Refactor image_tab function to update generator initialization and remove device parameter

tabs/images/handlers.py

@@ -63,6 +63,7 @@ def get_pipe(request: BaseReq | BaseImg2ImgReq | BaseInpaintReq):
                     pipe_args['pipeline'] = AutoPipelineForText2Image.from_pipe(**pipe_args)
             elif request.custom_addons:
                 ...
+            gr.Info(f"Pipeline Mode: {type(pipe_args['pipeline'])}")
             
             # Enable or Disable Vae
             if request.vae:
@@ -213,7 +214,7 @@ def gen_img(request: BaseReq | BaseImg2ImgReq | BaseInpaintReq, progress=gr.Prog
         progress(0.3, "Getting Prompt Embeddings")
         # Get Prompt Embeddings
         if isinstance(pipeline, flux_pipes):
-            positive_prompt_embeds, positive_prompt_pooled = get_weighted_text_embeddings_flux1(pipeline, request.prompt, device=device)
+            positive_prompt_embeds, positive_prompt_pooled = get_weighted_text_embeddings_flux1(pipeline, request.prompt)
         elif isinstance(pipeline, sd_pipes):
             positive_prompt_embeds, negative_prompt_embeds, positive_prompt_pooled, negative_prompt_pooled = get_weighted_text_embeddings_sdxl(pipeline, request.prompt, request.negative_prompt)
 
@@ -227,7 +228,7 @@ def gen_img(request: BaseReq | BaseImg2ImgReq | BaseInpaintReq, progress=gr.Prog
             'num_images_per_prompt': request.num_images_per_prompt,
             'num_inference_steps': request.num_inference_steps,
             'guidance_scale': request.guidance_scale,
-            'generator': [torch.Generator(device=device).manual_seed(request.seed + i) if not request.seed is any([None, 0, -1]) else torch.Generator(device=device).manual_seed(random.randint(0, 2**32 - 1)) for i in range(request.num_images_per_prompt)],
+            'generator': [torch.Generator().manual_seed(request.seed + i) if not request.seed is any([None, 0, -1]) else torch.Generator().manual_seed(random.randint(0, 2**32 - 1)) for i in range(request.num_images_per_prompt)],
         }
         
         if isinstance(pipeline, sd_pipes):