test gradio

app.py

@@ -1,32 +1,73 @@
 import gradio as gr
 import torch
-from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
-import gradio as gr
-from huggingface_hub import login
+from PIL import Image
+from models.transformer_sd3 import SD3Transformer2DModel
+from pipeline_stable_diffusion_3_ipa import StableDiffusion3Pipeline
 import os
-import spaces,tempfile
-import torch
-
+from huggingface_hub import login
 
 token = os.getenv("HF_TOKEN")
 login(token=token)
-model_id = "stabilityai/stable-diffusion-2-base"
-scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
-pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
-
-lora_path = "Jl-wei/ui-diffuser-v2"
-pipe.load_lora_weights(lora_path)
-pipe.to("cuda")
-
-@spaces.GPU
-def gui_generation(text, num_imgs):
-    prompt = f"Mobile app: {text}"
-    images = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, height=512, width=288, num_images_per_prompt=num_imgs).images
-    yield images
-
-gallery = gr.Gallery(columns=[3], rows=[1], object_fit="contain", height="auto")
-number_slider = gr.Slider(1, 30, value=2, step=1, label="Batch size")
-prompt_box = gr.Textbox(label="Prompt", placeholder="Health monittoring report")
-interface = gr.Interface(gui_generation, inputs=[prompt_box, number_slider], outputs=gallery)
+
+# Model and paths
+model_path = 'stabilityai/stable-diffusion-3.5-large'
+ip_adapter_path = './ip-adapter.bin'
+image_encoder_path = "google/siglip-so400m-patch14-384"
+ref_img_path = './assets/1.jpg'  # Reference image path
+
+# Load SD3.5 pipeline and components
+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=ip_adapter_path,
+    image_encoder_path=image_encoder_path,
+    nb_token=64,
+)
+
+
+@gr.Interface()
+def gui_generation(prompt: str, negative_prompt: str, ipadapter_scale: float, num_imgs: int):
+    """
+    Generate images based on prompt, negative prompt, and IP-Adapter scale.
+    """
+    ref_img = Image.open(ref_img_path).convert('RGB')  # Load reference image
+    generator = torch.Generator("cuda").manual_seed(42)  # Reproducibility
+
+    images = []
+    for _ in range(num_imgs):
+        output = pipe(
+            width=1024,
+            height=1024,
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            num_inference_steps=24,
+            guidance_scale=5.0,
+            generator=generator,
+            clip_image=ref_img,
+            ipadapter_scale=ipadapter_scale,
+        ).images[0]
+        images.append(output)
+    return images
+
+
+# Gradio UI elements
+prompt_box = gr.Textbox(label="Prompt", placeholder="Enter your generation prompt here")
+negative_prompt_box = gr.Textbox(label="Negative Prompt", placeholder="e.g., lowres, worst quality")
+ipadapter_slider = gr.Slider(0.1, 1.0, value=0.5, step=0.1, label="IP-Adapter Scale")
+number_slider = gr.Slider(1, 5, value=1, step=1, label="Number of Images")
+gallery = gr.Gallery(label="Generated Images", columns=[3], rows=[1], object_fit="contain", height="auto")
+
+interface = gr.Interface(
+    gui_generation,
+    inputs=[prompt_box, negative_prompt_box, ipadapter_slider, number_slider],
+    outputs=gallery,
+    title="Stable Diffusion 3.5 Image Generation with IP-Adapter",
+    description="Generate high-quality images with Stable Diffusion 3.5 Large and IP-Adapter guidance."
+)
 
 interface.launch()