Replace FluxPipeline with InferenceClient for image generation and update related code

app.py

@@ -16,12 +16,14 @@ from trellis.representations import Gaussian, MeshExtractResult
 from trellis.utils import render_utils, postprocessing_utils
 from gradio_client import Client
 from diffusers import FluxPipeline
+from huggingface_hub import InferenceClient
 
 llm_client = Client("Qwen/Qwen2.5-72B-Instruct")
+client = InferenceClient("black-forest-labs/FLUX.1-dev")
 
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cpu")
-print(f"Flux pipeline loaded on {pipe.device}")
+# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to("cpu")
+# print(f"Flux pipeline loaded on {pipe.device}")
 
 def generate_t2i_prompt(item_name):
     llm_prompt_template = """You are tasked with creating a concise yet highly detailed description of an item to be used for generating an image in a game development pipeline. The image should show the **entire item** with no parts cropped or hidden. The background should always be plain and monocolor, with no focus on it.  
@@ -52,9 +54,9 @@ Focus on the item itself, ensuring it is fully described, and specify a plain, w
 
     return object_t2i_prompt
 
-@spaces.GPU
 def generate_item_image(object_t2i_prompt):
-    image = pipe(prompt=object_t2i_prompt, guidance_scale=3.5, num_inference_steps=28, width=1024, height=1024, generator=torch.Generator("cpu").manual_seed(0), output_type="pil").images[0]
+    # image = pipe(prompt=object_t2i_prompt, guidance_scale=3.5, num_inference_steps=28, width=1024, height=1024, generator=torch.Generator("cpu").manual_seed(0), output_type="pil").images[0]
+    image = client.text_to_image(object_t2i_prompt, guidance_scale=3.5, num_inference_steps=28, width=1024, height=1024)
     trial_id, processed_image = preprocess_pil_image(image)
     return trial_id, processed_image