added fast wan 5B and fixed pytorch compilation

README.md

@@ -20,6 +20,10 @@ WanGP supports the Wan (and derived models), Hunyuan Video and LTV Video models
 **Follow DeepBeepMeep on Twitter/X to get the Latest News**: https://x.com/deepbeepmeep
 
 ## 🔥 Latest Updates : 
+### August 8 2025: WanGP v7.75 - Faster than the VAE ...
+We have a funny one here today: FastWan 2.2 5B, the Fastest Video Generator, only 20s to generate 121 frames at 720p. The snag is that VAE is twice as slow... 
+Thanks to Kijai for extracting the Lora that is used to build the corresponding finetune.
+
 ### August 8 2025: WanGP v7.74 - Qwen Rebirth part 2
 Added support for Qwen Lightning lora for a 8 steps generation (https://huggingface.co/lightx2v/Qwen-Image-Lightning/blob/main/Qwen-Image-Lightning-8steps-V1.0.safetensors). Lora is not normalized and you can use a multiplier around 0.1.
 

configs/i2v_2_2_multitalk.json

@@ -0,0 +1,15 @@
+{
+  "_class_name": "WanModel",
+  "_diffusers_version": "0.33.0",
+  "dim": 5120,
+  "eps": 1e-06,
+  "ffn_dim": 13824,
+  "freq_dim": 256,
+  "in_dim": 36,
+  "model_type": "i2v2_2",
+  "num_heads": 40,
+  "num_layers": 40,
+  "out_dim": 16,
+  "text_len": 512,
+  "multitalk_output_dim": 768
+}

defaults/i2v_2_2_multitalk.json

@@ -0,0 +1,18 @@
+{
+	"model":
+	{
+		"name": "Wan2.2 Multitalk 14B",
+		"architecture" : "i2v_2_2_multitalk",
+		"description": "The Multitalk module of Wan 2.1 has been combined with the Wan 2.2 image 2 video. It lets you have up to two people have a conversation.",
+		"modules": ["multitalk"],
+		"URLs": "i2v_2_2",
+		"URLs2": "i2v_2_2",
+		"group": "wan2_2",
+		"visible": false
+	},
+	"switch_threshold" : 900,
+	"guidance_scale" : 3.5,
+	"guidance2_scale" : 3.5,
+	"flow_shift" : 5
+	
+}

defaults/ti2v_2_2_fastwan.json

@@ -0,0 +1,15 @@
+{
+    "model": {
+        "name": "Wan2.2 FastWan TextImage2video 5B",
+        "architecture": "ti2v_2_2",
+        "description": "FastWan2.2-TI2V-5B-Full-Diffusers is built upon Wan-AI/Wan2.2-TI2V-5B-Diffusers. It supports efficient 3-step inference and produces high-quality videos at 121×704×1280 resolution",
+        "URLs": "ti2v_2_2",
+		"loras": ["https://huggingface.co/DeepBeepMeep/Wan2.2/resolve/main/loras_accelerators/Wan2_2_5B_FastWanFullAttn_lora_rank_128_bf16.safetensors"],
+        "group": "wan2_2"
+    },
+    "video_length": 121,
+    "guidance_scale": 1,
+    "flow_shift": 3,
+    "num_inference_steps": 3,
+    "resolution": "1280x720"
+}

models/flux/flux_handler.py

@@ -32,7 +32,7 @@ class family_handler():
         return {}, {}
 
     @staticmethod
-    def get_rgb_factors(model_type):
+    def get_rgb_factors(base_model_type ):
         from shared.RGB_factors import get_rgb_factors
         latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("flux")
         return latent_rgb_factors, latent_rgb_factors_bias

models/hyvideo/hunyuan_handler.py

@@ -72,7 +72,7 @@ class family_handler():
         return {"hunyuan":(20, "Hunyuan Video")}
 
