merge with latest branch
This commit is contained in:
Chris Malone
parent
c7a8c82398
commit
7e7c9c5593
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@ -19,11 +19,13 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
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## 🔥 Latest News!!
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* Mar 19 2022: 👋 Wan2.1GP v3.1: Faster launch and RAM optimizations (should require less RAM to run)\
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You will need one more *pip install -r requirements.txt*
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* Mar 18 2022: 👋 Wan2.1GP v3.0:
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- New Tab based interface, yon can switch from i2v to t2v conversely without restarting the app
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- 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.
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- 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)
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- Slight acceleration with loras
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- Slight acceleration with loras\
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You will need one more *pip install -r requirements.txt*
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Many thanks to *Tophness* who created the framework (and did a big part of the work) of the multitabs and saved settings features
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* 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\
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@ -255,8 +257,9 @@ You can define multiple lines of macros. If there is only one macro line, the ap
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--check-loras : filter loras that are incompatible (will take a few seconds while refreshing the lora list or while starting the app)\
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--advanced : turn on the advanced mode while launching the app\
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--i2v-settings : path to launch settings for i2v\
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--t2v-settings : path to launch settings for t2v
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--listen : make server accessible on network
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--t2v-settings : path to launch settings for t2v\
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--listen : make server accessible on network\
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--gpu device : run Wan on device for instance "cuda:1"
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### Profiles (for power users only)
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You can choose between 5 profiles, but two are really relevant here :
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110
gradio_server.py
110
gradio_server.py
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@ -15,7 +15,7 @@ import json
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import wan
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from wan.configs import MAX_AREA_CONFIGS, WAN_CONFIGS, SUPPORTED_SIZES
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from wan.utils.utils import cache_video
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from wan.modules.attention import get_attention_modes
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from wan.modules.attention import get_attention_modes, get_supported_attention_modes
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import torch
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import gc
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import traceback
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@ -24,7 +24,7 @@ import asyncio
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from wan.utils import prompt_parser
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PROMPT_VARS_MAX = 10
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target_mmgp_version = "3.3.1"
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target_mmgp_version = "3.3.3"
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from importlib.metadata import version
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mmgp_version = version("mmgp")
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if mmgp_version != target_mmgp_version:
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@ -55,16 +55,16 @@ def runner():
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while True:
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with lock:
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for item in queue:
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task_id = item['id']
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task_id_runner = item['id']
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with tracker_lock:
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progress = progress_tracker.get(task_id, {})
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progress = progress_tracker.get(task_id_runner, {})
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if item['state'] == "Processing":
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current_step = progress.get('current_step', 0)
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total_steps = progress.get('total_steps', 0)
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elapsed = time.time() - progress.get('start_time', time.time())
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status = progress.get('status', "")
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repeats = progress.get("repeats")
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repeats = progress.get("repeats", "0/0")
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item.update({
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'progress': f"{((current_step/total_steps)*100 if total_steps > 0 else 0):.1f}%",
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'steps': f"{current_step}/{total_steps}",
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@ -381,6 +381,13 @@ def _parse_args():
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help="Server name"
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)
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parser.add_argument(
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"--gpu",
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type=str,
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default="",
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help="Default GPU Device"
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)
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parser.add_argument(
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"--open-browser",
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action="store_true",
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@ -473,7 +480,8 @@ def get_lora_dir(i2v):
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return lora_dir_14B
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return root_lora_dir
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attention_modes_supported = get_attention_modes()
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attention_modes_installed = get_attention_modes()
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attention_modes_supported = get_supported_attention_modes()
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args = _parse_args()
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args.flow_reverse = True
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@ -587,6 +595,7 @@ vae_config = server_config.get("vae_config", 0)
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if len(args.vae_config) > 0:
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vae_config = int(args.vae_config)
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reload_needed = False
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default_ui = server_config.get("default_ui", "t2v")
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metadata = server_config.get("metadata_type", "metadata")
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save_path = server_config.get("save_path", os.path.join(os.getcwd(), "gradio_outputs"))
