+
+
+ """
+ return html
+
+def update_generation_status(html_content):
+ if(html_content):
+ return gr.update(value=html_content)
+
+def _parse_args():
+ parser = argparse.ArgumentParser(
+ description="Generate a video from a text prompt or image using Gradio")
+
+ parser.add_argument(
+ "--quantize-transformer",
+ action="store_true",
+ help="On the fly 'transformer' quantization"
+ )
+
+ parser.add_argument(
+ "--share",
+ action="store_true",
+ help="Create a shared URL to access webserver remotely"
+ )
+
+ parser.add_argument(
+ "--lock-config",
+ action="store_true",
+ help="Prevent modifying the configuration from the web interface"
+ )
+
+ parser.add_argument(
+ "--lock-model",
+ action="store_true",
+ help="Prevent switch models"
+ )
+
+ parser.add_argument(
+ "--preload",
+ type=str,
+ default="0",
+ help="Megabytes of the diffusion model to preload in VRAM"
+ )
+
+ parser.add_argument(
+ "--multiple-images",
+ action="store_true",
+ help="Allow inputting multiple images with image to video"
+ )
+
+
+ parser.add_argument(
+ "--lora-dir-i2v",
+ type=str,
+ default="",
+ help="Path to a directory that contains Wan i2v Loras "
+ )
+
+
+ parser.add_argument(
+ "--lora-dir",
+ type=str,
+ default="",
+ help="Path to a directory that contains Wan t2v Loras"
+ )
+
+ parser.add_argument(
+ "--lora-dir-hunyuan",
+ type=str,
+ default="loras_hunyuan",
+ help="Path to a directory that contains Hunyuan Video t2v Loras"
+ )
+
+ parser.add_argument(
+ "--lora-dir-hunyuan-i2v",
+ type=str,
+ default="loras_hunyuan_i2v",
+ help="Path to a directory that contains Hunyuan Video i2v Loras"
+ )
+
+
+ parser.add_argument(
+ "--lora-dir-ltxv",
+ type=str,
+ default="loras_ltxv",
+ help="Path to a directory that contains LTX Videos Loras"
+ )
+
+
+
+ parser.add_argument(
+ "--check-loras",
+ action="store_true",
+ help="Filter Loras that are not valid"
+ )
+
+
+ parser.add_argument(
+ "--lora-preset",
+ type=str,
+ default="",
+ help="Lora preset to preload"
+ )
+
+ parser.add_argument(
+ "--settings",
+ type=str,
+ default="settings",
+ help="Path to settings folder"
+ )
+
+
+ # parser.add_argument(
+ # "--lora-preset-i2v",
+ # type=str,
+ # default="",
+ # help="Lora preset to preload for i2v"
+ # )
+
+ parser.add_argument(
+ "--profile",
+ type=str,
+ default=-1,
+ help="Profile No"
+ )
+
+ parser.add_argument(
+ "--verbose",
+ type=str,
+ default=1,
+ help="Verbose level"
+ )
+
+ parser.add_argument(
+ "--steps",
+ type=int,
+ default=0,
+ help="default denoising steps"
+ )
+
+
+ parser.add_argument(
+ "--teacache",
+ type=float,
+ default=-1,
+ help="teacache speed multiplier"
+ )
+
+ parser.add_argument(
+ "--frames",
+ type=int,
+ default=0,
+ help="default number of frames"
+ )
+
+ parser.add_argument(
+ "--seed",
+ type=int,
+ default=-1,
+ help="default generation seed"
+ )
+
+ parser.add_argument(
+ "--advanced",
+ action="store_true",
+ help="Access advanced options by default"
+ )
+
+ parser.add_argument(
+ "--fp16",
+ action="store_true",
+ help="For using fp16 transformer model"
+ )
+
+ parser.add_argument(
+ "--bf16",
+ action="store_true",
+ help="For using bf16 transformer model"
+ )
+
+ parser.add_argument(
+ "--server-port",
+ type=str,
+ default=0,
+ help="Server port"
+ )
+
+ parser.add_argument(
+ "--theme",
+ type=str,
+ default="",
+ help="set UI Theme"
+ )
+
+ parser.add_argument(
+ "--perc-reserved-mem-max",
+ type=float,
+ default=0,
+ help="% of RAM allocated to Reserved RAM"
+ )
+
+
+
+ parser.add_argument(
+ "--server-name",
+ type=str,
+ default="",
+ help="Server name"
+ )
+ parser.add_argument(
+ "--gpu",
+ type=str,
+ default="",
+ help="Default GPU Device"
+ )
+
+ parser.add_argument(
+ "--open-browser",
+ action="store_true",
+ help="open browser"
+ )
+
+ parser.add_argument(
+ "--t2v",
+ action="store_true",
+ help="text to video mode"
+ )
+
+ parser.add_argument(
+ "--i2v",
+ action="store_true",
+ help="image to video mode"
+ )
+
+ parser.add_argument(
+ "--t2v-14B",
+ action="store_true",
+ help="text to video mode 14B model"
+ )
+
+ parser.add_argument(
+ "--t2v-1-3B",
+ action="store_true",
+ help="text to video mode 1.3B model"
+ )
+
+ parser.add_argument(
+ "--vace-1-3B",
+ action="store_true",
+ help="Vace ControlNet 1.3B model"
+ )
+ parser.add_argument(
+ "--i2v-1-3B",
+ action="store_true",
+ help="Fun InP image to video mode 1.3B model"
+ )
+
+ parser.add_argument(
+ "--i2v-14B",
+ action="store_true",
+ help="image to video mode 14B model"
+ )
+
+
+ parser.add_argument(
+ "--compile",
+ action="store_true",
+ help="Enable pytorch compilation"
+ )
+
+ parser.add_argument(
+ "--listen",
+ action="store_true",
+ help="Server accessible on local network"
+ )
+
+ # parser.add_argument(
+ # "--fast",
+ # action="store_true",
+ # help="use Fast model"
+ # )
+
+ # parser.add_argument(
+ # "--fastest",
+ # action="store_true",
+ # help="activate the best config"
+ # )
+
+ parser.add_argument(
+ "--attention",
+ type=str,
+ default="",
+ help="attention mode"
+ )
+
+ parser.add_argument(
+ "--vae-config",
+ type=str,
+ default="",
+ help="vae config mode"
+ )
+
+ args = parser.parse_args()
+
+ return args
+
+def get_lora_dir(model_filename):
+
+ model_family = get_model_family(model_filename)
+ i2v = test_class_i2v(model_filename)
+ if model_family == "wan":
+ lora_dir =args.lora_dir
+ if i2v and len(lora_dir)==0:
+ lora_dir =args.lora_dir_i2v
+ if len(lora_dir) > 0:
+ return lora_dir
+ root_lora_dir = "loras_i2v" if i2v else "loras"
+
+ if "1.3B" in model_filename :
+ lora_dir_1_3B = os.path.join(root_lora_dir, "1.3B")
+ if os.path.isdir(lora_dir_1_3B ):
+ return lora_dir_1_3B
+ else:
+ lora_dir_14B = os.path.join(root_lora_dir, "14B")
+ if os.path.isdir(lora_dir_14B ):
+ return lora_dir_14B
+ return root_lora_dir
+ elif model_family == "ltxv":
+ return args.lora_dir_ltxv
+ elif model_family =="hunyuan":
+ if i2v:
+ return args.lora_dir_hunyuan_i2v
+ else:
+ return args.lora_dir_hunyuan
+ else:
+ raise Exception("loras unknown")
+
+attention_modes_installed = get_attention_modes()
+attention_modes_supported = get_supported_attention_modes()
+args = _parse_args()
+
+major, minor = torch.cuda.get_device_capability(args.gpu if len(args.gpu) > 0 else None)
+if major < 8:
+ print("Switching to FP16 models when possible as GPU architecture doesn't support optimed BF16 Kernels")
+ bfloat16_supported = False
+else:
+ bfloat16_supported = True
+
+args.flow_reverse = True
+processing_device = args.gpu
+if len(processing_device) == 0:
+ processing_device ="cuda"
+# torch.backends.cuda.matmul.allow_fp16_accumulation = True
+lock_ui_attention = False
+lock_ui_transformer = False
+lock_ui_compile = False
+
+force_profile_no = int(args.profile)
+verbose_level = int(args.verbose)
+quantizeTransformer = args.quantize_transformer
+check_loras = args.check_loras ==1
+advanced = args.advanced
+
+server_config_filename = "wgp_config.json"
+if not os.path.isdir("settings"):
+ os.mkdir("settings")
+if os.path.isfile("t2v_settings.json"):
+ for f in glob.glob(os.path.join(".", "*_settings.json*")):
+ target_file = os.path.join("settings", Path(f).parts[-1] )
+ shutil.move(f, target_file)
+
+if not os.path.isfile(server_config_filename) and os.path.isfile("gradio_config.json"):
+ shutil.move("gradio_config.json", server_config_filename)
+
+if not os.path.isdir("ckpts/umt5-xxl/"):
+ os.makedirs("ckpts/umt5-xxl/")
+src_move = [ "ckpts/models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors", "ckpts/models_t5_umt5-xxl-enc-bf16.safetensors", "ckpts/models_t5_umt5-xxl-enc-quanto_int8.safetensors" ]
+tgt_move = [ "ckpts/xlm-roberta-large/", "ckpts/umt5-xxl/", "ckpts/umt5-xxl/"]
+for src,tgt in zip(src_move,tgt_move):
+ if os.path.isfile(src):
+ try:
+ if os.path.isfile(tgt):
+ shutil.remove(src)
+ else:
+ shutil.move(src, tgt)
+ except:
+ pass
+
+
+if not Path(server_config_filename).is_file():
+ server_config = {"attention_mode" : "auto",
+ "transformer_types": [],
+ "transformer_quantization": "int8",
+ "text_encoder_quantization" : "int8",
+ "save_path": "outputs", #os.path.join(os.getcwd(),
+ "compile" : "",
+ "metadata_type": "metadata",
+ "default_ui": "t2v",
+ "boost" : 1,
+ "clear_file_list" : 5,
+ "vae_config": 0,
+ "profile" : profile_type.LowRAM_LowVRAM,
+ "preload_model_policy": [],
+ "UI_theme": "default" }
+
+ with open(server_config_filename, "w", encoding="utf-8") as writer:
+ writer.write(json.dumps(server_config))
+else:
+ with open(server_config_filename, "r", encoding="utf-8") as reader:
+ text = reader.read()
+ server_config = json.loads(text)
+
+# Deprecated models
+for path in ["wan2.1_Vace_1.3B_preview_bf16.safetensors", "sky_reels2_diffusion_forcing_1.3B_bf16.safetensors","sky_reels2_diffusion_forcing_720p_14B_bf16.safetensors",
+"sky_reels2_diffusion_forcing_720p_14B_quanto_int8.safetensors", "sky_reels2_diffusion_forcing_720p_14B_quanto_fp16_int8.safetensors", "wan2.1_image2video_480p_14B_bf16.safetensors", "wan2.1_image2video_480p_14B_quanto_int8.safetensors",
+"wan2.1_image2video_720p_14B_quanto_int8.safetensors", "wan2.1_image2video_720p_14B_quanto_fp16_int8.safetensors", "wan2.1_image2video_720p_14B_bf16.safetensors"
+]:
+ if Path(os.path.join("ckpts" , path)).is_file():
+ print(f"Removing old version of model '{path}'. A new version of this model will be downloaded next time you use it.")
