_loras"] = activated_indices ui_defaults["activated_loras"] = activated_indices
return wan_model, offloadobj, loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets return wan_model, offloadobj, loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets
wan_model, offloadobj, loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets = load_models(use_image2video, lora_dir, lora_preselected_preset ) wan_model, offloadobj, loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets = load_models(use_image2video, lora_dir, lora_preselected_preset )
@ -904,15 +900,25 @@ def generate_video(
slg_end, slg_end,
state, state,
metadata_choice, metadata_choice,
image2video,
progress=gr.Progress() #track_tqdm= True progress=gr.Progress() #track_tqdm= True
): ):
global use_image2video, wan_model, offloadobj, loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets
if use_image2video != image2video:
if offloadobj is not None:
offloadobj.release()
offloadobj = None
wan_model = None
lora_dir = get_lora_dir(root_lora_dir)
wan_model, offloadobj, loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets = load_models(image2video, lora_dir, lora_preselected_preset)
use_image2video = image2video
from PIL import Image from PIL import Image
import numpy as np import numpy as np
import tempfile import tempfile
if wan_model == None: if wan_model == None:
raise gr.Error("Unable to generate a Video while a new configuration is being applied.") raise gr.Error("Unable to generate a Video while a new configuration is being applied.")
if attention_mode == "auto": if attention_mode == "auto":
@ -930,7 +936,7 @@ def generate_video(
if slg_switch == 0: if slg_switch == 0:
slg_layers = None slg_layers = None
if use_image2video: if image2video:
if "480p" in transformer_filename_i2v and width * height > 848*480: if "480p" in transformer_filename_i2v and width * height > 848*480:
gr.Info("You must use the 720P image to video model to generate videos with a resolution equivalent to 720P") gr.Info("You must use the 720P image to video model to generate videos with a resolution equivalent to 720P")
return return
@ -980,7 +986,7 @@ def generate_video(
prompts = [prompt.strip() for prompt in prompts if len(prompt.strip())>0 and not prompt.startswith("#")] prompts = [prompt.strip() for prompt in prompts if len(prompt.strip())>0 and not prompt.startswith("#")]
if len(prompts) ==0: if len(prompts) ==0:
return return
if use_image2video: if image2video:
if image_to_continue is not None: if image_to_continue is not None:
if isinstance(image_to_continue, list): if isinstance(image_to_continue, list):
image_to_continue = [ tup[0] for tup in image_to_continue ] image_to_continue = [ tup[0] for tup in image_to_continue ]
@ -1081,7 +1087,7 @@ def generate_video(
trans.rel_l1_thresh = 0 trans.rel_l1_thresh = 0
trans.teacache_start_step = int(tea_cache_start_step_perc*num_inference_steps/100) trans.teacache_start_step = int(tea_cache_start_step_perc*num_inference_steps/100)
if use_image2video: if image2video:
if '480p' in transformer_filename_i2v: if '480p' in transformer_filename_i2v:
# teacache_thresholds = [0.13, .19, 0.26] # teacache_thresholds = [0.13, .19, 0.26]
trans.coefficients = [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01] trans.coefficients = [-3.02331670e+02, 2.23948934e+02, -5.25463970e+01, 5.87348440e+00, -2.01973289e-01]
@ -1152,7 +1158,7 @@ def generate_video(
torch.cuda.empty_cache() torch.cuda.empty_cache()
wan_model._interrupt = False wan_model._interrupt = False
try: try:
if use_image2video: if image2video:
samples = wan_model.generate( samples = wan_model.generate(
prompt, prompt,
image_to_continue[no].convert('RGB'), image_to_continue[no].convert('RGB'),
@ -1373,7 +1379,7 @@ def delete_lset(lset_name):
return gr.Dropdown(choices=lset_choices, value= lset_choices[pos][1]), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Checkbox(visible= False) return gr.Dropdown(choices=lset_choices, value= lset_choices[pos][1]), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= True), gr.Button(visible= False), gr.Checkbox(visible= False)
def refresh_lora_list(lset_name, loras_choices): def refresh_lora_list(lset_name, loras_choices):
global loras,loras_names, loras_presets global loras,loras_names, loras_presets, wan_model
prev_lora_names_selected = [ loras_names[int(i)] for i in loras_choices] prev_lora_names_selected = [ loras_names[int(i)] for i in loras_choices]
loras, loras_names, _, _, _, _, loras_presets = setup_loras(wan_model.model, lora_dir, lora_preselected_preset, None) loras, loras_names, _, _, _, _, loras_presets = setup_loras(wan_model.model, lora_dir, lora_preselected_preset, None)
@ -1553,7 +1559,6 @@ def switch_prompt_type(state, prompt, wizard_prompt, *prompt_vars):
state["apply_success"] = 1 state["apply_success"] = 1
return fill_wizard_prompt(state, prompt, wizard_prompt) return fill_wizard_prompt(state, prompt, wizard_prompt)
visible= False visible= False
def switch_advanced(new_advanced, lset_name): def switch_advanced(new_advanced, lset_name):
global advanced global advanced
@ -1570,15 +1575,12 @@ def switch_advanced(new_advanced, lset_name):
def download_loras(): def download_loras():
from huggingface_hub import snapshot_download from huggingface_hub import snapshot_download