     @staticmethod
-    def get_rgb_factors(model_type):
+    def get_rgb_factors(base_model_type ):
         from shared.RGB_factors import get_rgb_factors
         latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("hunyuan")
         return latent_rgb_factors, latent_rgb_factors_bias

models/ltx_video/ltxv_handler.py

@@ -38,7 +38,7 @@ class family_handler():
         return {}, {}
 
     @staticmethod
-    def get_rgb_factors(model_type):
+    def get_rgb_factors(base_model_type ):
         from shared.RGB_factors import get_rgb_factors
         latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("ltxv")
         return latent_rgb_factors, latent_rgb_factors_bias

models/qwen/transformer_qwenimage.py

@@ -469,13 +469,20 @@ class QwenImageTransformer2DModel(nn.Module):
 
 
     def preprocess_loras(self, model_type, sd):
-        new_sd = {}
+
-        for k,v in sd.items():
+        first = next(iter(sd), None)
-            if k.startswith("transformer_blocks"):
+        if first == None:
-                k = "diffusion_model." + k
+            return sd
-                new_sd[k] = v
+        if first.startswith("transformer_blocks"):
-        sd = new_sd  
+            new_sd = {}
-        return sd
+            for k,v in sd.items():
+                if k.startswith("transformer_blocks"):
+                    k = "diffusion_model." + k
+                    new_sd[k] = v
+            sd = new_sd  
+            return sd
+        else:
+            return sd
 
     def __init__(
         self,

models/wan/any2video.py

@@ -87,17 +87,17 @@ class WanAny2V:
             dtype=config.t5_dtype,
             device=torch.device('cpu'),
             checkpoint_path=text_encoder_filename,
-            tokenizer_path=os.path.join(checkpoint_dir, config.t5_tokenizer),
+            tokenizer_path=os.path.join(checkpoint_dir, "umt5-xxl"),
             shard_fn= None)
 
         # base_model_type = "i2v2_2"
-        if hasattr(config, "clip_checkpoint") and not base_model_type in ["i2v_2_2"]:
+        if hasattr(config, "clip_checkpoint") and not base_model_type in ["i2v_2_2", "i2v_2_2_multitalk"]:
             self.clip = CLIPModel(
                 dtype=config.clip_dtype,
                 device=self.device,
                 checkpoint_path=os.path.join(checkpoint_dir , 
                                             config.clip_checkpoint),
-                tokenizer_path=os.path.join(checkpoint_dir ,  config.clip_tokenizer))
+                tokenizer_path=os.path.join(checkpoint_dir ,  "clip_vit_large_patch14"))
 
 
         if base_model_type in ["ti2v_2_2"]:
@@ -495,7 +495,7 @@ class WanAny2V:
         vace = model_type in ["vace_1.3B","vace_14B", "vace_multitalk_14B"]
         phantom = model_type in ["phantom_1.3B", "phantom_14B"]
         fantasy = model_type in ["fantasy"]
-        multitalk = model_type in ["multitalk", "vace_multitalk_14B"]
+        multitalk = model_type in ["multitalk", "vace_multitalk_14B", "i2v_2_2_multitalk"]
         recam = model_type in ["recam_1.3B"]
         ti2v = model_type in ["ti2v_2_2"]
         start_step_no = 0
@@ -505,7 +505,7 @@ class WanAny2V:
         timestep_injection = False
         lat_frames = int((frame_num - 1) // self.vae_stride[0]) + 1
         # image2video 
-        if model_type in ["i2v", "i2v_2_2", "fun_inp_1.3B", "fun_inp", "fantasy", "multitalk", "flf2v_720p"]:
+        if model_type in ["i2v", "i2v_2_2", "fun_inp_1.3B", "fun_inp", "fantasy", "multitalk", "i2v_2_2_multitalk", "flf2v_720p"]:
             any_end_frame = False
             if image_start is None:
                 _ , preframes_count, height, width = input_video.shape

models/wan/configs/wan_i2v_14B.py

@@ -10,7 +10,7 @@ i2v_14B = EasyDict(__name__='Config: Wan I2V 14B')
 i2v_14B.update(wan_shared_cfg)
 
 i2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
-i2v_14B.t5_tokenizer = 'google/umt5-xxl'
+i2v_14B.t5_tokenizer = 'umt5-xxl'
 
 # clip
 i2v_14B.clip_model = 'clip_xlm_roberta_vit_h_14'

models/wan/configs/wan_t2v_14B.py

@@ -10,7 +10,7 @@ t2v_14B.update(wan_shared_cfg)
 
 # t5
 t2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
-t2v_14B.t5_tokenizer = 'google/umt5-xxl'
+t2v_14B.t5_tokenizer = 'umt5-xxl'
 
 # vae
 t2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth'

models/wan/configs/wan_t2v_1_3B.py

@@ -10,7 +10,7 @@ t2v_1_3B.update(wan_shared_cfg)
 
 # t5
 t2v_1_3B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
-t2v_1_3B.t5_tokenizer = 'google/umt5-xxl'
+t2v_1_3B.t5_tokenizer = 'umt5-xxl'
 