+ os.remove( os.path.join("ckpts" , path))
+
+
+wan_choices_t2v=["ckpts/wan2.1_text2video_1.3B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_bf16.safetensors", "ckpts/wan2.1_text2video_14B_quanto_int8.safetensors",
+ "ckpts/wan2.1_recammaster_1.3B_bf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_1.3B_mbf16.safetensors", "ckpts/sky_reels2_diffusion_forcing_14B_bf16.safetensors",
+ "ckpts/sky_reels2_diffusion_forcing_14B_quanto_int8.safetensors", "ckpts/sky_reels2_diffusion_forcing_720p_14B_mbf16.safetensors","ckpts/sky_reels2_diffusion_forcing_720p_14B_quanto_mbf16_int8.safetensors",
+ "ckpts/wan2.1_Vace_1.3B_mbf16.safetensors", "ckpts/wan2.1_Vace_14B_mbf16.safetensors", "ckpts/wan2.1_Vace_14B_quanto_mbf16_int8.safetensors",
+ "ckpts/wan2.1_moviigen1.1_14B_mbf16.safetensors", "ckpts/wan2.1_moviigen1.1_14B_quanto_mbf16_int8.safetensors",
+ "ckpts/wan2_1_phantom_1.3B_mbf16.safetensors", "ckpts/wan2.1_phantom_14B_mbf16.safetensors", "ckpts/wan2.1_phantom_14B_quanto_mbf16_int8.safetensors",
+ ]
+wan_choices_i2v=["ckpts/wan2.1_image2video_480p_14B_mbf16.safetensors", "ckpts/wan2.1_image2video_480p_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_image2video_720p_14B_mbf16.safetensors",
+ "ckpts/wan2.1_image2video_720p_14B_quanto_mbf16_int8.safetensors", "ckpts/wan2.1_Fun_InP_1.3B_bf16.safetensors", "ckpts/wan2.1_Fun_InP_14B_bf16.safetensors",
+ "ckpts/wan2.1_Fun_InP_14B_quanto_int8.safetensors", "ckpts/wan2.1_FLF2V_720p_14B_bf16.safetensors", "ckpts/wan2.1_FLF2V_720p_14B_quanto_int8.safetensors",
+ "ckpts/wan2.1_fantasy_speaking_14B_bf16.safetensors"]
+ltxv_choices= ["ckpts/ltxv_0.9.7_13B_dev_bf16.safetensors", "ckpts/ltxv_0.9.7_13B_dev_quanto_bf16_int8.safetensors", "ckpts/ltxv_0.9.7_13B_distilled_lora128_bf16.safetensors"]
+
+hunyuan_choices= ["ckpts/hunyuan_video_720_bf16.safetensors", "ckpts/hunyuan_video_720_quanto_int8.safetensors", "ckpts/hunyuan_video_i2v_720_bf16v2.safetensors", "ckpts/hunyuan_video_i2v_720_quanto_int8v2.safetensors",
+ "ckpts/hunyuan_video_custom_720_bf16.safetensors", "ckpts/hunyuan_video_custom_720_quanto_bf16_int8.safetensors",
+ "ckpts/hunyuan_video_custom_audio_720_bf16.safetensors", "ckpts/hunyuan_video_custom_audio_720_quanto_bf16_int8.safetensors",
+ "ckpts/hunyuan_video_custom_edit_720_bf16.safetensors", "ckpts/hunyuan_video_custom_edit_720_quanto_bf16_int8.safetensors",
+ "ckpts/hunyuan_video_avatar_720_bf16.safetensors", "ckpts/hunyuan_video_avatar_720_quanto_bf16_int8.safetensors",
+ ]
+
+transformer_choices = wan_choices_t2v + wan_choices_i2v + ltxv_choices + hunyuan_choices
+def get_dependent_models(model_filename, quantization, dtype_policy ):
+ if "fantasy" in model_filename:
+ return [get_model_filename("i2v_720p", quantization, dtype_policy)]
+ elif "ltxv_0.9.7_13B_distilled_lora128" in model_filename:
+ return [get_model_filename("ltxv_13B", quantization, dtype_policy)]
+ else:
+ return []
+model_types = [ "t2v_1.3B", "t2v", "i2v", "i2v_720p", "flf2v_720p", "vace_1.3B","vace_14B","moviigen", "phantom_1.3B", "phantom_14B", "fantasy", "fun_inp_1.3B", "fun_inp", "recam_1.3B", "sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B", "ltxv_13B", "ltxv_13B_distilled", "hunyuan", "hunyuan_i2v", "hunyuan_custom", "hunyuan_custom_audio", "hunyuan_custom_edit", "hunyuan_avatar"]
+model_signatures = {"t2v": "text2video_14B", "t2v_1.3B" : "text2video_1.3B", "fun_inp_1.3B" : "Fun_InP_1.3B", "fun_inp" : "Fun_InP_14B",
+ "i2v" : "image2video_480p", "i2v_720p" : "image2video_720p" , "vace_1.3B" : "Vace_1.3B", "vace_14B" : "Vace_14B","recam_1.3B": "recammaster_1.3B",
+ "flf2v_720p" : "FLF2V_720p", "sky_df_1.3B" : "sky_reels2_diffusion_forcing_1.3B", "sky_df_14B" : "sky_reels2_diffusion_forcing_14B",
+ "sky_df_720p_14B" : "sky_reels2_diffusion_forcing_720p_14B", "moviigen" :"moviigen",
+ "phantom_1.3B" : "phantom_1.3B", "phantom_14B" : "phantom_14B", "fantasy" : "fantasy", "ltxv_13B" : "ltxv_0.9.7_13B_dev", "ltxv_13B_distilled" : "ltxv_0.9.7_13B_distilled",
+ "hunyuan" : "hunyuan_video_720", "hunyuan_i2v" : "hunyuan_video_i2v_720", "hunyuan_custom" : "hunyuan_video_custom_720", "hunyuan_custom_audio" : "hunyuan_video_custom_audio", "hunyuan_custom_edit" : "hunyuan_video_custom_edit",
+ "hunyuan_avatar" : "hunyuan_video_avatar" }
+
+
+def get_model_type(model_filename):
+ for model_type, signature in model_signatures.items():
+ if signature in model_filename:
+ return model_type
+ raise Exception("Unknown model:" + model_filename)
+
+def get_model_family(model_filename):
+ if "wan" in model_filename or "sky" in model_filename:
+ return "wan"
+ elif "ltxv" in model_filename:
+ return "ltxv"
+ elif "hunyuan" in model_filename:
+ return "hunyuan"
+ else:
+ raise Exception(f"Unknown model family for model'{model_filename}'")
+
+def test_class_i2v(model_filename):
+ return "image2video" in model_filename or "Fun_InP" in model_filename or "FLF2V" in model_filename or "fantasy" in model_filename or "hunyuan_video_i2v" in model_filename
+
+def get_model_name(model_filename, description_container = [""]):
+ if "Fun" in model_filename:
+ model_name = "Fun InP image2video"
+ model_name += " 14B" if "14B" in model_filename else " 1.3B"
+ description = "The Fun model is an alternative image 2 video that supports out the box End Image fixing (contrary to the original Wan image 2 video model). The 1.3B adds also image 2 to video capability to the 1.3B model."
+ elif "Vace" in model_filename:
+ model_name = "Vace ControlNet"
+ model_name += " 14B" if "14B" in model_filename else " 1.3B"
+ description = "The Vace ControlNet model is a powerful model that allows you to control the content of the generated video based of additional custom data : pose or depth video, images or objects you want to see in the video."
