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2025-11-18 14:08:38 +00:00 · 2025-11-18 14:03:31 +00:00 · 2025-11-18 14:00:18 +00:00 · 2025-11-17 19:20:02 +00:00 · 2025-11-17 19:07:21 +00:00 · 2025-11-17 18:03:37 +00:00
37 changed files with 48 additions and 1418 deletions
--- a/3d-generation-pipeline/.env.example
+++ b/3d-generation-pipeline/.env.example
@ -1,5 +0,0 @@
 3D_GENERATION_URL=
 MODEL_FOLDER=
 CLOUDFLARE_ACCOUNT_ID=
 CLOUDFLARE_API_TOKEN=
--- a/3d-generation-pipeline/.gitignore
+++ b/3d-generation-pipeline/.gitignore
@ -1,6 +0,0 @@
 .venv
 .env
 __pycache__
 images/
 models/
 logs/
--- a/3d-generation-pipeline/README.md
+++ b/3d-generation-pipeline/README.md
@ -1,6 +0,0 @@
 ### TODO
 * Artikkel text-to-3d prompt engineeringu kohta: "Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation"
 * TRELLIS: postprocessing_utils: texture baking mode: 'opt' vs 'fast' - hardcoded 'opt', kui võimaldada 'fast' siis tuleb error
 ### Notes
 * TRELLIS: added functionality to specify texture baking optimisation total steps as an argument (`texture_opt_total_steps`), to replace the hardcoded 2500. But this is not tracked in Git (because modified this https://github.com/IgorAherne/trellis-stable-projectorz/releases/tag/latest)
--- a/3d-generation-pipeline/cloudflare_api.py
+++ b/3d-generation-pipeline/cloudflare_api.py
@ -1,61 +0,0 @@
 import base64
 import requests
 import os
 from dotenv import load_dotenv
 load_dotenv()
 ACCOUNT_ID = os.environ["CLOUDFLARE_ACCOUNT_ID"]
 API_TOKEN = os.environ["CLOUDFLARE_API_TOKEN"]
 def text_to_image_cloudflare(prompt, output_path):
    MODEL = "@cf/black-forest-labs/flux-1-schnell"
    URL = f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}"
    payload = {
        "prompt": prompt,
    }
    headers = {
        "Authorization": f"Bearer {API_TOKEN}",
        "Content-Type": "application/json",
    }
    resp = requests.post(URL, json=payload, headers=headers, timeout=60)
    resp.raise_for_status()
    data = resp.json()
    b64 = data["result"]["image"]
    if not b64:
        raise RuntimeError(f"Unexpected response structure: {data}")
    img_bytes = base64.b64decode(b64)
    with open(output_path, "wb") as f:
        f.write(img_bytes)
    return True
 def refine_text_prompt(prompt):
    MODEL = "@cf/meta/llama-3.2-3b-instruct"
    URL = f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}"
    instructions = """
    User is talking about some object. Your task is to generate a short and concise description of it. Use only user's own words, keep it as short as possible.
    Example:
    User: 'Umm, okay, I would like a really cool sword, with for example a bright orange crossguard. And also it should be slightly curved.'
    You: 'a slightly curved sword with bright orange crossguard'
    """
    response = requests.post(URL,
    headers={"Authorization": f"Bearer {API_TOKEN}"},
    json={
        "messages": [
        {"role": "system", "content": instructions},
        {"role": "user", "content": prompt}
        ]
    }
    )
    data = response.json()
    return data["result"]["response"]
--- a/3d-generation-pipeline/generate_image_local.py
+++ b/3d-generation-pipeline/generate_image_local.py
@ -1,86 +0,0 @@
 import requests
 from invokeai_mcp_server import create_text2img_graph, enqueue_graph, wait_for_completion, get_image_url
 from urllib.parse import urljoin
 INVOKEAI_BASE_URL = "http://127.0.0.1:9090"
 async def generate_image(arguments: dict):
    # Extract parameters
    prompt = arguments["prompt"]
    negative_prompt = arguments.get("negative_prompt", "")
    width = arguments.get("width", 512)
    height = arguments.get("height", 512)
    steps = arguments.get("steps", 30)
    cfg_scale = arguments.get("cfg_scale", 7.5)
    scheduler = arguments.get("scheduler", "euler")
    seed = arguments.get("seed")
    model_key = arguments.get("model_key")
    lora_key = arguments.get("lora_key")
    lora_weight = arguments.get("lora_weight", 1.0)
    vae_key = arguments.get("vae_key")
    print(f"Generating image with prompt: {prompt[:50]}...")