yield "<B><FONT SIZE=3>Please wait while the Loras are being downloaded</B></FONT>", *[gr.Column(visible=False)] * 2 yield "<B><FONT SIZE=3>Please wait while the Loras are being downloaded</B></FONT>", *[gr.Column(visible=False)] * 2
log_path = os.path.join(lora_dir, "log.txt") log_path = os.path.join(lora_dir, "log.txt")
if not os.path.isfile(log_path) or True: if not os.path.isfile(log_path) or True:
import shutil import shutil
tmp_path = os.path.join(lora_dir, "tmp_lora_dowload") tmp_path = os.path.join(lora_dir, "tmp_lora_dowload")
import glob
import shutil, glob
snapshot_download(repo_id="DeepBeepMeep/Wan2.1", allow_patterns="loras_i2v/*", local_dir= tmp_path) snapshot_download(repo_id="DeepBeepMeep/Wan2.1", allow_patterns="loras_i2v/*", local_dir= tmp_path)
for f in glob.glob(os.path.join(tmp_path, "loras_i2v", "*.*")): for f in glob.glob(os.path.join(tmp_path, "loras_i2v", "*.*")):
target_file = os.path.join(lora_dir, Path(f).parts[-1] ) target_file = os.path.join(lora_dir, Path(f).parts[-1] )
@ -1586,76 +1588,296 @@ def download_loras():
os.remove(f) os.remove(f)
else: else:
shutil.move(f, lora_dir) shutil.move(f, lora_dir)
try: try:
os.remove(tmp_path) os.remove(tmp_path)
except: except:
pass pass
yield "<B><FONT SIZE=3>Loras have been completely downloaded</B></FONT>", *[gr.Column(visible=True)] * 2 yield "<B><FONT SIZE=3>Loras have been completely downloaded</B></FONT>", *[gr.Column(visible=True)] * 2
from datetime import datetime from datetime import datetime
dt = datetime.today().strftime('%Y-%m-%d') dt = datetime.today().strftime('%Y-%m-%d')
with open( log_path, "w", encoding="utf-8") as writer: with open( log_path, "w", encoding="utf-8") as writer:
writer.write(f"Loras downloaded on the {dt} at {time.time()} on the {time.time()}") writer.write(f"Loras downloaded on the {dt} at {time.time()} on the {time.time()}")
return return
def create_demo():
css= """ def generate_video_tab(image2video=False):
.title-with-lines {
display: flex;
align-items: center;
margin: 30px 0;
}
.line {
flex-grow: 1;
height: 1px;
background-color: #333;
}
h2 {
margin: 0 20px;
white-space: nowrap;
}
"""
with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="sky", neutral_hue="slate", text_size= "md")) as demo:
state_dict = {} state_dict = {}
gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v2.1 <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A> / <A HREF='https://github.com/Wan-Video/Wan2.1'>Original by Alibaba</A>)</FONT SIZE=3></H1></div>")
if use_image2video:
gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v2.1 - Image To Video <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A> / <A HREF='https://github.com/Wan-Video/Wan2.1'>Original by Alibaba</A>)</FONT SIZE=3></H1></div>")
else:
gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v2.1 - Text To Video <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A> / <A HREF='https://github.com/Wan-Video/Wan2.1'>Original by Alibaba</A>)</FONT SIZE=3></H1></div>")
gr.Markdown("<FONT SIZE=3>Welcome to Wan 2.1GP a super fast and low VRAM AI Video Generator !</FONT>") gr.Markdown("<FONT SIZE=3>Welcome to Wan 2.1GP a super fast and low VRAM AI Video Generator !</FONT>")
with gr.Accordion("Click here for some Info on how to use Wan2GP", open = False): # and to download 20+ Loras with gr.Accordion("Click here for some Info on how to use Wan2GP", open = False):
if use_image2video and False:
pass
else:
gr.Markdown("The VRAM requirements will depend greatly of the resolution and the duration of the video, for instance :") gr.Markdown("The VRAM requirements will depend greatly of the resolution and the duration of the video, for instance :")
gr.Markdown("- 848 x 480 with a 14B model: 80 frames (5s) : 8 GB of VRAM") gr.Markdown("- 848 x 480 with a 14B model: 80 frames (5s) : 8 GB of VRAM")
gr.Markdown("- 848 x 480 with the 1.3B model: 80 frames (5s) : 5 GB of VRAM") gr.Markdown("- 848 x 480 with the 1.3B model: 80 frames (5s) : 5 GB of VRAM")
gr.Markdown("- 1280 x 720 with a 14B model: 80 frames (5s): 11 GB of VRAM") gr.Markdown("- 1280 x 720 with a 14B model: 80 frames (5s): 11 GB of VRAM")
gr.Markdown("It is not recommmended to generate a video longer than 8s (128 frames) even if there is still some VRAM left as some artifacts may appear") gr.Markdown("It is not recommmended to generate a video longer than 8s (128 frames) even if there is still some VRAM left as some artifacts may appear")
gr.Markdown("Please note that if your turn on compilation, the first denoising step of the first video generation will be slow due to the compilation. Therefore all your tests should be done with compilation turned off.") gr.Markdown("Please note that if your turn on compilation, the first denoising step of the first video generation will be slow due to the compilation. Therefore all your tests should be done with compilation turned off.")
with gr.Row(visible= image2video):
with gr.Row(visible= use_image2video):
with gr.Row(scale =3): with gr.Row(scale =3):
gr.Markdown("<I>Wan2GP's Lora Festival ! Press the following button to download i2v <B>Remade</B> Loras collection (and bonuses Loras). Dont't forget first to make a backup of your Loras just in case.") gr.Markdown("<I>Wan2GP's Lora Festival ! Press the following button to download i2v <B>Remade</B> Loras collection (and bonuses Loras). Dont't forget first to make a backup of your Loras just in case.")
