Changes in sound attributions

Just a small reference change

3d-generation-pipeline/.env.example

@@ -1,5 +0,0 @@
-INVOKEAI_URL=
-TRELLIS_URL=
-
-CLOUDFLARE_ACCOUNT_ID=
-CLOUDFLARE_API_TOKEN=

3d-generation-pipeline/.gitignore

@@ -1,7 +0,0 @@
-.venv
-.env
-__pycache__
-images/
-models/
-logs/
-notebooks/test_resources/

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)

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"]

3d-generation-pipeline/generate_image_local.py

@@ -1,489 +0,0 @@
-# Based on: https://github.com/coinstax/invokeai-mcp-server
-
-import requests
-import asyncio
-import json
-import logging
-import httpx
-import os
-
-from typing import Optional
-from urllib.parse import urljoin
-from dotenv import load_dotenv
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("invokeai-mcp")
-
-load_dotenv()
-
-INVOKEAI_BASE_URL = os.environ["INVOKEAI_URL"]
-DEFAULT_QUEUE_ID = "default"
-
-# HTTP client
-http_client: Optional[httpx.AsyncClient] = None
-
-
-
-def get_client() -> httpx.AsyncClient:
-    """Get or create HTTP client."""
-    global http_client
-    if http_client is None:
-        http_client = httpx.AsyncClient(base_url=INVOKEAI_BASE_URL, timeout=120.0)
-    return http_client
-
-
-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)
-
-async def wait_for_completion(batch_id: str, queue_id: str = DEFAULT_QUEUE_ID, timeout: int = 300) -> dict:
-    """Wait for a batch to complete and return the most recent image."""
-    client = get_client()
-    start_time = asyncio.get_event_loop().time()
-
-    while True:
-        # Check if we've exceeded timeout
-        if asyncio.get_event_loop().time() - start_time > timeout:
-            raise TimeoutError(f"Image generation timed out after {timeout} seconds")
-
-        # Get batch status
-        response = await client.get(f"/api/v1/queue/{queue_id}/b/{batch_id}/status")
-        response.raise_for_status()
-        status_data = response.json()
-
-        # Check for failures
-        failed_count = status_data.get("failed", 0)
-        if failed_count > 0:
-            # Try to get error details from the queue
-            queue_status_response = await client.get(f"/api/v1/queue/{queue_id}/status")
-            queue_status_response.raise_for_status()
-            queue_data = queue_status_response.json()
-
-            raise RuntimeError(
-                f"Image generation failed. Batch {batch_id} has {failed_count} failed item(s). "
-                f"Queue status: {json.dumps(queue_data, indent=2)}"
-            )
-
-        # Check completion
-        completed = status_data.get("completed", 0)
-        total = status_data.get("total", 0)
-
-        if completed == total and total > 0:
-            # Get most recent non-intermediate image
-            images_response = await client.get("/api/v1/images/?is_intermediate=false&limit=10")
-            images_response.raise_for_status()
-            images_data = images_response.json()
-
-            # Return the most recent image (first in the list)
-            if images_data.get("items"):
-                return {
-                    "batch_id": batch_id,
-                    "status": "completed",
-                    "result": {
-                        "outputs": {
-                            "save_image": {
-                                "type": "image_output",
-                                "image": {
-                                    "image_name": images_data["items"][0]["image_name"]
-                                }
-                            }
-                        }
-                    }
-                }
-
-            # If no images found, return status
-            return status_data
-
-        # Wait before checking again
-        await asyncio.sleep(1)
-
-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 get_image_url(image_name: str) -> str:
-    """Get the URL for an image."""
-    client = get_client()
-    response = await client.get(f"/api/v1/images/i/{image_name}/urls")
-    response.raise_for_status()
-    data = response.json()
-    return data.get("image_url", "")
-    
-
-async def enqueue_graph(graph: dict, queue_id: str = DEFAULT_QUEUE_ID) -> dict:
-    """Enqueue a graph for processing."""
-    client = get_client()
-
-    batch = {
-        "batch": {
-            "graph": graph,
-            "runs": 1,
-            "data": None
-        }
-    }
-
-    response = await client.post(
-        f"/api/v1/queue/{queue_id}/enqueue_batch",
-        json=batch
-    )
-    response.raise_for_status()
-    return response.json()
-
-async def list_models(model_type: str = "main") -> list:
-    """List available models."""
-    client = get_client()
-    response = await client.get("/api/v2/models/", params={"model_type": model_type})
-    response.raise_for_status()
-    data = response.json()
-    return data.get("models", [])
-
-async def get_model_info(model_key: str) -> Optional[dict]:
-    """Get information about a specific model."""
-    client = get_client()
-    try:
-        response = await client.get(f"/api/v2/models/i/{model_key}")
-        response.raise_for_status()
-        model_data = response.json()
-
-        # Ensure we have a valid dictionary
-        if not isinstance(model_data, dict):
-            logger.error(f"Model info for {model_key} is not a dictionary: {type(model_data)}")
-            return None
-
-        return model_data
-    except Exception as e:
-        logger.error(f"Error fetching model info for {model_key}: {e}")
-        return None
