try to inport inside gpu

app.py

@@ -8,101 +8,101 @@ import gradio as gr
 from PIL import Image
 import os
 
-from src.flux.xflux_pipeline import XFluxPipeline
 import random
 
-def run_xflux_pipeline(
-    prompt, image, repo_id, name, device,
-    model_type, width, height, timestep_to_start_cfg, num_steps, true_gs, guidance,
-    neg_prompt="",
-    negative_image=None,
-    save_path='results', control_type='depth', use_controlnet=False, seed=None, num_images_per_prompt=1, use_lora=False, lora_weight=0.7, lora_repo_id="XLabs-AI/flux-lora-collection", lora_name="realism_lora.safetensors", use_ip=False 
-):
-    # Montando os argumentos simulando a linha de comando
-    class Args:
-        def __init__(self):
-            self.prompt = prompt
-            self.image = image
-            self.control_type = control_type
-            self.repo_id = repo_id
-            self.name = name
-            self.device = device
-            self.use_controlnet = use_controlnet
-            self.model_type = model_type
-            self.width = width
-            self.height = height
-            self.timestep_to_start_cfg = timestep_to_start_cfg
-            self.num_steps = num_steps
-            self.true_gs = true_gs
-            self.guidance = guidance
-            self.num_images_per_prompt = num_images_per_prompt
-            self.seed = seed if seed else 123456789
-            self.neg_prompt = neg_prompt
-            self.img_prompt = Image.open(image)
-            self.neg_img_prompt = Image.open(negative_image) if negative_image else None
-            self.ip_scale = 1.0
-            self.neg_ip_scale = 1.0
-            self.local_path = None
-            self.ip_repo_id = "XLabs-AI/flux-ip-adapter"
-            self.ip_name = "flux-ip-adapter.safetensors"
-            self.ip_local_path = None
-            self.lora_repo_id = lora_repo_id
-            self.lora_name = lora_name
-            self.lora_local_path = None
-            self.offload = False
-            self.use_ip = use_ip
-            self.use_lora = use_lora
-            self.lora_weight = lora_weight
-            self.save_path = save_path
-
-    args = Args()
-
-    # Carregar a imagem se fornecida
-    if args.image:
-        image = Image.open(args.image)
-    else:
-        image = None
-    
-    # Inicializar o pipeline com os parâmetros necessários
-    xflux_pipeline = XFluxPipeline(args.model_type, args.device, args.offload)
-    
-    # Configurar ControlNet se necessário
-    if args.use_controlnet:
-        print('Loading ControlNet:', args.local_path, args.repo_id, args.name)
-        xflux_pipeline.set_controlnet(args.control_type, args.local_path, args.repo_id, args.name)
-    if args.use_ip:
-        print('load ip-adapter:', args.ip_local_path, args.ip_repo_id, args.ip_name)
-        xflux_pipeline.set_ip(args.ip_local_path, args.ip_repo_id, args.ip_name)
-    if args.use_lora:
-        print('load lora:', args.lora_local_path, args.lora_repo_id, args.lora_name)
-        xflux_pipeline.set_lora(args.lora_local_path, args.lora_repo_id, args.lora_name, args.lora_weight)
-    
-    # Laço para gerar imagens
-    images = []
-    for _ in range(args.num_images_per_prompt):
-        seed = random.randint(0, 2147483647)
-        result = xflux_pipeline(
-            prompt=args.prompt,
-            controlnet_image=image,
-            width=args.width,
-            height=args.height,
-            guidance=args.guidance,
-            num_steps=args.num_steps,
-            seed=seed,
-            true_gs=args.true_gs,
-            neg_prompt=args.neg_prompt,
-            timestep_to_start_cfg=args.timestep_to_start_cfg,
-            image_prompt=args.img_prompt, 
-            neg_image_prompt=args.neg_img_prompt, 
-            ip_scale=args.ip_scale, 
-            neg_ip_scale=args.neg_ip_scale, 
-        )
-        images.append(result)
-
-    return images
-
 @spaces.GPU(duration=300)
 def process_image(image, prompt, steps, use_lora, use_controlnet, use_depth, use_hed, use_ip, lora_name, lora_path, lora_weight, negative_image, neg_prompt, true_gs, guidance, cfg):
+    from src.flux.xflux_pipeline import XFluxPipeline