with gr.Row(scale =1): with gr.Row(scale =1):
download_loras_btn = gr.Button("---> Let the Lora's Festival Start !", scale =1) download_loras_btn = gr.Button("---> Let the Lora's Festival Start !", scale =1)
with gr.Row(visible= use_image2video): with gr.Row(visible= image2video):
download_status = gr.Markdown() download_status = gr.Markdown()
model_filename = transformer_filename_i2v if image2video else transformer_filename_t2v
header = gr.Markdown(generate_header(model_filename, compile, attention_mode))
with gr.Row():
with gr.Column():
with gr.Row(visible= len(loras)>0) as presets_column:
lset_choices = [ (preset, preset) for preset in loras_presets ] + [(get_new_preset_msg(advanced), "")]
with gr.Column(scale=6):
lset_name = gr.Dropdown(show_label=False, allow_custom_value= True, scale=5, filterable=True, choices= lset_choices, value=default_lora_preset)
with gr.Column(scale=1):
with gr.Row(height=17):
apply_lset_btn = gr.Button("Apply Lora Preset", size="sm", min_width= 1)
refresh_lora_btn = gr.Button("Refresh", size="sm", min_width= 1, visible=advanced or not only_allow_edit_in_advanced)
save_lset_prompt_drop= gr.Dropdown(
choices=[
("Save Prompt Comments Only", 0),
("Save Full Prompt", 1)
], show_label= False, container=False, value =1, visible= False
)
with gr.Row(height=17, visible=False) as refresh2_row:
refresh_lora_btn2 = gr.Button("Refresh", size="sm", min_width= 1)
# css = """<STYLE> with gr.Row(height=17, visible=advanced or not only_allow_edit_in_advanced) as preset_buttons_rows:
# h2 { width: 100%; text-align: center; border-bottom: 1px solid #000; line-height: 0.1em; margin: 10px 0 20px; } confirm_save_lset_btn = gr.Button("Go Ahead Save it !", size="sm", min_width= 1, visible=False)
# h2 span {background:#fff; padding:0 10px; }</STYLE>""" confirm_delete_lset_btn = gr.Button("Go Ahead Delete it !", size="sm", min_width= 1, visible=False)
# gr.HTML(css) save_lset_btn = gr.Button("Save", size="sm", min_width= 1)
delete_lset_btn = gr.Button("Delete", size="sm", min_width= 1)
cancel_lset_btn = gr.Button("Don't do it !", size="sm", min_width= 1 , visible=False)
video_to_continue = gr.Video(label= "Video to continue", visible= image2video and False) #######
if args.multiple_images:
image_to_continue = gr.Gallery(
label="Images as a starting point for new videos", type ="pil", #file_types= "image",
columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible=image2video)
else:
image_to_continue = gr.Image(label= "Image as a starting point for a new video", type ="pil", visible=image2video)
advanced_prompt = advanced
prompt_vars=[]
if advanced_prompt:
default_wizard_prompt, variables, values= None, None, None
else:
default_wizard_prompt, variables, values, errors = extract_wizard_prompt(default_prompt)
advanced_prompt = len(errors) > 0
with gr.Column(visible= advanced_prompt) as prompt_column_advanced:
prompt = gr.Textbox( visible= advanced_prompt, label="Prompts (each new line of prompt will generate a new video, # lines = comments, ! lines = macros)", value=ui_defaults["prompts"], lines=3)
header = gr.Markdown(generate_header(transformer_filename_i2v if use_image2video else transformer_filename_t2v, compile, attention_mode) ) with gr.Column(visible=not advanced_prompt and len(variables) > 0) as prompt_column_wizard_vars:
gr.Markdown("<B>Please fill the following input fields to adapt automatically the Prompt:</B>")
with gr.Row():
if not advanced_prompt:
for variable in variables:
value = values.get(variable, "")
prompt_vars.append(gr.Textbox( placeholder=variable, min_width=80, show_label= False, info= variable, visible= True, value= "\n".join(value) ))
state_dict["wizard_prompt"] = 1
state_dict["variables"] = variables
for _ in range( PROMPT_VARS_MAX - len(prompt_vars)):
prompt_vars.append(gr.Textbox(visible= False, min_width=80, show_label= False))
with gr.Column(not advanced_prompt) as prompt_column_wizard:
wizard_prompt = gr.Textbox(visible = not advanced_prompt, label="Prompts (each new line of prompt will generate a new video, # lines = comments)", value=default_wizard_prompt, lines=3)
state = gr.State(state_dict)
with gr.Row():
if image2video:
resolution = gr.Dropdown(
choices=[
# 720p
("720p", "1280x720"),
("480p", "832x480"),
],
value=ui_defaults["resolution"],
label="Resolution (video will have the same height / width ratio than the original image)"
)
else:
resolution = gr.Dropdown(
choices=[
# 720p
("1280x720 (16:9, 720p)", "1280x720"),
("720x1280 (9:16, 720p)", "720x1280"),
("1024x1024 (4:3, 720p)", "1024x024"),
# ("832x1104 (3:4, 720p)", "832x1104"),
# ("960x960 (1:1, 720p)", "960x960"),
# 480p
# ("960x544 (16:9, 480p)", "960x544"),
("832x480 (16:9, 480p)", "832x480"),
("480x832 (9:16, 480p)", "480x832"),
# ("832x624 (4:3, 540p)", "832x624"),
# ("624x832 (3:4, 540p)", "624x832"),
# ("720x720 (1:1, 540p)", "720x720"),
],
value=ui_defaults["resolution"],
label="Resolution"
)
with gr.Row():
with gr.Column():
video_length = gr.Slider(5, 193, value=ui_defaults["video_length"], step=4, label="Number of frames (16 = 1s)")
with gr.Column():
num_inference_steps = gr.Slider(1, 100, value=ui_defaults["num_inference_steps"], step=1, label="Number of Inference Steps")
with gr.Row():
max_frames = gr.Slider(1, 100, value=9, step=1, label="Number of input frames to use for Video2World prediction", visible=image2video and False) #########
show_advanced = gr.Checkbox(label="Advanced Mode", value=advanced)
with gr.Row(visible=advanced) as advanced_row:
with gr.Column():
seed = gr.Slider(-1, 999999999, value=ui_defaults["seed"], step=1, label="Seed (-1 for random)")
with gr.Row():
repeat_generation = gr.Slider(1, 25.0, value=ui_defaults["repeat_generation"], step=1, label="Default Number of Generated Videos per Prompt")
multi_images_gen_type = gr.Dropdown(
choices=[
("Generate every combination of images and texts prompts", 0),
("Match images and text prompts", 1),
], visible= args.multiple_images, label= "Multiple Images as Prompts"
)
with gr.Row():
guidance_scale = gr.Slider(1.0, 20.0, value=ui_defaults["guidance_scale"], step=0.5, label="Guidance Scale", visible=True)
embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale", visible=False)
flow_shift = gr.Slider(0.0, 25.0, value=ui_defaults["flow_shift"], step=0.1, label="Shift Scale")
with gr.Row():
negative_prompt = gr.Textbox(label="Negative Prompt", value=ui_defaults["negative_prompt"])
with gr.Column(visible = len(loras)>0) as loras_column:
