various fixes

README.md

@@ -122,7 +122,9 @@ See full changelog: **[Changelog](docs/CHANGELOG.md)**
 
 ## 🚀 Quick Start
 
-**One-click installation:** Get started instantly with [Pinokio App](https://pinokio.computer/)
+**One-click installation:** 
+- Get started instantly with [Pinokio App](https://pinokio.computer/)
+- Use Redtash1 [One Click Install with Sage](https://github.com/Redtash1/Wan2GP-Windows-One-Click-Install-With-Sage)
 
 **Manual installation:**
 ```bash
@@ -136,8 +138,7 @@ pip install -r requirements.txt
 
 **Run the application:**
 ```bash
-python wgp.py  # Text-to-video (default)
+python wgp.py
-python wgp.py --i2v  # Image-to-video
 ```
 
 **Update the application:**

models/qwen/pipeline_qwenimage.py

@@ -232,7 +232,7 @@ class QwenImagePipeline(): #DiffusionPipeline
         drop_idx = self.prompt_template_encode_start_idx
         txt = [template.format(e) for e in prompt]
 
-        if self.processor is not None and image is not None:
+        if self.processor is not None and image is not None and len(image) > 0:
             img_prompt_template = "Picture {}: <|vision_start|><|image_pad|><|vision_end|>"
             if isinstance(image, list):
                 base_img_prompt = ""
@@ -972,7 +972,7 @@ class QwenImagePipeline(): #DiffusionPipeline
 
             if callback is not None:
                 preview = self._unpack_latents(latents, height, width, self.vae_scale_factor)
-                preview = preview.squeeze(0)
+                preview = preview.transpose(0,2).squeeze(0)
                 callback(i, preview, False)         
 
 

models/wan/any2video.py

@@ -595,7 +595,7 @@ class WanAny2V:
                 color_reference_frame = input_frames[:, -1:].clone()
                 if prefix_frames_count > 0:
                     overlapped_frames_num = prefix_frames_count
-                    overlapped_latents_frames_num = (overlapped_latents_frames_num -1 // 4) + 1 
+                    overlapped_latents_frames_num = (overlapped_frames_num -1 // 4) + 1 
                     # overlapped_latents_frames_num = overlapped_latents.shape[2]
                     # overlapped_frames_num = (overlapped_latents_frames_num-1) * 4 + 1
                 else: 
@@ -735,11 +735,20 @@ class WanAny2V:
         if callback != None:
             callback(-1, None, True)
 
+
+        clear_caches()
         offload.shared_state["_chipmunk"] =  False
         chipmunk = offload.shared_state.get("_chipmunk", False)        
         if chipmunk:
             self.model.setup_chipmunk()
 
+        offload.shared_state["_radial"] =  offload.shared_state["_attention"]=="radial"
+        radial = offload.shared_state.get("_radial", False)        
+        if radial:
+            radial_cache = get_cache("radial")
+            from shared.radial_attention.attention import fill_radial_cache
+            fill_radial_cache(radial_cache, len(self.model.blocks), *target_shape[1:])
+
         # init denoising
         updated_num_steps= len(timesteps)
 

models/wan/modules/model.py

@@ -315,6 +315,8 @@ class WanSelfAttention(nn.Module):
             x_ref_attn_map = None
 
         chipmunk = offload.shared_state.get("_chipmunk", False) 
+        radial = offload.shared_state.get("_radial", False) 
+
         if chipmunk and self.__class__ == WanSelfAttention:
             q = q.transpose(1,2)
             k = k.transpose(1,2)
@@ -322,12 +324,17 @@ class WanSelfAttention(nn.Module):
             attn_layers = offload.shared_state["_chipmunk_layers"]
             x = attn_layers[self.block_no](q, k, v)
             x = x.transpose(1,2)
+        elif radial and self.__class__ == WanSelfAttention:
+            qkv_list = [q,k,v]
+            del q,k,v
+            radial_cache = get_cache("radial")
+            no_step_no = offload.shared_state["step_no"] 
+            x = radial_cache[self.block_no](qkv_list=qkv_list, timestep_no=no_step_no)
         elif block_mask == None:
             qkv_list = [q,k,v]
             del q,k,v
-            x = pay_attention(
+
-                qkv_list,
+            x = pay_attention( qkv_list, window_size=self.window_size)
-                window_size=self.window_size)
 
         else:
             with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
@@ -1311,9 +1318,8 @@ class WanModel(ModelMixin, ConfigMixin):
             del causal_mask
 
         offload.shared_state["embed_sizes"] = grid_sizes 
-        offload.shared_state["step_no"] = real_step_no 
+        offload.shared_state["step_no"] = current_step_no 
         offload.shared_state["max_steps"] = max_steps
-        if current_step_no == 0 and x_id == 0: clear_caches()
         # arguments
 
         kwargs = dict(

models/wan/wan_handler.py

@@ -184,6 +184,15 @@ class family_handler():
                 "visible": False
             }
 
+            # extra_model_def["image_ref_choices"] = {
+            #         "choices": [("None", ""),
+            #             ("People / Objects", "I"),
+            #             ("Landscape followed by People / Objects (if any)", "KI"),
+            #             ],
+            #         "visible": False,
+            #         "letters_filter":  "KFI",
+            # }
+
