Merge branch 'main' into bindweave

Seems to need smaller resolutions

nodes.py

@@ -765,6 +765,98 @@ class WanVideoAddStandInLatent:
         updated = dict(embeds)
         updated["standin_input"] = new_entry
         return (updated,)
+    
+class WanVideoAddBindweaveEmbeds:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "embeds": ("WANVIDIMAGE_EMBEDS",),
+                    "reference_latents": ("LATENT", {"tooltip": "Reference image to encode"}),
+                }, 
+                "optional": {
+                    "ref_masks": ("MASK", {"tooltip": "Reference mask to encode"}),
+                    "qwenvl_embeds_pos": ("QWENVL_EMBEDS", {"tooltip": "Qwen-VL image embeddings for the reference image"}),
+                    "qwenvl_embeds_neg": ("QWENVL_EMBEDS", {"tooltip": "Qwen-VL image embeddings for the reference image"}),
+                }
+        }
+
+    RETURN_TYPES = ("WANVIDIMAGE_EMBEDS", "LATENT", "MASK",)
+    RETURN_NAMES = ("image_embeds", "image_embed_preview", "mask_preview",)
+    FUNCTION = "add"
+    CATEGORY = "WanVideoWrapper"
+
+    def add(self, embeds, reference_latents, ref_masks=None, qwenvl_embeds_pos=None, qwenvl_embeds_neg=None):
+        updated = dict(embeds)
+        image_embeds = embeds["image_embeds"]
+        max_refs = 4
+        num_refs = reference_latents["samples"].shape[0]
+        pad = torch.zeros(image_embeds.shape[0], max_refs-num_refs, image_embeds.shape[2], image_embeds.shape[3], device=image_embeds.device, dtype=image_embeds.dtype)
+        if num_refs < max_refs:
+            image_embeds = torch.cat([pad, image_embeds], dim=1)
+        ref_latents = [ref_latent for ref_latent in reference_latents["samples"]]
+        image_embeds = torch.cat([*ref_latents, image_embeds], dim=1)
+        
+        mask = embeds.get("mask", None)
+        if mask is not None:
+            mask_pad = torch.zeros(mask.shape[0], max_refs-num_refs, mask.shape[2], mask.shape[3], device=mask.device, dtype=mask.dtype)
+            if num_refs < max_refs:
+                mask = torch.cat([mask_pad, mask], dim=1)
+            if ref_masks is not None:
+                ref_mask_ = common_upscale(ref_masks.unsqueeze(1), mask.shape[3], mask.shape[2], "nearest", "disabled").movedim(0,1)
+                ref_mask_ = torch.cat([ref_mask_, torch.zeros(3, ref_mask_.shape[1], ref_mask_.shape[2], ref_mask_.shape[3], device=ref_mask_.device, dtype=ref_mask_.dtype)])
+                mask = torch.cat([ref_mask_, mask], dim=1)
+            else:
+                mask = torch.cat([torch.ones(mask.shape[0], num_refs, mask.shape[2], mask.shape[3], device=mask.device, dtype=mask.dtype), mask], dim=1)
+
+            updated["mask"] = mask
+
+        clip_embeds = updated.get("clip_context", None)
+        if clip_embeds is not None:
+            B, T, C = clip_embeds.shape
+            target_len = max_refs * 257  # 4 * 257 = 1028
+            if T < target_len:
+                pad = torch.zeros(B, target_len - T, C, device=clip_embeds.device, dtype=clip_embeds.dtype)
+                padded_embeds = torch.cat([clip_embeds, pad], dim=1)
+                log.info(f"Padded clip embeds from {clip_embeds.shape} to {padded_embeds.shape} for Bindweave")
+                updated["clip_context"] = padded_embeds
+            else:
+                updated["clip_context"] = clip_embeds
+
+        updated["image_embeds"] = image_embeds
+        updated["qwenvl_embeds_pos"] = qwenvl_embeds_pos
+        updated["qwenvl_embeds_neg"] = qwenvl_embeds_neg
+        return (updated, {"samples": image_embeds.unsqueeze(0)}, mask[0].float())
+    
+class TextImageEncodeQwenVL():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "clip": ("CLIP",),
+                    "prompt": ("STRING", {"default": "", "multiline": True}),
+                }, 
+                "optional": {
+                    "image": ("IMAGE", ),
+                }
+        }
+
+    RETURN_TYPES = ("QWENVL_EMBEDS",)
+    RETURN_NAMES = ("qwenvl_embeds",)
+    FUNCTION = "add"
+    CATEGORY = "WanVideoWrapper"
+
+    def add(cls, clip, prompt, image=None):
+        if image is None:
+            input_images = []
+            llama_template = None
+        else:
+            input_images = [image[:, :, :, :3]]
+
+            llama_template = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
+
+        tokens = clip.tokenize(prompt, images=input_images, llama_template=llama_template)
+        conditioning = clip.encode_from_tokens_scheduled(tokens)
+        print("Qwen-VL embeds shape:", conditioning[0][0].shape)
+        return (conditioning[0][0],)
 