gr.Markdown("<B>Loras can be used to create special effects on the video by mentioning a trigger word in the Prompt. You can save Loras combinations in presets.</B>")
loras_choices = gr.Dropdown(
choices=[
(lora_name, str(i) ) for i, lora_name in enumerate(loras_names)
],
value= ui_defaults["activated_loras"],
multiselect= True,
label="Activated Loras"
)
loras_mult_choices = gr.Textbox(label="Loras Multipliers (1.0 by default) separated by space characters or carriage returns, line that starts with # are ignored", value=ui_defaults["loras_multipliers"])
with gr.Row():
gr.Markdown("<B>Tea Cache accelerates by skipping intelligently some steps, the more steps are skipped the lower the quality of the video (Tea Cache consumes also VRAM)</B>")
with gr.Row():
tea_cache_setting = gr.Dropdown(
choices=[
("Tea Cache Disabled", 0),
("around x1.5 speed up", 1.5),
("around x1.75 speed up", 1.75),
("around x2 speed up", 2.0),
("around x2.25 speed up", 2.25),
("around x2.5 speed up", 2.5),
],
value=float(ui_defaults["tea_cache"]),
visible=True,
label="Tea Cache Global Acceleration"
)
tea_cache_start_step_perc = gr.Slider(0, 100, value=ui_defaults["tea_cache_start_step_perc"], step=1, label="Tea Cache starting moment in % of generation")
with gr.Accordion("Video Engine Configuration - click here to change it", open = False, visible= not args.lock_config): gr.Markdown("<B>With Riflex you can generate videos longer than 5s which is the default duration of videos used to train the model</B>")
RIFLEx_setting = gr.Dropdown(
choices=[
("Auto (ON if Video longer than 5s)", 0),
("Always ON", 1),
("Always OFF", 2),
],
value=ui_defaults["RIFLEx_setting"],
label="RIFLEx positional embedding to generate long video"
)
with gr.Row():
gr.Markdown("<B>Experimental: Skip Layer guidance,should improve video quality</B>")
with gr.Row():
slg_switch = gr.Dropdown(
choices=[
("OFF", 0),
("ON", 1),
],
value=ui_defaults["slg_switch"],
visible=True,
scale = 1,
label="Skip Layer guidance"
)
slg_layers = gr.Dropdown(
choices=[
(str(i), i ) for i in range(40)
],
value=ui_defaults["slg_layers"],
multiselect= True,
label="Skip Layers",
scale= 3
)
with gr.Row():
slg_start_perc = gr.Slider(0, 100, value=ui_defaults["slg_start_perc"], step=1, label="Denoising Steps % start")
slg_end_perc = gr.Slider(0, 100, value=ui_defaults["slg_end_perc"], step=1, label="Denoising Steps % end")
metadata_choice = gr.Dropdown(
choices=[
("Export JSON files", "json"),
("Add metadata to video", "metadata"),
("Neither", "none")
],
value=metadata,
label="Metadata Handling"
)
show_advanced.change(fn=switch_advanced, inputs=[show_advanced, lset_name], outputs=[advanced_row, preset_buttons_rows, refresh_lora_btn, refresh2_row ,lset_name ]).then(
fn=switch_prompt_type, inputs = [state, prompt, wizard_prompt, *prompt_vars], outputs = [prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars])
with gr.Column():
gen_status = gr.Text(label="Status", interactive= False)
output = gr.Gallery(
label="Generated videos", show_label=False, elem_id="gallery"
, columns=[3], rows=[1], object_fit="contain", height=450, selected_index=0, interactive= False)
generate_btn = gr.Button("Generate")
onemore_btn = gr.Button("One More Please !", visible= False)
abort_btn = gr.Button("Abort")
gen_info = gr.Text(label="Current prompt", visible= False , interactive= False)
save_lset_btn.click(validate_save_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop])
confirm_save_lset_btn.click(fn=validate_wizard_prompt, inputs =[state, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]).then(
save_lset, inputs=[state, lset_name, loras_choices, loras_mult_choices, prompt, save_lset_prompt_drop], outputs=[lset_name, apply_lset_btn,refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop])
delete_lset_btn.click(validate_delete_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ])
confirm_delete_lset_btn.click(delete_lset, inputs=[lset_name], outputs=[lset_name, apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ])
cancel_lset_btn.click(cancel_lset, inputs=[], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_delete_lset_btn,confirm_save_lset_btn, cancel_lset_btn,save_lset_prompt_drop ])
apply_lset_btn.click(apply_lset, inputs=[state, lset_name,loras_choices, loras_mult_choices, prompt], outputs=[loras_choices, loras_mult_choices, prompt]).then(
fn = fill_wizard_prompt, inputs = [state, prompt, wizard_prompt], outputs = [ prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars]
)
refresh_lora_btn.click(refresh_lora_list, inputs=[lset_name,loras_choices], outputs=[lset_name, loras_choices])
refresh_lora_btn2.click(refresh_lora_list, inputs=[lset_name,loras_choices], outputs=[lset_name, loras_choices])
download_loras_btn.click(fn=download_loras, inputs=[], outputs=[download_status, presets_column, loras_column]).then(fn=refresh_lora_list, inputs=[lset_name,loras_choices], outputs=[lset_name, loras_choices])
gen_status.change(refresh_gallery, inputs = [state, gen_info], outputs = [output, gen_info] )
abort_btn.click(abort_generation,state,abort_btn )
output.select(select_video, state, None )
onemore_btn.click(fn=one_more_video,inputs=[state], outputs= [state])
generate_btn.click(fn=prepare_generate_video,inputs=[], outputs= [generate_btn, onemore_btn]
).then(
fn=validate_wizard_prompt, inputs =[state, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]
).then(
fn=generate_video,
inputs=[
prompt,
negative_prompt,
resolution,
video_length,
seed,
num_inference_steps,
guidance_scale,
flow_shift,
embedded_guidance_scale,
repeat_generation,
multi_images_gen_type,
tea_cache_setting,
tea_cache_start_step_perc,
loras_choices,
loras_mult_choices,
image_to_continue,
video_to_continue,
max_frames,
RIFLEx_setting,
slg_switch,
slg_layers,
slg_start_perc,
slg_end_perc,
state,
metadata_choice,
gr.State(image2video)
],
outputs= [gen_status]
).then(
finalize_gallery,
[state],
[output , abort_btn, generate_btn, onemore_btn, gen_info]
)
return loras_choices, lset_name
def generate_settings_tab():
state_dict = {}
state = gr.State(state_dict)
gr.Markdown("For the changes to be effective you will need to restart the gradio_server. Some choices below may be locked if the app has been launched by specifying a config preset.") gr.Markdown("For the changes to be effective you will need to restart the gradio_server. Some choices below may be locked if the app has been launched by specifying a config preset.")