+ elif "image" in model_filename:
+ model_name = "Wan2.1 image2video"
+ model_name += " 720p" if "720p" in model_filename else " 480p"
+ model_name += " 14B"
+ if "720p" in model_filename:
+ description = "The standard Wan Image 2 Video specialized to generate 720p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)"
+ else:
+ description = "The standard Wan Image 2 Video specialized to generate 480p images. It also offers Start and End Image support (End Image is not supported in the original model but seems to work well)"
+ elif "recam" in model_filename:
+ model_name = "ReCamMaster"
+ model_name += " 14B" if "14B" in model_filename else " 1.3B"
+ description = "The Recam Master in theory should allow you to replay a video by applying a different camera movement. The model supports only video that are at least 81 frames long (any frame beyond will be ignored)"
+ elif "FLF2V" in model_filename:
+ model_name = "Wan2.1 FLF2V"
+ model_name += " 720p" if "720p" in model_filename else " 480p"
+ model_name += " 14B"
+ description = "The First Last Frame 2 Video model is the official model Image 2 Video model that support Start and End frames."
+ elif "sky_reels2_diffusion_forcing" in model_filename:
+ model_name = "SkyReels2 Diffusion Forcing"
+ if "720p" in model_filename :
+ model_name += " 720p"
+ elif not "1.3B" in model_filename :
+ model_name += " 540p"
+ model_name += " 14B" if "14B" in model_filename else " 1.3B"
+ description = "The SkyReels 2 Diffusion Forcing model has been designed to generate very long videos that exceeds the usual 5s limit. You can also use this model to extend any existing video."
+ elif "phantom" in model_filename:
+ model_name = "Wan2.1 Phantom"
+ if "14B" in model_filename:
+ model_name += " 14B"
+ description = "The Phantom model is specialized to transfer people or objects of your choice into a generated Video. It seems to produce better results if you keep the original background of the Image Referendes."
+ else:
+ model_name += " 1.3B"
+ description = "The Phantom model is specialized to transfer people or objects of your choice into a generated Video. It produces very nice results when used at 720p."
+ elif "fantasy" in model_filename:
+ model_name = "Wan2.1 Fantasy Speaking 720p"
+ model_name += " 14B" if "14B" in model_filename else " 1.3B"
+ description = "The Fantasy Speaking model corresponds to the original Wan image 2 video model combined with the Fantasy Speaking extension to process an audio Input."
+ elif "movii" in model_filename:
+ model_name = "Wan2.1 MoviiGen 1080p 14B"
+ description = "MoviiGen 1.1, a cutting-edge video generation model that excels in cinematic aesthetics and visual quality. Use it to generate videos in 720p or 1080p in the 21:9 ratio."
+ elif "ltxv_0.9.7_13B_dev" in model_filename:
+ model_name = "LTX Video 0.9.7 13B"
+ description = "LTX Video is a fast model that can be used to generate long videos (up to 260 frames).It is recommended to keep the number of steps to 30 or you will need to update the file 'ltxv_video/configs/ltxv-13b-0.9.7-dev.yaml'.The LTX Video model expects very long prompts, so don't hesitate to use the Prompt Enhancer."
+ elif "ltxv_0.9.7_13B_distilled" in model_filename:
+ model_name = "LTX Video 0.9.7 Distilled 13B"
+ description = "LTX Video is a fast model that can be used to generate long videos (up to 260 frames).This distilled version is a very fast version and retains a high level of quality. The LTX Video model expects very long prompts, so don't hesitate to use the Prompt Enhancer."
+ elif "hunyuan_video_720" in model_filename:
+ model_name = "Hunyuan Video text2video 720p 13B"
+ description = "Probably the best text 2 video model available."
+ elif "hunyuan_video_i2v" in model_filename:
+ model_name = "Hunyuan Video image2video 720p 13B"
+ description = "A good looking image 2 video model, but not so good in prompt adherence."
+ elif "hunyuan_video_custom" in model_filename:
+ if "audio" in model_filename:
+ model_name = "Hunyuan Video Custom Audio 720p 13B"
+ description = "The Hunyuan Video Custom Audio model can be used to generate scenes of a person speaking given a Reference Image and a Recorded Voice or Song. The reference image is not a start image and therefore one can represent the person in a different context.The video length can be anything up to 10s. It is also quite good to generate no sound Video based on a person."
+ elif "edit" in model_filename:
+ model_name = "Hunyuan Video Custom Edit 720p 13B"
+ description = "The Hunyuan Video Custom Edit model can be used to do Video inpainting on a person (add accessories or completely replace the person). You will need in any case to define a Video Mask which will indicate which area of the Video should be edited."
+ else:
+ model_name = "Hunyuan Video Custom 720p 13B"
+ description = "The Hunyuan Video Custom model is probably the best model to transfer people (only people for the momment) as it is quite good to keep their identity. However it is slow as to get good results, you need to generate 720p videos with 30 steps."
+ elif "hunyuan_video_avatar" in model_filename:
+ model_name = "Hunyuan Video Avatar 720p 13B"
+ description = "With the Hunyuan Video Avatar model you can animate a person based on the content of an audio input. Please note that the video generator works by processing 128 frames segment at a time (even if you ask less). The good news is that it will concatenate multiple segments for long video generation (max 3 segments recommended as the quality will get worse)."
+ else:
+ model_name = "Wan2.1 text2video"
+ model_name += " 14B" if "14B" in model_filename else " 1.3B"
+ description = "The original Wan Text 2 Video model. Most other models have been built on top of it"
+ description_container[0] = description
+ return model_name
+
+
+def get_model_filename(model_type, quantization ="int8", dtype_policy = ""):
+ signature = model_signatures[model_type]
+ choices = [ name for name in transformer_choices if signature in name]
+ if len(quantization) == 0:
+ quantization = "bf16"
+
+ model_family = get_model_family(choices[0])
+ dtype = get_transformer_dtype(model_family, dtype_policy)
+ if len(choices) <= 1:
+ raw_filename = choices[0]
+ else:
+ sub_choices = [ name for name in choices if quantization in name]
+ if len(sub_choices) > 0:
+ dtype_str = "fp16" if dtype == torch.float16 else "bf16"
+ new_sub_choices = [ name for name in sub_choices if dtype_str in name]
+ sub_choices = new_sub_choices if len(new_sub_choices) > 0 else sub_choices
+ raw_filename = sub_choices[0]
+ else:
+ raw_filename = choices[0]
+
+ if dtype == torch.float16 and not "fp16" in raw_filename and model_family == "wan" :
+ if "quanto_int8" in raw_filename:
+ raw_filename = raw_filename.replace("quanto_int8", "quanto_fp16_int8")
+ elif "quanto_bf16_int8" in raw_filename:
+ raw_filename = raw_filename.replace("quanto_bf16_int8", "quanto_fp16_int8")
+ elif "quanto_mbf16_int8" in raw_filename:
+ raw_filename= raw_filename.replace("quanto_mbf16_int8", "quanto_mfp16_int8")
+ return raw_filename
+
+def get_transformer_dtype(model_family, transformer_dtype_policy):
+ if len(transformer_dtype_policy) == 0:
+ if not bfloat16_supported:
+ return torch.float16
+ else:
+ if model_family == "wan"and False:
+ return torch.float16
+ else:
+ return torch.bfloat16
+ return transformer_dtype
+ elif transformer_dtype_policy =="fp16":
+ return torch.float16
+ else:
+ return torch.bfloat16
+
+def get_settings_file_name(model_filename):
+ return os.path.join(args.settings, get_model_type(model_filename) + "_settings.json")
+
+def get_default_settings(filename):
+ def get_default_prompt(i2v):
+ if i2v:
+ return "Several giant wooly mammoths approach treading through a snowy meadow, their long wooly fur lightly blows in the wind as they walk, snow covered trees and dramatic snow capped mountains in the distance, mid afternoon light with wispy clouds and a sun high in the distance creates a warm glow, the low camera view is stunning capturing the large furry mammal with beautiful photography, depth of field."
+ else:
+ return "A large orange octopus is seen resting on the bottom of the ocean floor, blending in with the sandy and rocky terrain. Its tentacles are spread out around its body, and its eyes are closed. The octopus is unaware of a king crab that is crawling towards it from behind a rock, its claws raised and ready to attack. The crab is brown and spiny, with long legs and antennae. The scene is captured from a wide angle, showing the vastness and depth of the ocean. The water is clear and blue, with rays of sunlight filtering through. The shot is sharp and crisp, with a high dynamic range. The octopus and the crab are in focus, while the background is slightly blurred, creating a depth of field effect."