    # Create graph
    graph = await create_text2img_graph(
        prompt=prompt,
        negative_prompt=negative_prompt,
        model_key=model_key,
        lora_key=lora_key,
        lora_weight=lora_weight,
        vae_key=vae_key,
        width=width,
        height=height,
        steps=steps,
        cfg_scale=cfg_scale,
        scheduler=scheduler,
        seed=seed
    )
    # Enqueue and wait for completion
    result = await enqueue_graph(graph)
    batch_id = result["batch"]["batch_id"]
    print(f"Enqueued batch {batch_id}, waiting for completion...")
    completed = await wait_for_completion(batch_id)
    # Extract image name from result
    if "result" in completed and "outputs" in completed["result"]:
        outputs = completed["result"]["outputs"]
        # Find the image output
        for node_id, output in outputs.items():
            if output.get("type") == "image_output":
                image_name = output["image"]["image_name"]
                image_url = await get_image_url(image_name)
                return urljoin(INVOKEAI_BASE_URL, image_url)
    raise RuntimeError("Failed to generate image!")
 def download_file(url, filepath):
    response = requests.get(url)
    if response.status_code == 200:
        with open(filepath, "wb") as file:
            file.write(response.content)
    else:
        raise RuntimeError(f"Failed to download image. Status code: {response.status_code}")
 async def text_to_image_invoke_ai(prompt, output_path):
    # see available model keys via GET http://127.0.0.1:9090/api/v2/models/?model_type=main
    args = {
        "prompt": prompt,
        "width": 512,
        "height": 512,
        "model_key": "79401292-0a6b-428d-b7d7-f1e86caeba2b" # Juggernaut XL v9
        #"model_key": "735f6485-6703-498f-929e-07cf0bbbd179" # Dreamshaper 8
    }
    image_url = await generate_image(args)
    print("got image url: ", image_url)
    download_file(image_url, output_path)
--- a/3d-generation-pipeline/generate_model_local.py
+++ b/3d-generation-pipeline/generate_model_local.py
@ -1,97 +0,0 @@
 import subprocess
 import os
 import time
 import requests
 import base64
 from dotenv import load_dotenv
 load_dotenv()
 MODEL_FOLDER = os.environ["MODEL_FOLDER"]
 API_URL = os.environ["3D_GENERATION_URL"]
 def image_to_3d_subprocess(image_path, output_path):
    venv_python = MODEL_FOLDER + r"\.venv\Scripts\python.exe"
    script_path = MODEL_FOLDER + r"\run.py"
    args = [image_path, "--output-dir", output_path]
    command = [venv_python, script_path] + args
    try:
        # Run the subprocess
        result = subprocess.run(command, capture_output=True, text=True)
        # Print output and errors
        print("STDOUT:\n", result.stdout)
        print("STDERR:\n", result.stderr)
        print("Return Code:", result.returncode)
    except Exception as e:
        print(f"Error occurred: {e}")
 def generate_no_preview(image_base64: str):
    """Generate 3D model from a single base64-encoded image without previews.
    Args:
        image_base64: Base64 string of the image (without 'data:image/...' prefix)
    """
    try:
        # Set generation parameters
        params = {
            'image_base64': image_base64,
            'seed': 42,
            'ss_guidance_strength': 7.5,
            'ss_sampling_steps': 10,
            'slat_guidance_strength': 7.5,
            'slat_sampling_steps': 10,
            'mesh_simplify_ratio': 0.99,
            'texture_size': 1024,
            #'texture_baking_mode': 'opt',
            'texture_opt_total_steps': 1000,
            'output_format': 'glb'
        }
        # Start generation
        print("Starting generation...")
        response = requests.post(f"{API_URL}/generate_no_preview", data=params)
        response.raise_for_status()
        # Poll status until complete
        while True:
            status = requests.get(f"{API_URL}/status").json()
            print(f"Progress: {status['progress']}%")
            if status['status'] == 'COMPLETE':
                break
            elif status['status'] == 'FAILED':
                raise Exception(f"Generation failed: {status['message']}")
            time.sleep(1)
        # Download the model
        print("Downloading model...")
