Create app.py

app.py

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+import os, io, math, time, base64
+from typing import Tuple
+import gradio as gr
+from PIL import Image, ImageFilter, ImageOps
+import numpy as np
+import torch
+
+# ---- Модель по умолчанию (можно переопределить в Settings → Variables) ----
+MODEL_ID = os.getenv("MODEL_ID", "Qwen/Qwen-Image-Edit")
+HF_TOKEN = os.getenv("HF_TOKEN", "").strip() or None  # если модель gated
+
+# ---- Подгружаем подходящий пайплайн inpainting ----
+# Многие редактирующие модели следуют API diffusers InpaintPipeline
+# Попробуем сначала специализированный пайплайн, затем универсальный.
+from diffusers import AutoPipelineForInpainting, StableDiffusionInpaintPipeline
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+dtype = torch.float16 if device.type == "cuda" else torch.float32
+
+def _load_pipeline():
+    auth = {"token": HF_TOKEN} if HF_TOKEN else {}
+    try:
+        pipe = AutoPipelineForInpainting.from_pretrained(
+            MODEL_ID, torch_dtype=dtype, **auth
+        )
+    except Exception:
+        # fallback на классический SD inpaint, если у модели нет auto-конфига
+        pipe = StableDiffusionInpaintPipeline.from_pretrained(
+            MODEL_ID, torch_dtype=dtype, **auth
+        )
+    pipe = pipe.to(device)
+    if hasattr(pipe, "enable_attention_slicing"):
+        pipe.enable_attention_slicing()
+    return pipe
+
+PIPE = None
+LOAD_ERR = None
+try:
+    PIPE = _load_pipeline()
+except Exception as e:
+    LOAD_ERR = str(e)
+
+# ----------------- Утилиты -----------------
+def _pad_canvas(img: Image.Image, left: int, right: int, top: int, bottom: int,
+                fill=(127,127,127)) -> Tuple[Image.Image, Tuple[int,int,int,int]]:
+    """Создаёт расширенный холст и возвращает (canvas, bbox вставки исходника)."""
+    w, h = img.size
+    canvas = Image.new("RGB", (w + left + right, h + top + bottom), fill)
+    canvas.paste(img, (left, top))
+    return canvas, (left, top, left + w, top + h)
+
+def _feather_mask(size: Tuple[int,int], bbox: Tuple[int,int,int,int], feather_px: int = 32) -> Image.Image:
+    """Маска: 255 = дорисовать, 0 = оставить исходник. По краю делаем плавный градиент."""
+    W, H = size
+    L, T, R, B = bbox
+    mask = Image.new("L", (W, H), 255)
+    base = Image.new("L", (W, H), 0)
+    base.paste(255, (L, T, R, B))
+    # invert: центральная область = 0 (не трогать), снаружи = 255
+    inv = ImageOps.invert(base)
+    if feather_px > 0:
+        inv = inv.filter(ImageFilter.GaussianBlur(radius=feather_px))
+    return inv
+
+def expand_with_model(image: Image.Image, left: int, right: int, top: int, bottom: int,
+                      prompt: str, neg: str, steps: int, guidance: float, seed: int | None):
+    if LOAD_ERR:
+        raise gr.Error(f"Не удалось загрузить модель '{MODEL_ID}'.\n{LOAD_ERR}")
+
+    for name, val in [("left",left),("right",right),("top",top),("bottom",bottom)]:
+        if val is None or val < 0 or val > 2048:
+            raise gr.Error(f"{name} должен быть в диапазоне 0..2048")
+
+    # 1) строим холст и маску
+    canvas, bbox = _pad_canvas(image.convert("RGB"), left, right, top, bottom)
+    mask = _feather_mask(canvas.size, bbox, feather_px=48)
+
+    # 2) аккуратный промпт для бесшовного расширения
+    clean_prompt = (prompt or "").strip()
+    if not clean_prompt:
+        clean_prompt = (
+            "Seamlessly continue the existing background so it looks like a natural, "
+            "wider version of the same image. Match colors, textures, lighting and perspective. "
+            "No frames, no collage, no new subjects."
+        )
+    negative_prompt = (neg or "frames, borders, phone mockup, collage tiles, UI elements, captions, watermark").strip()
+
+    generator = None
+    if isinstance(seed, int) and seed >= 0:
+        generator = torch.Generator(device=device).manual_seed(seed)
+
+    # 3) инференс
+    out = PIPE(
+        prompt=clean_prompt,
+        negative_prompt=negative_prompt,
+        image=canvas,
+        mask_image=mask,
+        num_inference_steps=int(steps),
+        guidance_scale=float(guidance),
+        generator=generator
+    ).images[0]
+
+    return out, clean_prompt, negative_prompt
+
+# ----------------- Gradio UI -----------------
+with gr.Blocks(title="Qwen Image — Seamless Expand") as demo:
+    gr.Markdown("## Qwen Image — Seamless Background Expand\n"
+                "Загрузи картинку, задай сколько пикселей дорисовать, при желании уточни промпт.")
+
+    if LOAD_ERR:
+        gr.Markdown(
+            f"**⚠️ Модель не загрузил��сь.** Проверь `MODEL_ID` в Settings → Variables. Текущая: `{MODEL_ID}`  "
+            f"\nСообщение: `{LOAD_ERR}`"
+        )
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            img_in = gr.Image(type="pil", label="Картинка")
+            prompt = gr.Textbox(label="Prompt (EN)", placeholder="Describe seamless continuation…")
+            neg = gr.Textbox(label="Negative prompt", value="frames, borders, phone mockup, collage tiles, UI elements, captions, watermark")
+            with gr.Row():
+                left = gr.Number(value=256, label="Left (px)", precision=0)
+                right = gr.Number(value=256, label="Right (px)", precision=0)
+            with gr.Row():
+                top = gr.Number(value=256, label="Top (px)", precision=0)
+                bottom = gr.Number(value=256, label="Bottom (px)", precision=0)
+            with gr.Row():
+                steps = gr.Slider(10, 60, value=30, step=1, label="Steps")
+                guidance = gr.Slider(0.5, 12.0, value=5.5, step=0.1, label="Guidance scale")
+                seed = gr.Number(value=-1, label="Seed (-1 = random)", precision=0)
+            btn = gr.Button("Expand", variant="primary")
+        with gr.Column(scale=1):
+            img_out = gr.Image(label="Результат")
+            used_prompt = gr.Textbox(label="Использованный prompt", interactive=False)
+            used_neg = gr.Textbox(label="Использованный negative", interactive=False)
+
+    btn.click(
+        expand_with_model,
+        inputs=[img_in, left, right, top, bottom, prompt, neg, steps, guidance, seed],
+        outputs=[img_out, used_prompt, used_neg]
+    )
+
+demo.launch()