Update app.py

app.py

@@ -8,6 +8,8 @@ import spaces
 from PIL import Image
 hf_token = os.environ.get("HF_TOKEN")
 from diffusers import AutoPipelineForText2Image
+from diffusers.utils import load_image
+
 
 
 device = "cuda" #if torch.cuda.is_available() else "cpu"
@@ -19,15 +21,19 @@ pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 
 @spaces.GPU(enable_queue=True)
-def predict(prompt, ip_adapter_images, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=False, center_crop=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
+def predict(prompt, files, ip_adapter_scale=0.5, negative_prompt="", seed=100, randomize_seed=False, center_crop=False, width=1024, height=1024, guidance_scale=5.0, num_inference_steps=50, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    ip_adapter_images = [Image.open(image) for image in ip_adapter_images]
+    ip_adapter_images = []
+    for img in upload_images:
+        ip_adapter_images.append(load_image(img))
+        
+    # ip_adapter_images = [Image.open(image) for image in ip_adapter_images]
 
-    # Optionally resize images if center crop is not selected
-    if not center_crop:
-        ip_adapter_images = [image.resize((224, 224)) for image in ip_adapter_images]
+    # # Optionally resize images if center crop is not selected
+    # if not center_crop:
+    #     ip_adapter_images = [image.resize((224, 224)) for image in ip_adapter_images]
     
     generator = torch.Generator(device="cuda").manual_seed(seed)
     pipe.set_ip_adapter_scale([ip_adapter_scale])
@@ -72,7 +78,16 @@ with gr.Blocks(css=css) as demo:
         with gr.Row():
             with gr.Column():
                 # ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery").style(grid=[2], preview=True)
-                ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery", show_label=True)#.style(grid=[2])
+                # ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery", show_label=True)#.style(grid=[2])
+                ip_adapter_images = gr.Gallery(columns=4, interactive=True, label="Input Images")
+
+                files = gr.File(
+                            label="Input Image/s",
+                            file_types=["image"],
+                            file_count="multiple"
+                        )
+                uploaded_files = gr.Gallery(label="Your images", visible=False, columns=5, rows=1, height=200)
+                
 
                 ip_adapter_scale = gr.Slider(
                     label="Image Input Scale",
@@ -156,6 +171,8 @@ with gr.Blocks(css=css) as demo:
                     step=1,
                     value=25,
                 )
+        files.upload(fn=swap_to_gallery, inputs=files, outputs=[uploaded_files, clear_button, files])
+        remove_and_reupload.click(fn=remove_back_to_files, outputs=[uploaded_files, clear_button, files])
             
         
         # gr.Examples(
@@ -169,7 +186,7 @@ with gr.Blocks(css=css) as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=predict,
-        inputs=[prompt, ip_adapter_images, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps],
+        inputs=[prompt, files, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed]
     )