        response = requests.get(f"{API_URL}/download/model")
        response.raise_for_status()
        return response.content
    except Exception as e:
        print(f"Error: {str(e)}")
        return None
 def image_to_3d_api(image_path, output_path):
    with open(image_path, 'rb') as image_file:
        image_data = image_file.read()
    base64_encoded = base64.b64encode(image_data).decode('utf-8')
    model_binary = generate_no_preview(base64_encoded)
    output_file = f"{output_path}.glb"
    with open(output_file, 'wb') as f:
        f.write(model_binary)
    return output_file
--- a/3d-generation-pipeline/notebooks/cloudflare_API_test.ipynb
+++ b/3d-generation-pipeline/notebooks/cloudflare_API_test.ipynb
@ -1,152 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1dc6faae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import base64\n",
    "import requests\n",
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3107275",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv()\n",
    "\n",
    "ACCOUNT_ID = os.environ[\"CLOUDFLARE_ACCOUNT_ID\"]\n",
    "API_TOKEN = os.environ[\"CLOUDFLARE_API_TOKEN\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "999adf95",
   "metadata": {},
   "source": [
    "## Text to image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40b35163",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved: output.jpg  (263282 bytes)\n",
      "Saved: image9.jpg  (263282 bytes)\n"
     ]
    }
   ],
   "source": [
    "MODEL = \"@cf/black-forest-labs/flux-1-schnell\"\n",
    "URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
    "\n",
    "payload = {\n",
    "    \"prompt\": \"a slightly curved broadsword with a fancy golden crossguard\",\n",
    "}\n",
    "\n",
    "headers = {\n",
    "    \"Authorization\": f\"Bearer {API_TOKEN}\",\n",
    "    \"Content-Type\": \"application/json\",\n",
    "}\n",
    "\n",
    "resp = requests.post(URL, json=payload, headers=headers, timeout=60)\n",
    "resp.raise_for_status()\n",
    "\n",
    "data = resp.json()\n",
    "b64 = data[\"result\"][\"image\"]\n",
    "if not b64:\n",
    "    raise RuntimeError(f\"Unexpected response structure: {data}\")\n",
    "\n",
    "img_bytes = base64.b64decode(b64)\n",
    "\n",
    "out_path = \"output.jpg\"\n",
    "with open(out_path, \"wb\") as f:\n",
    "    f.write(img_bytes)\n",
    "\n",
    "print(f\"Saved: {out_path}  ({len(img_bytes)} bytes)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14a874c4",
   "metadata": {},
   "source": [
    "## Text prompt refinement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "485f6f46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"dark wooden battleaxe with bronze blade\"\n"
     ]
    }
   ],
   "source": [
    "MODEL = \"@cf/meta/llama-3.2-3b-instruct\"\n",
    "URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
    "\n",
    "instructions = \"\"\"\n",
    "User is talking about some object. Your task is to generate a short and concise description of it. Use only user's own words, keep it as short as possible.\n",
    "Example:\n",
    "User: 'Umm, okay, I would like a really cool sword, with for example a bright orange crossguard. And also it should be slightly curved.'\n",
    "You: 'a slightly curved sword with bright orange crossguard'\n",
    "\"\"\"\n",
    "prompt = \"Umm, alright, can you please give me an epic battleaxe? It should have a dark wooden shaft and bronze blade.\"\n",
    "\n",
    "response = requests.post(URL,\n",
    "  headers={\"Authorization\": f\"Bearer {API_TOKEN}\"},\n",
    "  json={\n",
    "    \"messages\": [\n",
    "      {\"role\": \"system\", \"content\": instructions},\n",
    "      {\"role\": \"user\", \"content\": prompt}\n",
    "    ]\n",
    "  }\n",
    ")\n",
    "data = response.json()\n",
    "result_text = data[\"result\"][\"response\"]\n",
    "print(result_text)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
--- a/3d-generation-pipeline/notebooks/local_image_generation.ipynb
+++ b/3d-generation-pipeline/notebooks/local_image_generation.ipynb
@ -1,122 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50e24baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from invokeai_mcp_server import create_text2img_graph, enqueue_graph, wait_for_completion, get_image_url\n",
    "from urllib.parse import urljoin\n",
    "\n",
