Update app.py

app.py

@@ -1,154 +1,159 @@
 import gradio as gr
 import numpy as np
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
 import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-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)
-
+from PIL import Image
+import os
+from pipeline_flux_ipa import FluxPipeline
+from transformer_flux import FluxTransformer2DModel
+from attention_processor import IPAFluxAttnProcessor2_0
+from transformers import AutoProcessor, SiglipVisionModel
+from infer_flux_ipa_siglip import MLPProjModel, IPAdapter
+from huggingface_hub import hf_hub_download
+import spaces
+
+# Constants
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+image_encoder_path = "google/siglip-so400m-patch14-384"
+ipadapter_path = hf_hub_download(repo_id="InstantX/FLUX.1-dev-IP-Adapter", filename="ip-adapter.bin")
+
+transformer = FluxTransformer2DModel.from_pretrained(
+    "black-forest-labs/FLUX.1-dev", 
+    subfolder="transformer", 
+    torch_dtype=torch.bfloat16
+)
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev", 
+    transformer=transformer, 
+    torch_dtype=torch.bfloat16
+)
+ip_model = IPAdapter(pipe, image_encoder_path, ipadapter_path, device="cuda", num_tokens=128)
+
+
+def resize_img(image, max_size=1024):
+    width, height = image.size
+    scaling_factor = min(max_size / width, max_size / height)
+    new_width = int(width * scaling_factor)
+    new_height = int(height * scaling_factor)
+    return image.resize((new_width, new_height), Image.LANCZOS)
+
+@spaces.GPU
+def process_image(
+    image,
     prompt,
-    negative_prompt,
+    scale,
     seed,
     randomize_seed,
     width,
     height,
-    guidance_scale,
-    num_inference_steps,
     progress=gr.Progress(track_tqdm=True),
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
+    
+    if image is None:
+        return None, seed
+    
+    # Convert to PIL Image if needed
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(image)
+    
+    # Resize image
+    image = resize_img(image)
+    
+    # Generate the image
+    result = ip_model.generate(
+        pil_image=image,
         prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
+        scale=scale,
         width=width,
         height=height,
-        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",
-]
+        seed=seed
+    )
+    
+    return result[0], seed
 
+# UI CSS
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 960px;
 }
 """
 
+# Create the Gradio interface
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
+        gr.Markdown("# Image Processing Model")
+        
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
+            with gr.Column():
+                input_image = gr.Image(
+                    label="Input Image",
+                    type="pil"
+                )
+                prompt = gr.Text(
+                    label="Prompt",
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                )
+                run_button = gr.Button("Process", variant="primary")
+            
+            with gr.Column():
+                result = gr.Image(label="Result")
+        
         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(
                 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():
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                    value=960,
                 )
-
+                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
                     maximum=MAX_IMAGE_SIZE,
                     step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            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
+                    value=1280,
                 )
-
-                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
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
+            
+            scale = gr.Slider(
+                label="Scale",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.1,
+                value=0.7,
+            )
+    
+    run_button.click(
+        fn=process_image,
         inputs=[
+            input_image,
             prompt,
-            negative_prompt,
+            scale,
             seed,
             randomize_seed,
             width,
             height,
-            guidance_scale,
-            num_inference_steps,
         ],
         outputs=[result, seed],
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()