 # vae
 t2v_1_3B.vae_checkpoint = 'Wan2.1_VAE.pth'

models/wan/modules/t5.py

@@ -504,7 +504,6 @@ class T5EncoderModel:
         else:
             self.model.to(self.device)
         # init tokenizer
-        tokenizer_path= "google/umt5-xxl"
         self.tokenizer = HuggingfaceTokenizer(
             name=tokenizer_path, seq_len=text_len, clean='whitespace')
 

models/wan/wan_handler.py

@@ -2,7 +2,7 @@ import torch
 import numpy as np
 
 def test_class_i2v(base_model_type):    
-    return base_model_type in ["i2v", "i2v_2_2", "fun_inp_1.3B", "fun_inp", "flf2v_720p",  "fantasy",  "multitalk",  ] #"hunyuan_i2v",
+    return base_model_type in ["i2v", "i2v_2_2", "fun_inp_1.3B", "fun_inp", "flf2v_720p",  "fantasy",  "multitalk", "i2v_2_2_multitalk" ] #"hunyuan_i2v",
 
 def test_class_1_3B(base_model_type):    
     return base_model_type in [ "vace_1.3B", "t2v_1.3B", "recam_1.3B","phantom_1.3B","fun_inp_1.3B"]
@@ -79,7 +79,7 @@ class family_handler():
             extra_model_def["no_steps_skipping"] = True
         i2v =  test_class_i2v(base_model_type)
         extra_model_def["i2v_class"] = i2v
-
+        extra_model_def["multitalk_class"] = base_model_type in ["multitalk", "vace_multitalk_14B", "i2v_2_2_multitalk"]
         vace_class = base_model_type in ["vace_14B", "vace_1.3B", "vace_multitalk_14B"] 
         extra_model_def["vace_class"] = vace_class
 
@@ -118,7 +118,7 @@ class family_handler():
         return ["multitalk", "fantasy", "vace_14B", "vace_multitalk_14B",
                     "t2v_1.3B", "t2v", "vace_1.3B", "phantom_1.3B", "phantom_14B", 
                     "recam_1.3B", 
-                    "i2v", "i2v_2_2", "ti2v_2_2", "flf2v_720p", "fun_inp_1.3B", "fun_inp"]
+                    "i2v", "i2v_2_2", "i2v_2_2_multitalk", "ti2v_2_2", "flf2v_720p", "fun_inp_1.3B", "fun_inp"]
 
 
     @staticmethod
@@ -133,6 +133,7 @@ class family_handler():
                     "vace_14B" : [ "vace_multitalk_14B"],
                     "t2v" : [ "vace_14B", "vace_1.3B" "vace_multitalk_14B", "t2v_1.3B", "phantom_1.3B","phantom_14B"],
                     "i2v" : [ "fantasy", "multitalk", "flf2v_720p" ],
+                    "i2v_2_2" : ["i2v_2_2_multitalk"],
                     "fantasy": ["multitalk"],
                     }
         return models_eqv_map, models_comp_map
@@ -150,9 +151,9 @@ class family_handler():
         return 32 if base_model_type == "ti2v_2_2" else 16
 
     @staticmethod
-    def get_rgb_factors(model_type):
+    def get_rgb_factors(base_model_type ):
         from shared.RGB_factors import get_rgb_factors
-        if model_type == "ti2v_2_2": return None, None
+        if base_model_type == "ti2v_2_2": return None, None
         latent_rgb_factors, latent_rgb_factors_bias = get_rgb_factors("wan")
         return latent_rgb_factors, latent_rgb_factors_bias
     

requirements.txt

@@ -17,7 +17,7 @@ gradio==5.23.0
 numpy>=1.23.5,<2
 einops
 moviepy==1.0.3
-mmgp==3.5.7
+mmgp==3.5.8
 peft==0.15.0
 mutagen
 pydantic==2.10.6

wgp.py

@@ -44,13 +44,16 @@ from preprocessing.matanyone  import app as matanyone_app
 from tqdm import tqdm
 import requests
 