    "import asyncio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0407cd9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "INVOKEAI_BASE_URL = \"http://127.0.0.1:9090\"\n",
    "\n",
    "\n",
    "async def generate_image(arguments: dict):\n",
    "\n",
    "    # Extract parameters\n",
    "    prompt = arguments[\"prompt\"]\n",
    "    negative_prompt = arguments.get(\"negative_prompt\", \"\")\n",
    "    width = arguments.get(\"width\", 512)\n",
    "    height = arguments.get(\"height\", 512)\n",
    "    steps = arguments.get(\"steps\", 30)\n",
    "    cfg_scale = arguments.get(\"cfg_scale\", 7.5)\n",
    "    scheduler = arguments.get(\"scheduler\", \"euler\")\n",
    "    seed = arguments.get(\"seed\")\n",
    "    model_key = arguments.get(\"model_key\")\n",
    "    lora_key = arguments.get(\"lora_key\")\n",
    "    lora_weight = arguments.get(\"lora_weight\", 1.0)\n",
    "    vae_key = arguments.get(\"vae_key\")\n",
    "\n",
    "    #logger.info(f\"Generating image with prompt: {prompt[:50]}...\")\n",
    "\n",
    "    # Create graph\n",
    "    graph = await create_text2img_graph(\n",
    "        prompt=prompt,\n",
    "        negative_prompt=negative_prompt,\n",
    "        model_key=model_key,\n",
    "        lora_key=lora_key,\n",
    "        lora_weight=lora_weight,\n",
    "        vae_key=vae_key,\n",
    "        width=width,\n",
    "        height=height,\n",
    "        steps=steps,\n",
    "        cfg_scale=cfg_scale,\n",
    "        scheduler=scheduler,\n",
    "        seed=seed\n",
    "    )\n",
    "\n",
    "    # Enqueue and wait for completion\n",
    "    result = await enqueue_graph(graph)\n",
    "    batch_id = result[\"batch\"][\"batch_id\"]\n",
    "\n",
    "    #logger.info(f\"Enqueued batch {batch_id}, waiting for completion...\")\n",
    "\n",
    "    completed = await wait_for_completion(batch_id)\n",
    "\n",
    "    # Extract image name from result\n",
    "    if \"result\" in completed and \"outputs\" in completed[\"result\"]:\n",
    "        outputs = completed[\"result\"][\"outputs\"]\n",
    "        # Find the image output\n",
    "        for node_id, output in outputs.items():\n",
    "            if output.get(\"type\") == \"image_output\":\n",
    "                image_name = output[\"image\"][\"image_name\"]\n",
    "                image_url = await get_image_url(image_name)\n",
    "\n",
    "                text=f\"Image generated successfully!\\n\\nImage Name: {image_name}\\nImage URL: {image_url}\\n\\nYou can view the image at: {urljoin(INVOKEAI_BASE_URL, f'/api/v1/images/i/{image_name}/full')}\"\n",
    "                print(text)\n",
    "\n",
    "    # Fallback if we couldn't find image output\n",
    "    #text=f\"Image generation completed but output format was unexpected. Batch ID: {batch_id}\\n\\nResult: {json.dumps(completed, indent=2)}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cf9d879",
   "metadata": {},
   "outputs": [],
   "source": [
    "async def main():\n",
    "    args = {\n",
    "        \"prompt\": \"a golden katana with a fancy pommel\"\n",
    "    }\n",
    "    await generate_image(args)\n",
    "\n",
    "asyncio.run(main())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
--- a/3d-generation-pipeline/notebooks/local_model_generation_test.ipynb
+++ b/3d-generation-pipeline/notebooks/local_model_generation_test.ipynb
--- a/3d-generation-pipeline/notebooks/subprocess_test.ipynb
+++ b/3d-generation-pipeline/notebooks/subprocess_test.ipynb
@ -1,165 +0,0 @@
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4826c91d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2025-10-18-16-35-47'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9419e692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STDOUT:\n",
      " Device used:  cuda\n",
      "After Remesh 9998 19996\n",
      "\n",
      "STDERR:\n",
      " D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\timm\\models\\layers\\__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers\n",
      "  warnings.warn(f\"Importing from {__name__} is deprecated, please import via timm.layers\", FutureWarning)\n",
      "\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
      "  0%|          | 0/1 [00:00<?, ?it/s]\n",
      "Traceback (most recent call last):\n",
      "  File \"D:\\users\\henrisel\\stable-fast-3d\\run.py\", line 122, in <module>\n",
      "    mesh, glob_dict = model.run_image(\n",
      "  File \"D:\\users\\henrisel\\stable-fast-3d\\sf3d\\system.py\", line 286, in run_image\n",
