Create app.py

app.py

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+# ───────────────────────────────────────────────────────────
+# app.py – Gradio Space: Text ➜ 2D (Kontext) ➜ 3D (Hunyuan3D)
+# -----------------------------------------------------------
+# Requirements (add to requirements.txt):
+#   torch>=2.2.0
+#   diffusers>=0.27.0
+#   hy3dgen   # Hunyuan3D official PyPI after Jan‑2025
+#   trimesh
+#   gradio==4.26.0
+#   pillow
+# -----------------------------------------------------------
+# NOTE: • Set the following secrets in the Space **Settings → Secrets**
+#         HF_TOKEN            – your Hugging Face access token (for gated models)
+#         BFL_API_KEY         – optional, required if using Black‑Forest Labs usage tracking
+#       • GPU (A10G/16 GB↑) is strongly recommended.
+#       • Hunyuan3D installs a CUDA‑based custom rasteriser at runtime; build
+#         wheels are provided on Linux/Windows. See model card instructions.
+# ---------------------------------------------------------------------------
+
+import os
+import tempfile
+from typing import List, Tuple
+
+import gradio as gr
+import torch
+from PIL import Image
+from huggingface_hub import login as hf_login
+
+# ─────────── Login / device ───────────
+HF_TOKEN = os.getenv("HF_TOKEN")
+if HF_TOKEN:
+    hf_login(token=HF_TOKEN, add_to_git_credential=False)
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# ─────────── Load FLUX .1 Kontext (2D) ───────────
+from diffusers import FluxKontextPipeline, FluxPipeline  # FluxPipeline = text‑to‑image variant
+
+print("[+] Loading FLUX.1 Kontext [dev] …")
+kontext_pipe = FluxKontextPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=DTYPE
+).to(DEVICE)
+kontext_pipe.set_progress_bar_config(disable=True)
+
+print("[+] Loading FLUX.1 [dev] (text‑to‑image) …")
+text2img_pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev", torch_dtype=DTYPE
+).to(DEVICE)
+text2img_pipe.set_progress_bar_config(disable=True)
+
+# ─────────── Load Hunyuan3D‑2 (3D) ───────────
+print("[+] Loading Hunyuan3D‑2 shape+texture … (this may take a while)")
+from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline
+from hy3dgen.texgen import Hunyuan3DPaintPipeline
+
+shape_pipe = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(
+    "tencent/Hunyuan3D-2", torch_dtype=DTYPE
+).to(DEVICE)
+shape_pipe.set_progress_bar_config(disable=True)
+
+paint_pipe = Hunyuan3DPaintPipeline.from_pretrained(
+    "tencent/Hunyuan3D-2", torch_dtype=DTYPE
+).to(DEVICE)
+paint_pipe.set_progress_bar_config(disable=True)
+
+# ───────────────────────────────────────────────
+# Helper functions
+# ───────────────────────────────────────────────
+
+def generate_single_2d(prompt: str, image: Image.Image | None, guidance_scale: float) -> Image.Image:
+    """Either edit an existing image via Kontext or generate a fresh one via Flux text2img."""
+    if image is None:
+        result = text2img_pipe(prompt=prompt, guidance_scale=guidance_scale).images[0]
+    else:
+        result = kontext_pipe(image=image, prompt=prompt, guidance_scale=guidance_scale).images[0]
+    return result
+
+
+def generate_multiview(prompt: str, base_image: Image.Image, guidance_scale: float) -> List[Image.Image]:
+    """Generate four canonical views (front / back / left / right) by re‑prompting Kontext."""
+    views = [
+        ("front view", base_image),
+        (
+            "left side view",
+            kontext_pipe(image=base_image, prompt=f"{prompt}, left side view", guidance_scale=guidance_scale).images[0],
+        ),
+        (
+            "right side view",
+            kontext_pipe(image=base_image, prompt=f"{prompt}, right side view", guidance_scale=guidance_scale).images[0],
+        ),
+        (
+            "back view",
+            kontext_pipe(image=base_image, prompt=f"{prompt}, back view", guidance_scale=guidance_scale).images[0],
+        ),
+    ]
+    # Return only images, keep order [front, left, right, back]
+    return [v[1] for v in views]
+
+
+def build_3d_mesh(prompt: str, images: List[Image.Image]) -> str:
+    """Call Hunyuan3D pipelines to build geometry then paint texture. Returns path to GLB."""
+    # For single‑view use first image; multi‑view (≤6) accepted by Hunyuan3D
+    single_or_multi = images if len(images) > 1 else images[0]
+    mesh = shape_pipe(image=single_or_multi, prompt=prompt)[0]
+    mesh = paint_pipe(mesh, image=single_or_multi)
+
+    tmpdir = tempfile.mkdtemp()
+    out_path = os.path.join(tmpdir, "mesh.glb")
+    mesh.export(out_path)  # trimesh export
+    return out_path
+
+
+# ─────────── Gradio interface ───────────
+CSS = """
+footer {visibility: hidden;}
+"""
+
+def workflow(prompt: str, input_image: Image.Image | None, multiview: bool, guidance_scale: float) -> Tuple[List[Image.Image], str, str]:
+    """Main inference wrapper."""
+    if not prompt:
+        raise gr.Error("프롬프트(설명)를 입력하세요 📌")
+
+    # 1️⃣ 2D Generation / Editing
+    base_img = generate_single_2d(prompt, input_image, guidance_scale)
+    images = [base_img]
+
+    if multiview:
+        images = generate_multiview(prompt, base_img, guidance_scale)
+
+    # 2️⃣ 3D Generation
+    model_path = build_3d_mesh(prompt, images)
+
+    return images, model_path, model_path  # gallery, viewer, file download
+
+
+def build_ui():
+    with gr.Blocks(css=CSS, title="Text ➜ 2D ➜ 3D (Kontext × Hunyuan3D)") as demo:
+        gr.Markdown("# 🌀 텍스트 → 2D → 3D 생성기")
+        gr.Markdown(
+            "Kontext로 일관된 2D 이미지를 만든 뒤, Hunyuan3D‑2로 텍스처 3D 메시에스를 생성합니다.\n"
+            "⏱️ 첫 실행은 모델 로딩으로 시간이 걸립니다."
+        )
+
+        with gr.Row():
+            with gr.Column():
+                prompt = gr.Textbox(label="프롬프트 / 설명", placeholder="예: 파란 모자를 쓴 귀여운 로봇")
+                input_image = gr.Image(label="(선택) 편집할 참조 이미지", type="pil")
+                multiview = gr.Checkbox(label="멀티뷰(좌/우/후면 포함) 3D 품질 향상", value=True)
+                guidance = gr.Slider(0.5, 7.5, 2.5, step=0.1, label="Guidance Scale (Kontext)")
+                run_btn = gr.Button("🚀 생성하기", variant="primary")
+            with gr.Column():
+                gallery = gr.Gallery(label="🎨 2D 결과", show_label=True, columns=2, height="auto")
+                model3d = gr.Model3D(label="🧱 3D 미리보기", clear_color=[1, 1, 1, 0])
+                download = gr.File(label="⬇️ GLB 다운로드")
+
+        run_btn.click(
+            fn=workflow,
+            inputs=[prompt, input_image, multiview, guidance],
+            outputs=[gallery, model3d, download],
+            api_name="generate",
+            scroll_to_output=True,
+            show_progress="full",
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    build_ui().queue(max_size=3, concurrency_count=1).launch()