Update app.py

app.py

@@ -1,60 +1,77 @@
 import gradio as gr
-import numpy as np
+from gradio_client import Client
 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)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
+# API client for the external Space
+space_client = Client("prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast")
+
+LORA_STYLES = [
+    'Multiple-Angles',
+    'Photo-to-Anime',
+    'Anime-V2',
+    'Light-Migration',
+    'Upscaler',
+    'Style-Transfer',
+    'Manga-Tone',
+    'Anything2Real',
+    'Fal-Multiple-Angles',
+    'Polaroid-Photo',
+    'Unblur-Anything',
+    'Midnight-Noir-Eyes-Spotlight',
+    'Hyper-Realistic-Portrait',
+    'Ultra-Realistic-Portrait',
+    'Pixar-Inspired-3D',
+    'Noir-Comic-Book',
+    'Any-light',
+    'Studio-DeLight',
+    'Cinematic-FlatLog',
+]
+MAX_SEED = 2**32-1
 
-# @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
+    image,
     prompt,
-    negative_prompt,
+    lora_adapter,
     seed,
     randomize_seed,
-    width,
-    height,
     guidance_scale,
-    num_inference_steps,
+    steps,
     progress=gr.Progress(track_tqdm=True),
 ):
+    # Prepare images input as per API (expects Gallery [list of dicts])
+    images = []
+    if image is not None:
+        if isinstance(image, list):  # Gradio Gallery
+            for im in image:
+                images.append({"image": {"path": im}, "caption": None})
+        else:
+            images.append({"image": {"path": image}, "caption": None})
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
+    try:
+        result = space_client.predict(
+            images=images,
+            prompt=prompt,
+            lora_adapter=lora_adapter,
+            seed=float(seed),
+            randomize_seed=bool(randomize_seed),
+            guidance_scale=float(guidance_scale),
+            steps=float(steps),
+            api_name="/infer",
+        )
+        # result is a tuple: (image_dict, seed)
+        image_info, seed_used = result
+        # The API may return image at .url or .path, we use .url if available
+        img_url = image_info.get("url") or image_info.get("path")
+        return img_url, seed_used
+    except Exception as e:
+        return None, seed
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    ["", "Astronaut in jungle, anime style", "Photo-to-Anime", 0, True, 1.0, 4],
+    ["", "A delicious ceviche cheesecake slice", "Style-Transfer", 0, True, 1.0, 4],
 ]
 
 css = """
@@ -66,89 +83,80 @@ css = """
 
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+        gr.Markdown(" # 图像编辑 API Demo (基于 prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast)")
 
+        with gr.Row():
+            image = gr.Image(
+                label="上传图片",
+                source="upload",
+                tool=None,
+                type="filepath",
+                elem_id="input-image"
+            )
         with gr.Row():
             prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
+                label="编辑描述（Prompt）",
+                placeholder="请输入图片编辑描述...",
             )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+        with gr.Row():
+            lora_adapter = gr.Dropdown(
+                label="编辑风格（Style）",
+                choices=LORA_STYLES,
+                value="Photo-to-Anime"
+            )
 
-        result = gr.Image(label="Result", show_label=False)
+        run_button = gr.Button("执行编辑", scale=1, variant="primary")
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
+        result = gr.Image(label="结果图片", show_label=True)
 
+        with gr.Accordion("高级设置", open=False):
             seed = gr.Slider(
-                label="Seed",
+                label="随机种子",
                 minimum=0,
                 maximum=MAX_SEED,
                 step=1,
                 value=0,
             )
 
-            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
-                )
-
-                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
-                )
-
-                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])
+            randomize_seed = gr.Checkbox(label="随机种子", value=True)
+
+            guidance_scale = gr.Slider(
+                label="引导强度(Guidance Scale)",
+                minimum=0.1,
+                maximum=10.0,
+                step=0.1,
+                value=1.0,
+            )
+
+            steps = gr.Slider(
+                label="推理步数(Steps)",
+                minimum=1,
+                maximum=50,
+                step=1,
+                value=4,
+            )
+
+        gr.Examples(
+            examples=examples,
+            inputs=[image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
+            label="示例",
+        )
+
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
         inputs=[
+            image,
             prompt,
-            negative_prompt,
+            lora_adapter,
             seed,
             randomize_seed,
-            width,
-            height,
             guidance_scale,
-            num_inference_steps,
+            steps,
         ],
         outputs=[result, seed],
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()