      "    meshes, global_dict = self.generate_mesh(\n",
      "  File \"D:\\users\\henrisel\\stable-fast-3d\\sf3d\\system.py\", line 369, in generate_mesh\n",
      "    rast = self.baker.rasterize(\n",
      "  File \"D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\texture_baker\\baker.py\", line 28, in rasterize\n",
      "    return torch.ops.texture_baker_cpp.rasterize(\n",
      "  File \"D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\torch\\_ops.py\", line 1243, in __call__\n",
      "    return self._op(*args, **kwargs)\n",
      "NotImplementedError: Could not run 'texture_baker_cpp::rasterize' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'texture_baker_cpp::rasterize' is only available for these backends: [CPU, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMTIA, AutogradMAIA, AutogradMeta, Tracer, AutocastCPU, AutocastMTIA, AutocastMAIA, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n",
      "\n",
      "CPU: registered at texture_baker\\csrc\\baker.cpp:543 [kernel]\n",
      "Meta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\MetaFallbackKernel.cpp:23 [backend fallback]\n",
      "BackendSelect: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\n",
      "Python: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:194 [backend fallback]\n",
      "FuncTorchDynamicLayerBackMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:479 [backend fallback]\n",
      "Functionalize: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:375 [backend fallback]\n",
      "Named: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\n",
      "Conjugate: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\n",
      "Negative: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\native\\NegateFallback.cpp:18 [backend fallback]\n",
      "ZeroTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\n",
      "ADInplaceOrView: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:104 [backend fallback]\n",
      "AutogradOther: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:63 [backend fallback]\n",
      "AutogradCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:67 [backend fallback]\n",
      "AutogradCUDA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:75 [backend fallback]\n",
      "AutogradXLA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:87 [backend fallback]\n",
      "AutogradMPS: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:95 [backend fallback]\n",
      "AutogradXPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:71 [backend fallback]\n",
      "AutogradHPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:108 [backend fallback]\n",
      "AutogradLazy: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:91 [backend fallback]\n",
      "AutogradMTIA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:79 [backend fallback]\n",
      "AutogradMAIA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:83 [backend fallback]\n",
      "AutogradMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:99 [backend fallback]\n",
      "Tracer: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\torch\\csrc\\autograd\\TraceTypeManual.cpp:294 [backend fallback]\n",
      "AutocastCPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:322 [backend fallback]\n",
      "AutocastMTIA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:466 [backend fallback]\n",
      "AutocastMAIA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:504 [backend fallback]\n",
      "AutocastXPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:542 [backend fallback]\n",
      "AutocastMPS: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:209 [backend fallback]\n",
      "AutocastCUDA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:165 [backend fallback]\n",
      "FuncTorchBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:731 [backend fallback]\n",
      "BatchedNestedTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:758 [backend fallback]\n",
      "FuncTorchVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:27 [backend fallback]\n",
      "Batched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\n",
      "VmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\n",
      "FuncTorchGradWrapper: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:210 [backend fallback]\n",
      "PythonTLSSnapshot: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:202 [backend fallback]\n",
      "FuncTorchDynamicLayerFrontMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:475 [backend fallback]\n",
      "PreDispatch: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:206 [backend fallback]\n",
      "PythonDispatcher: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:198 [backend fallback]\n",
      "\n",
      "\n",
      "Return Code: 1\n"
     ]
    }
   ],
   "source": [
    "import subprocess\n",
    "\n",
    "MODEL_FOLDER = r\"D:\\users\\henrisel\\stable-fast-3d\"\n",
    "PROJECT_FOLDER = r\"D:\\users\\henrisel\\DeltaVR3DModelGeneration\\3d-generation-pipeline\"\n",
    "\n",
    "# Path to the Python interpreter in the other virtual environment\n",
    "venv_python = MODEL_FOLDER + r\"\\.venv\\Scripts\\python.exe\"\n",
    "\n",
    "# Path to the .py file you want to run\n",
    "script_path = MODEL_FOLDER + r\"\\run.py\"\n",
    "\n",
    "# Optional: arguments to pass to the script\n",
    "args = [MODEL_FOLDER + r\"\\demo_files\\examples\\chair1.png\", \"--output-dir\", PROJECT_FOLDER + r\"\\images\"]\n",
    "\n",
    "# Build the command\n",
    "command = [venv_python, script_path] + args\n",
    "\n",
    "try:\n",
    "    # Run the subprocess\n",
    "    result = subprocess.run(command, capture_output=True, text=True)\n",
    "\n",
    "    # Print output and errors\n",
    "    print(\"STDOUT:\\n\", result.stdout)\n",
    "    print(\"STDERR:\\n\", result.stderr)\n",
    "    print(\"Return Code:\", result.returncode)\n",
    "\n",
    "except Exception as e:\n",
    "    print(f\"Error occurred: {e}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee480ba6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
--- a/3d-generation-pipeline/requirements.txt
+++ b/3d-generation-pipeline/requirements.txt
@ -1,7 +0,0 @@
 #torch==2.8.0+cu129 https://pytorch.org/get-started/previous-versions/
 transformers==4.57.0
 git+https://github.com/huggingface/diffusers.git
 accelerate==1.10.1
 huggingface_hub[hf_xet]==1.1.10
 sentencepiece==0.2.1
 protobuf==6.32.1
--- a/3d-generation-pipeline/start_pipeline.py
+++ b/3d-generation-pipeline/start_pipeline.py
@ -1,62 +0,0 @@
 import argparse
 import asyncio
 import logging
 import time
 from pathlib import Path
 from datetime import datetime
 from dotenv import load_dotenv
 from generate_image_local import text_to_image_invoke_ai
 from generate_model_local import image_to_3d_api
 load_dotenv()
 def get_timestamp():
    return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
 def setup_logger(base_folder, timestamp):
    log_dir = base_folder / Path("logs")
    log_dir.mkdir(parents=True, exist_ok=True)
    logging.basicConfig(
        filename=log_dir / f"{timestamp}.log",
        level=logging.INFO,
        force=True
    )
 async def main():
    parser = argparse.ArgumentParser(description="Text to 3D model pipeline")
    parser.add_argument("--prompt", type=str, required=True, help="User text prompt")
    args = parser.parse_args()
    input_prompt = args.prompt
    print(f"Input prompt: {input_prompt}")
    image_generation_prompt = input_prompt + ", single object, fully visible, plain neutral background, high-resolution, sharp details, soft studio lighting, front and side visible, realistic style, true-to-scale"
    pipeline_folder = Path(__file__).resolve().parent
    timestamp = get_timestamp()
    setup_logger(pipeline_folder, timestamp)
    time_checkpoint = time.time()
    image_path = pipeline_folder / "images" / f"{timestamp}.jpg"
    # TODO: use Invoke AI or Cloudflare, depending on env var
    #text_to_image_cloudflare(image_generation_prompt, image_path)
    await text_to_image_invoke_ai(image_generation_prompt, image_path)
    image_generation_time = time.time() - time_checkpoint
    time_checkpoint = time.time()
    logging.info(f"Image generation time: {round(image_generation_time, 1)} s")
    print(f"Generated image file: {image_path}")
    model_path = pipeline_folder / "models" / timestamp
    model_file = image_to_3d_api(image_path, model_path)
    model_generation_time = time.time() - time_checkpoint
    logging.info(f"Model generation time: {round(model_generation_time, 1)} s")
    print(f"Generated 3D model file: {model_file}")
 if __name__ == "__main__":
    asyncio.run(main())
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--- a/Assets/_PROJECT/Scenes/DeltaBuilding_base.unity