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@ -686,7 +695,7 @@ def download_models(transformer_filename, text_encoder_filename):
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from huggingface_hub import hf_hub_download, snapshot_download
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repoId = "DeepBeepMeep/Wan2.1"
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sourceFolderList = ["xlm-roberta-large", "", ]
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fileList = [ [], ["Wan2.1_VAE.pth", "models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth" ] + computeList(text_encoder_filename) + computeList(transformer_filename) ]
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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) ]
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targetRoot = "ckpts/"
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for sourceFolder, files in zip(sourceFolderList,fileList ):
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if len(files)==0:
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@ -703,6 +712,14 @@ def download_models(transformer_filename, text_encoder_filename):
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offload.default_verboseLevel = verbose_level
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to_remove = ["models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth", "Wan2.1_VAE.pth"]
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for file_name in to_remove:
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file_name = os.path.join("ckpts",file_name)
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if os.path.isfile(file_name):
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try:
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os.remove(file_name)
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except:
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pass
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download_models(transformer_filename_i2v if use_image2video else transformer_filename_t2v, text_encoder_filename)
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@ -875,6 +892,8 @@ def load_models(i2v):
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elif profile == 3:
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kwargs["budgets"] = { "*" : "70%" }
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offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", **kwargs)
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if len(args.gpu) > 0:
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torch.set_default_device(args.gpu)
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return wan_model, offloadobj, pipe["transformer"]
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@ -914,8 +933,10 @@ def generate_header(model_filename, compile, attention_mode):
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header += model_name
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header += " (attention mode: " + (attention_mode if attention_mode!="auto" else "auto/" + get_auto_attention() )
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if attention_mode not in attention_modes_supported:
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if attention_mode not in attention_modes_installed:
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header += " -NOT INSTALLED-"
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elif attention_mode not in attention_modes_supported:
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header += " -NOT SUPPORTED-"
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if compile:
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header += ", pytorch compilation ON"
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@ -979,11 +1000,7 @@ def apply_changes( state,
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if v != v_old:
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changes.append(k)
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state["config_changes"] = changes
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state["config_new"] = server_config
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state["config_old"] = old_server_config
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global attention_mode, profile, compile, transformer_filename_t2v, transformer_filename_i2v, text_encoder_filename, vae_config, boost, lora_dir
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global attention_mode, profile, compile, transformer_filename_t2v, transformer_filename_i2v, text_encoder_filename, vae_config, boost, lora_dir, reload_needed
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attention_mode = server_config["attention_mode"]
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profile = server_config["profile"]
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compile = server_config["compile"]
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@ -995,7 +1012,7 @@ def apply_changes( state,
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if all(change in ["attention_mode", "vae_config", "default_ui", "boost", "save_path", "metadata_choice"] for change in changes ):
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pass
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else:
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state["_reload_needed"] = True
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reload_needed = True
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yield "<DIV ALIGN=CENTER>The new configuration has been succesfully applied</DIV>"
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@ -1013,7 +1030,7 @@ def save_video(final_frames, output_path, fps=24):
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def build_callback(taskid, state, pipe, num_inference_steps, repeats):
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start_time = time.time()
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def update_progress(step_idx, latents, read_state = False):
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def update_progress(step_idx, _):
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with tracker_lock:
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step_idx += 1
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if state.get("abort", False):
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@ -1094,8 +1111,7 @@ def generate_video(
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progress=gr.Progress() #track_tqdm= True
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):
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global wan_model, offloadobj, last_model_type
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reload_needed = state.get("_reload_needed", False)
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global wan_model, offloadobj, reload_needed, last_model_type
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file_model_needed = model_needed(image2video)
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with lock:
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queue_not_empty = len(queue) > 0
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@ -1108,7 +1124,7 @@ def generate_video(
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print(f"Loading model {get_model_name(file_model_needed)}...")
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wan_model, offloadobj, trans = load_models(image2video)
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print(f"Model loaded")
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state["_reload_needed"] = False
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reload_needed= False
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from PIL import Image
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import numpy as np
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@ -1121,11 +1137,12 @@ def generate_video(
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elif attention_mode in attention_modes_supported:
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attn = attention_mode
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else:
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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.")
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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.")