+ i2v = test_class_i2v(filename)
+ defaults_filename = get_settings_file_name(filename)
+ if not Path(defaults_filename).is_file():
+ ui_defaults = {
+ "prompt": get_default_prompt(i2v),
+ "resolution": "1280x720" if "720p" in filename else "832x480",
+ "video_length": 81,
+ "num_inference_steps": 30,
+ "seed": -1,
+ "repeat_generation": 1,
+ "multi_images_gen_type": 0,
+ "guidance_scale": 5.0,
+ "embedded_guidance_scale" : 6.0,
+ "audio_guidance_scale": 5.0,
+ "flow_shift": get_default_flow(filename, i2v),
+ "negative_prompt": "",
+ "activated_loras": [],
+ "loras_multipliers": "",
+ "tea_cache": 0.0,
+ "tea_cache_start_step_perc": 0,
+ "RIFLEx_setting": 0,
+ "slg_switch": 0,
+ "slg_layers": [9],
+ "slg_start_perc": 10,
+ "slg_end_perc": 90
+ }
+
+ if get_model_type(filename) in ("hunyuan","hunyuan_i2v"):
+ ui_defaults.update({
+ "guidance_scale": 7.0,
+ })
+
+ if get_model_type(filename) in ("sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B"):
+ ui_defaults.update({
+ "guidance_scale": 6.0,
+ "flow_shift": 8,
+ "sliding_window_discard_last_frames" : 0,
+ "resolution": "1280x720" if "720p" in filename else "960x544",
+ "sliding_window_size" : 121 if "720p" in filename else 97,
+ "RIFLEx_setting": 2,
+ "guidance_scale": 6,
+ "flow_shift": 8,
+ })
+
+
+ if get_model_type(filename) in ("phantom_1.3B", "phantom_14B"):
+ ui_defaults.update({
+ "guidance_scale": 7.5,
+ "flow_shift": 5,
+ "remove_background_images_ref": 0,
+ # "resolution": "1280x720"
+ })
+
+ elif get_model_type(filename) in ("hunyuan_custom"):
+ ui_defaults.update({
+ "guidance_scale": 7.5,
+ "flow_shift": 13,
+ "resolution": "1280x720",
+ })
+ elif get_model_type(filename) in ("hunyuan_custom_edit"):
+ ui_defaults.update({
+ "guidance_scale": 7.5,
+ "flow_shift": 13,
+ "video_prompt_type": "MV",
+ "sliding_window_size": 129,
+ })
+ elif get_model_type(filename) in ("hunyuan_avatar"):
+ ui_defaults.update({
+ "guidance_scale": 7.5,
+ "flow_shift": 5,
+ "tea_cache_start_step_perc": 25,
+ "video_length": 129,
+ "video_prompt_type": "I",
+ })
+ elif get_model_type(filename) in ("vace_14B"):
+ ui_defaults.update({
+ "sliding_window_discard_last_frames": 0,
+ })
+
+
+ with open(defaults_filename, "w", encoding="utf-8") as f:
+ json.dump(ui_defaults, f, indent=4)
+ else:
+ with open(defaults_filename, "r", encoding="utf-8") as f:
+ ui_defaults = json.load(f)
+ prompts = ui_defaults.get("prompts", "")
+ if len(prompts) > 0:
+ ui_defaults["prompt"] = prompts
+ image_prompt_type = ui_defaults.get("image_prompt_type", None)
+ if image_prompt_type !=None and not isinstance(image_prompt_type, str):
+ ui_defaults["image_prompt_type"] = "S" if image_prompt_type == 0 else "SE"
+
+ default_seed = args.seed
+ if default_seed > -1:
+ ui_defaults["seed"] = default_seed
+ default_number_frames = args.frames
+ if default_number_frames > 0:
+ ui_defaults["video_length"] = default_number_frames
+ default_number_steps = args.steps
+ if default_number_steps > 0:
+ ui_defaults["num_inference_steps"] = default_number_steps
+ return ui_defaults
+
+transformer_types = server_config.get("transformer_types", [])
+transformer_type = transformer_types[0] if len(transformer_types) > 0 else model_types[0]
+
+transformer_quantization =server_config.get("transformer_quantization", "int8")
+
+transformer_dtype_policy = server_config.get("transformer_dtype_policy", "")
+if args.fp16:
+ transformer_dtype_policy = "fp16"
+if args.bf16:
+ transformer_dtype_policy = "bf16"
+transformer_filename = get_model_filename(transformer_type, transformer_quantization, transformer_dtype_policy)
+text_encoder_quantization =server_config.get("text_encoder_quantization", "int8")
+attention_mode = server_config["attention_mode"]
+if len(args.attention)> 0:
+ if args.attention in ["auto", "sdpa", "sage", "sage2", "flash", "xformers"]:
+ attention_mode = args.attention
+ lock_ui_attention = True
+ else:
+ raise Exception(f"Unknown attention mode '{args.attention}'")
+
+profile = force_profile_no if force_profile_no >=0 else server_config["profile"]
+compile = server_config.get("compile", "")
+boost = server_config.get("boost", 1)
+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")
+save_path = server_config.get("save_path", os.path.join(os.getcwd(), "gradio_outputs"))
+preload_model_policy = server_config.get("preload_model_policy", [])
+
+
+if args.t2v_14B or args.t2v:
+ transformer_filename = get_model_filename("t2v", transformer_quantization, transformer_dtype_policy)
+
+if args.i2v_14B or args.i2v:
+ transformer_filename = get_model_filename("i2v", transformer_quantization, transformer_dtype_policy)
+
+if args.t2v_1_3B:
+ transformer_filename = get_model_filename("t2v_1.3B", transformer_quantization, transformer_dtype_policy)
+
+if args.i2v_1_3B:
+ transformer_filename = get_model_filename("fun_inp_1.3B", transformer_quantization, transformer_dtype_policy)
+
+if args.vace_1_3B:
+ transformer_filename = get_model_filename("vace_1.3B", transformer_quantization, transformer_dtype_policy)
+
+only_allow_edit_in_advanced = False
+lora_preselected_preset = args.lora_preset
+lora_preset_model = transformer_filename
+
+if args.compile: #args.fastest or
+ compile="transformer"
+ lock_ui_compile = True
+
+model_filename = ""
+#attention_mode="sage"
+#attention_mode="sage2"
+#attention_mode="flash"
+#attention_mode="sdpa"
+#attention_mode="xformers"
+# compile = "transformer"
+
+def get_loras_preprocessor(transformer, model_filename):
+ preprocessor = getattr(transformer, "preprocess_loras", None)
+ if preprocessor == None:
+ return None
+
+ def preprocessor_wrapper(sd):
+ return preprocessor(model_filename, sd)
+
+ return preprocessor_wrapper
+
+
+def get_model_manager(model_family):
+ if model_family == "wan":
+ return None
+ elif model_family == "ltxv":
+ from ltxv import model_def
+ return model_def
+ else:
+ raise Exception("model family not supported")
+
+def get_wan_text_encoder_filename(text_encoder_quantization):
+ text_encoder_filename = "ckpts/umt5-xxl/models_t5_umt5-xxl-enc-bf16.safetensors"
+ if text_encoder_quantization =="int8":
+ text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_int8")
+ return text_encoder_filename
+
+def get_ltxv_text_encoder_filename(text_encoder_quantization):
+ text_encoder_filename = "ckpts/T5_xxl_1.1/T5_xxl_1.1_enc_bf16.safetensors"
+ if text_encoder_quantization =="int8":
+ text_encoder_filename = text_encoder_filename.replace("bf16", "quanto_bf16_int8")
+ return text_encoder_filename
+
+def get_hunyuan_text_encoder_filename(text_encoder_quantization):
+ if text_encoder_quantization =="int8":
+ text_encoder_filename = "ckpts/llava-llama-3-8b/llava-llama-3-8b-v1_1_vlm_quanto_int8.safetensors"
+ else:
+ text_encoder_filename = "ckpts/llava-llama-3-8b/llava-llama-3-8b-v1_1_vlm_fp16.safetensors"
+
+ return text_encoder_filename
+
+
+def download_models(transformer_filename):
+ def computeList(filename):
+ pos = filename.rfind("/")
+ filename = filename[pos+1:]
+ return [filename]
+
+ def process_files_def(repoId, sourceFolderList, fileList):
+ targetRoot = "ckpts/"
+ for sourceFolder, files in zip(sourceFolderList,fileList ):
+ if len(files)==0:
+ if not Path(targetRoot + sourceFolder).exists():
+ snapshot_download(repo_id=repoId, allow_patterns=sourceFolder +"/*", local_dir= targetRoot)
+ else:
+ for onefile in files:
+ if len(sourceFolder) > 0:
+ if not os.path.isfile(targetRoot + sourceFolder + "/" + onefile ):
+ hf_hub_download(repo_id=repoId, filename=onefile, local_dir = targetRoot, subfolder=sourceFolder)
+ else:
+ if not os.path.isfile(targetRoot + onefile ):
+ hf_hub_download(repo_id=repoId, filename=onefile, local_dir = targetRoot)
+
+ from huggingface_hub import hf_hub_download, snapshot_download
+
+ shared_def = {
+ "repoId" : "DeepBeepMeep/Wan2.1",
+ "sourceFolderList" : [ "pose", "depth", "mask", "wav2vec", "" ],
+ "fileList" : [ [],[], ["sam_vit_h_4b8939_fp16.safetensors"], ["config.json", "feature_extractor_config.json", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer_config.json", "vocab.json"],
+ [ "flownet.pkl" ] ]
+ }
+ process_files_def(**shared_def)
+
+
+ if server_config.get("enhancer_enabled", 0) == 1:
+ enhancer_def = {
+ "repoId" : "DeepBeepMeep/LTX_Video",
