Add experimental GLIGEN node

__init__.py

@@ -102,6 +102,7 @@ NODE_CLASS_MAPPINGS = {
     "LoadResAdapterNormalization": LoadResAdapterNormalization,
     "Superprompt": Superprompt,
     "GLIGENTextBoxApplyBatch": GLIGENTextBoxApplyBatch,
+    "GLIGENTextBoxApplyBatchCoords": GLIGENTextBoxApplyBatchCoords,
     "Intrinsic_lora_sampling": Intrinsic_lora_sampling,
 
 }

nodes/curve_nodes.py

@@ -55,7 +55,8 @@ class SplineEditor:
             }
         }
 
-    RETURN_TYPES = ("MASK", "STRING", "FLOAT")
+    RETURN_TYPES = ("MASK", "STRING", "FLOAT", "INT")
+    RETURN_NAMES = ("mask", "string", "float", "count")
     FUNCTION = "splinedata"
     CATEGORY = "KJNodes/experimental"
     DESCRIPTION = """
@@ -126,7 +127,7 @@ output types:
         masks_out = torch.stack(mask_tensors)
         masks_out = masks_out.repeat(repeat_output, 1, 1, 1)
         masks_out = masks_out.mean(dim=-1)
-        return (masks_out, str(coordinates), out_floats,)
+        return (masks_out, str(coordinates), out_floats, len(out_floats))
 
 class CreateShapeMaskOnPath:
     
@@ -485,4 +486,115 @@ Creates a sigmas tensor from list of float values.
 
 """
     def customsigmas(self, float_list):
-        return torch.tensor(float_list, dtype=torch.float32),
+        return torch.tensor(float_list, dtype=torch.float32),
+
+class GLIGENTextBoxApplyBatchCoords:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ),
+                              "latents": ("LATENT", ),
+                              "clip": ("CLIP", ),
+                              "gligen_textbox_model": ("GLIGEN", ),
+                              "coordinates": ("STRING", {"forceInput": True}),
+                              "text": ("STRING", {"multiline": True}),
+                              "width": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 8}),
+                              "height": ("INT", {"default": 64, "min": 8, "max": 4096, "step": 8}),
+                            },
+                }
+    RETURN_TYPES = ("CONDITIONING", "IMAGE", )
+    FUNCTION = "append"
+    CATEGORY = "KJNodes/experimental"
+    DESCRIPTION = """
+Experimental, does not function yet as ComfyUI base changes are needed
+"""
+
+    def append(self, latents, coordinates, conditioning_to, clip, gligen_textbox_model, text, width, height):
+        coordinates = json.loads(coordinates.replace("'", '"'))
+        coordinates = [(coord['x'], coord['y']) for coord in coordinates]
+
+        batch_size = sum(tensor.size(0) for tensor in latents.values())
+        assert len(coordinates) == batch_size, "The number of coordinates does not match the number of latents"
+        c = []
+        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+
+        image_height = latents['samples'].shape[-1] * 8
+        image_width = latents['samples'].shape[-2] * 8
+        plot_image_tensor = self.plot_coordinates_to_tensor(coordinates, image_height, image_width, height)
+
+        for t in conditioning_to:
+            n = [t[0], t[1].copy()]
+            
+            position_params_batch = [[] for _ in range(batch_size)]  # Initialize a list of empty lists for each batch item
+            
+            for i in range(batch_size):
+                x_position, y_position = coordinates[i] 
+                position_param = (cond_pooled, height // 8, width // 8, y_position // 8, x_position // 8)
+                position_params_batch[i].append(position_param)  # Append position_param to the correct sublist
+
+            prev = []
+            if "gligen" in n[1]:
+                prev = n[1]['gligen'][2]
+            else:
+                prev = [[] for _ in range(batch_size)]
+            # Concatenate prev and position_params_batch, ensuring both are lists of lists
+            # and each sublist corresponds to a batch item
+            combined_position_params = [prev_item + batch_item for prev_item, batch_item in zip(prev, position_params_batch)]
+            n[1]['gligen'] = ("position", gligen_textbox_model, combined_position_params)
+            c.append(n)
+        
+        return (c, plot_image_tensor,)
+    
+    def plot_coordinates_to_tensor(self, coordinates, height, width, box_size):
+        import matplotlib
+        matplotlib.use('Agg')
+        from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+
+        # Convert coordinates to separate x and y lists
+        #x_coords, y_coords = zip(*coordinates)
+
+        fig, ax = matplotlib.pyplot.subplots(figsize=(width/100, height/100), dpi=100)
+        #ax.scatter(x_coords, y_coords, color='yellow', label='_nolegend_')
+
+        # Draw a box at each coordinate
+        for x, y in coordinates:
+            rect = matplotlib.patches.Rectangle((x - box_size/2, y - box_size/2), box_size, box_size,
+                                    linewidth=1, edgecolor='green', facecolor='none', alpha=0.5)
+            ax.add_patch(rect)
+
+         # Draw arrows from one point to another to indicate direction
+        for i in range(len(coordinates) - 1):
+            x1, y1 = coordinates[i]
+            x2, y2 = coordinates[i + 1]
+            ax.annotate("", xy=(x2, y2), xytext=(x1, y1),
+                        arrowprops=dict(arrowstyle="->",
+                                        linestyle="-",
+                                        lw=1,
+                                        color='orange',
+                                        mutation_scale=10))
+        matplotlib.pyplot.rcParams['text.color'] = '#999999'
+        fig.patch.set_facecolor('#353535')
+        ax.set_facecolor('#353535')
+        ax.grid(color='#999999', linestyle='-', linewidth=0.5)
+        ax.set_xlabel('x', color='#999999')
+        ax.set_ylabel('y', color='#999999')
+        for text in ax.get_xticklabels() + ax.get_yticklabels():
+            text.set_color('#999999')
+        ax.set_title('Gligen positions')
+        ax.set_xlabel('X Coordinate')
+        ax.set_ylabel('Y Coordinate')
+        ax.legend().remove()
+        ax.set_xlim(0, width) # Set the x-axis to match the input latent width
+        ax.set_ylim(height, 0) # Set the y-axis to match the input latent height, with (0,0) at top-left
+        # Adjust the margins of the subplot
+        matplotlib.pyplot.subplots_adjust(left=0.08, right=0.95, bottom=0.05, top=0.95, wspace=0.2, hspace=0.2)
+        canvas = FigureCanvas(fig)
+        canvas.draw()
+        matplotlib.pyplot.close(fig)
+
+        width, height = fig.get_size_inches() * fig.get_dpi()        
+
+        image_np = np.frombuffer(canvas.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
+        image_tensor = torch.from_numpy(image_np).float() / 255.0
+        image_tensor = image_tensor.unsqueeze(0)
+
+        return image_tensor

web/js/spline_editor.js

@@ -181,7 +181,7 @@ app.registerExtension({
               }
             });
             
-            this.setSize([550, 900]);
+            this.setSize([550, 920]);
             this.resizable = false;
             this.splineEditor.parentEl = document.createElement("div");
             this.splineEditor.parentEl.className = "spline-editor";
@@ -190,7 +190,7 @@ app.registerExtension({
 
             chainCallback(this, "onGraphConfigured", function() {
               createSplineEditor(this);
-              this.setSize([550, 900]);
+              this.setSize([550, 920]);
               });
               
           }); // onAfterGraphConfigured