Update app.py

app.py

@@ -18,20 +18,17 @@ vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype
 repo = "fluently/Fluently-XL-Final"
 
 pipe_best = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
-pipe_best.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_best.scheduler.config)
 pipe_best.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
 pipe_best.load_lora_weights("KingNish/Better-Image-XL-Lora", weight_name="example-03.safetensors", adapter_name="lora")
 pipe_best.set_adapters(["lora","dalle"], adapter_weights=[1.5, 0.7])
 pipe_best.to("cuda")
 
 pipe_3D = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
-pipe_3D.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_3D.scheduler.config)
 pipe_3D.load_lora_weights("artificialguybr/3DRedmond-V1", weight_name="3DRedmond-3DRenderStyle-3DRenderAF.safetensors", adapter_name="3D")
 pipe_3D.set_adapters(["3D"])
 pipe_3D.to("cuda")
 
 pipe_logo = StableDiffusionXLPipeline.from_pretrained(repo, torch_dtype=torch.float16, vae=vae)
-pipe_logo.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe_logo.scheduler.config)
 pipe_logo.load_lora_weights("artificialguybr/LogoRedmond-LogoLoraForSDXL", weight_name="LogoRedmond_LogoRedAF.safetensors", adapter_name="logo")
 pipe_logo.set_adapters(["logo"])
 pipe_logo.to("cuda")
@@ -104,12 +101,12 @@ def king(type ,
         generator = torch.Generator().manual_seed(seed)
         if style=="3D":
             instruction = f"3DRenderAF, 3D Render, {instruction}"
-            image = pipe_3D( prompt = instruction, guidance_scale = 4, num_inference_steps = steps, width = width, height = height, generator = generator).images[0]         
+            image = pipe_3D( prompt = instruction, guidance_scale = 5, num_inference_steps = steps, width = width, height = height, generator = generator).images[0]         
         elif style=="Logo":
             instruction = f"LogoRedAF, {instruction}"
-            image = pipe_logo( prompt = instruction, guidance_scale = 4, num_inference_steps = steps, width = width, height = height, generator = generator).images[0]         
+            image = pipe_logo( prompt = instruction, guidance_scale = 5, num_inference_steps = steps, width = width, height = height, generator = generator).images[0]         
         else:
-            image = pipe_best( prompt = instruction, guidance_scale = 4, num_inference_steps = steps, width = width, height = height, generator = generator).images[0]         
+            image = pipe_best( prompt = instruction, guidance_scale = 5, num_inference_steps = steps, width = width, height = height, generator = generator).images[0]         
         return seed, image
 
 client = InferenceClient()