+ "sourceFolderList" : [ "Florence2", "Llama3_2" ],
+ "fileList" : [ ["config.json", "configuration_florence2.py", "model.safetensors", "modeling_florence2.py", "preprocessor_config.json", "processing_florence2.py", "tokenizer.json", "tokenizer_config.json"],["config.json", "generation_config.json", "Llama3_2_quanto_bf16_int8.safetensors", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json"] ]
+ }
+ process_files_def(**enhancer_def)
+
+
+ model_family = get_model_family(transformer_filename)
+ if model_family == "wan":
+ text_encoder_filename = get_wan_text_encoder_filename(text_encoder_quantization)
+ model_def = {
+ "repoId" : "DeepBeepMeep/Wan2.1",
+ "sourceFolderList" : ["xlm-roberta-large", "umt5-xxl", "" ],
+ "fileList" : [ [ "models_clip_open-clip-xlm-roberta-large-vit-huge-14-bf16.safetensors", "sentencepiece.bpe.model", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json"], ["special_tokens_map.json", "spiece.model", "tokenizer.json", "tokenizer_config.json"] + computeList(text_encoder_filename) , ["Wan2.1_VAE.safetensors", "fantasy_proj_model.safetensors" ] + computeList(transformer_filename) ]
+ }
+ elif model_family == "ltxv":
+ text_encoder_filename = get_ltxv_text_encoder_filename(text_encoder_quantization)
+ model_def = {
+ "repoId" : "DeepBeepMeep/LTX_Video",
+ "sourceFolderList" : ["T5_xxl_1.1", "" ],
+ "fileList" : [ ["added_tokens.json", "special_tokens_map.json", "spiece.model", "tokenizer_config.json"] + computeList(text_encoder_filename), ["ltxv_0.9.7_VAE.safetensors", "ltxv_0.9.7_spatial_upscaler.safetensors", "ltxv_scheduler.json"] + computeList(transformer_filename) ]
+ }
+ elif model_family == "hunyuan":
+ text_encoder_filename = get_hunyuan_text_encoder_filename(text_encoder_quantization)
+ model_def = {
+ "repoId" : "DeepBeepMeep/HunyuanVideo",
+ "sourceFolderList" : [ "llava-llama-3-8b", "clip_vit_large_patch14", "whisper-tiny" , "det_align", "" ],
+ "fileList" :[ ["config.json", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "preprocessor_config.json"] + computeList(text_encoder_filename) ,
+ ["config.json", "merges.txt", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "vocab.json"],
+ ["config.json", "model.safetensors", "preprocessor_config.json", "special_tokens_map.json", "tokenizer_config.json"],
+ ["detface.pt"],
+ [ "hunyuan_video_720_quanto_int8_map.json", "hunyuan_video_custom_VAE_fp32.safetensors", "hunyuan_video_custom_VAE_config.json", "hunyuan_video_VAE_fp32.safetensors", "hunyuan_video_VAE_config.json" , "hunyuan_video_720_quanto_int8_map.json" ] + computeList(transformer_filename)
+ ]
+ }
+
+ else:
+ model_manager = get_model_manager(model_family)
+ model_def = model_manager.get_files_def(transformer_filename, text_encoder_quantization)
+
+ process_files_def(**model_def)
+
+
+offload.default_verboseLevel = verbose_level
+
+
+# download_models(transformer_filename)
+
+def sanitize_file_name(file_name, rep =""):
+ return file_name.replace("/",rep).replace("\\",rep).replace(":",rep).replace("|",rep).replace("?",rep).replace("<",rep).replace(">",rep).replace("\"",rep).replace("\n",rep).replace("\r",rep)
+
+def extract_preset(model_filename, lset_name, loras):
+ loras_choices = []
+ loras_choices_files = []
+ loras_mult_choices = ""
+ prompt =""
+ full_prompt =""
+ lset_name = sanitize_file_name(lset_name)
+ lora_dir = get_lora_dir(model_filename)
+ if not lset_name.endswith(".lset"):
+ lset_name_filename = os.path.join(lora_dir, lset_name + ".lset" )
+ else:
+ lset_name_filename = os.path.join(lora_dir, lset_name )
+ error = ""
+ if not os.path.isfile(lset_name_filename):
+ error = f"Preset '{lset_name}' not found "
+ else:
+ missing_loras = []
+
+ with open(lset_name_filename, "r", encoding="utf-8") as reader:
+ text = reader.read()
+ lset = json.loads(text)
+
+ loras_choices_files = lset["loras"]
+ for lora_file in loras_choices_files:
+ choice = os.path.join(lora_dir, lora_file)
+ if choice not in loras:
+ missing_loras.append(lora_file)
+ else:
+ loras_choice_no = loras.index(choice)
+ loras_choices.append(str(loras_choice_no))
+
+ if len(missing_loras) > 0:
+ error = f"Unable to apply Lora preset '{lset_name} because the following Loras files are missing or invalid: {missing_loras}"
+
+ loras_mult_choices = lset["loras_mult"]
+ prompt = lset.get("prompt", "")
+ full_prompt = lset.get("full_prompt", False)
+ return loras_choices, loras_mult_choices, prompt, full_prompt, error
+
+
+
+def setup_loras(model_filename, transformer, lora_dir, lora_preselected_preset, split_linear_modules_map = None):
+ loras =[]
+ loras_names = []
+ default_loras_choices = []
+ default_loras_multis_str = ""
+ loras_presets = []
+ default_lora_preset = ""
+ default_lora_preset_prompt = ""
+
+ from pathlib import Path
+
+ lora_dir = get_lora_dir(model_filename)
+ if lora_dir != None :
+ if not os.path.isdir(lora_dir):
+ raise Exception("--lora-dir should be a path to a directory that contains Loras")
+
+
+ if lora_dir != None:
+ dir_loras = glob.glob( os.path.join(lora_dir , "*.sft") ) + glob.glob( os.path.join(lora_dir , "*.safetensors") )
+ dir_loras.sort()
+ loras += [element for element in dir_loras if element not in loras ]
+
+ dir_presets = glob.glob( os.path.join(lora_dir , "*.lset") )
+ dir_presets.sort()
+ loras_presets = [ Path(Path(file_path).parts[-1]).stem for file_path in dir_presets]
+
+ if transformer !=None:
+ loras = offload.load_loras_into_model(transformer, loras, activate_all_loras=False, check_only= True, preprocess_sd=get_loras_preprocessor(transformer, model_filename), split_linear_modules_map = split_linear_modules_map) #lora_multiplier,
+
+ if len(loras) > 0:
+ loras_names = [ Path(lora).stem for lora in loras ]
+
+ if len(lora_preselected_preset) > 0:
+ if not os.path.isfile(os.path.join(lora_dir, lora_preselected_preset + ".lset")):
+ raise Exception(f"Unknown preset '{lora_preselected_preset}'")
+ default_lora_preset = lora_preselected_preset
+ default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, _ , error = extract_preset(model_filename, default_lora_preset, loras)
+ if len(error) > 0:
+ print(error[:200])
+ return loras, loras_names, loras_presets, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset
+
+
+def load_wan_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
+ filename = model_filename[-1]
+ print(f"Loading '{filename}' model...")
+
+ if test_class_i2v(model_filename[0]):
+ cfg = WAN_CONFIGS['i2v-14B']
+ model_factory = wan.WanI2V
+ else:
+ cfg = WAN_CONFIGS['t2v-14B']
+ # cfg = WAN_CONFIGS['t2v-1.3B']
+ if get_model_type(filename) in ("sky_df_1.3B", "sky_df_14B", "sky_df_720p_14B"):
+ model_factory = wan.DTT2V
+ else:
+ model_factory = wan.WanT2V
+
+ wan_model = model_factory(
+ config=cfg,
+ checkpoint_dir="ckpts",
+ model_filename=model_filename,
+ text_encoder_filename= get_wan_text_encoder_filename(text_encoder_quantization),
+ quantizeTransformer = quantizeTransformer,
+ dtype = dtype,
+ VAE_dtype = VAE_dtype,
+ mixed_precision_transformer = mixed_precision_transformer
+ )
+
+ pipe = {"transformer": wan_model.model, "text_encoder" : wan_model.text_encoder.model, "vae": wan_model.vae.model }
+ if hasattr(wan_model, "clip"):
+ pipe["text_encoder_2"] = wan_model.clip.model
+ return wan_model, pipe
+
+def load_ltxv_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
+ filename = model_filename[-1]
+ print(f"Loading '{filename}' model...")
+ from ltx_video.ltxv import LTXV
+
+ ltxv_model = LTXV(
+ model_filepath = model_filename,
+ text_encoder_filepath = get_ltxv_text_encoder_filename(text_encoder_quantization),
+ dtype = dtype,
+ # quantizeTransformer = quantizeTransformer,
+ VAE_dtype = VAE_dtype,
+ mixed_precision_transformer = mixed_precision_transformer
+ )
+
+ pipeline = ltxv_model.pipeline
+ pipe = {"transformer" : pipeline.video_pipeline.transformer, "vae" : pipeline.vae, "text_encoder" : pipeline.video_pipeline.text_encoder, "latent_upsampler" : pipeline.latent_upsampler}
+
+ return ltxv_model, pipe
+
+def load_hunyuan_model(model_filename, quantizeTransformer = False, dtype = torch.bfloat16, VAE_dtype = torch.float32, mixed_precision_transformer = False):
+ filename = model_filename[-1]
+ print(f"Loading '{filename}' model...")
