Update app.py

app.py

@@ -5,7 +5,7 @@ from diffusers import DiffusionPipeline
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 if torch.cuda.is_available():
-    PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000}
+    #PYTORCH_CUDA_ALLOC_CONF={'max_split_size_mb': 6000}
     torch.cuda.max_memory_allocated(device=device)
     torch.cuda.empty_cache()
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
@@ -27,7 +27,7 @@ else:
 
 def genie (prompt, steps, seed):
     generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
-    int_image = pipe(prompt=prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0] 
+    int_image = pipe(prompt=prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0] 8
     #image = refiner(prompt=prompt, prompt_2=prompt_2, negative_prompt=negative_prompt, negative_prompt_2=negative_prompt_2, image=int_image, denoising_start=high_noise_frac).images[0]   
     return int_image
     
@@ -43,6 +43,6 @@ gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generat
     #gr.Slider(minimum=.7, maximum=.99, value=.95, step=.01, label='Refiner Denoise Start %')
     ],
     outputs='image', 
-    title="Stable Diffusion XL 1.0 CPU or GPU", 
-    description="SDXL 1.0 CPU or GPU. Currently running on CPU. <br><br><b>WARNING:</b> Extremely Slow. 65s/Iteration. Expect 25-50mins an image for 25-50 iterations respectively. This model is capable of producing NSFW (Softcore) images.", 
+    title="Stable Diffusion Turbo CPU or GPU", 
+    description="SDXL Turbo CPU or GPU. Currently running on CPU. <br><br><b>WARNING:</b> Extremely Slow. 65s/Iteration. Expect 25-50mins an image for 25-50 iterations respectively. This model is capable of producing NSFW (Softcore) images.", 
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