with gr.Column(): with gr.Column():
index = transformer_choices_t2v.index(transformer_filename_t2v) index = transformer_choices_t2v.index(transformer_filename_t2v)
index = 0 if index ==0 else index index = 0 if index ==0 else index
@ -1670,7 +1892,6 @@ def create_demo():
interactive= not lock_ui_transformer, interactive= not lock_ui_transformer,
visible=not use_image2video visible=not use_image2video
) )
index = transformer_choices_i2v.index(transformer_filename_i2v) index = transformer_choices_i2v.index(transformer_filename_i2v)
index = 0 if index ==0 else index index = 0 if index ==0 else index
transformer_i2v_choice = gr.Dropdown( transformer_i2v_choice = gr.Dropdown(
@ -1683,12 +1904,10 @@ def create_demo():
value= index, value= index,
label="Transformer model for Image to Video", label="Transformer model for Image to Video",
interactive= not lock_ui_transformer, interactive= not lock_ui_transformer,
visible = use_image2video, ############### visible = use_image2video,
) )
index = text_encoder_choices.index(text_encoder_filename) index = text_encoder_choices.index(text_encoder_filename)
index = 0 if index ==0 else index index = 0 if index ==0 else index
text_encoder_choice = gr.Dropdown( text_encoder_choice = gr.Dropdown(
choices=[ choices=[
("UMT5 XXL 16 bits - unquantized text encoder, better quality uses more RAM", 0), ("UMT5 XXL 16 bits - unquantized text encoder, better quality uses more RAM", 0),
@ -1729,8 +1948,6 @@ def create_demo():
label="Compile Transformer (up to 50% faster and 30% more frames but requires Linux / WSL and Flash or Sage attention)", label="Compile Transformer (up to 50% faster and 30% more frames but requires Linux / WSL and Flash or Sage attention)",
interactive= not lock_ui_compile interactive= not lock_ui_compile
) )
vae_config_choice = gr.Dropdown( vae_config_choice = gr.Dropdown(
choices=[ choices=[
("Auto", 0), ("Auto", 0),
@ -1741,7 +1958,6 @@ def create_demo():
value= vae_config, value= vae_config,
label="VAE Tiling - reduce the high VRAM requirements for VAE decoding and VAE encoding (if enabled it will be slower)" label="VAE Tiling - reduce the high VRAM requirements for VAE decoding and VAE encoding (if enabled it will be slower)"
) )
boost_choice = gr.Dropdown( boost_choice = gr.Dropdown(
choices=[ choices=[
# ("Auto (ON if Video longer than 5s)", 0), # ("Auto (ON if Video longer than 5s)", 0),
@ -1751,7 +1967,6 @@ def create_demo():
value=boost, value=boost,
label="Boost: Give a 10% speed speedup without losing quality at the cost of a litle VRAM (up to 1GB for max frames and resolution)" label="Boost: Give a 10% speed speedup without losing quality at the cost of a litle VRAM (up to 1GB for max frames and resolution)"
) )
profile_choice = gr.Dropdown( profile_choice = gr.Dropdown(
choices=[ choices=[
("HighRAM_HighVRAM, profile 1: at least 48 GB of RAM and 24 GB of VRAM, the fastest for short videos a RTX 3090 / RTX 4090", 1), ("HighRAM_HighVRAM, profile 1: at least 48 GB of RAM and 24 GB of VRAM, the fastest for short videos a RTX 3090 / RTX 4090", 1),
@ -1763,7 +1978,6 @@ def create_demo():
value= profile, value= profile,
label="Profile (for power users only, not needed to change it)" label="Profile (for power users only, not needed to change it)"
) )
default_ui_choice = gr.Dropdown( default_ui_choice = gr.Dropdown(
choices=[ choices=[
("Text to Video", "t2v"), ("Text to Video", "t2v"),
@ -1771,9 +1985,7 @@ def create_demo():
], ],
value= default_ui, value= default_ui,
label="Default mode when launching the App if not '--t2v' ot '--i2v' switch is specified when launching the server ", label="Default mode when launching the App if not '--t2v' ot '--i2v' switch is specified when launching the server ",
# visible= True ############
) )
metadata_choice = gr.Dropdown( metadata_choice = gr.Dropdown(
choices=[ choices=[
("Export JSON files", "json"), ("Export JSON files", "json"),
@ -1783,281 +1995,8 @@ def create_demo():
value=metadata, value=metadata,
label="Metadata Handling" label="Metadata Handling"
) )
msg = gr.Markdown() msg = gr.Markdown()
apply_btn = gr.Button("Apply Changes") apply_btn = gr.Button("Apply Changes")
with gr.Row():
with gr.Column():
with gr.Row(visible= len(loras)>0) as presets_column:
lset_choices = [ (preset, preset) for preset in loras_presets ] + [(get_new_preset_msg(advanced), "")]
with gr.Column(scale=6):
lset_name = gr.Dropdown(show_label=False, allow_custom_value= True, scale=5, filterable=True, choices= lset_choices, value=default_lora_preset)
with gr.Column(scale=1):
# with gr.Column():
with gr.Row(height=17):
apply_lset_btn = gr.Button("Apply Lora Preset", size="sm", min_width= 1)
refresh_lora_btn = gr.Button("Refresh", size="sm", min_width= 1, visible=advanced or not only_allow_edit_in_advanced)
# save_lset_prompt_cbox = gr.Checkbox(label="Save Prompt Comments in Preset", value=False, visible= False)
save_lset_prompt_drop= gr.Dropdown(
choices=[
("Save Prompt Comments Only", 0),
("Save Full Prompt", 1)