+ from hyvideo.hunyuan import HunyuanVideoSampler
+
+ hunyuan_model = HunyuanVideoSampler.from_pretrained(
+ model_filepath = model_filename,
+ text_encoder_filepath = get_hunyuan_text_encoder_filename(text_encoder_quantization),
+ dtype = dtype,
+ # quantizeTransformer = quantizeTransformer,
+ VAE_dtype = VAE_dtype,
+ mixed_precision_transformer = mixed_precision_transformer
+ )
+
+ pipe = { "transformer" : hunyuan_model.model, "text_encoder" : hunyuan_model.text_encoder, "text_encoder_2" : hunyuan_model.text_encoder_2, "vae" : hunyuan_model.vae }
+
+ if hunyuan_model.wav2vec != None:
+ pipe["wav2vec"] = hunyuan_model.wav2vec
+
+
+ # if hunyuan_model.align_instance != None:
+ # pipe["align_instance"] = hunyuan_model.align_instance.facedet.model
+
+
+ from hyvideo.modules.models import get_linear_split_map
+
+ split_linear_modules_map = get_linear_split_map()
+ hunyuan_model.model.split_linear_modules_map = split_linear_modules_map
+ offload.split_linear_modules(hunyuan_model.model, split_linear_modules_map )
+
+
+ return hunyuan_model, pipe
+
+def get_transformer_model(model):
+ if hasattr(model, "model"):
+ return model.model
+ elif hasattr(model, "transformer"):
+ return model.transformer
+ else:
+ raise Exception("no transformer found")
+
+
+def load_models(model_filename):
+ global transformer_filename, transformer_loras_filenames
+ model_family = get_model_family(model_filename)
+ perc_reserved_mem_max = args.perc_reserved_mem_max
+ preload =int(args.preload)
+ if preload == 0:
+ preload = server_config.get("preload_in_VRAM", 0)
+ new_transformer_loras_filenames = None
+ dependent_models = get_dependent_models(model_filename, quantization= transformer_quantization, dtype_policy = transformer_dtype_policy)
+ new_transformer_loras_filenames = [model_filename] if "_lora" in model_filename else None
+ model_filelist = dependent_models + [model_filename]
+ for filename in model_filelist:
+ download_models(filename)
+ transformer_dtype = get_transformer_dtype(model_family, transformer_dtype_policy)
+ VAE_dtype = torch.float16 if server_config.get("vae_precision","16") == "16" else torch.float
+ mixed_precision_transformer = server_config.get("mixed_precision","0") == "1"
+ transformer_filename = None
+ transformer_loras_filenames = None
+ new_transformer_filename = model_filelist[-1]
+ if model_family == "wan" :
+ wan_model, pipe = load_wan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
+ elif model_family == "ltxv":
+ wan_model, pipe = load_ltxv_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
+ elif model_family == "hunyuan":
+ wan_model, pipe = load_hunyuan_model(model_filelist, quantizeTransformer = quantizeTransformer, dtype = transformer_dtype, VAE_dtype = VAE_dtype, mixed_precision_transformer = mixed_precision_transformer)
+ else:
+ raise Exception(f"Model '{new_transformer_filename}' not supported.")
+ wan_model._model_file_name = new_transformer_filename
+ kwargs = { "extraModelsToQuantize": None }
+ if profile in (2, 4, 5):
+ kwargs["budgets"] = { "transformer" : 100 if preload == 0 else preload, "text_encoder" : 100 if preload == 0 else preload, "*" : max(1000 if profile==5 else 3000 , preload) }
+ elif profile == 3:
+ kwargs["budgets"] = { "*" : "70%" }
+
+ global prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer
+ if server_config.get("enhancer_enabled", 0) == 1:
+ from transformers import ( AutoModelForCausalLM, AutoProcessor, AutoTokenizer, LlamaForCausalLM )
+ prompt_enhancer_image_caption_model = AutoModelForCausalLM.from_pretrained( "ckpts/Florence2", trust_remote_code=True)
+ prompt_enhancer_image_caption_processor = AutoProcessor.from_pretrained( "ckpts/Florence2", trust_remote_code=True)
+ prompt_enhancer_llm_model = offload.fast_load_transformers_model("ckpts/Llama3_2/Llama3_2_quanto_bf16_int8.safetensors") #, configKwargs= {"_attn_implementation" :"XXXsdpa"}
+ prompt_enhancer_llm_tokenizer = AutoTokenizer.from_pretrained("ckpts/Llama3_2")
+ pipe["prompt_enhancer_image_caption_model"] = prompt_enhancer_image_caption_model
+ pipe["prompt_enhancer_llm_model"] = prompt_enhancer_llm_model
+ prompt_enhancer_image_caption_model._model_dtype = torch.float
+ if "budgets" in kwargs:
+ kwargs["budgets"]["prompt_enhancer_llm_model"] = 5000
+ else:
+ prompt_enhancer_image_caption_model = None
+ prompt_enhancer_image_caption_processor = None
+ prompt_enhancer_llm_model = None
+ prompt_enhancer_llm_tokenizer = None
+
+
+ offloadobj = offload.profile(pipe, profile_no= profile, compile = compile, quantizeTransformer = quantizeTransformer, loras = "transformer", coTenantsMap= {}, perc_reserved_mem_max = perc_reserved_mem_max , convertWeightsFloatTo = transformer_dtype, **kwargs)
+ if len(args.gpu) > 0:
+ torch.set_default_device(args.gpu)
+ transformer_filename = new_transformer_filename
+ transformer_loras_filenames = new_transformer_loras_filenames
+ return wan_model, offloadobj, pipe["transformer"]
+
+if not "P" in preload_model_policy:
+ wan_model, offloadobj, transformer = None, None, None
+ reload_needed = True
+else:
+ wan_model, offloadobj, transformer = load_models(transformer_filename)
+ if check_loras:
+ setup_loras(model_filename, transformer, get_lora_dir(transformer_filename), "", None)
+ exit()
+ del transformer
+
+gen_in_progress = False
+
+def get_auto_attention():
+ for attn in ["sage2","sage","sdpa"]:
+ if attn in attention_modes_supported:
+ return attn
+ return "sdpa"
+
+def get_default_flow(filename, i2v):
+ return 7.0 if "480p" in filename and i2v else 5.0
+
+
+
+
+
+def generate_header(model_filename, compile, attention_mode):
+
+ description_container = [""]
+ get_model_name(model_filename, description_container)
+ description = description_container[0]
+ header = "" + description + "
"
+
+ header += "Attention mode " + (attention_mode if attention_mode!="auto" else "auto/" + get_auto_attention() )
+ if attention_mode not in attention_modes_installed:
+ header += " -NOT INSTALLED-"
+ elif attention_mode not in attention_modes_supported:
+ header += " -NOT SUPPORTED-"
+ header += ""
+
+ if compile:
+ header += ", Pytorch compilation ON"
+ if "fp16" in model_filename:
+ header += ", Data Type FP16"
+ else:
+ header += ", Data Type BF16"
+
+ if "int8" in model_filename:
+ header += ", Quantization Scaled Int8"
+ header += "
"
+
+ return header
+
+def apply_changes( state,
+ transformer_types_choices,
+ transformer_dtype_policy_choice,
+ text_encoder_quantization_choice,
+ VAE_precision_choice,
+ mixed_precision_choice,
+ save_path_choice,
+ attention_choice,
+ compile_choice,
+ profile_choice,
+ vae_config_choice,
+ metadata_choice,
+ quantization_choice,
+ boost_choice = 1,
+ clear_file_list = 0,
+ preload_model_policy_choice = 1,
+ UI_theme_choice = "default",
+ enhancer_enabled_choice = 0,
+ fit_canvas_choice = 0,
+ preload_in_VRAM_choice = 0
+):
+ if args.lock_config:
+ return
+ if gen_in_progress:
+ return "Unable to change config when a generation is in progress
", gr.update(), gr.update()
+ global offloadobj, wan_model, server_config, loras, loras_names, default_loras_choices, default_loras_multis_str, default_lora_preset_prompt, default_lora_preset, loras_presets
+ server_config = {"attention_mode" : attention_choice,
+ "transformer_types": transformer_types_choices,
+ "text_encoder_quantization" : text_encoder_quantization_choice,
+ "save_path" : save_path_choice,
+ "compile" : compile_choice,
+ "profile" : profile_choice,
+ "vae_config" : vae_config_choice,
+ "vae_precision" : VAE_precision_choice,
+ "mixed_precision" : mixed_precision_choice,
+ "metadata_type": metadata_choice,
+ "transformer_quantization" : quantization_choice,
+ "transformer_dtype_policy" : transformer_dtype_policy_choice,
+ "boost" : boost_choice,
+ "clear_file_list" : clear_file_list,
+ "preload_model_policy" : preload_model_policy_choice,
+ "UI_theme" : UI_theme_choice,
+ "fit_canvas": fit_canvas_choice,
+ "enhancer_enabled" : enhancer_enabled_choice,
+ "preload_in_VRAM" : preload_in_VRAM_choice
+ }
+
+ if Path(server_config_filename).is_file():
+ with open(server_config_filename, "r", encoding="utf-8") as reader:
+ text = reader.read()
+ old_server_config = json.loads(text)
+ if lock_ui_transformer:
+ server_config["transformer_filename"] = old_server_config["transformer_filename"]
+ if lock_ui_attention:
+ server_config["attention_mode"] = old_server_config["attention_mode"]
+ if lock_ui_compile:
+ server_config["compile"] = old_server_config["compile"]
+
+ with open(server_config_filename, "w", encoding="utf-8") as writer:
+ writer.write(json.dumps(server_config))
+
+ changes = []
+ for k, v in server_config.items():
+ v_old = old_server_config.get(k, None)
+ if v != v_old:
+ changes.append(k)
+
+ global attention_mode, profile, compile, vae_config, boost, lora_dir, reload_needed, preload_model_policy, transformer_quantization, transformer_dtype_policy, transformer_types, text_encoder_quantization
+ attention_mode = server_config["attention_mode"]
+ profile = server_config["profile"]
+ compile = server_config["compile"]
+ text_encoder_quantization = server_config["text_encoder_quantization"]
+ vae_config = server_config["vae_config"]
+ boost = server_config["boost"]
+ preload_model_policy = server_config["preload_model_policy"]
+ transformer_quantization = server_config["transformer_quantization"]