+    def run_xflux_pipeline(
+        prompt, image, repo_id, name, device,
+        model_type, width, height, timestep_to_start_cfg, num_steps, true_gs, guidance,
+        neg_prompt="",
+        negative_image=None,
+        save_path='results', control_type='depth', use_controlnet=False, seed=None, num_images_per_prompt=1, use_lora=False, lora_weight=0.7, lora_repo_id="XLabs-AI/flux-lora-collection", lora_name="realism_lora.safetensors", use_ip=False 
+    ):
+        # Montando os argumentos simulando a linha de comando
+        class Args:
+            def __init__(self):
+                self.prompt = prompt
+                self.image = image
+                self.control_type = control_type
+                self.repo_id = repo_id
+                self.name = name
+                self.device = device
+                self.use_controlnet = use_controlnet
+                self.model_type = model_type
+                self.width = width
+                self.height = height
+                self.timestep_to_start_cfg = timestep_to_start_cfg
+                self.num_steps = num_steps
+                self.true_gs = true_gs
+                self.guidance = guidance
+                self.num_images_per_prompt = num_images_per_prompt
+                self.seed = seed if seed else 123456789
+                self.neg_prompt = neg_prompt
+                self.img_prompt = Image.open(image)
+                self.neg_img_prompt = Image.open(negative_image) if negative_image else None
+                self.ip_scale = 1.0
+                self.neg_ip_scale = 1.0
+                self.local_path = None
+                self.ip_repo_id = "XLabs-AI/flux-ip-adapter"
+                self.ip_name = "flux-ip-adapter.safetensors"
+                self.ip_local_path = None
+                self.lora_repo_id = lora_repo_id
+                self.lora_name = lora_name
+                self.lora_local_path = None
+                self.offload = False
+                self.use_ip = use_ip
+                self.use_lora = use_lora
+                self.lora_weight = lora_weight
+                self.save_path = save_path
+
+        args = Args()
+
+        # Carregar a imagem se fornecida
+        if args.image:
+            image = Image.open(args.image)
+        else:
+            image = None
+        
+        # Inicializar o pipeline com os parâmetros necessários
+        xflux_pipeline = XFluxPipeline(args.model_type, args.device, args.offload)
+        
+        # Configurar ControlNet se necessário
+        if args.use_controlnet:
+            print('Loading ControlNet:', args.local_path, args.repo_id, args.name)
+            xflux_pipeline.set_controlnet(args.control_type, args.local_path, args.repo_id, args.name)
+        if args.use_ip:
+            print('load ip-adapter:', args.ip_local_path, args.ip_repo_id, args.ip_name)
+            xflux_pipeline.set_ip(args.ip_local_path, args.ip_repo_id, args.ip_name)
+        if args.use_lora:
+            print('load lora:', args.lora_local_path, args.lora_repo_id, args.lora_name)
+            xflux_pipeline.set_lora(args.lora_local_path, args.lora_repo_id, args.lora_name, args.lora_weight)
+        
+        # Laço para gerar imagens
+        images = []
+        for _ in range(args.num_images_per_prompt):
+            seed = random.randint(0, 2147483647)
+            result = xflux_pipeline(
+                prompt=args.prompt,
+                controlnet_image=image,
+                width=args.width,
+                height=args.height,
+                guidance=args.guidance,
+                num_steps=args.num_steps,
+                seed=seed,
+                true_gs=args.true_gs,
+                neg_prompt=args.neg_prompt,
+                timestep_to_start_cfg=args.timestep_to_start_cfg,
+                image_prompt=args.img_prompt, 
+                neg_image_prompt=args.neg_img_prompt, 
+                ip_scale=args.ip_scale, 
+                neg_ip_scale=args.neg_ip_scale, 
+            )
+            images.append(result)
+
+        return images
+
     return run_xflux_pipeline(
           prompt=prompt,
           neg_prompt=neg_prompt,