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return
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#if state.get("validate_success",0) != 1:
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# return
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raw_resolution = resolution
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width, height = resolution.split("x")
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width, height = int(width), int(height)
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@ -1289,7 +1306,7 @@ def generate_video(
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'total_steps': num_inference_steps,
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'start_time': time.time(),
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'last_update': time.time(),
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'repeats': f"0/{repeat_generation}",
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'repeats': f"{video_no}/{repeat_generation}",
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'status': "Encoding Prompt"
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}
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video_no += 1
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@ -1401,14 +1418,8 @@ def generate_video(
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normalize=True,
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value_range=(-1, 1))
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configs = {
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'prompt': prompt,
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'negative_prompt': negative_prompt,
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'resolution': resolution,
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'video_length': video_length,
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'seed': seed,
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'num_inference_steps': num_inference_steps,
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}
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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,
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loras_mult_choices, tea_cache , tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start, slg_end)
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metadata_choice = server_config.get("metadata_choice","metadata")
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if metadata_choice == "json":
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@ -1715,19 +1726,14 @@ def switch_advanced(state, new_advanced, lset_name):
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else:
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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)
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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,
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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,
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loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc):
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if state.get("validate_success",0) != 1:
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return
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loras_choices
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loras = state["loras"]
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activated_loras = [Path( loras[int(no)]).parts[-1] for no in loras_choices ]
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ui_defaults = {
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ui_settings = {
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"prompts": prompt,
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"image_prompt_type": image_prompt_type,
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"resolution": resolution,
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"video_length": video_length,
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"num_inference_steps": num_inference_steps,
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@ -1747,10 +1753,25 @@ def save_settings(state, prompt, image_prompt_type, video_length, resolution, nu
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"slg_start_perc": slg_start_perc,
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"slg_end_perc": slg_end_perc
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}
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if i2v:
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ui_settings["type"] = "Wan2.1GP by DeepBeepMeep - image2video"
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ui_settings["image_prompt_type"] = image_prompt_type
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else:
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ui_settings["type"] = "Wan2.1GP by DeepBeepMeep - text2video"
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return ui_settings
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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,
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loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc):
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if state.get("validate_success",0) != 1:
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return
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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,
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loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers, slg_start_perc, slg_end_perc)
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defaults_filename = get_settings_file_name(use_image2video)
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with open(defaults_filename, "w", encoding="utf-8") as f:
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json.dump(ui_defaults, f, indent=4)
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json.dump(ui_settings , f, indent=4)
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gr.Info("New Default Settings saved")
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@ -1864,7 +1885,8 @@ def generate_video_tab(image2video=False):
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cancel_lset_btn = gr.Button("Don't do it !", size="sm", min_width= 1 , visible=False)
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video_to_continue = gr.Video(label= "Video to continue", visible= image2video and False) #######
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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)
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image_prompt_type= ui_defaults.get("image_prompt_type",0)
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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)
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if args.multiple_images:
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image_to_continue = gr.Gallery(
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@ -1876,9 +1898,9 @@ def generate_video_tab(image2video=False):
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if args.multiple_images:
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image_to_end = gr.Gallery(
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label="Images as ending points for new videos", type ="pil", #file_types= "image",
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columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible=False)
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columns=[3], rows=[1], object_fit="contain", height="auto", selected_index=0, interactive= True, visible=image_prompt_type==1)
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else:
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image_to_end = gr.Image(label= "Last Image for a new video", type ="pil", visible= False)
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image_to_end = gr.Image(label= "Last Image for a new video", type ="pil", visible=image_prompt_type==1)