], show_label= False, container=False, value =1, visible= False
)
with gr.Row(height=17, visible=False) as refresh2_row:
refresh_lora_btn2 = gr.Button("Refresh", size="sm", min_width= 1)
with gr.Row(height=17, visible=advanced or not only_allow_edit_in_advanced) as preset_buttons_rows:
confirm_save_lset_btn = gr.Button("Go Ahead Save it !", size="sm", min_width= 1, visible=False)
confirm_delete_lset_btn = gr.Button("Go Ahead Delete it !", size="sm", min_width= 1, visible=False)
save_lset_btn = gr.Button("Save", size="sm", min_width= 1)
delete_lset_btn = gr.Button("Delete", size="sm", min_width= 1)
cancel_lset_btn = gr.Button("Don't do it !", size="sm", min_width= 1 , visible=False)
video_to_continue = gr.Video(label= "Video to continue", visible= use_image2video and False) #######
if args.multiple_images:
image_to_continue = gr.Gallery(
label="Images as a starting point for new videos", type ="pil", #file_types= "image",
columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible=use_image2video)
else:
image_to_continue = gr.Image(label= "Image as a starting point for a new video", type ="pil", visible=use_image2video)
advanced_prompt = advanced
prompt_vars=[]
if advanced_prompt:
default_wizard_prompt, variables, values= None, None, None
else:
default_wizard_prompt, variables, values, errors = extract_wizard_prompt(default_prompt)
advanced_prompt = len(errors) > 0
with gr.Column(visible= advanced_prompt) as prompt_column_advanced: #visible= False
prompt = gr.Textbox( visible= advanced_prompt, label="Prompts (each new line of prompt will generate a new video, # lines = comments, ! lines = macros)", value=ui_defaults["prompts"], lines=3)
with gr.Column(visible=not advanced_prompt and len(variables) > 0) as prompt_column_wizard_vars: #visible= False
gr.Markdown("<B>Please fill the following input fields to adapt automatically the Prompt:</B>")
with gr.Row(): #visible= not advanced_prompt and len(variables) > 0
if not advanced_prompt:
for variable in variables:
value = values.get(variable, "")
prompt_vars.append(gr.Textbox( placeholder=variable, min_width=80, show_label= False, info= variable, visible= True, value= "\n".join(value) ))
state_dict["wizard_prompt"] = 1
state_dict["variables"] = variables
for _ in range( PROMPT_VARS_MAX - len(prompt_vars)):
prompt_vars.append(gr.Textbox(visible= False, min_width=80, show_label= False))
with gr.Column(not advanced_prompt) as prompt_column_wizard:
wizard_prompt = gr.Textbox(visible = not advanced_prompt, label="Prompts (each new line of prompt will generate a new video, # lines = comments)", value=default_wizard_prompt, lines=3)
state = gr.State(state_dict)
with gr.Row():
if use_image2video:
resolution = gr.Dropdown(
choices=[
# 720p
("720p", "1280x720"),
("480p", "832x480"),
],
value=ui_defaults["resolution"],
label="Resolution (video will have the same height / width ratio than the original image)"
)
else:
resolution = gr.Dropdown(
choices=[
# 720p
("1280x720 (16:9, 720p)", "1280x720"),
("720x1280 (9:16, 720p)", "720x1280"),
("1024x1024 (4:3, 720p)", "1024x024"),
# ("832x1104 (3:4, 720p)", "832x1104"),
# ("960x960 (1:1, 720p)", "960x960"),
# 480p
# ("960x544 (16:9, 480p)", "960x544"),
("832x480 (16:9, 480p)", "832x480"),
("480x832 (9:16, 480p)", "480x832"),
# ("832x624 (4:3, 540p)", "832x624"),
# ("624x832 (3:4, 540p)", "624x832"),
# ("720x720 (1:1, 540p)", "720x720"),
],
value=ui_defaults["resolution"],
label="Resolution"
)
with gr.Row():
with gr.Column():
video_length = gr.Slider(5, 193, value=ui_defaults["video_length"], step=4, label="Number of frames (16 = 1s)")
with gr.Column():
num_inference_steps = gr.Slider(1, 100, value=ui_defaults["num_inference_steps"], step=1, label="Number of Inference Steps")
with gr.Row():
max_frames = gr.Slider(1, 100, value=9, step=1, label="Number of input frames to use for Video2World prediction", visible=use_image2video and False) #########
show_advanced = gr.Checkbox(label="Advanced Mode", value=advanced)
with gr.Row(visible=advanced) as advanced_row:
with gr.Column():
seed = gr.Slider(-1, 999999999, value=ui_defaults["seed"], step=1, label="Seed (-1 for random)")
with gr.Row():
repeat_generation = gr.Slider(1, 25.0, value=ui_defaults["repeat_generation"], step=1, label="Default Number of Generated Videos per Prompt")
multi_images_gen_type = gr.Dropdown(
choices=[
("Generate every combination of images and texts prompts", 0),
("Match images and text prompts", 1),
], visible= args.multiple_images, label= "Multiple Images as Prompts"
)
with gr.Row():
guidance_scale = gr.Slider(1.0, 20.0, value=ui_defaults["guidance_scale"], step=0.5, label="Guidance Scale", visible=True)
embedded_guidance_scale = gr.Slider(1.0, 20.0, value=6.0, step=0.5, label="Embedded Guidance Scale", visible=False)
flow_shift = gr.Slider(0.0, 25.0, value=ui_defaults["flow_shift"], step=0.1, label="Shift Scale")
with gr.Row():
negative_prompt = gr.Textbox(label="Negative Prompt", value=ui_defaults["negative_prompt"])