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--- a/Assets/_PROJECT/Scripts/3DModeGeneration/ModelGenerationPipelineStarter.cs
+++ b/Assets/_PROJECT/Scripts/3DModeGeneration/ModelGenerationPipelineStarter.cs
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 using UnityEngine;
 using System.Diagnostics;
 using System.Threading.Tasks;
 using GLTFast;
 public class ModelGenerationPipelineStarter : MonoBehaviour
 {
    public Material activeMaterial;
    public Material inactiveMaterial;
    public Transform modelSpawnPoint;
    private MeshRenderer meshRenderer;
    public string inputPrompt;
    // Start is called before the first frame update
    void Start()
    {
        meshRenderer = GetComponent<MeshRenderer>();
    }
    // Update is called once per frame
    void Update()
    {
    }
    void OnTriggerEnter(Collider other)
    {
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        if (controller != null)
        {
            meshRenderer.material = activeMaterial;
            StartModeGenerationPipeline();
            //LoadModel("D:\\henrisel\\DeltaVR3DModelGeneration\\3d-generation-pipeline\\models\\2025-11-17-16-13-33\\mesh.glb");
        }
    }
    private void OnTriggerExit(Collider other)
    {
        KbmController controller = other.GetComponent<KbmController>();
        if (controller != null)
        {
            meshRenderer.material = inactiveMaterial;
        }
    }
    private async Task<string> GenerateModelAsync()
    {
        return await Task.Run(() =>
        {
            // Path to your virtual environment's python.exe
            string pythonExe = @"D:\users\henrisel\DeltaVR3DModelGeneration\3d-generation-pipeline\.venv\Scripts\python.exe";
            // Path to your Python script
            string scriptPath = @"D:\users\henrisel\DeltaVR3DModelGeneration\3d-generation-pipeline\start_pipeline.py";
            // Arguments to pass to the script
            string arguments = $"{scriptPath} --prompt \"{inputPrompt}\"";
            ProcessStartInfo psi = new ProcessStartInfo
            {
                FileName = pythonExe,
                Arguments = arguments,
                UseShellExecute = false,
                RedirectStandardOutput = true,
                RedirectStandardError = true,
                CreateNoWindow = true
            };
            using (Process process = new Process())
            {
                process.StartInfo = psi;
                process.OutputDataReceived += (sender, e) => UnityEngine.Debug.Log(e.Data);
                process.ErrorDataReceived += (sender, e) => UnityEngine.Debug.LogError(e.Data);
                process.Start();
                string output = process.StandardOutput.ReadToEnd();
                string error = process.StandardError.ReadToEnd();
                process.WaitForExit();
                // Extract model path from output
                foreach (string line in output.Split('\n'))
                {
                    if (line.StartsWith("Generated 3D model file: "))
                    {
                        return line.Replace("Generated 3D model file: ", "").Trim();
                    }
                }
                return null;
            }
        });
    }
    private async void StartModeGenerationPipeline()
    {
        string modelPath = await GenerateModelAsync();
        if (!string.IsNullOrEmpty(modelPath))
        {
            UnityEngine.Debug.Log("Got generated model path: " + modelPath);
            await LoadModel(modelPath);
        }
        else
        {
            UnityEngine.Debug.LogError("Model path not found in Python output.");
        }
    }
    private async Task LoadModel(string modelPath)
    {
        var gltf = new GltfImport();
        bool loadSuccess = await gltf.Load(modelPath);
        UnityEngine.Debug.Log("Load model success: " + loadSuccess);
        if (loadSuccess)
        {
            GameObject spawnedObject = new GameObject("spawned model");
            spawnedObject.transform.parent = modelSpawnPoint;
            spawnedObject.transform.position = modelSpawnPoint.position;
            bool spawnSuccess = await gltf.InstantiateMainSceneAsync(spawnedObject.transform);
            UnityEngine.Debug.Log("Spawn model success: " + spawnSuccess);
            Transform spawnedObjectMainTransform = spawnedObject.transform.GetChild(0).transform;
            GameObject spawnedObjectBody = spawnedObjectMainTransform.GetChild(0).transform.gameObject;
