Refactor flux_helpers.py to update pipeline mode based on request type

Refactor flux_helpers.py to add cleanup in finally block
Refactor image_tab.py to update custom Lora selection order
Refactor flux_events.py to add GPU duration for image generation
Refactor flux_helpers.py to use CUDA device for generator seed

modules/events/flux_events.py

@@ -217,82 +217,85 @@ def generate_image(
         image_num_inference_steps, image_guidance_scale, image_seed,
         refiner, vae
     ):
-        base_args = {
-            "model": model,
-            "prompt": prompt,
-            "fast_generation": fast_generation,
-            "loras": None,
-            "resize_mode": resize_mode,
-            "scheduler": scheduler,
-            "height": int(image_height),
-            "width": int(image_width),
-            "num_images_per_prompt": float(image_num_images_per_prompt),
-            "num_inference_steps": float(image_num_inference_steps),
-            "guidance_scale": float(image_guidance_scale),
-            "seed": int(image_seed),
-            "refiner": refiner,
-            "vae": vae,
-            "controlnet_config": None,
-        }
-        base_args = BaseReq(**base_args)
-        
-        if len(enabled_loras) > 0:
-            base_args.loras = []
-            for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]):
-                if enabled_lora['repo_id']:
-                    base_args.loras.append({
-                        "repo_id": enabled_lora['repo_id'],
-                        "weight": slider
-                    })
-        
-        image = None
-        mask_image = None
-        strength = None
-        
-        if img2img_image:
-            image = img2img_image
-            strength = float(img2img_strength)
+        try:
+            base_args = {
+                "model": model,
+                "prompt": prompt,
+                "fast_generation": fast_generation,
+                "loras": None,
+                "resize_mode": resize_mode,
+                "scheduler": scheduler,
+                "height": int(image_height),
+                "width": int(image_width),
+                "num_images_per_prompt": float(image_num_images_per_prompt),
+                "num_inference_steps": float(image_num_inference_steps),
+                "guidance_scale": float(image_guidance_scale),
+                "seed": int(image_seed),
+                "refiner": refiner,
+                "vae": vae,
+                "controlnet_config": None,
+            }
+            base_args = BaseReq(**base_args)
             
-            base_args = BaseImg2ImgReq(
-                **base_args.__dict__,
-                image=image,
-                strength=strength
-            )
-        elif inpaint_image:
-            image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None
-            mask_image = inpaint_image['layers'][0] if image else None
-            strength = float(inpaint_strength)
+            if len(enabled_loras) > 0:
+                base_args.loras = []
+                for enabled_lora, slider in zip(enabled_loras, [lora_slider_0, lora_slider_1, lora_slider_2, lora_slider_3, lora_slider_4, lora_slider_5]):
+                    if enabled_lora['repo_id']:
+                        base_args.loras.append({
+                            "repo_id": enabled_lora['repo_id'],
+                            "weight": slider
+                        })
+            
+            image = None
+            mask_image = None
+            strength = None
             
-            if image and mask_image:
-                base_args = BaseInpaintReq(
+            if img2img_image:
+                image = img2img_image
+                strength = float(img2img_strength)
+                
+                base_args = BaseImg2ImgReq(
                     **base_args.__dict__,
                     image=image,
-                    mask_image=mask_image,
                     strength=strength
                 )
-        elif any([canny_image, pose_image, depth_image]):
-            base_args.controlnet_config = ControlNetReq(
-                controlnets=[],
-                control_images=[],
-                controlnet_conditioning_scale=[]
-            )
+            elif inpaint_image:
+                image = inpaint_image['background'] if not all(pixel == (0, 0, 0) for pixel in list(inpaint_image['background'].getdata())) else None
+                mask_image = inpaint_image['layers'][0] if image else None
+                strength = float(inpaint_strength)
+                
+                if image and mask_image:
+                    base_args = BaseInpaintReq(
+                        **base_args.__dict__,
+                        image=image,
+                        mask_image=mask_image,
+                        strength=strength
+                    )
+            elif any([canny_image, pose_image, depth_image]):
+                base_args.controlnet_config = ControlNetReq(
+                    controlnets=[],
+                    control_images=[],
+                    controlnet_conditioning_scale=[]
+                )
+                
+                if canny_image:
+                    base_args.controlnet_config.controlnets.append("canny")
+                    base_args.controlnet_config.control_images.append(canny_image)
+                    base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength))
+                if pose_image:
+                    base_args.controlnet_config.controlnets.append("pose")
+                    base_args.controlnet_config.control_images.append(pose_image)
+                    base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength))
+                if depth_image:
+                    base_args.controlnet_config.controlnets.append("depth")
+                    base_args.controlnet_config.control_images.append(depth_image)
+                    base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength))
+            else:
+                base_args = BaseReq(**base_args.__dict__)
             
-            if canny_image:
-                base_args.controlnet_config.controlnets.append("canny")
-                base_args.controlnet_config.control_images.append(canny_image)
-                base_args.controlnet_config.controlnet_conditioning_scale.append(float(canny_strength))
-            if pose_image:
-                base_args.controlnet_config.controlnets.append("pose")
-                base_args.controlnet_config.control_images.append(pose_image)
-                base_args.controlnet_config.controlnet_conditioning_scale.append(float(pose_strength))
-            if depth_image:
-                base_args.controlnet_config.controlnets.append("depth")
-                base_args.controlnet_config.control_images.append(depth_image)
-                base_args.controlnet_config.controlnet_conditioning_scale.append(float(depth_strength))
-        else:
-            base_args = BaseReq(**base_args.__dict__)
-        
-        return gr.update(
-            value=gen_img(base_args),
-            interactive=True
-        )
+            return gr.update(
+                value=gen_img(base_args),
+                interactive=True
+            )
+        except Exception as e:
+            raise gr.Error(f"Error: {e}") from e

modules/helpers/flux_helpers.py

@@ -113,6 +113,7 @@ def gen_img(request: BaseReq | BaseImg2ImgReq | BaseInpaintReq):
             args['mask_image'] = resize_images([request.mask_image], request.height, request.width, request.resize_mode)[0]
 
         # Generate
+        gr.Info(f"Using {pipeline}", duration=150)
         images = pipeline(**args).images
 
         # Refiner