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def switch_image_prompt_type_radio(image_prompt_type_radio):
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if args.multiple_images:
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@ -1886,7 +1908,7 @@ def generate_video_tab(image2video=False):
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else:
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return gr.Image(visible = (image_prompt_type_radio == 1) )
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image_prompt_type.change(fn=switch_image_prompt_type_radio, inputs=[image_prompt_type], outputs=[image_to_end])
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image_prompt_type_radio.change(fn=switch_image_prompt_type_radio, inputs=[image_prompt_type_radio], outputs=[image_to_end])
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advanced_prompt = advanced
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@ -2080,7 +2102,7 @@ def generate_video_tab(image2video=False):
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outputs=[output]
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)
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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(
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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,
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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,
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loras_choices, loras_mult_choices, tea_cache_setting, tea_cache_start_step_perc, RIFLEx_setting, slg_switch, slg_layers,
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slg_start_perc, slg_end_perc ], outputs = [])
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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])
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@ -2182,8 +2204,10 @@ def generate_configuration_tab():
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value=server_config.get("save_path", save_path)
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)
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def check(mode):
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if not mode in attention_modes_supported:
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if not mode in attention_modes_installed:
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return " (NOT INSTALLED)"
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elif not mode in attention_modes_supported:
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return " (NOT SUPPORTED)"
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else:
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return ""
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attention_choice = gr.Dropdown(
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@ -2435,7 +2459,7 @@ def create_demo():
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}
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"""
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with gr.Blocks(css=css, theme=gr.themes.Soft(primary_hue="sky", neutral_hue="slate", text_size="md")) as demo:
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gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v3.0 <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A>)</FONT SIZE=3></H1></div>")
|
||||
gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v3.1 <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</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>")
|
||||
|
||||
with gr.Accordion("Click here for some Info on how to use Wan2GP", open = False):
|
||||
|
|
|
|||
|
|
@ -16,6 +16,6 @@ gradio>=5.0.0
|
|||
numpy>=1.23.5,<2
|
||||
einops
|
||||
moviepy==1.0.3
|
||||
mmgp==3.3.0
|
||||
mmgp==3.3.3
|
||||
peft==0.14.0
|
||||
mutagen
|
||||
|
|
@ -177,7 +177,7 @@ class WanI2V:
|
|||
logging.info(f"Creating WanModel from {model_filename}")
|
||||
from mmgp import offload
|
||||
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel)
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel, writable_tensors= False)
|
||||
self.model.eval().requires_grad_(False)
|
||||
|
||||
if t5_fsdp or dit_fsdp or use_usp:
|
||||
|
|
|
|||
|
|
@ -30,6 +30,7 @@ try:
|
|||
max_seqlen_kv,
|
||||
):
|
||||
return sageattn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_kv, max_seqlen_q, max_seqlen_kv)
|
||||
|
||||
except ImportError:
|
||||
sageattn_varlen_wrapper = None
|
||||
|
||||
|
|
@ -38,11 +39,12 @@ import warnings
|
|||
|
||||
try:
|
||||
from sageattention import sageattn
|
||||
from .sage2_core import sageattn as alt_sageattn
|
||||
from .sage2_core import sageattn as alt_sageattn, is_sage_supported
|
||||
sage_supported = is_sage_supported()
|
||||
except ImportError:
|
||||
sageattn = None
|
||||
alt_sageattn = None
|
||||
|
||||
sage_supported = False
|
||||
# @torch.compiler.disable()
|
||||
def sageattn_wrapper(
|
||||
qkv_list,
|
||||
|
|
@ -129,6 +131,14 @@ def get_attention_modes():
|
|||
|
||||
return ret
|
||||
|
||||
def get_supported_attention_modes():
|
||||
ret = get_attention_modes()
|
||||
if not sage_supported:
|
||||
if "sage" in ret:
|
||||
ret.remove("sage")
|
||||
if "sage2" in ret:
|
||||
ret.remove("sage2")
|
||||
return ret
|
||||
|
||||
__all__ = [
|
||||
'pay_attention',
|
||||
|
|
|
|||
|
|
@ -519,8 +519,11 @@ class CLIPModel:
|
|||
device=device)
|
||||
self.model = self.model.eval().requires_grad_(False)
|
||||
logging.info(f'loading {checkpoint_path}')
|
||||
self.model.load_state_dict(
|
||||
torch.load(checkpoint_path, map_location='cpu'), assign= True)
|
||||
from mmgp import offload
|
||||
# self.model.load_state_dict(
|
||||
# torch.load(checkpoint_path, map_location='cpu'), assign= True)
|
||||
|
||||
offload.load_model_data(self.model, checkpoint_path.replace(".pth", "-bf16.safetensors"), writable_tensors= False)
|
||||
|
||||
# init tokenizer
|
||||
self.tokenizer = HuggingfaceTokenizer(
|
||||
|
|
|
|||
|
|
@ -51,6 +51,15 @@ from sageattention.quant import per_channel_fp8
|
|||
|
||||
from typing import Any, List, Literal, Optional, Tuple, Union
|
||||
import warnings
|
||||
import os
|
||||
|
||||
def is_sage_supported():
|
||||
device_count = torch.cuda.device_count()
|
||||
for i in range(device_count):
|
||||
major, minor = torch.cuda.get_device_capability(i)
|
||||
if major < 8:
|
||||
return False
|
||||
return True
|
||||
|
||||
def get_cuda_arch_versions():
|
||||
cuda_archs = []
|
||||
|
|
|
|||
|
|
@ -496,7 +496,7 @@ class T5EncoderModel:
|
|||
device=device).eval().requires_grad_(False)
|
||||
logging.info(f'loading {checkpoint_path}')
|
||||
from mmgp import offload
|
||||
offload.load_model_data(model,checkpoint_path )
|
||||
offload.load_model_data(model,checkpoint_path, writable_tensors= False )
|
||||
|
||||
self.model = model
|
||||
if shard_fn is not None:
|
||||
|
|
|
|||
|
|
@ -744,11 +744,12 @@ def _video_vae(pretrained_path=None, z_dim=None, device='cpu', **kwargs):
|
|||
with torch.device('meta'):
|
||||
model = WanVAE_(**cfg)
|
||||
|
||||
from mmgp import offload
|
||||
# load checkpoint
|
||||
logging.info(f'loading {pretrained_path}')
|
||||
model.load_state_dict(
|
||||
torch.load(pretrained_path, map_location=device), assign=True)
|
||||
|
||||
# model.load_state_dict(
|
||||
# torch.load(pretrained_path, map_location=device), assign=True)
|
||||
offload.load_model_data(model, pretrained_path.replace(".pth", "_bf16.safetensors"), writable_tensors= False)
|
||||
return model
|
||||
|
||||
|
||||
|
|
@ -778,7 +779,7 @@ class WanVAE:
|
|||
self.model = _video_vae(
|
||||
pretrained_path=vae_pth,
|
||||
z_dim=z_dim,
|
||||
).eval().requires_grad_(False).to(device)
|
||||
).eval() #.requires_grad_(False).to(device)
|
||||
|
||||
def encode(self, videos, tile_size = 256, any_end_frame = False):
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -89,7 +89,7 @@ class WanT2V:
|
|||
from mmgp import offload
|
||||
|
||||
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel)
|
||||
self.model = offload.fast_load_transformers_model(model_filename, modelClass=WanModel, writable_tensors= False)
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue