diff --git a/README.md b/README.md
index c69848b..e37c10d 100644
--- a/README.md
+++ b/README.md
@@ -19,11 +19,13 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
## 🔥 Latest News!!
+* Mar 19 2022: 👋 Wan2.1GP v3.1: Faster launch and RAM optimizations (should require less RAM to run)\
+ You will need one more *pip install -r requirements.txt*
* Mar 18 2022: 👋 Wan2.1GP v3.0:
- New Tab based interface, yon can switch from i2v to t2v conversely without restarting the app
- Experimental Dual Frames mode for i2v, you can also specify an End frame. It doesn't always work, so you will need a few attempts.
- You can save default settings in the files *i2v_settings.json* and *t2v_settings.json* that will be used when launching the app (you can also specify the path to different settings files)
- - Slight acceleration with loras
+ - Slight acceleration with loras\
You will need one more *pip install -r requirements.txt*
Many thanks to *Tophness* who created the framework (and did a big part of the work) of the multitabs and saved settings features
* Mar 18 2022: 👋 Wan2.1GP v2.11: Added more command line parameters to prefill the generation settings + customizable output directory and choice of type of metadata for generated videos. Many thanks to *Tophness* for his contributions. You will need one more *pip install -r requirements.txt* to reflect new dependencies\
@@ -255,8 +257,9 @@ You can define multiple lines of macros. If there is only one macro line, the ap
--check-loras : filter loras that are incompatible (will take a few seconds while refreshing the lora list or while starting the app)\
--advanced : turn on the advanced mode while launching the app\
--i2v-settings : path to launch settings for i2v\
---t2v-settings : path to launch settings for t2v
---listen : make server accessible on network
+--t2v-settings : path to launch settings for t2v\
+--listen : make server accessible on network\
+--gpu device : run Wan on device for instance "cuda:1"
### Profiles (for power users only)
You can choose between 5 profiles, but two are really relevant here :
diff --git a/gradio_server.py b/gradio_server.py
index bcc8dda..27d88c5 100644
--- a/gradio_server.py
+++ b/gradio_server.py
@@ -15,7 +15,7 @@ import json
import wan
from wan.configs import MAX_AREA_CONFIGS, WAN_CONFIGS, SUPPORTED_SIZES
from wan.utils.utils import cache_video
-from wan.modules.attention import get_attention_modes
+from wan.modules.attention import get_attention_modes, get_supported_attention_modes
import torch
import gc
import traceback
@@ -24,7 +24,7 @@ import asyncio
from wan.utils import prompt_parser
PROMPT_VARS_MAX = 10
-target_mmgp_version = "3.3.1"
+target_mmgp_version = "3.3.3"
from importlib.metadata import version
mmgp_version = version("mmgp")
if mmgp_version != target_mmgp_version:
@@ -55,16 +55,16 @@ def runner():
while True:
with lock:
for item in queue:
- task_id = item['id']
+ task_id_runner = item['id']
with tracker_lock:
- progress = progress_tracker.get(task_id, {})
+ progress = progress_tracker.get(task_id_runner, {})
if item['state'] == "Processing":
current_step = progress.get('current_step', 0)
total_steps = progress.get('total_steps', 0)
elapsed = time.time() - progress.get('start_time', time.time())
status = progress.get('status', "")
- repeats = progress.get("repeats")
+ repeats = progress.get("repeats", "0/0")
item.update({
'progress': f"{((current_step/total_steps)*100 if total_steps > 0 else 0):.1f}%",
'steps': f"{current_step}/{total_steps}",
@@ -381,6 +381,13 @@ def _parse_args():
help="Server name"
)
+ parser.add_argument(
+ "--gpu",
+ type=str,
+ default="",
+ help="Default GPU Device"
+ )
+
parser.add_argument(
"--open-browser",
action="store_true",
@@ -473,7 +480,8 @@ def get_lora_dir(i2v):
return lora_dir_14B
return root_lora_dir
-attention_modes_supported = get_attention_modes()
+attention_modes_installed = get_attention_modes()
+attention_modes_supported = get_supported_attention_modes()
args = _parse_args()
args.flow_reverse = True
@@ -587,6 +595,7 @@ vae_config = server_config.get("vae_config", 0)
if len(args.vae_config) > 0:
vae_config = int(args.vae_config)
+reload_needed = False
default_ui = server_config.get("default_ui", "t2v")
metadata = server_config.get("metadata_type", "metadata")
save_path = server_config.get("save_path", os.path.join(os.getcwd(), "gradio_outputs"))
@@ -686,7 +695,7 @@ def download_models(transformer_filename, text_encoder_filename):
from huggingface_hub import hf_hub_download, snapshot_download
repoId = "DeepBeepMeep/Wan2.1"
sourceFolderList = ["xlm-roberta-large", "", ]
- fileList = [ [], ["Wan2.1_VAE.pth", "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" ] + computeList(text_encoder_filename) + computeList(transformer_filename) ]
+ fileList = [ [], ["Wan2.1_VAE_bf16.safetensors", "models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors" ] + computeList(text_encoder_filename) + computeList(transformer_filename) ]
targetRoot = "ckpts/"
for sourceFolder, files in zip(sourceFolderList,fileList ):
if len(files)==0:
@@ -703,6 +712,14 @@ def download_models(transformer_filename, text_encoder_filename):
offload.default_verboseLevel = verbose_level
+to_remove = ["models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", "Wan2.1_VAE.pth"]
+for file_name in to_remove:
+ file_name = os.path.join("ckpts",file_name)
+ if os.path.isfile(file_name):
+ try:
+ os.remove(file_name)
+ except:
+ pass
download_models(transformer_filename_i2v if use_image2video else transformer_filename_t2v, text_encoder_filename)
@@ -875,6 +892,8 @@ def load_models(i2v):
elif profile == 3:
kwargs["budgets"] = { "*" : "70%" }
offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", **kwargs)
+ if len(args.gpu) > 0:
+ torch.set_default_device(args.gpu)
return wan_model, offloadobj, pipe["transformer"]
@@ -914,8 +933,10 @@ def generate_header(model_filename, compile, attention_mode):
header += model_name
header += " (attention mode: " + (attention_mode if attention_mode!="auto" else "auto/" + get_auto_attention() )
- if attention_mode not in attention_modes_supported:
+ if attention_mode not in attention_modes_installed:
header += " -NOT INSTALLED-"
+ elif attention_mode not in attention_modes_supported:
+ header += " -NOT SUPPORTED-"
if compile:
header += ", pytorch compilation ON"
@@ -979,11 +1000,7 @@ def apply_changes( state,
if v != v_old:
changes.append(k)
- state["config_changes"] = changes
- state["config_new"] = server_config
- state["config_old"] = old_server_config
-
- global attention_mode, profile, compile, transformer_filename_t2v, transformer_filename_i2v, text_encoder_filename, vae_config, boost, lora_dir
+ global attention_mode, profile, compile, transformer_filename_t2v, transformer_filename_i2v, text_encoder_filename, vae_config, boost, lora_dir, reload_needed
attention_mode = server_config["attention_mode"]
profile = server_config["profile"]
compile = server_config["compile"]
@@ -995,7 +1012,7 @@ def apply_changes( state,
if all(change in ["attention_mode", "vae_config", "default_ui", "boost", "save_path", "metadata_choice"] for change in changes ):
pass
else:
- state["_reload_needed"] = True
+ reload_needed = True
yield "The new configuration has been succesfully applied
"
@@ -1013,7 +1030,7 @@ def save_video(final_frames, output_path, fps=24):
def build_callback(taskid, state, pipe, num_inference_steps, repeats):
start_time = time.time()
- def update_progress(step_idx, latents, read_state = False):
+ def update_progress(step_idx, _):
with tracker_lock:
step_idx += 1
if state.get("abort", False):
@@ -1094,8 +1111,7 @@ def generate_video(
progress=gr.Progress() #track_tqdm= True
):
- global wan_model, offloadobj, last_model_type
- reload_needed = state.get("_reload_needed", False)
+ global wan_model, offloadobj, reload_needed, last_model_type
file_model_needed = model_needed(image2video)
with lock:
queue_not_empty = len(queue) > 0
@@ -1108,7 +1124,7 @@ def generate_video(
print(f"Loading model {get_model_name(file_model_needed)}...")
wan_model, offloadobj, trans = load_models(image2video)
print(f"Model loaded")
- state["_reload_needed"] = False
+ reload_needed= False
from PIL import Image
import numpy as np
@@ -1121,11 +1137,12 @@ def generate_video(
elif attention_mode in attention_modes_supported:
attn = attention_mode
else:
- gr.Info(f"You have selected attention mode '{attention_mode}'. However it is not installed on your system. You should either install it or switch to the default 'sdpa' attention.")
+ gr.Info(f"You have selected attention mode '{attention_mode}'. However it is not installed or supported on your system. You should either install it or switch to the default 'sdpa' attention.")
return
#if state.get("validate_success",0) != 1:
# return
+ raw_resolution = resolution
width, height = resolution.split("x")
width, height = int(width), int(height)
@@ -1289,7 +1306,7 @@ def generate_video(
'total_steps': num_inference_steps,
'start_time': time.time(),
'last_update': time.time(),
- 'repeats': f"0/{repeat_generation}",
+ 'repeats': f"{video_no}/{repeat_generation}",
'status': "Encoding Prompt"
}
video_no += 1
@@ -1401,14 +1418,8 @@ def generate_video(
normalize=True,
value_range=(-1, 1))
- configs = {
- 'prompt': prompt,
- 'negative_prompt': negative_prompt,
- 'resolution': resolution,
- 'video_length': video_length,
- 'seed': seed,
- 'num_inference_steps': num_inference_steps,
- }
+ configs = get_settings_dict(state, use_image2video, prompt, 0 if image_to_end == None else 1 , video_length, raw_resolution, num_inference_steps, seed, repeat_generation, multi_images_gen_type, guidance_scale, flow_shift, negative_prompt, loras_choices,
+ loras_mult_choices, tea_cache , tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start, slg_end)
metadata_choice = server_config.get("metadata_choice","metadata")
if metadata_choice == "json":
@@ -1715,19 +1726,14 @@ def switch_advanced(state, new_advanced, lset_name):
else:
return gr.Row(visible=new_advanced), gr.Row(visible=True), gr.Button(visible=True), gr.Row(visible= False), gr.Dropdown(choices=lset_choices, value= lset_name)
-def save_settings(state, prompt, image_prompt_type, video_length, resolution, num_inference_steps, seed, repeat_generation, multi_images_gen_type, guidance_scale, flow_shift, negative_prompt, loras_choices,
+def get_settings_dict(state, i2v, prompt, image_prompt_type, video_length, resolution, num_inference_steps, seed, repeat_generation, multi_images_gen_type, guidance_scale, flow_shift, negative_prompt, loras_choices,
loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc):
- if state.get("validate_success",0) != 1:
- return
-
- loras_choices
loras = state["loras"]
activated_loras = [Path( loras[int(no)]).parts[-1] for no in loras_choices ]
- ui_defaults = {
+ ui_settings = {
"prompts": prompt,
- "image_prompt_type": image_prompt_type,
"resolution": resolution,
"video_length": video_length,
"num_inference_steps": num_inference_steps,
@@ -1747,10 +1753,25 @@ def save_settings(state, prompt, image_prompt_type, video_length, resolution, nu
"slg_start_perc": slg_start_perc,
"slg_end_perc": slg_end_perc
}
+
+ if i2v:
+ ui_settings["type"] = "Wan2.1GP by DeepBeepMeep - image2video"
+ ui_settings["image_prompt_type"] = image_prompt_type
+ else:
+ ui_settings["type"] = "Wan2.1GP by DeepBeepMeep - text2video"
+ return ui_settings
+
+def save_settings(state, prompt, image_prompt_type, video_length, resolution, num_inference_steps, seed, repeat_generation, multi_images_gen_type, guidance_scale, flow_shift, negative_prompt, loras_choices,
+ loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc):
+ if state.get("validate_success",0) != 1:
+ return