            MeshCollider collider = spawnedObjectBody.AddComponent<MeshCollider>();
            collider.convex = true;
            MeshRenderer renderer = spawnedObjectBody.GetComponent<MeshRenderer>();
            renderer.material.SetFloat("metallicFactor", 0);
            spawnedObjectMainTransform.gameObject.AddComponent<Rigidbody>();
        }
    }
 }
--- a/Assets/_PROJECT/Scripts/3DModeGeneration/ModelGenerationPipelineStarter.cs.meta
+++ b/Assets/_PROJECT/Scripts/3DModeGeneration/ModelGenerationPipelineStarter.cs.meta
@ -1,11 +0,0 @@
 fileFormatVersion: 2
 guid: 46e67223dce9b7a4783ed36b8ed65f19
 MonoImporter:
  externalObjects: {}
  serializedVersion: 2
  defaultReferences: []
  executionOrder: 0
  icon: {instanceID: 0}
  userData: 
  assetBundleName: 
  assetBundleVariant: 
--- a/Doc/clips/Bolt-Car-Network-Problem-Clip.gif
+++ b/Doc/clips/Bolt-Car-Network-Problem-Clip.gif
--- a/Doc/clips/Bolt-Car-Network-Results-Clip.gif
+++ b/Doc/clips/Bolt-Car-Network-Results-Clip.gif
--- a/Doc/clips/Camera-Collider-Clip.gif
+++ b/Doc/clips/Camera-Collider-Clip.gif
--- a/Doc/clips/Elevator-network-problem-Clip.gif
+++ b/Doc/clips/Elevator-network-problem-Clip.gif
--- a/Doc/clips/Elevator-network-results-Clip.gif
+++ b/Doc/clips/Elevator-network-results-Clip.gif
--- a/Doc/clips/Hand-Collider-Prototype-Clip.gif
+++ b/Doc/clips/Hand-Collider-Prototype-Clip.gif
--- a/Doc/clips/Player-Hand-Collision-Complete-Clip.gif
+++ b/Doc/clips/Player-Hand-Collision-Complete-Clip.gif
--- a/Doc/clips/VR-Elevator-Example-Clip.gif
+++ b/Doc/clips/VR-Elevator-Example-Clip.gif
--- a/Doc/designs/Elevator-buttons-inside.png
+++ b/Doc/designs/Elevator-buttons-inside.png
--- a/Doc/designs/Model-Collider-Rework-Concept.png
+++ b/Doc/designs/Model-Collider-Rework-Concept.png
--- a/Doc/designs/Moder-Collider-Rework-Results.png
+++ b/Doc/designs/Moder-Collider-Rework-Results.png
--- a/Doc/designs/Network-Player-mirror.png
+++ b/Doc/designs/Network-Player-mirror.png
--- a/Doc/designs/Original-elevator-state-diagram.png
+++ b/Doc/designs/Original-elevator-state-diagram.png
--- a/Doc/designs/Player-Hand-Colliders.png
+++ b/Doc/designs/Player-Hand-Colliders.png
--- a/Packages/manifest.json
+++ b/Packages/manifest.json
@ -4,7 +4,6 @@
    "com.unity.2d.tilemap": "1.0.0",
    "com.unity.ai.navigation": "1.1.1",
    "com.unity.cinemachine": "2.9.5",
    "com.unity.cloud.gltfast": "6.14.1",
    "com.unity.collab-proxy": "2.0.1",
    "com.unity.ext.nunit": "1.0.6",
    "com.unity.feature.vr": "1.0.0",
--- a/Packages/packages-lock.json
+++ b/Packages/packages-lock.json
@ -31,12 +31,11 @@
      "url": "https://packages.unity.com"
    },
    "com.unity.burst": {
-      "version": "1.8.24",
+      "version": "1.8.3",
      "depth": 1,
      "source": "registry",
      "dependencies": {
-        "com.unity.mathematics": "1.2.1",
+        "com.unity.mathematics": "1.2.1"
        "com.unity.modules.jsonserialize": "1.0.0"
      },
      "url": "https://packages.unity.com"
    },
@ -49,19 +48,6 @@
      },
      "url": "https://packages.unity.com"
    },
    "com.unity.cloud.gltfast": {
      "version": "6.14.1",
      "depth": 0,
      "source": "registry",
      "dependencies": {
        "com.unity.burst": "1.8.24",
        "com.unity.collections": "1.2.4",
        "com.unity.mathematics": "1.2.6",
        "com.unity.modules.jsonserialize": "1.0.0",
        "com.unity.modules.unitywebrequest": "1.0.0"
      },
      "url": "https://packages.unity.com"
    },
    "com.unity.collab-proxy": {
      "version": "2.0.1",
      "depth": 0,
@ -69,16 +55,6 @@
      "dependencies": {},
      "url": "https://packages.unity.com"
    },
    "com.unity.collections": {
      "version": "1.2.4",
      "depth": 1,
      "source": "registry",
      "dependencies": {
        "com.unity.burst": "1.6.6",
        "com.unity.test-framework": "1.1.31"
      },
      "url": "https://packages.unity.com"
    },
    "com.unity.editorcoroutines": {
      "version": "1.0.0",
      "depth": 1,
Author	SHA1	Message	Date
rannosam	b563be1158	Upload files to "Doc/clips"	2025-11-18 14:08:38 +00:00
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rannosam	15c2e62e92	Upload files to "Doc/clips"	2025-11-04 17:28:46 +00:00
rannosam	c4fafd1dd3	Delete Doc/clips/Hand-Collider-Prototype-Clip.gif	2025-11-04 17:26:11 +00:00
rannosam	1c03f1773b	Upload files to "Doc/clips"	2025-11-04 17:25:57 +00:00
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