Update app.py

app.py

@@ -54,10 +54,10 @@ def update_language(new_language):
 text2img = None
 img2img = None
 def Generate(image_input, prompt, negative_prompt, strength, guidance_scale, num_inference_steps, width, height, seed):
+	global text2img, img2img
 	if seed == -1:
 		seed = generate_new_seed()
 	generator = torch.Generator().manual_seed(int(seed))
-	global text2img, img2img
 	start_time = time.time()
 	if image_input is None:
 		image = text2img(prompt=prompt, negative_prompt=negative_prompt, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps, width=width, height=height, num_images_per_prompt=1, generator=generator).images[0]
@@ -67,9 +67,11 @@ def Generate(image_input, prompt, negative_prompt, strength, guidance_scale, num
 	return image, f"{minutes:02d}:{seconds:02d}"
 def Loading(model_name, is_xl, is_cuda):
 	global text2img, img2img
-	device = "cuda" if is_xl else "cpu"
+	if is_xl == False:
+		is_xl ='xl' in model_name.lower()
+	device = "cuda" if is_cuda else "cpu"
 	pipeline_class = StableDiffusionXLPipeline if is_xl else StableDiffusionPipeline
-	if is_xl:
+	if is_cuda:
 		text2img = pipeline_class.from_pretrained(model_name, torch_dtype=torch.float16, variant="fp16", use_safetensors=True).to(device)
 	else:
 		text2img = pipeline_class.from_pretrained(model_name, use_safetensors=True).to(device)