Update app.py

app.py

@@ -6,14 +6,14 @@ import torch
 import random
 from PIL import Image
 
-from kontext_pipeline import FluxKontextPipeline
+from pipeline_flux_kontext import FluxKontextPipeline
 from diffusers import FluxTransformer2DModel
 from diffusers.utils import load_image
 
 from huggingface_hub import hf_hub_download
 
 
-kontext_path = hf_hub_download(repo_id="diffusers/kontext", filename="kontext.safetensors")
+kontext_path = hf_hub_download(repo_id="diffusers/kontext-v2", filename="dev-opt-2-a-3.safetensors")
 
 MAX_SEED = np.iinfo(np.int32).max
 
@@ -27,32 +27,32 @@ def infer(input_image, prompt, seed=42, randomize_seed=False, guidance_scale=2.5
         seed = random.randint(0, MAX_SEED)
         
     input_image = input_image.convert("RGB")
-    original_width, original_height = input_image.size
+    # original_width, original_height = input_image.size
     
-    if original_width >= original_height:
-        new_width = 1024
-        new_height = int(original_height * (new_width / original_width))
-        new_height = round(new_height / 64) * 64
-    else:
-        new_height = 1024
-        new_width = int(original_width * (new_height / original_height))
-        new_width = round(new_width / 64) * 64
+    # if original_width >= original_height:
+    #     new_width = 1024
+    #     new_height = int(original_height * (new_width / original_width))
+    #     new_height = round(new_height / 64) * 64
+    # else:
+    #     new_height = 1024
+    #     new_width = int(original_width * (new_height / original_height))
+    #     new_width = round(new_width / 64) * 64
     
-    input_image_resized = input_image.resize((new_width, new_height), Image.LANCZOS)
+    #input_image_resized = input_image.resize((new_width, new_height), Image.LANCZOS)
     image = pipe(
-        image=input_image_resized, 
+        image=input_image, 
         prompt=prompt,
         guidance_scale=guidance_scale,
-        width=new_width,
-        height=new_height,
+        # width=new_width,
+        # height=new_height,
         generator=torch.Generator().manual_seed(seed),
     ).images[0]
-    return image, seed
+    return image, seed, gr.update(visible=True)
 
 css="""
 #col-container {
     margin: 0 auto;
-    max-width: 520px;
+    max-width: 960px;
 }
 """
 
@@ -62,46 +62,54 @@ with gr.Blocks(css=css) as demo:
         gr.Markdown(f"""# FLUX.1 Kontext [dev]
         """)
 
-        input_image = gr.Image(label="Upload the image for editing", type="pil")
         with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(label="Upload the image for editing", type="pil")
+                with gr.Row():
+                    prompt = gr.Text(
+                        label="Prompt",
+                        show_label=False,
+                        max_lines=1,
+                        placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
+                        container=False,
+                    )
+                    run_button = gr.Button("Run", scale=0)
+                with gr.Accordion("Advanced Settings", open=False):
             
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt for editing (e.g., 'Remove glasses', 'Add a hat')",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", scale=0)
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=MAX_SEED,
+                        step=1,
+                        value=0,
+                    )
+                    
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+                    
+                    guidance_scale = gr.Slider(
+                        label="Guidance Scale",
+                        minimum=1,
+                        maximum=10,
+                        step=0.1,
+                        value=2.5,
+                    )       
+                    
+            with gr.Column():
+                result = gr.Image(label="Result", show_label=False, interactive=False)
+                reuse_button = gr.Button("Reuse this image", visible=False)
         
-        result = gr.Image(label="Result", show_label=False)
         
-        with gr.Accordion("Advanced Settings", open=False):
-            
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            guidance_scale = gr.Slider(
-                label="Guidance Scale",
-                minimum=1,
-                maximum=10,
-                step=0.1,
-                value=2.5,
-            )
 
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn = infer,
         inputs = [input_image, prompt, seed, randomize_seed, guidance_scale],
-        outputs = [result, seed]
+        outputs = [result, seed, reuse_button]
+    )
+    reuse_button.click(
+        fn = lambda image: image,
+        inputs = [result],
+        outputs = [input_image]
     )
 
 demo.launch()