lora

nodes/image_node.py

@@ -240,6 +240,65 @@ def generate_image(self, prompt, image_size, width, height, num_images, safety_t
             print(f"Error generating image with FluxPro 1.1: {str(e)}")
             return self.create_blank_image()
 
+class FluxLora:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"default": "", "multiline": True}),
+                "image_size": (["square_hd", "square", "portrait_4_3", "portrait_16_9", "landscape_4_3", "landscape_16_9", "custom"], {"default": "landscape_4_3"}),
+                "width": ("INT", {"default": 1024, "min": 512, "max": 1536, "step": 16}),
+                "height": ("INT", {"default": 768, "min": 512, "max": 1536, "step": 16}),
+                "num_inference_steps": ("INT", {"default": 28, "min": 1, "max": 50}),
+                "guidance_scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 20.0, "step": 0.1}),
+                "num_images": ("INT", {"default": 1, "min": 1, "max": 4}),
+                "enable_safety_checker": ("BOOLEAN", {"default": True}),
+                "lora_path_1": ("STRING", {"default": ""}),
+                "lora_scale_1": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.1}),
+            },
+            "optional": {
+                "seed": ("INT", {"default": -1}),
+                "lora_path_2": ("STRING", {"default": ""}),
+                "lora_scale_2": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.1}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "generate_image"
+    CATEGORY = "FAL/Image"
+
+    def generate_image(self, prompt, image_size, width, height, num_inference_steps, guidance_scale, num_images, enable_safety_checker, lora_path_1, lora_scale_1, seed=-1, lora_path_2="", lora_scale_2=0.7):
+        arguments = {
+            "prompt": prompt,
+            "num_inference_steps": num_inference_steps,
+            "guidance_scale": guidance_scale,
+            "num_images": num_images,
+            "enable_safety_checker": enable_safety_checker,
+        }
+        if image_size == "custom":
+            arguments["image_size"] = {"width": width, "height": height}
+        else:
+            arguments["image_size"] = image_size
+        if seed != -1:
+            arguments["seed"] = seed
+
+        # Add LoRAs
+        loras = []
+        if lora_path_1:
+            loras.append({"path": lora_path_1, "scale": lora_scale_1})
+        if lora_path_2:
+            loras.append({"path": lora_path_2, "scale": lora_scale_2})
+        if loras:
+            arguments["loras"] = loras
+
+        try:
+            handler = submit("fal-ai/flux-lora", arguments=arguments)
+            result = handler.get()
+            return self.process_result(result)
+        except Exception as e:
+            print(f"Error generating image with FluxLora: {str(e)}")
+            return self.create_blank_image()
+
 class FluxGeneral:
     @classmethod
     def INPUT_TYPES(cls):
@@ -285,14 +344,18 @@ def INPUT_TYPES(cls):
                 "control_mask": ("MASK",),
                 "ip_adapter_image": ("IMAGE",),
                 "ip_adapter_mask": ("MASK",),
+                "lora_path_1": ("STRING", {"default": ""}),
+                "lora_scale_1": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
+                "lora_path_2": ("STRING", {"default": ""}),
+                "lora_scale_2": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
             }
         }
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "generate_image"
     CATEGORY = "FAL/Image"
 
-    def generate_image(self, prompt, image_size, width, height, num_inference_steps, guidance_scale, real_cfg_scale, num_images, enable_safety_checker, use_real_cfg, sync_mode, seed=-1, loras="None", lora_scale=1.0, ip_adapter_scale=0.6, controlnet_conditioning_scale=0.6, controlnet_union_control_mode="canny", ip_adapters="None", controlnets="None", controlnet_unions="None", control_image=None, control_mask=None, ip_adapter_image=None, ip_adapter_mask=None):
+    def generate_image(self, prompt, image_size, width, height, num_inference_steps, guidance_scale, real_cfg_scale, num_images, enable_safety_checker, use_real_cfg, sync_mode, seed=-1, loras="None", lora_scale=1.0, ip_adapter_scale=0.6, controlnet_conditioning_scale=0.6, controlnet_union_control_mode="canny", ip_adapters="None", controlnets="None", controlnet_unions="None", control_image=None, control_mask=None, ip_adapter_image=None, ip_adapter_mask=None, lora_path_1="", lora_scale_1=1.0, lora_path_2="", lora_scale_2=1.0):
         arguments = {
             "prompt": prompt,
             "num_inference_steps": num_inference_steps,
@@ -404,6 +467,15 @@ def generate_image(self, prompt, image_size, width, height, num_inference_steps,
                     if mask_image_url:
                         arguments["ip_adapters"][0]["mask_image_url"] = mask_image_url
 
+        # Add LoRAs if provided
+        loras = []
+        if lora_path_1:
+            loras.append({"path": lora_path_1, "scale": lora_scale_1})
+        if lora_path_2:
+            loras.append({"path": lora_path_2, "scale": lora_scale_2})
+        if loras:
+            arguments["loras"] = loras
+
         try:
             handler = submit("fal-ai/flux-general", arguments=arguments)
             result = handler.get()
@@ -451,7 +523,8 @@ def create_blank_image(self):
     "FluxDev_fal": FluxDev,
     "FluxSchnell_fal": FluxSchnell,
     "FluxPro11_fal": FluxPro11,
-    "FluxGeneral_fal": FluxGeneral
+    "FluxGeneral_fal": FluxGeneral,
+    "FluxLora_fal": FluxLora
 }
 
 # Node display name mappings
@@ -460,5 +533,6 @@ def create_blank_image(self):
     "FluxDev_fal": "Flux Dev (fal)",
     "FluxSchnell_fal": "Flux Schnell (fal)",
     "FluxPro11_fal": "Flux Pro 1.1 (fal)",
-    "FluxGeneral_fal": "Flux General (fal)"
-}
+    "FluxGeneral_fal": "Flux General (fal)",
+    "FluxLora_fal": "Flux LoRA (fal)"
+}