Update app.py

app.py

@@ -1,154 +1,86 @@
-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)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    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(
-        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
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        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.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,
-            )
-
-            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])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import streamlit as st
+import io
+import requests
+from PIL import Image
+from io import BytesIO
+
+# ----------------------------
+# 配置 Hugging Face Inference API
+# ----------------------------
+import os
+
+
+
+
+API_URL = "https://api-inference.huggingface.co/models/GGPENG/StyleDiffusion"  # 替换为你上传的模型仓库
+API_TOKEN = os.getenv("HF_TOKEN")
+
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+# ----------------------------
+# Streamlit 页面设置
+# ----------------------------
+st.set_page_config(page_title="Fine-tuning style diffusion (API)", layout="wide")
+
+st.title("Fine-tuning style diffusion 推理 Demo (API)")
+st.write("只是训练了一个提示词 'A <new1> reference.'")
+st.write("示例：A <new1> reference. New Year image with a rabbit as the main element, in a 2D or anime style, and a festive background")
+
+# ----------------------------
+# Prompt 输入
+# ----------------------------
+prompt = st.text_input(
+    "Prompt",
+    "A <new1> reference. New Year image with a rabbit as the main element, in a 2D or anime style, and a festive background"
+)
+
+# ----------------------------
+# 参数调节
+# ----------------------------
+steps = st.slider("Steps", 10, 320, 100)
+guidance = st.slider("Guidance", 1.0, 18.0, 6.0)
+
+# ----------------------------
+# 生成函数（调用 API）
+# ----------------------------
+def generate(prompt):
+    payload = {
+        "inputs": prompt,
+        "parameters": {
+            "num_inference_steps": steps,
+            "guidance_scale": guidance,
+            # "height": 512,
+            # "width": 512
+        }
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload)
+    
+    if response.status_code != 200:
+        st.error(f"API请求失败：{response.status_code}, {response.text}")
+        return None
+    
+    # 将返回的字节流或 Base64 数据转换为 PIL Image
+    try:
+        image = Image.open(BytesIO(response.content))
+    except:
+        st.error("生成图像失败，请检查模型是否支持图像输出。")
+        return None
+    return image
+
+# ----------------------------
+# 生成按钮
+# ----------------------------
+if st.button("Generate"):
+    with st.spinner("Generating via Hugging Face API..."):
+        image = generate(prompt)
+    
+    if image:
+        st.image(image, caption="Result", width=512)
+        buf = io.BytesIO()
+        image.save(buf, format="PNG")
+        st.download_button(
+            "Download",
+            buf.getvalue(),
+            "result.png"
+        )