🔥 add generate settings

app.py

@@ -1,111 +1,3 @@
-# import os
-# import subprocess
-
-# # 🧹 Убираем pyenv, если вдруг остался .python-version
-# os.environ.pop("PYENV_VERSION", None)
-
-# # ⚙️ Устанавливаем torch и diso
-# subprocess.run(["pip", "install", "torch", "wheel"], check=True)
-
-# subprocess.run([
-#     "pip", "install", "--no-build-isolation", 
-#     "diso@git+https://github.com/SarahWeiii/diso.git"
-# ], check=True)
-
-# # ✅ Только теперь импортируем всё остальное
-# import gradio as gr
-# import uuid
-# import torch
-# import zipfile
-# import requests
-
-# from inference_triposg import run_triposg
-# from triposg.pipelines.pipeline_triposg import TripoSGPipeline
-# from briarmbg import BriaRMBG
-
-# # === Настройки устройства ===
-# # device = "cuda" if torch.cuda.is_available() else "cpu"
-# # dtype = torch.float16 if torch.cuda.is_available() else torch.float32
-# # dtype = torch.float32
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-# dtype = torch.float16 if device == "cuda" else torch.float32
-
-# # === Проверка и загрузка весов ===
-# weights_dir = "pretrained_weights"
-# triposg_path = os.path.join(weights_dir, "TripoSG")
-# rmbg_path = os.path.join(weights_dir, "RMBG-1.4")
-
-# if not (os.path.exists(triposg_path) and os.path.exists(rmbg_path)):
-#     print("📦 Downloading pretrained weights from Hugging Face Dataset...")
-#     url = "https://huggingface.co/datasets/endlesstools/pretrained-assets/resolve/main/pretrained_models.zip"
-#     zip_path = "pretrained_models.zip"
-
-#     with requests.get(url, stream=True) as r:
-#         r.raise_for_status()
-#         with open(zip_path, "wb") as f:
-#             for chunk in r.iter_content(chunk_size=8192):
-#                 f.write(chunk)
-
-#     print("📦 Extracting weights...")
-#     with zipfile.ZipFile(zip_path, "r") as zip_ref:
-#         zip_ref.extractall(weights_dir)
-
-#     os.remove(zip_path)
-#     print("✅ Weights ready.")
-
-# # === Загрузка моделей ===
-# pipe = TripoSGPipeline.from_pretrained(triposg_path).to(device, dtype)
-# rmbg_net = BriaRMBG.from_pretrained(rmbg_path).to(device)
-# rmbg_net.eval()
-
-# # === Функция генерации ===
-# def generate(file):
-#     temp_id = str(uuid.uuid4())
-#     input_path = f"/tmp/{temp_id}.png"
-#     output_path = f"/tmp/{temp_id}.glb"
-
-#     with open(input_path, "wb") as f:
-#         f.write(file)
-
-#     print("[DEBUG] Generating mesh...")
-#     try:
-#         mesh = run_triposg(
-#             pipe=pipe,
-#             image_input=input_path,
-#             rmbg_net=rmbg_net,
-#             seed=42,
-#             num_inference_steps=25,
-#             guidance_scale=5.0,
-#             faces=-1,
-#         )
-#         # mesh.export(output_path)
-#         if mesh is None:
-#             raise ValueError("Mesh generation failed")
-#         mesh.export(output_path)
-#         print(f"[DEBUG] Mesh saved to {output_path}")
-#         # return output_path
-#         if os.path.exists(output_path):
-#             return output_path
-#         else:
-#             return "Error: mesh export failed or file not found"
-#     except Exception as e:
-#         print("[ERROR]", e)
-#         return f"Error: {e}"
-
-# # === Gradio-интерфейс ===
-# demo = gr.Interface(
-#     fn=generate,
-#     inputs=gr.File(type="binary", label="Upload image"),
-#     outputs=gr.File(label="Generated .glb model"),
-#     title="TripoSG Image-to-3D",
-#     description="Upload an image and get back a 3D GLB model.",
-# )
-
-# # # === ВАЖНО: переменная должна называться `app` ===
-# # app = demo.launch(inline=True, share=False, prevent_thread_lock=True)
-# demo.launch()
-
-
 
 
 import os
@@ -166,7 +58,8 @@ rmbg_net = BriaRMBG.from_pretrained(rmbg_path).to(device)
 rmbg_net.eval()
 
 # Генерация .glb
-def generate(image_path):
+# def generate(image_path):
+def generate(image_path, face_number=50000, guidance_scale=5.0, num_steps=25):
     print("[API CALL] image_path received:", image_path)
     print("[API CALL] File exists:", os.path.exists(image_path))
 
@@ -176,14 +69,23 @@ def generate(image_path):
     print("[DEBUG] Generating mesh from:", image_path)
 
     try:
+        # mesh = run_triposg(
+        #     pipe=pipe,
+        #     image_input=image_path,
+        #     rmbg_net=rmbg_net,
+        #     seed=42,
+        #     num_inference_steps=25,
+        #     guidance_scale=5.0,
+        #     faces=-1,
+        # )
         mesh = run_triposg(
             pipe=pipe,
             image_input=image_path,
             rmbg_net=rmbg_net,
             seed=42,
-            num_inference_steps=25,
-            guidance_scale=5.0,
-            faces=-1,
+            num_inference_steps=int(num_steps),
+            guidance_scale=float(guidance_scale),
+            faces=int(face_number),
         )
 
         if mesh is None:
@@ -217,33 +119,3 @@ demo.launch()
 
 
 
-
-
-# import gradio as gr
-# import uuid
-# import os
-# import traceback
-
-# def generate(image_path):
-#     try:
-#         print("[DEBUG] got image path:", image_path)
-#         print("[DEBUG] file exists:", os.path.exists(image_path))
-
-#         out_path = f"/tmp/{uuid.uuid4()}.txt"
-#         with open(out_path, "w") as f:
-#             f.write(f"Received: {image_path}")
-
-#         return out_path
-
-#     except Exception as e:
-#         print("[ERROR]", e)
-#         traceback.print_exc()
-#         return f"Error: {e}"
-
-# demo = gr.Interface(
-#     fn=generate,
-#     inputs=gr.Image(type="filepath"),
-#     outputs=gr.File()
-# )
-
-# demo.launch()