with gr.Column(visible = len(loras)>0) as loras_column:
gr.Markdown("<B>Loras can be used to create special effects on the video by mentioning a trigger word in the Prompt. You can save Loras combinations in presets.</B>")
loras_choices = gr.Dropdown(
choices=[
(lora_name, str(i) ) for i, lora_name in enumerate(loras_names)
],
value= ui_defaults["activated_loras"],
multiselect= True,
label="Activated Loras"
)
loras_mult_choices = gr.Textbox(label="Loras Multipliers (1.0 by default) separated by space characters or carriage returns, line that starts with # are ignored", value=ui_defaults["loras_multipliers"])
with gr.Row():
gr.Markdown("<B>Tea Cache accelerates by skipping intelligently some steps, the more steps are skipped the lower the quality of the video (Tea Cache consumes also VRAM)</B>")
with gr.Row():
tea_cache_setting = gr.Dropdown(
choices=[
("Tea Cache Disabled", 0),
("around x1.5 speed up", 1.5),
("around x1.75 speed up", 1.75),
("around x2 speed up", 2.0),
("around x2.25 speed up", 2.25),
("around x2.5 speed up", 2.5),
],
value=float(ui_defaults["tea_cache"]),
visible=True,
label="Tea Cache Global Acceleration"
)
tea_cache_start_step_perc = gr.Slider(0, 100, value=ui_defaults["tea_cache_start_step_perc"], step=1, label="Tea Cache starting moment in % of generation")
gr.Markdown("<B>With Riflex you can generate videos longer than 5s which is the default duration of videos used to train the model</B>")
RIFLEx_setting = gr.Dropdown(
choices=[
("Auto (ON if Video longer than 5s)", 0),
("Always ON", 1),
("Always OFF", 2),
],
value=ui_defaults["RIFLEx_setting"],
label="RIFLEx positional embedding to generate long video"
)
with gr.Row():
gr.Markdown("<B>Experimental: Skip Layer guidance,should improve video quality</B>")
with gr.Row():
slg_switch = gr.Dropdown(
choices=[
("OFF", 0),
("ON", 1),
],
value=ui_defaults["slg_switch"],
visible=True,
scale = 1,
label="Skip Layer guidance"
)
slg_layers = gr.Dropdown(
choices=[
(str(i), i ) for i in range(40)
],
value=ui_defaults["slg_layers"],
multiselect= True,
label="Skip Layers",
scale= 3
)
with gr.Row():
slg_start_perc = gr.Slider(0, 100, value=ui_defaults["slg_start_perc"], step=1, label="Denoising Steps % start")
slg_end_perc = gr.Slider(0, 100, value=ui_defaults["slg_end_perc"], step=1, label="Denoising Steps % end")
show_advanced.change(fn=switch_advanced, inputs=[show_advanced, lset_name], outputs=[advanced_row, preset_buttons_rows, refresh_lora_btn, refresh2_row ,lset_name ]).then(
fn=switch_prompt_type, inputs = [state, prompt, wizard_prompt, *prompt_vars], outputs = [prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars])
with gr.Column():
gen_status = gr.Text(label="Status", interactive= False)
output = gr.Gallery(
label="Generated videos", show_label=False, elem_id="gallery"
, columns=[3], rows=[1], object_fit="contain", height=450, selected_index=0, interactive= False)
generate_btn = gr.Button("Generate")
onemore_btn = gr.Button("One More Please !", visible= False)
abort_btn = gr.Button("Abort")
gen_info = gr.Text(label="Current prompt", visible= False , interactive= False) #gr.Markdown("Current prompt") #, ,
save_lset_btn.click(validate_save_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop])
confirm_save_lset_btn.click(fn=validate_wizard_prompt, inputs =[state, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]).then(
save_lset, inputs=[state, lset_name, loras_choices, loras_mult_choices, prompt, save_lset_prompt_drop], outputs=[lset_name, apply_lset_btn,refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_save_lset_btn, cancel_lset_btn, save_lset_prompt_drop])
delete_lset_btn.click(validate_delete_lset, inputs=[lset_name], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ])
confirm_delete_lset_btn.click(delete_lset, inputs=[lset_name], outputs=[lset_name, apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn,confirm_delete_lset_btn, cancel_lset_btn ])
cancel_lset_btn.click(cancel_lset, inputs=[], outputs=[apply_lset_btn, refresh_lora_btn, delete_lset_btn, save_lset_btn, confirm_delete_lset_btn,confirm_save_lset_btn, cancel_lset_btn,save_lset_prompt_drop ])
apply_lset_btn.click(apply_lset, inputs=[state, lset_name,loras_choices, loras_mult_choices, prompt], outputs=[loras_choices, loras_mult_choices, prompt]).then(
fn = fill_wizard_prompt, inputs = [state, prompt, wizard_prompt], outputs = [ prompt, wizard_prompt, prompt_column_advanced, prompt_column_wizard, prompt_column_wizard_vars, *prompt_vars]
)
refresh_lora_btn.click(refresh_lora_list, inputs=[lset_name,loras_choices], outputs=[lset_name, loras_choices])
refresh_lora_btn2.click(refresh_lora_list, inputs=[lset_name,loras_choices], outputs=[lset_name, loras_choices])
download_loras_btn.click(fn=download_loras, inputs=[], outputs=[download_status, presets_column, loras_column]).then(fn=refresh_lora_list, inputs=[lset_name,loras_choices], outputs=[lset_name, loras_choices])
gen_status.change(refresh_gallery, inputs = [state, gen_info], outputs = [output, gen_info] )
