Fixe Sage test

README.md

@@ -19,25 +19,25 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
 
 
 ## 🔥 Latest News!!
-* Mar 19 2022: 👋 Wan2.1GP v3.2: 
+* Mar 19 2025: 👋 Wan2.1GP v3.2: 
     - Added Classifier-Free Guidance Zero Star. The video should match better the text prompt (especially with text2video) at no performance cost: many thanks to the **CFG Zero * Team:**\
     Dont hesitate to give them a star if you appreciate the results:  https://github.com/WeichenFan/CFG-Zero-star 
     - Added back support for Pytorch compilation with Loras. It seems it had been broken for some time
     - Added possibility to keep a number of pregenerated videos in the Video Gallery (useful to compare outputs of different settings)
     You will need one more *pip install -r requirements.txt*
-* Mar 19 2022: 👋 Wan2.1GP v3.1: Faster launch and RAM optimizations (should require less RAM to run)\ 
+* Mar 19 2025: 👋 Wan2.1GP v3.1: Faster launch and RAM optimizations (should require less RAM to run)\ 
     You will need one more *pip install -r requirements.txt*
-* Mar 18 2022: 👋 Wan2.1GP v3.0: 
+* Mar 18 2025: 👋 Wan2.1GP v3.0: 
     - New Tab based interface, yon can switch from i2v to t2v conversely without restarting the app
     - Experimental Dual Frames mode for i2v, you can also specify an End frame. It doesn't always work, so you will need a few attempts.
     - You can save default settings in the files *i2v_settings.json* and *t2v_settings.json* that will be used when launching the app (you can also specify the path to different settings files)
     - Slight acceleration with loras\
     You will need one more *pip install -r requirements.txt*
     Many thanks to *Tophness* who created the framework (and did a big part of the work) of the multitabs and saved settings features 
-* Mar 18 2022: 👋 Wan2.1GP v2.11: Added more command line parameters to prefill the generation settings + customizable output directory and choice of type of metadata for generated videos. Many thanks to *Tophness* for his contributions. You will need one more *pip install -r requirements.txt* to reflect new dependencies\
-* Mar 18 2022: 👋 Wan2.1GP v2.1: More Loras !: added support for 'Safetensors' and 'Replicate' Lora formats.\
+* Mar 18 2025: 👋 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\
+* Mar 18 2025: 👋 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:
+* Mar 17 2025: 👋 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

@@ -847,14 +847,15 @@ def abort_generation(state):
 
 def refresh_gallery(state, txt):
     file_list = state.get("file_list", None)      
+    choice = state.get("selected",0)
+    if "in_progress" in state:
+        if state.get("last_selected", True):
+            choice = max(len(file_list) - 1,0)  
+
     prompt = state.get("prompt", "")
     if len(prompt) == 0:
         return file_list, gr.Text(visible= False, value="")
     else:
-        choice = 0
-        if "in_progress" in state:
-            choice = state.get("selected",0)
-
         prompts_max = state.get("prompts_max",0) 
         prompt_no = state.get("prompt_no",0)
         if prompts_max >1 : 
@@ -869,7 +870,9 @@ def finalize_gallery(state):
     if "in_progress" in state:
         del state["in_progress"]
         choice = state.get("selected",0)
-        # file_list = state.get("file_list", [])      
+        if state.get("last_selected", True):
+            file_list = state.get("file_list", [])
+            choice = len(file_list) - 1
             
 
     state["extra_orders"] = 0
@@ -881,7 +884,10 @@ def finalize_gallery(state):
 def select_video(state , event_data: gr.EventData):
     data=  event_data._data
     if data!=None:
-        state["selected"] = data.get("index",0)
+        choice = data.get("index",0)
+        file_list = state.get("file_list", [])
+        state["last_selected"] = (choice + 1) >= len(file_list)
+        state["selected"] = choice
     return 
 
 def expand_slist(slist, num_inference_steps ):
@@ -1199,6 +1205,7 @@ def generate_video(
     state["selected"] = choice         
     state["file_list"] = file_list    
 
+
     global save_path
     os.makedirs(save_path, exist_ok=True)
     video_no = 0

wan/image2video.py

@@ -409,11 +409,10 @@ class WanI2V:
 
 
                 if (i <= cfg_zero_step):
-                    noise_pred = noise_pred_text*0.
+                    noise_pred *= 0. # it would be faster not to compute noise_pred...
                 else:
-                    noise_pred = noise_pred_uncond * alpha + guide_scale * (noise_pred_text - noise_pred_uncond * alpha)
-            else:
-                noise_pred = noise_pred_uncond + guide_scale * (noise_pred_text - noise_pred_uncond)        
+                    noise_pred_uncond *= alpha
+            noise_pred = noise_pred_uncond + guide_scale * (noise_pred_text - noise_pred_uncond)            
 
             del noise_pred_uncond
 

wan/modules/attention.py

@@ -39,12 +39,12 @@ import warnings
 
 try:
     from sageattention import sageattn
-    from .sage2_core import sageattn as alt_sageattn, is_sage_supported
-    sage_supported =  is_sage_supported()
+    from .sage2_core import sageattn as alt_sageattn, is_sage2_supported
+    sage2_supported =  is_sage2_supported()
 except ImportError:
     sageattn = None
     alt_sageattn = None
-    sage_supported = False
+    sage2_supported = False
 # @torch.compiler.disable()
 def sageattn_wrapper(
         qkv_list,
@@ -134,9 +134,7 @@ def get_attention_modes():
 
 def get_supported_attention_modes():
     ret = get_attention_modes()
-    if not sage_supported:
-        if "sage" in ret:
-            ret.remove("sage")
+    if not sage2_supported:
         if "sage2" in ret:
             ret.remove("sage2")
     return ret

wan/modules/sage2_core.py

@@ -53,7 +53,7 @@ from typing import Any, List, Literal, Optional, Tuple, Union
 import warnings
 import os
 
-def is_sage_supported():
+def is_sage2_supported():
     device_count = torch.cuda.device_count()
     for i in range(device_count):
         major, minor = torch.cuda.get_device_capability(i)

wan/text2video.py

@@ -305,13 +305,11 @@ class WanT2V:
                 alpha = optimized_scale(positive_flat,negative_flat)
                 alpha = alpha.view(batch_size, 1, 1, 1)
 
-
                 if (i <= cfg_zero_step):
-                    noise_pred = noise_pred_text*0.
+                    noise_pred *= 0. # it would be faster not to compute noise_pred...
                 else:
-                    noise_pred = noise_pred_uncond * alpha + guide_scale * (noise_pred_text - noise_pred_uncond * alpha)
-            else:
-                noise_pred = noise_pred_uncond + guide_scale * (noise_pred_text - noise_pred_uncond)            
+                    noise_pred_uncond *= alpha
+            noise_pred = noise_pred_uncond + guide_scale * (noise_pred_text - noise_pred_uncond)            
             del noise_pred_uncond
 
             temp_x0 = sample_scheduler.step(