Update app.py

app.py

@@ -10,15 +10,13 @@ from diffusers import StableDiffusionXLImg2ImgPipeline, StableDiffusionXLPipelin
 from huggingface_hub import hf_hub_download, InferenceClient
 
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-pipe = StableDiffusionXLPipeline.from_pretrained("fluently/Fluently-XL-Final", torch_dtype=torch.float16, vae=vae)
-pipe.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
-pipe.set_adapters("lora")
-pipe.to("cuda")
 
 refiner = StableDiffusionXLImg2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
 refiner.to("cuda")
 
-pipe_fast = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V4.0_Lightning", torch_dtype=torch.float16)
+pipe_fast = StableDiffusionXLPipeline.from_pretrained("SG161222/RealVisXL_V4.0_Lightning", torch_dtype=torch.float16, vae=vae)
+pipe_fast.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
+pipe_fast.set_adapters("lora")
 pipe_fast.to("cuda")
 
 help_text = """
@@ -100,33 +98,33 @@ def king(type ,
             steps=int(steps/2.5)
             guidance_scale2=(guidance_scale/3)
             
-            image = pipe_fast( prompt = instruction,
+            refine = pipe_fast( prompt = instruction,
             guidance_scale = guidance_scale2, 
             num_inference_steps = steps, 
             width = width, height = height,
-            use_resolution_binning = True,
-            generator = generator, output_type="latent",
-            ).images 
+            generator = generator,
+            ).images[0]
         else:
             if enhance_prompt:
                 print(f"BEFORE: {instruction} ")
                 instruction = promptifier(instruction)
                 print(f"AFTER: {instruction} ")
-            image = pipe( prompt = instruction,
+            guidance_scale2=(guidance_scale/2)
+            
+            image = pipe_fast( prompt = instruction,
             negative_prompt=negative_prompt,
-            guidance_scale = guidance_scale, 
+            guidance_scale = guidance_scale2, 
             num_inference_steps = steps, 
             width = width, height = height,
-            use_resolution_binning = True,
             generator = generator, output_type="latent",
             ).images 
 
-        refine = refiner( prompt=instruction,
-            negative_prompt = negative_prompt,
-            guidance_scale = guidance_scale,
-            num_inference_steps=  steps,
-            image=image, generator=generator,
-        ).images[0]        
+            refine = refiner( prompt=instruction,
+                negative_prompt = negative_prompt,
+                guidance_scale = guidance_scale,
+                num_inference_steps=  steps,
+                image=image, generator=generator,
+            ).images[0]        
         return seed, refine
 
 client = InferenceClient()