abort_btn.click(abort_generation,state,abort_btn )
output.select(select_video, state, None )
onemore_btn.click(fn=one_more_video,inputs=[state], outputs= [state])
generate_btn.click(fn=prepare_generate_video,inputs=[], outputs= [generate_btn, onemore_btn]
).then(
fn=validate_wizard_prompt, inputs =[state, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]
).then(
fn=generate_video,
inputs=[
prompt,
negative_prompt,
resolution,
video_length,
seed,
num_inference_steps,
guidance_scale,
flow_shift,
embedded_guidance_scale,
repeat_generation,
multi_images_gen_type,
tea_cache_setting,
tea_cache_start_step_perc,
loras_choices,
loras_mult_choices,
image_to_continue,
video_to_continue,
max_frames,
RIFLEx_setting,
slg_switch,
slg_layers,
slg_start_perc,
slg_end_perc,
state,
metadata_choice,
],
outputs= [gen_status] #,state
).then(
finalize_gallery,
[state],
[output , abort_btn, generate_btn, onemore_btn, gen_info]
)
apply_btn.click( apply_btn.click(
fn=apply_changes, fn=apply_changes,
inputs=[ inputs=[
@ -2075,28 +2014,90 @@ def create_demo():
boost_choice, boost_choice,
], ],
outputs= msg outputs= msg
).then(
update_defaults,
[state, num_inference_steps, flow_shift,lset_name , loras_choices],
[num_inference_steps, flow_shift, header, lset_name , loras_choices ]
) )
def on_tab_select(evt: gr.SelectData):
global use_image2video, wan_model, offloadobj, loras, loras_names, loras_presets, root_lora_dir, lora_dir
new_i2v = evt.index == 1
if new_i2v == use_image2video:
return [gr.Dropdown(visible=not new_i2v), gr.Dropdown(visible=not new_i2v),
gr.Dropdown(visible=new_i2v), gr.Dropdown(visible=new_i2v), gr.Markdown()]
if wan_model is not None:
if offloadobj is not None:
offloadobj.release()
offloadobj = None
wan_model = None
gc.collect()
torch.cuda.empty_cache()
use_image2video = new_i2v
get_defaults()
lora_dir = args.lora_dir_i2v if new_i2v and args.lora_dir_i2v else args.lora_dir if not new_i2v and args.lora_dir else "loras_i2v" if new_i2v else "loras"
root_lora_dir = lora_dir
lora_dir = get_lora_dir(root_lora_dir)
wan_model, offloadobj, loras, loras_names, default_loras_choices, default_loras_multis_str, default_prompt, default_lora_preset, loras_presets = load_models(use_image2video, lora_dir, lora_preselected_preset)
new_loras_choices = [(name, str(i)) for i, name in enumerate(loras_names)]
lset_choices = [(preset, preset) for preset in loras_presets] + [(get_new_preset_msg(advanced), "")]
model_filename = transformer_filename_i2v if use_image2video else transformer_filename_t2v
if new_i2v:
return [
gr.Dropdown(visible=False),
gr.Dropdown(visible=False),
gr.Dropdown(choices=new_loras_choices, visible=True),
gr.Dropdown(choices=lset_choices, value=default_lora_preset, visible=True)
]
else:
return [
gr.Dropdown(choices=new_loras_choices, visible=True),
gr.Dropdown(choices=lset_choices, value=default_lora_preset, visible=True),
gr.Dropdown(visible=False),
gr.Dropdown(visible=False)
]
def create_demo():
css = """
.title-with-lines {
display: flex;
align-items: center;
margin: 30px 0;
}
.line {
flex-grow: 1;
height: 1px;
background-color: #333;
}
h2 {
margin: 0 20px;
white-space: nowrap;
}
"""
with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="sky", neutral_hue="slate", text_size="md")) as demo:
with gr.Tabs(selected=default_ui) as main_tabs:
with gr.Tab("Text To Video", id="t2v") as t2v_tab:
t2v_loras_choices, t2v_lset_name = generate_video_tab()
with gr.Tab("Image To Video", id="i2v") as i2v_tab:
i2v_loras_choices, i2v_lset_name = generate_video_tab(True)
with gr.Tab("Settings"):
generate_settings_tab()
main_tabs.select(
fn=on_tab_select,
inputs=[],
outputs=[
t2v_loras_choices, t2v_lset_name,
i2v_loras_choices, i2v_lset_name
]
)
return demo return demo
if __name__ == "__main__": if __name__ == "__main__":
os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
server_port = int(args.server_port) server_port = int(args.server_port)
if os.name == "nt": if os.name == "nt":
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
if server_port == 0: if server_port == 0:
server_port = int(os.getenv("SERVER_PORT", "7860")) server_port = int(os.getenv("SERVER_PORT", "7860"))
server_name = args.server_name server_name = args.server_name
if len(server_name) == 0: if len(server_name) == 0:
server_name = os.getenv("SERVER_NAME", "localhost") server_name = os.getenv("SERVER_NAME", "localhost")
demo = create_demo() demo = create_demo()
if args.open_browser: if args.open_browser:
import webbrowser import webbrowser
@ -2105,7 +2106,4 @@ if __name__ == "__main__":
else: else:
url = "http://" + server_name url = "http://" + server_name
webbrowser.open(url + ":" + str(server_port), new = 0, autoraise = True) webbrowser.open(url + ":" + str(server_port), new = 0, autoraise = True)
demo.launch(server_name=server_name, server_port=server_port, share=args.share, allowed_paths=[save_path]) demo.launch(server_name=server_name, server_port=server_port, share=args.share, allowed_paths=[save_path])