+# import torch._dynamo as dynamo
+# dynamo.config.recompile_limit = 2000   # default is 256
+# dynamo.config.accumulated_recompile_limit = 2000  # or whatever limit you want
 
 global_queue_ref = []
 AUTOSAVE_FILENAME = "queue.zip"
 PROMPT_VARS_MAX = 10
 
-target_mmgp_version = "3.5.7"
+target_mmgp_version = "3.5.8"
-WanGP_version = "7.74"
+WanGP_version = "7.75"
 settings_version = 2.23
 max_source_video_frames = 3000
 prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
@@ -1616,7 +1619,8 @@ def _parse_args():
 
 def get_lora_dir(model_type):
     model_family = get_model_family(model_type)
-    i2v = test_class_i2v(model_type) and not get_base_model_type(model_type) == "i2v_2_2"
+    base_model_type = get_base_model_type(model_type)
+    i2v = test_class_i2v(model_type) and  base_model_type == "i2v_2_2"
     if model_family == "wan":
         lora_dir =args.lora_dir
         if i2v and len(lora_dir)==0:
@@ -1629,7 +1633,7 @@ def get_lora_dir(model_type):
             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
-        elif model_type == "ti2v_2_2":
+        elif base_model_type == "ti2v_2_2":
             lora_dir_5B = os.path.join(root_lora_dir, "5B")
             if os.path.isdir(lora_dir_5B ):
                 return lora_dir_5B
@@ -3641,7 +3645,7 @@ def perform_spatial_upsampling(sample, spatial_upsampling):
 
 def any_audio_track(model_type):
     base_model_type = get_base_model_type(model_type)
-    return base_model_type in ["fantasy", "multitalk", "hunyuan_avatar", "hunyuan_custom_audio", "vace_multitalk_14B"]
+    return base_model_type in ["fantasy", "hunyuan_avatar", "hunyuan_custom_audio"] or get_model_def(model_type).get("multitalk_class", False)
 
 def get_available_filename(target_path, video_source, suffix = "", force_extension = None):
     name, extension =  os.path.splitext(os.path.basename(video_source))
@@ -3950,6 +3954,12 @@ def generate_video(
     model_filename,
     mode,
 ):
+    # import os
+    # os.environ.pop("TORCH_LOGS", None)  # make sure no env var is suppressing/overriding
+    # import torch._logging as tlog
+    # tlog.set_logs(recompiles=True, guards=True, graph_breaks=True)    
+
+
 
     def remove_temp_filenames(temp_filenames_list):
         for temp_filename in temp_filenames_list: 
@@ -4094,7 +4104,7 @@ def generate_video(
     hunyuan_custom_edit =  hunyuan_custom and "edit" in model_filename
     hunyuan_avatar = "hunyuan_video_avatar" in model_filename
     fantasy = base_model_type in ["fantasy"]
-    multitalk = base_model_type in ["multitalk", "vace_multitalk_14B"]
+    multitalk = model_def.get("multitalk_class", False)
     flux = base_model_type in ["flux"]
 
     if "B" in audio_prompt_type or "X" in audio_prompt_type:
@@ -4821,8 +4831,9 @@ def generate_preview(model_type, latents):
     import einops
     if latents is None: return None
     model_handler = get_model_handler(model_type)
+    base_model_type = get_base_model_type(model_type)
     if hasattr(model_handler, "get_rgb_factors"):
-        latent_rgb_factors, latent_rgb_factors_bias = model_handler.get_rgb_factors(model_type)
+        latent_rgb_factors, latent_rgb_factors_bias = model_handler.get_rgb_factors(base_model_type )
     else:
         return None
     if latent_rgb_factors is None: return None
@@ -5520,7 +5531,7 @@ def prepare_inputs_dict(target, inputs, model_type = None, model_filename = None
     if not test_any_sliding_window( base_model_type):
         pop += ["sliding_window_size", "sliding_window_overlap", "sliding_window_overlap_noise", "sliding_window_discard_last_frames", "sliding_window_color_correction_strength"]
 
-    if not base_model_type in ["fantasy", "multitalk", "vace_multitalk_14B"]:
+    if not (base_model_type in ["fantasy"] or model_def.get("multitalk_class", False)):
         pop += ["audio_guidance_scale", "speakers_locations"]
 
     if not model_def.get("embedded_guidance", False) or model_def.get("no_guidance", False):
@@ -6505,7 +6516,7 @@ def generate_video_tab(update_form = False, state_dict = None, ui_defaults = Non
             vace = test_vace_module(base_model_type)
             phantom = base_model_type in ["phantom_1.3B", "phantom_14B"]
             fantasy = base_model_type in ["fantasy"]
-            multitalk = base_model_type in ["multitalk", "vace_multitalk_14B"]
+            multitalk = model_def.get("multitalk_class", False)            
             hunyuan_t2v = "hunyuan_video_720" in model_filename
             hunyuan_i2v = "hunyuan_video_i2v" in model_filename
             hunyuan_video_custom = "hunyuan_video_custom" in model_filename