Added support for more Loras format

README.md

@@ -19,7 +19,9 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
 
 
 ## 🔥 Latest News!!
-* Marc 17 2022: 👋 Wan2.1GP v2.0: The Lora festival continues:
+* Mar 18 2022: 👋 Wan2.1GP v2.1: More Loras !: added support for 'Safetensors' and 'Replicate' Lora formats.\
+You will need to refresh the requirements with a *pip install -r requirements.txt*
+* Mar 17 2022: 👋 Wan2.1GP v2.0: The Lora festival continues:
     - Clearer user interface
     - Download 30 Loras in one click to try them all (expand the info section)
     - Very to use Loras as now Lora presets can input the subject (or other need terms) of the Lora so that you dont have to modify manually a prompt 

gradio_server.py

@@ -23,7 +23,12 @@ import asyncio
 from wan.utils import prompt_parser
 PROMPT_VARS_MAX = 10
 
-
+target_mmgp_version = "3.2.8"
+from importlib.metadata import version
+mmgp_version = version("mmgp")
+if mmgp_version != target_mmgp_version:
+    print(f"Incorrect version of mmgp ({mmgp_version}), version {target_mmgp_version} is needed. Please upgrade with the command 'pip install -r requirements.txt'")
+    exit()
 def _parse_args():
     parser = argparse.ArgumentParser(
         description="Generate a video from a text prompt or image using Gradio")
@@ -77,8 +82,7 @@ def _parse_args():
 
     parser.add_argument(
         "--check-loras",
-        type=str,
+        action="store_true",
-        default=0, 
         help="Filter Loras that are not valid"
     )
 
@@ -346,6 +350,54 @@ if  args.compile: #args.fastest or
 #attention_mode="xformers"
 # compile = "transformer"
 
+def preprocess_loras(sd):
+    if not use_image2video:
+        return sd
+    
+    new_sd = {}
+    first = next(iter(sd), None)
+    if first == None:
+        return sd
+    if  not first.startswith("lora_unet_"):
+        return sd
+    print("Converting Lora Safetensors format to Lora Diffusers format")
+    alphas = {}
+    repl_list = ["cross_attn", "self_attn", "ffn"]
+    src_list = ["_" + k + "_" for k in repl_list]
+    tgt_list = ["." + k + "." for k in repl_list]
+
+    for k,v in sd.items():
+        k = k.replace("lora_unet_blocks_","diffusion_model.blocks.")
+
+        for s,t in zip(src_list, tgt_list):
+            k = k.replace(s,t)
+
+        k = k.replace("lora_up","lora_B")
+        k = k.replace("lora_down","lora_A")
+
+        if "alpha" in k:
+            alphas[k] = v
+        else:
+            new_sd[k] = v
+
+    new_alphas = {}
+    for k,v in new_sd.items():
+        if "lora_B" in k:
+            dim = v.shape[1]
+        elif "lora_A" in k:
+            dim = v.shape[0]
+        else:
+            continue
+        alpha_key = k[:-len("lora_X.weight")] +"alpha"
+        if alpha_key in alphas:
+            scale = alphas[alpha_key] / dim
+            new_alphas[alpha_key] = scale
+        else:
+            print(f"Lora alpha'{alpha_key}' is missing")
+    new_sd.update(new_alphas)
+    return new_sd
+
+
 def download_models(transformer_filename, text_encoder_filename):
     def computeList(filename):
         pos = filename.rfind("/")
@@ -450,7 +502,7 @@ def setup_loras(transformer,  lora_dir, lora_preselected_preset, split_linear_mo
         loras_presets = [ Path(Path(file_path).parts[-1]).stem for file_path in dir_presets]
 
     if check_loras:
-        loras = offload.load_loras_into_model(transformer, loras,  activate_all_loras=False, check_only= True, split_linear_modules_map = split_linear_modules_map) #lora_multiplier,
+        loras = offload.load_loras_into_model(transformer, loras,  activate_all_loras=False, check_only= True, preprocess_sd=preprocess_loras, split_linear_modules_map = split_linear_modules_map) #lora_multiplier,
 
     if len(loras) > 0:
         loras_names = [ Path(lora).stem for lora in loras  ]
@@ -986,7 +1038,7 @@ def generate_video(
             list_mult_choices_nums  += [1.0] * ( len(loras_choices) - len(list_mult_choices_nums ) )
         loras_selected = [ lora for i, lora in enumerate(loras) if str(i) in loras_choices]
         pinnedLora = False # profile !=5
-        offload.load_loras_into_model(trans, loras_selected, list_mult_choices_nums, activate_all_loras=True, pinnedLora=pinnedLora, split_linear_modules_map = None) 
+        offload.load_loras_into_model(trans, loras_selected, list_mult_choices_nums, activate_all_loras=True, preprocess_sd=preprocess_loras, pinnedLora=pinnedLora, split_linear_modules_map = None) 
         errors = trans._loras_errors
         if len(errors) > 0:
             error_files = [msg for _ ,  msg  in errors]
@@ -1534,9 +1586,9 @@ def create_demo():
         state_dict = {}
        
         if use_image2video:
-            gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v2.0 - Image To Video <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A> / <A HREF='https://github.com/Wan-Video/Wan2.1'>Original by Alibaba</A>)</FONT SIZE=3></H1></div>")
+            gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v2.1 - Image To Video <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A> / <A HREF='https://github.com/Wan-Video/Wan2.1'>Original by Alibaba</A>)</FONT SIZE=3></H1></div>")
         else:
-            gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v2.0 - Text To Video <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A> / <A HREF='https://github.com/Wan-Video/Wan2.1'>Original by Alibaba</A>)</FONT SIZE=3></H1></div>")
+            gr.Markdown("<div align=center><H1>Wan 2.1<SUP>GP</SUP> v2.1 - Text To Video <FONT SIZE=4>by <I>DeepBeepMeep</I></FONT> <FONT SIZE=3> (<A HREF='https://github.com/deepbeepmeep/Wan2GP'>Updates</A> / <A HREF='https://github.com/Wan-Video/Wan2.1'>Original by Alibaba</A>)</FONT SIZE=3></H1></div>")
 
         gr.Markdown("<FONT SIZE=3>Welcome to Wan 2.1GP a super fast and low VRAM AI Video Generator !</FONT>")
 
@@ -1727,7 +1779,9 @@ def create_demo():
 
                 advanced_prompt = advanced
                 prompt_vars=[]
-                if not advanced_prompt:
+                if advanced_prompt:
+                    default_wizard_prompt, variables, values= None, None, None
+                else:
                     default_wizard_prompt, variables, values, errors =  extract_wizard_prompt(default_prompt)
                     advanced_prompt  = len(errors) > 0
 

requirements.txt

@@ -16,5 +16,5 @@ gradio>=5.0.0
 numpy>=1.23.5,<2
 einops
 moviepy==1.0.3
-mmgp==3.2.7
+mmgp==3.2.8
 peft==0.14.0