+ ui_defaults = get_settings_dict(state, use_image2video, prompt, image_prompt_type, video_length, resolution, num_inference_steps, seed, repeat_generation, multi_images_gen_type, guidance_scale, flow_shift, negative_prompt, loras_choices,
+ loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc)
+
defaults_filename = get_settings_file_name(use_image2video)
with open(defaults_filename, "w", encoding="utf-8") as f:
- json.dump(ui_defaults, f, indent=4)
+ json.dump(ui_settings , f, indent=4)
gr.Info("New Default Settings saved")
@@ -1864,7 +1885,8 @@ def generate_video_tab(image2video=False):
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) #######
- image_prompt_type = gr.Radio( [("Use only a Start Image", 0),("Use both a Start and an End Image", 1)], value =ui_defaults["image_prompt_type"], label="Location", show_label= False, scale= 3, visible=image2video)
+ image_prompt_type= ui_defaults.get("image_prompt_type",0)
+ image_prompt_type_radio = gr.Radio( [("Use only a Start Image", 0),("Use both a Start and an End Image", 1)], value =image_prompt_type, label="Location", show_label= False, scale= 3, visible=image2video)
if args.multiple_images:
image_to_continue = gr.Gallery(
@@ -1876,9 +1898,9 @@ def generate_video_tab(image2video=False):
if args.multiple_images:
image_to_end = gr.Gallery(
label="Images as ending points for new videos", type ="pil", #file_types= "image",
- columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible=False)
+ columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible=image_prompt_type==1)
else:
- image_to_end = gr.Image(label= "Last Image for a new video", type ="pil", visible= False)
+ image_to_end = gr.Image(label= "Last Image for a new video", type ="pil", visible=image_prompt_type==1)
def switch_image_prompt_type_radio(image_prompt_type_radio):
if args.multiple_images:
@@ -1886,7 +1908,7 @@ def generate_video_tab(image2video=False):
else:
return gr.Image(visible = (image_prompt_type_radio == 1) )
- image_prompt_type.change(fn=switch_image_prompt_type_radio, inputs=[image_prompt_type], outputs=[image_to_end])
+ image_prompt_type_radio.change(fn=switch_image_prompt_type_radio, inputs=[image_prompt_type_radio], outputs=[image_to_end])
advanced_prompt = advanced
@@ -2080,7 +2102,7 @@ def generate_video_tab(image2video=False):
outputs=[output]
)
save_settings_btn.click( fn=validate_wizard_prompt, inputs =[state, wizard_prompt_activated_var, wizard_variables_var, prompt, wizard_prompt, *prompt_vars] , outputs= [prompt]).then(
- save_settings, inputs = [state, prompt, image_prompt_type, video_length, resolution, num_inference_steps, seed, repeat_generation, multi_images_gen_type, guidance_scale, flow_shift, negative_prompt,
+ save_settings, inputs = [state, prompt, image_prompt_type_radio, video_length, resolution, num_inference_steps, seed, repeat_generation, multi_images_gen_type, guidance_scale, flow_shift, negative_prompt,
loras_choices, loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers,
slg_start_perc, slg_end_perc ], outputs = [])
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])
@@ -2182,8 +2204,10 @@ def generate_configuration_tab():
value=server_config.get("save_path", save_path)
)
def check(mode):
- if not mode in attention_modes_supported:
+ if not mode in attention_modes_installed:
return " (NOT INSTALLED)"
+ elif not mode in attention_modes_supported:
+ return " (NOT SUPPORTED)"
else:
return ""
attention_choice = gr.Dropdown(
@@ -2435,7 +2459,7 @@ def create_demo():
}
"""
with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="sky", neutral_hue="slate", text_size="md")) as demo:
- gr.Markdown("Wan 2.1GP v3.0 by DeepBeepMeep (Updates)
Wan 2.1GP v3.1 by DeepBeepMeep (Updates)