             extra_model_def["video_guide_outpainting"] = [0,1]
             extra_model_def["keep_frames_video_guide_not_supported"] = True
             extra_model_def["extract_guide_from_window_start"] = True

shared/attention.py

@@ -42,6 +42,11 @@ try:
 except ImportError:
     sageattn_varlen_wrapper = None
 
+try:
+    from spas_sage_attn import block_sparse_sage2_attn_cuda
+except ImportError:
+    block_sparse_sage2_attn_cuda = None
+
 
 try:
     from .sage2_core import sageattn as sageattn2, is_sage2_supported
@@ -62,6 +67,8 @@ def sageattn2_wrapper(
 
     return o
 
+from sageattn import sageattn_blackwell as sageattn3
+
 try:
     from sageattn import sageattn_blackwell as sageattn3
 except ImportError:
@@ -144,6 +151,9 @@ def get_attention_modes():
         ret.append("sage")
     if sageattn2 != None and version("sageattention").startswith("2") :
         ret.append("sage2")
+    if block_sparse_sage2_attn_cuda != None and version("sageattention").startswith("2") :
+        ret.append("radial")
+
     if sageattn3 != None: # and version("sageattention").startswith("3") :
         ret.append("sage3")
         
@@ -159,6 +169,8 @@ def get_supported_attention_modes():
     if not sage2_supported:
         if "sage2" in ret:
             ret.remove("sage2")
+        if "radial" in ret:
+            ret.remove("radial")
 
     if  major < 7:
         if "sage" in ret:
@@ -225,6 +237,7 @@ def pay_attention(
     if attn == "chipmunk":
         from src.chipmunk.modules import SparseDiffMlp, SparseDiffAttn
         from src.chipmunk.util import LayerCounter, GLOBAL_CONFIG
+    if attn == "radial": attn ="sage2"
 
     if b > 1 and k_lens != None and attn in ("sage2", "sage3", "sdpa"):
         assert attention_mask == None

shared/sage2_core.py

@@ -28,18 +28,24 @@ from sageattention.triton.quant_per_thread import per_thread_int8 as per_thread_
 
 try:
     from sageattention import _qattn_sm80
+    if not hasattr(_qattn_sm80, "qk_int8_sv_f16_accum_f32_attn"): 
+        _qattn_sm80 = torch.ops.sageattention_qattn_sm80
     SM80_ENABLED = True
 except:
     SM80_ENABLED = False
 
 try:
     from sageattention import _qattn_sm89
+    if not hasattr(_qattn_sm89, "qk_int8_sv_f8_accum_f32_fuse_v_scale_attn_inst_buf"): 
+        _qattn_sm89 = torch.ops.sageattention_qattn_sm89
     SM89_ENABLED = True
 except:
     SM89_ENABLED = False
 
 try:
     from sageattention import _qattn_sm90
+    if not hasattr(_qattn_sm90, "qk_int8_sv_f8_accum_f32_fuse_v_scale_attn_inst_buf"): 
+        _qattn_sm90 = torch.ops.sageattention_qattn_sm90
     SM90_ENABLED = True
 except:
     SM90_ENABLED = False

wgp.py

@@ -63,7 +63,7 @@ AUTOSAVE_FILENAME = "queue.zip"
 PROMPT_VARS_MAX = 10
 
 target_mmgp_version = "3.6.0"
-WanGP_version = "8.74"
+WanGP_version = "8.75"
 settings_version = 2.36
 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
@@ -1394,6 +1394,12 @@ def _parse_args():
         help="View form generation / refresh time"
     )
 
+    parser.add_argument(
+        "--betatest",
+        action="store_true",
+        help="test unreleased features"
+    )
+
     parser.add_argument(
         "--vram-safety-coefficient",
         type=float,
@@ -4367,7 +4373,7 @@ def enhance_prompt(state, prompt, prompt_enhancer, multi_images_gen_type, overri
     if image_start is None or not "I" in prompt_enhancer:
         image_start = [None] * num_prompts
     else:
-        image_start = [img[0] for img in image_start]
+        image_start = [convert_image(img[0]) for img in image_start]
         if len(image_start) == 1:
             image_start = image_start * num_prompts
         else:
@@ -8712,9 +8718,9 @@ def generate_configuration_tab(state, blocks, header, model_family, model_choice
                         ("Flash" + check("flash")+ ": good quality - requires additional install (usually complex to set up on Windows without WSL)", "flash"),
                         ("Xformers" + check("xformers")+ ": good quality - requires additional install (usually complex, may consume less VRAM to set up on Windows without WSL)", "xformers"),
                         ("Sage" + check("sage")+ ": 30% faster but slightly worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage"),
-                        ("Sage2/2++" + check("sage2")+ ": 40% faster but slightly worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage2"),
+                        ("Sage2/2++" + check("sage2")+ ": 40% faster but slightly worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage2")]\
-                        ("Sage3" + check("sage3")+ ": x2 faster but worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage3"),
+                        + ([("Radial" + check("radial")+ ": x? faster but ? quality - requires Sparge & Sage 2 Attn (usually complex to set up on Windows without WSL)", "radial")] if args.betatest else [])\
-                    ],
+                        +  [("Sage3" + check("sage3")+ ": x2 faster but worse quality - requires additional install (usually complex to set up on Windows without WSL)", "sage3")],
                     value= attention_mode,
                     label="Attention Type",
                     interactive= not lock_ui_attention