Update app.py

app.py

@@ -5,9 +5,6 @@ import spaces
 import gradio as gr
 import numpy as np
 import torch
-import tempfile
-import os
-import uuid
 from PIL import Image
 from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipeline, EDMEulerScheduler, StableDiffusionXLInstructPix2PixPipeline, AutoencoderKL, DPMSolverMultistepScheduler
 from huggingface_hub import hf_hub_download, InferenceClient
@@ -32,11 +29,6 @@ To optimize image results:
 - **Increase the number of steps** for enhanced edits.
 """
 
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
 def set_timesteps_patched(self, num_inference_steps: int, device = None):
     self.num_inference_steps = num_inference_steps
     
@@ -58,7 +50,7 @@ pipe_edit = StableDiffusionXLInstructPix2PixPipeline.from_single_file( edit_file
 pipe_edit.scheduler = EDMEulerScheduler(sigma_min=0.002, sigma_max=120.0, sigma_data=1.0, prediction_type="v_prediction")
 pipe_edit.to("cuda")
 
-client1 = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
+client1 = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 system_instructions1 = "<s>[SYSTEM] Act as Image Prompt Generation expert, Your task is to modify prompt by USER to more better prompt for Image Generation in Stable Diffusion XL. \n Modify the user's prompt to generate a high-quality image by incorporating essential keywords and styles according to prompt if none style is mentioned than assume realistic. The optimized prompt may include keywords according to prompt for resolution (4K, HD, 16:9 aspect ratio, , etc.), image quality (cute, masterpiece, high-quality, vivid colors, intricate details, etc.), and desired art styles (realistic, anime, 3D, logo, futuristic, fantasy, etc.). Ensure the prompt is concise, yet comprehensive and choose keywords wisely, to generate an exceptional image that meets the user's expectations. \n Your task is to reply with final optimized prompt only. If you get big prompt make it concise. and Apply all keyword at last of prompt. Reply with optimized prompt only.[USER]"
 
 def promptifier(prompt):
@@ -99,9 +91,8 @@ def king(type ,
             num_inference_steps=steps,
             image=output_image,
             generator=generator,
-        ).images
-        image_paths = [save_image(img) for img in refine][0]
-        return seed, image_paths
+        ).images[0]  
+        return seed, refine
     else :
         if randomize_seed:
             seed = random.randint(0, 999999)
@@ -117,7 +108,7 @@ def king(type ,
             num_inference_steps = int(steps/2.5),
             width = width, height = height,
             generator = generator,
-            ).images
+            ).images[0]
         else:            
             image = pipe_fast( prompt = instruction,
             negative_prompt=negative_prompt,
@@ -132,9 +123,8 @@ def king(type ,
                     guidance_scale = 7.5,
                     num_inference_steps=  steps,
                     image=image, generator=generator,
-            ).images
-        image_paths = [save_image(img) for img in refine][0]
-        return seed, image_paths
+            ).images[0]        
+        return seed, refine
 
 client = InferenceClient()
 # Prompt classifier