Update app.py

app.py

@@ -4,36 +4,35 @@ import numpy as np
 import diffusers
 import os
 import random
-import spaces
 from PIL import Image
 
 hf_token = os.environ.get("HF_TOKEN")
 from diffusers import AutoPipelineForText2Image
 
-
-device = "cuda" #if torch.cuda.is_available() else "cpu"
+device = "cuda"  # if torch.cuda.is_available() else "cpu"
 pipe = AutoPipelineForText2Image.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16, force_zeros_for_empty_prompt=False).to(device)
 pipe.load_ip_adapter("briaai/Image-Prompt", subfolder='models', weight_name="ip_adapter_bria.bin")
 pipe.to(device)
-# default_negative_prompt= "" #"Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
 
 MAX_SEED = np.iinfo(np.int32).max
 
 @spaces.GPU(enable_queue=True)
-def predict(prompt, ip_adapter_image, 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, 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)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-
+    
     # Optionally resize images if center crop is not selected
     if not center_crop:
-        ip_adapter_image = [image.resize((224, 224)) for image in ip_adapter_image]
-
+        ip_adapter_images = [image.resize((224, 224)) for image in ip_adapter_images]
+    
+    # Create a generator for reproducible random seed
     generator = torch.Generator(device="cuda").manual_seed(seed)
     pipe.set_ip_adapter_scale([ip_adapter_scale])
     
-    image = pipe(
+    # Pass all images at once to the pipe
+    result_images = pipe(
         prompt=prompt,
-        ip_adapter_image=ip_adapter_image,
+        ip_adapter_image=ip_adapter_images,  # Pass the list of images
         negative_prompt=negative_prompt,
         height=height,
         width=width,
@@ -41,142 +40,61 @@ def predict(prompt, ip_adapter_image, ip_adapter_scale=0.5, negative_prompt="",
         guidance_scale=guidance_scale,
         num_images_per_prompt=1,
         generator=generator,
-    ).images[0]
+    ).images
     
-    return image, seed
+    return result_images, seed
 
-def swap_to_gallery(images):
-    return gr.update(value=images, visible=True), gr.update(visible=True), gr.update(visible=False)
-    
 examples = [
-    ["high quality", "example1.png", 1.0, "", 1000, False, False, 1152, 896],
-    ["capybara", "example2.png", 0.7, "", 1000, False, False, 1152, 896],
+    ["high quality", ["example1.png", "example2.png"], 1.0, "", 1000, False, False, 1152, 896],
 ]
 
-css="""
+css = """
 #col-container {
-    margin: 0 auto;
-    max-width: 1024px;
-}
-#result img{
-    object-position: top;
-}
-#result .image-container{
-    height: 100%
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    padding: 10px;
 }
 """
+
 with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Bria's Image-Prompt-Adapter
-        """)
-                
+    with gr.Column():
         with gr.Row():
-            with gr.Column():
-                # ip_adapter_image = gr.Image(label="IP-Adapter Image", type="pil")
-                ip_adapter_image = gr.Gallery(
-                    label="IP-Adapter Image/s", show_label=False, elem_id="image-gallery",
-                    columns=[3], rows=[1], object_fit="contain", height="auto")                
-                
-                ip_adapter_scale = gr.Slider(
-                    label="Image Input Scale",
-                    info="Use 1 for creating image variations",
-                    minimum=0.0,
-                    maximum=1.0,
-                    step=0.05,
-                    value=1.0,
-                )
-            with gr.Column():
-                result = gr.Image(label="Result", elem_id="result",  format="png")
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=True,
-                    lines=1,
-                    placeholder="Enter your prompt",
-                    container=True,
-                    info='For image variation, leave empty or try a prompt like: "high quality".'
-                )
-            
+            prompt = gr.Textbox(label="Prompt", lines=1)
+            ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery").style(grid=[2], preview=True)
+        
+        ip_adapter_scale = gr.Slider(label="IP Adapter Scale", minimum=0.0, maximum=1.0, step=0.1, value=0.5)
+        negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Optional", lines=1)
+        
         with gr.Row():
-            width = gr.Slider(
-                label="Width",
-                minimum=256,
-                maximum=2048,
-                step=32,
-                value=1024,
-            )
-            height = gr.Slider(
-                label="Height",
-                minimum=256,
-                maximum=2048,
-                step=32,
-                value=1024,
-            )
-            run_button = gr.Button("Run", scale=0)
-            
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
+            seed = gr.Number(label="Seed", value=100)
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            center_crop = gr.Checkbox(label="Center Crop Image", value=False, info="If not checked, the images will be resized.")
+        
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=7.0
             )
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=1000,
+            num_inference_steps = gr.Slider(
+                label="Number of Inference Steps", minimum=1, maximum=100, step=1, value=25
             )
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            center_crop = gr.Checkbox(label="Center Crop image", value=False, info="If not checked, the IP-Adapter image input would be resized to a square.")            
-            # with gr.Row():
-            #     width = gr.Slider(
-            #         label="Width",
-            #         minimum=256,
-            #         maximum=2048,
-            #         step=32,
-            #         value=1024,
-            #     )
-            #     height = gr.Slider(
-            #         label="Height",
-            #         minimum=256,
-            #         maximum=2048,
-            #         step=32,
-            #         value=1024,
-            #     )
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=7.0,
-                )
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=100,
-                    step=1,
-                    value=25,
-                )
-            
         
-        # gr.Examples(
-        #     examples=examples,
-        #     fn=predict,
-        #     inputs=[prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height],
-        #     outputs=[result, seed],
-        #     cache_examples="lazy"
-        # )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=predict,
-        inputs=[prompt, ip_adapter_image, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps],
+        result = gr.Gallery(label="Generated Images").style(grid=[2], preview=True)
+        
+        run_button = gr.Button("Run")
+    
+    run_button.click(
+        predict,
+        inputs=[prompt, ip_adapter_images, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height, guidance_scale, num_inference_steps],
         outputs=[result, seed]
     )
 
-demo.queue(max_size=25,api_open=False).launch(show_api=False)
+    gr.Examples(
+        examples=examples,
+        fn=predict,
+        inputs=[prompt, ip_adapter_images, ip_adapter_scale, negative_prompt, seed, randomize_seed, center_crop, width, height],
+        outputs=[result, seed],
+        cache_examples="lazy"
+    )
 
-# image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)
+demo.queue(max_size=25, api_open=False).launch(show_api=False)