lora, 7b model, cfg

comfy/ldm/seedvr/model.py

@@ -1331,15 +1331,14 @@ class NaDiT(nn.Module):
         **kwargs
     ):  
         transformer_options = kwargs.get("transformer_options", {})
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
         conditions = kwargs.get("condition")
 
-        pos_cond, neg_cond = context.squeeze(0).chunk(2, dim=0)
+        pos_cond, neg_cond = context.chunk(2, dim=0)
         pos_cond, neg_cond = pos_cond.squeeze(0), neg_cond.squeeze(0)
-        pos_cond, txt_shape = flatten([pos_cond])
-        neg_cond, _ = flatten([neg_cond])
-        txt = torch.cat([pos_cond, neg_cond], dim = 0)
+        txt, txt_shape = flatten([pos_cond, neg_cond])
 
-        vid = x
         vid, vid_shape = flatten(x)
         cond_latent, _ = flatten(conditions)
 
@@ -1360,6 +1359,28 @@ class NaDiT(nn.Module):
 
         cache = Cache(disable=disable_cache)
         for i, block in enumerate(self.blocks):
+            if ("block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["vid"], out["txt"], out["vid_shape"], out["txt_shape"] = block(
+                            vid=args["vid"],
+                            txt=args["txt"],
+                            vid_shape=args["vid_shape"],
+                            txt_shape=args["txt_shape"],
+                            emb=args["emb"],
+                            cache=args["cache"],
+                        )
+                    return out
+                out = blocks_replace[("block", i)]({
+                        "vid":vid,
+                        "txt":txt,
+                        "vid_shape":vid_shape,
+                        "txt_shape":txt_shape,
+                        "emb":emb,
+                        "cache":cache,
+                    }, {"original_block": block_wrap})
+                vid, txt, vid_shape, txt_shape = out["vid"], out["txt"], out["vid_shape"], out["txt_shape"]
+            else:
                 vid, txt, vid_shape, txt_shape = block(
                     vid=vid,
                     txt=txt,
@@ -1383,4 +1404,4 @@ class NaDiT(nn.Module):
 
         vid, vid_shape = self.vid_out(vid, vid_shape, cache, vid_shape_before_patchify = vid_shape_before_patchify)
         vid = unflatten(vid, vid_shape)
-        return vid[0]
+        return torch.stack(vid)

comfy/model_detection.py

@@ -342,6 +342,16 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["axes_lens"] = [300, 512, 512]
         return dit_config
     
+    elif "{}blocks.36.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 7b
+        dit_config = {}
+        dit_config["image_model"] = "seedvr2"
+        dit_config["vid_dim"] = 3072
+        dit_config["heads"] = 24
+        dit_config["num_layers"] = 36
+        dit_config["norm_eps"] = 1e-5
+        dit_config["qk_rope"] = True
+        dit_config["mlp_type"] = "normal"
+        return dit_config
     elif "{}blocks.31.mlp.all.proj_in_gate.weight".format(key_prefix) in state_dict_keys: # seedvr2 3b
         dit_config = {}
         dit_config["image_model"] = "seedvr2"
@@ -352,7 +362,6 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["qk_rope"] = None
         dit_config["mlp_type"] = "swiglu"
         dit_config["vid_out_norm"] = True
-
         return dit_config
 
     if '{}head.modulation'.format(key_prefix) in state_dict_keys:  # Wan 2.1

comfy/supported_models.py

@@ -1163,6 +1163,9 @@ class SeedVR2(supported_models_base.BASE):
     vae_key_prefix = ["vae."]
     text_encoder_key_prefix = ["text_encoders."]
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
+    sampling_settings = {
+        "shift": 1.0,
+    }
 
     def get_model(self, state_dict, prefix = "", device=None):
         out = model_base.SeedVR2(self, device=device)

comfy_extras/nodes_seedvr.py

@@ -198,9 +198,8 @@ class SeedVR2Conditioning(io.ComfyNode):
         else:
             pos_cond = F.pad(pos_cond, (0, 0, 0, diff))
 
-        cond = torch.cat([pos_cond.unsqueeze(0), neg_cond.unsqueeze(0)]).unsqueeze(0)
-        negative = [[cond, {"condition": condition}]]
-        positive = [[cond, {"condition": condition}]]
+        negative = [[neg_cond.unsqueeze(0), {"condition": condition}]]
+        positive = [[pos_cond.unsqueeze(0), {"condition": condition}]]
 
         return io.NodeOutput(positive, negative, {"samples": noises})