Update app.py

app.py

@@ -5,17 +5,18 @@ import random
 # import spaces #[uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
+from typing import Optional
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
+model_repo_id_default = "CompVis/stable-diffusion-v1-4"
 
 if torch.cuda.is_available():
     torch_dtype = torch.float16
 else:
     torch_dtype = torch.float32
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+# pipe = DiffusionPipeline.from_pretrained(model_repo_id_default, torch_dtype=torch_dtype)
+# pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
@@ -23,21 +24,22 @@ MAX_IMAGE_SIZE = 1024
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
+    prompt: str,
+    negative_prompt: str,
+    width: int,
+    height: int,
+    num_inference_steps: int,
     progress=gr.Progress(track_tqdm=True),
+    model_id: Optional[str] = 'CompVis/stable-diffusion-v1-4',
+    seed: Optional[int] = 42,
+    guidance_scale: Optional[float] = 7.0,
 ):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
     generator = torch.Generator().manual_seed(seed)
 
+    if model_id != model_repo_id_default:
+        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+        pipe = pipe.to(device)
+
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
@@ -48,14 +50,7 @@ def infer(
         generator=generator,
     ).images[0]
 
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    return image, pipe.name_or_path
 
 css = """
 #col-container {
@@ -66,74 +61,79 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image UI")
+        
+        gr.Markdown(" # DEMO Text-to-Image")
 
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
+            model_id = gr.Textbox(
+                label="Model ID",
                 max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+                placeholder="Enter model id like 'CompVis/stable-diffusion-v1-4'",
+                value="CompVis/stable-diffusion-v1-4"
             )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+        prompt = gr.Textbox(
+            label="Prompt",
+            max_lines=1,
+            placeholder="Enter your prompt",
+        )
 
-        result = gr.Image(label="Result", show_label=False)
+        negative_prompt = gr.Textbox(
+            label="Negative prompt",
+            max_lines=1,
+            placeholder="Enter a negative prompt",
+        )
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
+        with gr.Row():
+            seed = gr.Number(
                 label="Seed",
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
-                value=0,
+                value=42,
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance scale",
+                minimum=0.0,
+                maximum=10.0,
+                step=0.1,
+                value=7.0,
+            )
 
+        with gr.Accordion("Optional Settings", open=False):
             with gr.Row():
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
-
+            
+            with gr.Row():
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=1024,
                 )
 
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=2,  # Replace with defaults that work for your model
+                    value=2,
                 )
 
-        gr.Examples(examples=examples, inputs=[prompt])
+        run_button = gr.Button("Run", scale=1, variant="primary")
+        result = gr.Image(label="Result", show_label=False)
+        inferenced_model_name = gr.Label(show_label=True, value=model_id.value)
+    
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
@@ -141,13 +141,15 @@ with gr.Blocks(css=css) as demo:
             prompt,
             negative_prompt,
             seed,
-            randomize_seed,
             width,
             height,
             guidance_scale,
             num_inference_steps,
         ],
-        outputs=[result, seed],
+        outputs=[
+            result, 
+            inferenced_model_name,
+        ],
     )
 
 if __name__ == "__main__":