model_id

app.py

@@ -13,8 +13,7 @@ from DeepCache import DeepCacheSDHelper
 adapter_id = "latent-consistency/lcm-lora-sdv1-5"
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = OVStableDiffusionImg2ImgPipeline.from_pretrained("Lykon/dreamshaper-xl-lightning", torch_dtype=torch.float16, export=True) if torch.cuda.is_available() else AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo")
-pipe = pipe.to(device)
+
 # helper = DeepCacheSDHelper(pipe=pipe)
 # helper.set_params(
 #     cache_interval=3,
@@ -28,7 +27,9 @@ def resize(value,img):
     img = img.resize((value,value))
     return img
 
-def infer(source_img, prompt, steps, seed, Strength):
+def infer(model_id,source_img, prompt, steps, seed, Strength):
+    pipe = OVStableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float16, export=True) if torch.cuda.is_available() else AutoPipelineForImage2Image.from_pretrained("stabilityai/sdxl-turbo")
+    pipe = pipe.to(device)
     start_time = time.time()
     generator = torch.Generator(device).manual_seed(seed)  
     if int(steps * Strength) < 1:
@@ -42,6 +43,7 @@ def infer(source_img, prompt, steps, seed, Strength):
     return image
 
 gr.Interface(fn=infer, inputs=[
+    gr.Text(value="Lykon/dreamshaper-8", label="Checkpoint")
     gr.Image(sources=["upload", "webcam", "clipboard"], type="filepath", label="Raw Image."), 
     gr.Textbox(label = 'Prompt Input Text. 77 Token (Keyword or Symbol) Maximum'),
     gr.Slider(1, 5, value = 2, step = 1, label = 'Number of Iterations'),