+ transformer_dtype_policy = server_config["transformer_dtype_policy"]
+ text_encoder_quantization = server_config["text_encoder_quantization"]
+ transformer_types = server_config["transformer_types"]
+ model_filename = get_model_filename(get_model_type(state["model_filename"]), transformer_quantization, transformer_dtype_policy)
+ state["model_filename"] = model_filename
+ if all(change in ["attention_mode", "vae_config", "boost", "save_path", "metadata_type", "clear_file_list", "fit_canvas"] for change in changes ):
+ model_choice = gr.Dropdown()
+ else:
+ reload_needed = True
+ model_choice = generate_dropdown_model_list(model_filename)
+
+ header = generate_header(state["model_filename"], compile=compile, attention_mode= attention_mode)
+ return "The new configuration has been succesfully applied
", header, model_choice, gr.Row(visible= server_config["enhancer_enabled"] == 1)
+
+
+
+from moviepy.editor import ImageSequenceClip
+import numpy as np
+
+def save_video(final_frames, output_path, fps=24):
+ assert final_frames.ndim == 4 and final_frames.shape[3] == 3, f"invalid shape: {final_frames} (need t h w c)"
+ if final_frames.dtype != np.uint8:
+ final_frames = (final_frames * 255).astype(np.uint8)
+ ImageSequenceClip(list(final_frames), fps=fps).write_videofile(output_path, verbose= False)
+
+
+def get_gen_info(state):
+ cache = state.get("gen", None)
+ if cache == None:
+ cache = dict()
+ state["gen"] = cache
+ return cache
+
+def build_callback(state, pipe, send_cmd, status, num_inference_steps):
+ gen = get_gen_info(state)
+ gen["num_inference_steps"] = num_inference_steps
+ def callback(step_idx, latent, force_refresh, read_state = False, override_num_inference_steps = -1, pass_no = -1):
+ refresh_id = gen.get("refresh", -1)
+ if force_refresh or step_idx >= 0:
+ pass
+ else:
+ refresh_id = gen.get("refresh", -1)
+ if refresh_id < 0:
+ return
+ UI_refresh = state.get("refresh", 0)
+ if UI_refresh >= refresh_id:
+ return
+ if override_num_inference_steps > 0:
+ gen["num_inference_steps"] = override_num_inference_steps
+
+ num_inference_steps = gen.get("num_inference_steps", 0)
+ status = gen["progress_status"]
+ state["refresh"] = refresh_id
+ if read_state:
+ phase, step_idx = gen["progress_phase"]
+ else:
+ step_idx += 1
+ if gen.get("abort", False):
+ # pipe._interrupt = True
+ phase = "Aborting"
+ elif step_idx == num_inference_steps:
+ phase = "VAE Decoding"
+ else:
+ if pass_no <=0:
+ phase = "Denoising"
+ elif pass_no == 1:
+ phase = "Denoising First Pass"
+ elif pass_no == 2:
+ phase = "Denoising Second Pass"
+ elif pass_no == 3:
+ phase = "Denoising Third Pass"
+ else:
+ phase = f"Denoising {pass_no}th Pass"
+
+ gen["progress_phase"] = (phase, step_idx)
+ status_msg = merge_status_context(status, phase)
+ if step_idx >= 0:
+ progress_args = [(step_idx , num_inference_steps) , status_msg , num_inference_steps]
+ else:
+ progress_args = [0, status_msg]
+
+ # progress(*progress_args)
+ send_cmd("progress", progress_args)
+ if latent != None:
+ latent = latent.to("cpu", non_blocking=True)
+ send_cmd("preview", latent)
+
+ # gen["progress_args"] = progress_args
+
+ return callback
+def abort_generation(state):
+ gen = get_gen_info(state)
+ if "in_progress" in gen and wan_model != None:
+
+ wan_model._interrupt= True
+ msg = "Processing Request to abort Current Generation"
+ gen["status"] = msg
+ gr.Info(msg)
+ return gr.Button(interactive= False)
+ else:
+ return gr.Button(interactive= True)
+
+
+
+def refresh_gallery(state): #, msg
+ gen = get_gen_info(state)
+
+ # gen["last_msg"] = msg
+ file_list = gen.get("file_list", None)
+ choice = gen.get("selected",0)
+ in_progress = "in_progress" in gen
+ if in_progress:
+ if gen.get("last_selected", True):
+ choice = max(len(file_list) - 1,0)
+
+ queue = gen.get("queue", [])
+ abort_interactive = not gen.get("abort", False)
+ if not in_progress or len(queue) == 0:
+ return gr.Gallery(selected_index=choice, value = file_list), gr.HTML("", visible= False), gr.Button(visible=True), gr.Button(visible=False), gr.Row(visible=False), update_queue_data(queue), gr.Button(interactive= abort_interactive), gr.Button(visible= False)
+ else:
+ task = queue[0]
+ start_img_md = ""
+ end_img_md = ""
+ prompt = task["prompt"]
+ params = task["params"]
+ model_filename = params["model_filename"]
+ onemorewindow_visible = "Vace" in model_filename or "diffusion_forcing" in model_filename or "ltxv" in model_filename
+ enhanced = False
+ if prompt.startswith("!enhanced!\n"):
+ enhanced = True
+ prompt = prompt[len("!enhanced!\n"):]
+ if "\n" in prompt :
+ prompts = prompt.split("\n")
+ window_no= gen.get("window_no",1)
+ if window_no > len(prompts):
+ window_no = len(prompts)
+ window_no -= 1
+ prompts[window_no]="" + prompts[window_no] + ""
+ prompt = "".join(prompts) + if enhanced: + prompt = "Enhanced:
" + prompt + list_uri = [] + start_img_uri = task.get('start_image_data_base64') + if start_img_uri != None: + list_uri += start_img_uri + end_img_uri = task.get('end_image_data_base64') + if end_img_uri != None: + list_uri += end_img_uri + + thumbnail_size = "100px" + thumbnails = "" + for img_uri in list_uri: + thumbnails += f'
| " + prompt + " | " + thumbnails + "
WanGP - Wan 2.1 model for the GPU Poor by DeepBeepMeep (GitHub)
") + gr.Markdown("Original Wan 2.1 Model by Alibaba (GitHub)") + gr.Markdown("Many thanks to:") + gr.Markdown("- Alibaba Wan team for the best open source video generator") + gr.Markdown("- Alibaba Vace and Fun Teams for their incredible control net models") + gr.Markdown("- Tencent for the impressive Hunyuan Video models") + gr.Markdown("- Lightricks for the super fast LTX Video models") + gr.Markdown("- Cocktail Peanuts : QA and simple installation via Pinokio.computer") + gr.Markdown("- Tophness : created (former) multi tabs and queuing frameworks") + gr.Markdown("- AmericanPresidentJimmyCarter : added original support for Skip Layer Guidance") + gr.Markdown("- Remade_AI : for their awesome Loras collection") + gr.Markdown("- Reevoy24 : for his repackaging / completing the documentation") + gr.Markdown("Huge acknowlegments to these great open source projects used in WanGP:") + gr.Markdown("- Rife: temporal upsampler (https://github.com/hzwer/ECCV2022-RIFE)") + gr.Markdown("- DwPose: Open Pose extractor (https://github.com/IDEA-Research/DWPose)") + gr.Markdown("- Midas: Depth extractor (https://github.com/isl-org/MiDaS") + gr.Markdown("- Matanyone and SAM2: Mask Generation (https://github.com/pq-yang/MatAnyone) and (https://github.com/facebookresearch/sam2)") + + +def generate_info_tab(): + + + with open("docs/VACE.md", "r", encoding="utf-8") as reader: + vace= reader.read() + + with open("docs/MODELS.md", "r", encoding="utf-8") as reader: + models = reader.read() + + with open("docs/LORAS.md", "r", encoding="utf-8") as reader: + loras = reader.read() + + with gr.Tabs() : + with gr.Tab("Models", id="models"): + gr.Markdown(models) + with gr.Tab("Loras", id="loras"): + gr.Markdown(loras) + with gr.Tab("Vace", id="vace"): + gr.Markdown(vace) + + + +def generate_dropdown_model_list(model_filename): + dropdown_types= transformer_types if len(transformer_types) > 0 else model_types + current_model_type = get_model_type(model_filename) + if current_model_type not in dropdown_types: + dropdown_types.append(current_model_type) + model_list = [] + for model_type in dropdown_types: + choice = get_model_filename(model_type, transformer_quantization, transformer_dtype_policy) + model_list.append(choice) + dropdown_choices = [ ( get_model_name(choice), get_model_type(choice) ) for choice in model_list] + return gr.Dropdown( + choices= dropdown_choices, + value= current_model_type, + show_label= False, + scale= 2, + elem_id="model_list", + elem_classes="model_list_class", + ) + + +def select_tab(tab_state, evt:gr.SelectData): + tab_video_mask_creator = 2 + + old_tab_no = tab_state.get("tab_no",0) + new_tab_no = evt.index + if old_tab_no == tab_video_mask_creator: + vmc_event_handler(False) + elif new_tab_no == tab_video_mask_creator: + if gen_in_progress: + gr.Info("Unable to access this Tab while a Generation is in Progress. Please come back later") + tab_state["tab_no"] = 0 + return gr.Tabs(selected="video_gen") + else: + vmc_event_handler(True) + tab_state["tab_no"] = new_tab_no + return gr.Tabs() + +def get_js(): + start_quit_timer_js = """ + () => { + function findAndClickGradioButton(elemId) { + const gradioApp = document.querySelector('gradio-app') || document; + const button = gradioApp.querySelector(`#${elemId}`); + if (button) { button.click(); } + } + + if (window.quitCountdownTimeoutId) clearTimeout(window.quitCountdownTimeoutId); + + let js_click_count = 0; + const max_clicks = 5; + + function countdownStep() { + if (js_click_count < max_clicks) { + findAndClickGradioButton('trigger_info_single_btn'); + js_click_count++; + window.quitCountdownTimeoutId = setTimeout(countdownStep, 1000); + } else { + findAndClickGradioButton('force_quit_btn_hidden'); + } + } + + countdownStep(); + } + """ + + cancel_quit_timer_js = """ + () => { + if (window.quitCountdownTimeoutId) { + clearTimeout(window.quitCountdownTimeoutId); + window.quitCountdownTimeoutId = null; + console.log("Quit countdown cancelled (single trigger)."); + } + } + """ + + trigger_zip_download_js = """ + (base64String) => { + if (!base64String) { + console.log("No base64 zip data received, skipping download."); + return; + } + try { + const byteCharacters = atob(base64String); + const byteNumbers = new Array(byteCharacters.length); + for (let i = 0; i < byteCharacters.length; i++) { + byteNumbers[i] = byteCharacters.charCodeAt(i); + } + const byteArray = new Uint8Array(byteNumbers); + const blob = new Blob([byteArray], { type: 'application/zip' }); + + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.style.display = 'none'; + a.href = url; + a.download = 'queue.zip'; + document.body.appendChild(a); + a.click(); + + window.URL.revokeObjectURL(url); + document.body.removeChild(a); + console.log("Zip download triggered."); + } catch (e) { + console.error("Error processing base64 data or triggering download:", e); + } + } + """ + + trigger_settings_download_js = """ + (base64String) => { + if (!base64String) { + console.log("No base64 settings data received, skipping download."); + return; + } + try { + const byteCharacters = atob(base64String); + const byteNumbers = new Array(byteCharacters.length); + for (let i = 0; i < byteCharacters.length; i++) { + byteNumbers[i] = byteCharacters.charCodeAt(i); + } + const byteArray = new Uint8Array(byteNumbers); + const blob = new Blob([byteArray], { type: 'application/text' }); + + const url = URL.createObjectURL(blob); + const a = document.createElement('a'); + a.style.display = 'none'; + a.href = url; + a.download = 'settings.json'; + document.body.appendChild(a); + a.click(); + + window.URL.revokeObjectURL(url); + document.body.removeChild(a); + console.log("settings download triggered."); + } catch (e) { + console.error("Error processing base64 data or triggering download:", e); + } + } + """ + return start_quit_timer_js, cancel_quit_timer_js, trigger_zip_download_js, trigger_settings_download_js + +def create_ui(): + global vmc_event_handler + css = """ + #model_list{ + background-color:black; + padding:1px} + + #model_list input { + font-size:25px} + + .title-with-lines { + display: flex; + align-items: center; + margin: 25px 0; + } + .line { + flex-grow: 1; + height: 1px; + background-color: #333; + } + h2 { + margin: 0 20px; + white-space: nowrap; + } + .queue-item { + border: 1px solid #ccc; + padding: 10px; + margin: 5px 0; + border-radius: 5px; + } + .current { + background: #f8f9fa; + border-left: 4px solid #007bff; + } + .task-header { + display: flex; + justify-content: space-between; + margin-bottom: 5px; + } + .progress-container { + height: 10px; + background: #e9ecef; + border-radius: 5px; + overflow: hidden; + } + .progress-bar { + height: 100%; + background: #007bff; + transition: width 0.3s ease; + } + .task-details { + display: flex; + justify-content: space-between; + font-size: 0.9em; + color: #6c757d; + margin-top: 5px; + } + .task-prompt { + font-size: 0.8em; + color: #868e96; + margin-top: 5px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + } + #queue_df th { + pointer-events: none; + text-align: center; + vertical-align: middle; + font-size:11px; + } + #xqueue_df table { + width: 100%; + overflow: hidden !important; + } + #xqueue_df::-webkit-scrollbar { + display: none !important; + } + #xqueue_df { + scrollbar-width: none !important; + -ms-overflow-style: none !important; + } + .selection-button { + display: none; + } + .cell-selected { + --ring-color: none; + } + #queue_df th:nth-child(1), + #queue_df td:nth-child(1) { + width: 60px; + text-align: center; + vertical-align: middle; + cursor: default !important; + pointer-events: none; + } + #xqueue_df th:nth-child(2), + #queue_df td:nth-child(2) { + text-align: center; + vertical-align: middle; + white-space: normal; + } + #queue_df td:nth-child(2) { + cursor: default !important; + } + #queue_df th:nth-child(3), + #queue_df td:nth-child(3) { + width: 60px; + text-align: center; + vertical-align: middle; + cursor: default !important; + pointer-events: none; + } + #queue_df th:nth-child(4), + #queue_df td:nth-child(4) { + width: 60px; + text-align: center; + white-space: nowrap; + cursor: default !important; + pointer-events: none; + } + #queue_df th:nth-child(5), #queue_df td:nth-child(7), + #queue_df th:nth-child(6), #queue_df td:nth-child(8) { + width: 60px; + text-align: center; + vertical-align: middle; + } + #queue_df td:nth-child(5) img, + #queue_df td:nth-child(6) img { + max-width: 50px; + max-height: 50px; + object-fit: contain; + display: block; + margin: auto; + cursor: pointer; + } + #queue_df th:nth-child(7), #queue_df td:nth-child(9), + #queue_df th:nth-child(8), #queue_df td:nth-child(10), + #queue_df th:nth-child(9), #queue_df td:nth-child(11) { + width: 20px; + padding: 2px !important; + cursor: pointer; + text-align: center; + font-weight: bold; + vertical-align: middle; + } + #queue_df td:nth-child(5):hover, + #queue_df td:nth-child(6):hover, + #queue_df td:nth-child(7):hover, + #queue_df td:nth-child(8):hover, + #queue_df td:nth-child(9):hover { + background-color: #e0e0e0; + } + #image-modal-container { + position: fixed; + top: 0; + left: 0; + width: 100%; + height: 100%; + background-color: rgba(0, 0, 0, 0.7); + justify-content: center; + align-items: center; + z-index: 1000; + padding: 20px; + box-sizing: border-box; + } + #image-modal-container > div { + background-color: white; + padding: 15px; + border-radius: 8px; + max-width: 90%; + max-height: 90%; + overflow: auto; + position: relative; + display: flex; + flex-direction: column; + } + #image-modal-container img { + max-width: 100%; + max-height: 80vh; + object-fit: contain; + margin-top: 10px; + } + #image-modal-close-button-row { + display: flex; + justify-content: flex-end; + } + #image-modal-close-button-row button { + cursor: pointer; + } + .progress-container-custom { + width: 100%; + background-color: #e9ecef; + border-radius: 0.375rem; + overflow: hidden; + height: 25px; + position: relative; + margin-top: 5px; + margin-bottom: 5px; + } + .progress-bar-custom { + height: 100%; + background-color: #0d6efd; + transition: width 0.3s ease-in-out; + display: flex; + align-items: center; + justify-content: center; + color: white; + font-size: 0.9em; + font-weight: bold; + white-space: nowrap; + overflow: hidden; + } + .progress-bar-custom.idle { + background-color: #6c757d; + } + .progress-bar-text { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; + display: flex; + align-items: center; + justify-content: center; + color: white; + mix-blend-mode: difference; + font-size: 0.9em; + font-weight: bold; + white-space: nowrap; + z-index: 2; + pointer-events: none; + } + + """ + UI_theme = server_config.get("UI_theme", "default") + UI_theme = args.theme if len(args.theme) > 0 else UI_theme + if UI_theme == "gradio": + theme = None + else: + theme = gr.themes.Soft(font=["Verdana"], primary_hue="sky", neutral_hue="slate", text_size="md") + + with gr.Blocks(css=css, theme=theme, title= "WanGP") as main: + gr.Markdown(f"
WanGP v{WanGP_version} by DeepBeepMeep ") # (Updates)
" + get_model_name(transformer_filename) + "
") + model_choice = gr.Dropdown(visible=False, value= get_model_type(transformer_filename)) + else: + gr.Markdown("")
+ model_choice = generate_dropdown_model_list(transformer_filename)
+ gr.Markdown("
")
+ with gr.Row():
+ header = gr.Markdown(generate_header(transformer_filename, compile, attention_mode), visible= True)
+ with gr.Row():
+ ( state, loras_choices, lset_name, state,
+ video_guide, video_mask, image_refs, video_prompt_type_video_trigger, prompt_enhancer_row
+ ) = generate_video_tab(model_choice=model_choice, header=header, main = main)
+ with gr.Tab("Guides", id="info"):
+ generate_info_tab()
+ with gr.Tab("Video Mask Creator", id="video_mask_creator") as video_mask_creator:
+ from preprocessing.matanyone import app as matanyone_app
+ vmc_event_handler = matanyone_app.get_vmc_event_handler()
+
+ matanyone_app.display(main_tabs, model_choice, video_guide, video_mask, image_refs, video_prompt_type_video_trigger)
+ if not args.lock_config:
+ with gr.Tab("Downloads", id="downloads") as downloads_tab:
+ generate_download_tab(lset_name, loras_choices, state)
+ with gr.Tab("Configuration", id="configuration"):
+ generate_configuration_tab(state, main, header, model_choice, prompt_enhancer_row)
+ with gr.Tab("About"):
+ generate_about_tab()
+
+ main_tabs.select(fn=select_tab, inputs= [tab_state], outputs= main_tabs)
+ return main
+
+if __name__ == "__main__":
+ atexit.register(autosave_queue)
+ download_ffmpeg()
+ # threading.Thread(target=runner, daemon=True).start()
+ os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
+ server_port = int(args.server_port)
+ if os.name == "nt":
+ asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+ if server_port == 0:
+ server_port = int(os.getenv("SERVER_PORT", "7860"))
+ server_name = args.server_name
+ if args.listen:
+ server_name = "0.0.0.0"
+ if len(server_name) == 0:
+ server_name = os.getenv("SERVER_NAME", "localhost")
+ demo = create_ui()
+ if args.open_browser:
+ import webbrowser
+ if server_name.startswith("http"):
+ url = server_name
+ else:
+ url = "http://" + server_name
+ webbrowser.open(url + ":" + str(server_port), new = 0, autoraise = True)
+ demo.launch(server_name=server_name, server_port=server_port, share=True, allowed_paths=[save_path])
\ No newline at end of file