 class WanVideoAddMTVMotion:
     @classmethod
@@ -835,10 +927,6 @@ class WanVideoImageToVideoEncode:
                 start_latent_strength, end_latent_strength, start_image=None, end_image=None, control_embeds=None, fun_or_fl2v_model=False, 
                 temporal_mask=None, extra_latents=None, clip_embeds=None, tiled_vae=False, add_cond_latents=None, vae=None):
         
-        if start_image is None and end_image is None and add_cond_latents is None:
-            return WanVideoEmptyEmbeds().process(
-                num_frames, width, height, control_embeds=control_embeds, extra_latents=extra_latents,
-            )
         if vae is None:
             raise ValueError("VAE is required for image encoding.")
         H = height
@@ -956,7 +1044,7 @@ class WanVideoImageToVideoEncode:
             gc.collect()
 
         image_embeds = {
-            "image_embeds": y,
+            "image_embeds": y.cpu(),
             "clip_context": clip_embeds.get("clip_embeds", None) if clip_embeds is not None else None,
             "negative_clip_context": clip_embeds.get("negative_clip_embeds", None) if clip_embeds is not None else None,
             "max_seq_len": max_seq_len,
@@ -968,7 +1056,7 @@ class WanVideoImageToVideoEncode:
             "fun_or_fl2v_model": fun_or_fl2v_model,
             "has_ref": has_ref,
             "add_cond_latents": add_cond_latents,
-            "mask": mask
+            "mask": mask.cpu()
         }
 
         return (image_embeds,)
@@ -2246,6 +2334,8 @@ NODE_CLASS_MAPPINGS = {
     "WanVideoAnimateEmbeds": WanVideoAnimateEmbeds,
     "WanVideoAddLucyEditLatents": WanVideoAddLucyEditLatents,
     "WanVideoSchedulerSA_ODE": WanVideoSchedulerSA_ODE,
+    "WanVideoAddBindweaveEmbeds": WanVideoAddBindweaveEmbeds,
+    "TextImageEncodeQwenVL": TextImageEncodeQwenVL,
     "WanVideoUniLumosEmbeds": WanVideoUniLumosEmbeds,
     }
 
@@ -2286,5 +2376,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "WanVideoAnimateEmbeds": "WanVideo Animate Embeds",
     "WanVideoAddLucyEditLatents": "WanVideo Add LucyEdit Latents",
     "WanVideoSchedulerSA_ODE": "WanVideo Scheduler SA-ODE",
+    "WanVideoAddBindweaveEmbeds": "WanVideo Add Bindweave Embeds",
     "WanVideoUniLumosEmbeds": "WanVideo UniLumos Embeds",
 }

nodes_model_loading.py

@@ -1486,6 +1486,12 @@ class WanVideoModelLoader:
             transformer.add_proj = zero_module(torch.nn.Linear(inner_dim, inner_dim))
             transformer.attn_conv_in = torch.nn.Conv3d(attn_cond_in_dim, inner_dim, kernel_size=transformer.patch_size, stride=transformer.patch_size)
         
+        # Bindweave text_projection
+        if "text_projection.0.weight" in sd:
+            log.info("Bindweave model detected, adding text_projection to the model")
+            text_dim = sd["text_projection.0.weight"].shape[0]
+            transformer.text_projection = nn.Sequential(nn.Linear(sd["text_projection.0.weight"].shape[1], text_dim), nn.GELU(approximate='tanh'), nn.Linear(text_dim, text_dim))
+
         latent_format=Wan22 if dim == 3072 else Wan21
         comfy_model = WanVideoModel(
             WanVideoModelConfig(base_dtype, latent_format=latent_format),

nodes_sampler.py

@@ -342,7 +342,8 @@ class WanVideoSampler:
                 dtype=torch.float32,
                 generator=seed_g,
                 device=torch.device("cpu"))
-            seq_len = image_embeds["max_seq_len"]
+            
+            seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
 
             control_embeds = image_embeds.get("control_embeds", None)
             if control_embeds is not None:
@@ -411,7 +412,7 @@ class WanVideoSampler:
                     dtype=torch.float32,
                     device=torch.device("cpu"),
                     generator=seed_g)
-
+            
             seq_len = math.ceil((noise.shape[2] * noise.shape[3]) / 4 * noise.shape[1])
 
             recammaster = image_embeds.get("recammaster", None)
@@ -915,6 +916,9 @@ class WanVideoSampler:
             rope_function = "default" #echoshot does not support comfy rope function
             log.info(f"Number of shots in prompt: {shot_num}, Shot token lengths: {shot_len}")
 
