Update app.py

app.py

@@ -4,15 +4,17 @@ 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
 
@@ -20,19 +22,17 @@ MAX_SEED = np.iinfo(np.int32).max
 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_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])
     
-    # Pass all images at once to the pipe
-    result_images = pipe(
+    image = pipe(
         prompt=prompt,
-        ip_adapter_image=ip_adapter_images,  # Pass the list of images
+        ip_adapter_image=[ip_adapter_image],
         negative_prompt=negative_prompt,
         height=height,
         width=width,
@@ -40,61 +40,135 @@ def predict(prompt, ip_adapter_images, ip_adapter_scale=0.5, negative_prompt="",
         guidance_scale=guidance_scale,
         num_images_per_prompt=1,
         generator=generator,
-    ).images
+    ).images[0]
     
-    return result_images, seed
+    return image, seed
 
 examples = [
-    ["high quality", ["example1.png", "example2.png"], 1.0, "", 1000, False, False, 1152, 896],
+    ["high quality", "example1.png", 1.0, "", 1000, False, False, 1152, 896],
+    ["capybara", "example2.png", 0.7, "", 1000, False, False, 1152, 896],
 ]
 
-css = """
+css="""
 #col-container {
-    display: flex;
-    flex-direction: column;
-    align-items: center;
-    padding: 10px;
+    margin: 0 auto;
+    max-width: 1024px;
+}
+#result img{
+    object-position: top;
+}
+#result .image-container{
+    height: 100%
 }
 """
-
 with gr.Blocks(css=css) as demo:
-    with gr.Column():
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(f"""
+        # Bria's Image-Prompt-Adapter
+        """)
+                
         with gr.Row():
-            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():
-            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.Column():
+                ip_adapter_images = gr.Gallery(label="Input Images", elem_id="image-gallery").style(grid=[2], preview=True)
+                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".'
+                )
+            
         with gr.Row():
-            guidance_scale = gr.Slider(
-                label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=7.0
+            width = gr.Slider(
+                label="Width",
+                minimum=256,
+                maximum=2048,
+                step=32,
+                value=1024,
             )
-            num_inference_steps = gr.Slider(
-                label="Number of Inference Steps", minimum=1, maximum=100, step=1, value=25
+            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.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=1000,
+            )
+            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,
+                )
+            
         
-        result = gr.Gallery(label="Generated Images").style(grid=[2], preview=True)
-        
-        run_button = gr.Button("Run")
-    
-    run_button.click(
-        predict,
+        # 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"
+        # )
+
+    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],
         outputs=[result, seed]
     )
 
-    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"
-    )
+demo.queue(max_size=25,api_open=False).launch(show_api=False)
 
-demo.queue(max_size=25, api_open=False).launch(show_api=False)
+# image_blocks.queue(max_size=25,api_open=False).launch(show_api=False)