Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+import torch
+import spaces
+from PIL import Image
+import os
+
+from models.transformer_sd3 import SD3Transformer2DModel
+from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
+
+from transformers import AutoProcessor, SiglipVisionModel
+from huggingface_hub import hf_hub_download
+
+
+# Constants
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1024
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+model_path = 'stabilityai/stable-diffusion-3.5-large'
+image_encoder_path = "google/siglip-so400m-patch14-384"
+ipadapter_path = hf_hub_download(repo_id="InstantX/SD3.5-Large-IP-Adapter", filename="ip-adapter.bin")
+
+transformer = SD3Transformer2DModel.from_pretrained(
+    model_path, 
+    subfolder="transformer", 
+    torch_dtype=torch.bfloat16
+)
+
+pipe = StableDiffusion3Pipeline.from_pretrained(
+    model_path, 
+    transformer=transformer, 
+    torch_dtype=torch.bfloat16
+).to("cuda")
+
+pipe.init_ipadapter(
+    ip_adapter_path=ipadapter_path, 
+    image_encoder_path=image_encoder_path, 
+    nb_token=64, 
+)
+
+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,
+    scale,
+    seed,
+    randomize_seed,
+    width,
+    height,
+    progress=gr.Progress(track_tqdm=True),
+):
+    #pipe.to("cuda")
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    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 = pipe(
+        clip_image=image,
+        prompt=prompt,
+        ipadapter_scale=scale,
+        width=width,
+        height=height,
+        generator=torch.Generator().manual_seed(seed)
+    ).images[0]
+    
+    return result, seed
+
+# UI CSS
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 960px;
+}
+"""
+
+# Create the Gradio interface
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown("# InstantX's SD3.5 IP Adapter")
+        
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(
+                    label="Input Image",
+                    type="pil"
+                )
+                scale = gr.Slider(
+                    label="Image Scale",
+                    minimum=0.0,
+                    maximum=1.0,
+                    step=0.1,
+                    value=0.7,
+                )
+                prompt = gr.Text(
+                    label="Prompt",
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                )
+                run_button = gr.Button("Generate", variant="primary")
+            
+            with gr.Column():
+                result = gr.Image(label="Result")
+        
+        with gr.Accordion("Advanced Settings", open=False):
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                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,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=1024,
+                )
+    
+    run_button.click(
+        fn=process_image,
+        inputs=[
+            input_image,
+            prompt,
+            scale,
+            seed,
+            randomize_seed,
+            width,
+            height,
+        ],
+        outputs=[result, seed],
+    )
+
+if __name__ == "__main__":
+    demo.launch()