+        # Bindweave
+        qwenvl_embeds_pos = image_embeds.get("qwenvl_embeds_pos", None)
+        qwenvl_embeds_neg = image_embeds.get("qwenvl_embeds_neg", None)
 
         mm.unload_all_models()
         mm.soft_empty_cache()
@@ -1357,6 +1361,16 @@ class WanVideoSampler:
                     z = z * c_in
                     timestep = c_noise
 
+                if image_cond is not None:
+                    self.noise_front_pad_num = image_cond_input.shape[1] - z.shape[1]
+                    if self.noise_front_pad_num > 0:
+                        pad = torch.zeros((z.shape[0], self.noise_front_pad_num, z.shape[2], z.shape[3]), dtype=z.dtype, device=z.device)
+                        z = torch.concat([pad, z], dim=1)
+                        nonlocal seq_len
+                        seq_len = math.ceil((z.shape[2] * z.shape[3]) / 4 * z.shape[1])
+                else:
+                    self.noise_front_pad_num = 0
+
                 if background_latents is not None or foreground_latents is not None:
                     z = torch.cat([z, foreground_latents.to(z), background_latents.to(z)], dim=0)
 
@@ -1415,7 +1429,7 @@ class WanVideoSampler:
                     "ovi_negative_text_embeds": ovi_negative_text_embeds, # Audio latent model negative text embeds for Ovi
                     "flashvsr_LQ_latent": flashvsr_LQ_latent, # FlashVSR LQ latent for upsampling
                     "flashvsr_strength": flashvsr_strength, # FlashVSR strength
-                    "num_cond_latents": len(all_indices) if transformer.is_longcat else None # number of cond latents LongCat to separate attention
+                    "num_cond_latents": len(all_indices) if transformer.is_longcat else None,
                 }
 
                 batch_size = 1
@@ -1431,6 +1445,7 @@ class WanVideoSampler:
                         #conditional (positive) pass
                         if pos_latent is not None: # for humo
                             base_params['x'] = [torch.cat([z[:, :-humo_reference_count], pos_latent], dim=1)]
+                        base_params["add_text_emb"] = qwenvl_embeds_pos.to(device) if qwenvl_embeds_pos is not None else None # QwenVL embeddings for Bindweave
                         noise_pred_cond, noise_pred_ovi, cache_state_cond = transformer(
                             context=positive_embeds,
                             pred_id=cache_state[0] if cache_state else None,
@@ -1447,6 +1462,7 @@ class WanVideoSampler:
                         #unconditional (negative) pass
                         base_params['is_uncond'] = True
                         base_params['clip_fea'] = clip_fea_neg if clip_fea_neg is not None else clip_fea
+                        base_params["add_text_emb"] = qwenvl_embeds_neg.to(device) if qwenvl_embeds_neg is not None else None # QwenVL embeddings for Bindweave
                         if wananim_face_pixels is not None:
                             base_params['wananim_face_pixel_values'] = torch.zeros_like(wananim_face_pixels).to(device, torch.float32) - 1
                         if humo_audio_input_neg is not None:
@@ -2975,6 +2991,9 @@ class WanVideoSampler:
                     if flowedit_args is None:
                         latent = latent.to(intermediate_device)
 
+                        if self.noise_front_pad_num > 0:
+                            noise_pred = noise_pred[:, self.noise_front_pad_num:]
+
                         if use_tsr:
                             noise_pred = temporal_score_rescaling(noise_pred, latent, timestep, tsr_k, tsr_sigma)
 

wanvideo/modules/model.py

@@ -2226,6 +2226,7 @@ class WanModel(torch.nn.Module):
         x_ovi=None, seq_len_ovi=None, ovi_negative_text_embeds=None,
         flashvsr_LQ_latent=None, flashvsr_strength=1.0,
         num_cond_latents=None,
+        add_text_emb=None,
     ):
         r"""
         Forward pass through the diffusion model
@@ -2599,8 +2600,13 @@ class WanModel(torch.nn.Module):
                     torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context_ovi]).to(text_embed_dtype))
 
             tokens = context[0].shape[0]
-            context = self.text_embedding(
-            torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context]).to(text_embed_dtype))
+            context = torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context]).to(text_embed_dtype)
+
+            if add_text_emb is not None:
+                self.text_projection.to(self.main_device)
+                add_text_emb = self.text_projection(add_text_emb.to(self.text_projection[0].weight.dtype)).to(text_embed_dtype)
+                context = torch.cat([add_text_emb, context], dim=1)
+            context = self.text_embedding(context)
 
             if self.is_longcat:
                 context[:, tokens:] = 0