added input point number

README.md

@@ -66,7 +66,8 @@ ComfyUI/models/facebook/dinov3-vitl16-pretrain-lvd1689m/
 | `steps` | `INT` | Number of diffusion sampling steps (Default: 28). |
 | `guidance_scale` | `FLOAT` | CFG scale for image conditioning (Only active if `reference_image` is connected). |
 | `seed` | `INT` | Random seed for sampling latents. |
-| `num_verts` | `COMBO` | Target vertex resolution (1024 to 8192). Only active with the `meshflow_w_num_verts_control` model. |
+| `base_num_verts` | `COMBO` | The base resolution/point count of the loaded model checkpoint (1024, 2048, 4096, 8192, or 16384). |
+| `points` | `INT` | Target vertex resolution of the generated output mesh (1024 to 16384). Only active with the `meshflow_w_num_verts_control` model. |
 | `device` | `COMBO` | Execution device: `cuda` or `cpu`. |
 | `dtype` | `COMBO` | Computation precision: `fp16`, `bf16`, or `fp32`. |
 | `compile` | `BOOLEAN` | Uses `torch.compile` on CUDA for faster inference speeds. |

meshflow/pipelines/meshflow_pipeline.py

@@ -119,18 +119,16 @@ class MeshFlowPipeline:
         use_proj_cond_on_temb = bool(scfg.denoiser_model.get("use_proj_cond_on_temb", False))
 
         num_latents = int(scfg.mesh_model.num_latents)
-        if use_proj_cond_on_temb:
-            if num_verts is not None:
-                num_verts = int(num_verts)
-                config_num_verts = n_samples = input_size = num_verts
+        if num_verts is not None:
+            num_verts = int(num_verts)
+            config_num_verts = n_samples = input_size = num_verts
+            if use_proj_cond_on_temb:
+                pass
+            else:
+                num_latents = num_verts
         else:
-            if num_verts is not None:
-                print(
-                    "[MeshFlowPipeline] Ignoring num_verts: "
-                    "denoiser_model.use_proj_cond_on_temb is disabled in config"
-                )
-            num_verts = None
-            num_latents = None
+            if not use_proj_cond_on_temb:
+                num_latents = None
 
         dtype = resolve_torch_dtype(dtype)
         device = torch.device(device if torch.cuda.is_available() or device == "cpu" else "cpu")

nodes.py

@@ -50,7 +50,8 @@ class MeshFlowRemesh:
                 "steps": ("INT", {"default": 28, "min": 1, "max": 1000, "step": 1, "tooltip": "Number of diffusion sampling steps. Higher values can increase detail but take longer."}),
                 "guidance_scale": ("FLOAT", {"default": 2.5, "min": 0.0, "max": 100.0, "step": 0.1, "tooltip": "Classifier-Free Guidance (CFG) scale for visual conditioning. Only effective when reference_image is connected."}),
                 "seed": ("INT", {"default": 42, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Random seed for sampling latents."}),
-                "num_verts": ([1024, 2048, 3072, 4096, 5120, 6144, 7168, 8192], {"default": 4096, "tooltip": "Target vertex resolution. Roughly controls the generated mesh resolution."}),
+                "base_num_verts": ([1024, 2048, 4096, 8192, 16384], {"default": 4096, "tooltip": "The base resolution/point count of the loaded model checkpoint (sequence length)."}),
+                "points": ("INT", {"default": 4096, "min": 1024, "max": 16384, "step": 256, "tooltip": "Target resolution (points/vertices) of the generated output mesh."}),
                 "device": (["cuda", "cpu"], {"default": "cuda", "tooltip": "Computation device to run the model on (cuda or cpu)."}),
                 "dtype": (["fp16", "bf16", "fp32"], {"default": "fp16", "tooltip": "Precision model dtype (fp16, bf16, or fp32)."}),
                 "compile": ("BOOLEAN", {"default": False, "tooltip": "Whether to use torch.compile on CUDA for faster inference."}),
@@ -67,12 +68,12 @@ class MeshFlowRemesh:
     FUNCTION = "remesh"
     CATEGORY = "MeshFlow"
 
-    def remesh(self, trimesh, model_name, steps, guidance_scale, seed, num_verts, device, dtype, compile, use_rmbg, fill_holes, reference_image=None):
+    def remesh(self, trimesh, model_name, steps, guidance_scale, seed, base_num_verts, points, device, dtype, compile, use_rmbg, fill_holes, reference_image=None):
         model_path = os.path.join(folder_paths.models_dir, "facebook", "meshflow", model_name)
         if not os.path.isdir(model_path):
             raise FileNotFoundError(f"MeshFlow model path not found at {model_path}. Please download and place the config.yaml and model.pth in that directory.")
 
-        pipeline = get_pipeline(model_path, device, dtype, compile, num_verts)
+        pipeline = get_pipeline(model_path, device, dtype, compile, base_num_verts)
         pipeline.use_rmbg = use_rmbg
 
         if reference_image is not None:
@@ -115,7 +116,7 @@ class MeshFlowRemesh:
             steps=steps,
             guidance_scale=guidance_scale,
             seed=seed,
-            num_verts=num_verts,
+            num_verts=points,
             return_latent=True
         )