-
-
-async def create_text2img_graph(
-    prompt: str,
-    negative_prompt: str = "",
-    model_key: Optional[str] = None,
-    lora_key: Optional[str] = None,
-    lora_weight: float = 1.0,
-    vae_key: Optional[str] = None,
-    width: int = 512,
-    height: int = 512,
-    steps: int = 30,
-    cfg_scale: float = 7.5,
-    scheduler: str = "euler",
-    seed: Optional[int] = None
-) -> dict:
-    """Create a text-to-image generation graph with optional LoRA and VAE support."""
-
-    # Use default model if not specified
-    if model_key is None:
-        # Try to find an sd-1 model
-        models = await list_models("main")
-        for model in models:
-            if model.get("base") == "sd-1":
-                model_key = model["key"]
-                break
-        if model_key is None:
-            raise ValueError("No suitable model found")
-
-    # Get model information
-    model_info = await get_model_info(model_key)
-    if not model_info:
-        raise ValueError(f"Model {model_key} not found")
-
-    # Validate model info has required fields
-    if not isinstance(model_info, dict):
-        raise ValueError(f"Model {model_key} returned invalid data type: {type(model_info)}")
-
-    required_fields = ["key", "hash", "name", "base", "type"]
-    for field in required_fields:
-        if field not in model_info or model_info[field] is None:
-            raise ValueError(f"Model {model_key} is missing required field: {field}")
-
-    # Generate random seed if not provided
-    if seed is None:
-        import random
-        seed = random.randint(0, 2**32 - 1)
-
-    # Detect if this is an SDXL model
-    is_sdxl = model_info["base"] == "sdxl"
-
-    # Build nodes dictionary
-    nodes = {
-        # Main model loader - use sdxl_model_loader for SDXL models
-        "model_loader": {
-            "type": "sdxl_model_loader" if is_sdxl else "main_model_loader",
-            "id": "model_loader",
-            "model": {
-                "key": model_info["key"],
-                "hash": model_info["hash"],
-                "name": model_info["name"],
-                "base": model_info["base"],
-                "type": model_info["type"]
-            }
-        },
-
-        # Positive prompt encoding - use sdxl_compel_prompt for SDXL
-        "positive_prompt": {
-            "type": "sdxl_compel_prompt" if is_sdxl else "compel",
-            "id": "positive_prompt",
-            "prompt": prompt,
-            **({"style": prompt} if is_sdxl else {})
-        },
-
-        # Negative prompt encoding - use sdxl_compel_prompt for SDXL
-        "negative_prompt": {
-            "type": "sdxl_compel_prompt" if is_sdxl else "compel",
-            "id": "negative_prompt",
-            "prompt": negative_prompt,
-            **({"style": ""} if is_sdxl else {})
-        },
-
-        # Noise generation
-        "noise": {
-            "type": "noise",
-            "id": "noise",
-            "seed": seed,
-            "width": width,
-            "height": height,
-            "use_cpu": False
-        },
-
-        # Denoise latents (main generation step)
-        "denoise": {
-            "type": "denoise_latents",
-            "id": "denoise",
-            "steps": steps,
-            "cfg_scale": cfg_scale,
-            "scheduler": scheduler,
-            "denoising_start": 0,
-            "denoising_end": 1
-        },
-
-        # Convert latents to image
-        "latents_to_image": {
-            "type": "l2i",
-            "id": "latents_to_image"
-        },
-
-        # Save image
-        "save_image": {
-            "type": "save_image",
-            "id": "save_image",
-            "is_intermediate": False
-        }
-    }
-
-    # Add LoRA loader if requested
-    if lora_key is not None:
-        lora_info = await get_model_info(lora_key)
-        if not lora_info:
-            raise ValueError(f"LoRA model {lora_key} not found")
-
-        # Validate LoRA info has required fields
-        required_fields = ["key", "hash", "name", "base", "type"]
-        for field in required_fields:
-            if field not in lora_info or lora_info[field] is None:
-                raise ValueError(f"LoRA model {lora_key} is missing required field: {field}")
-
-        nodes["lora_loader"] = {
-            "type": "lora_loader",
-            "id": "lora_loader",
-            "lora": {
-                "key": lora_info["key"],
-                "hash": lora_info["hash"],
-                "name": lora_info["name"],
-                "base": lora_info["base"],
-                "type": lora_info["type"]
-            },
-            "weight": lora_weight
-        }
-
-    # Add VAE loader if requested (to override model's built-in VAE)
-    if vae_key is not None:
-        vae_info = await get_model_info(vae_key)
-        if not vae_info:
-            raise ValueError(f"VAE model {vae_key} not found")
-
-        # Validate VAE info has required fields
-        required_fields = ["key", "hash", "name", "base", "type"]
-        for field in required_fields:
-            if field not in vae_info or vae_info[field] is None:
-                raise ValueError(f"VAE model {vae_key} is missing required field: {field}")
-
-        nodes["vae_loader"] = {
-            "type": "vae_loader",
-            "id": "vae_loader",
-            "vae_model": {
-                "key": vae_info["key"],
-                "hash": vae_info["hash"],
-                "name": vae_info["name"],
-                "base": vae_info["base"],
-                "type": vae_info["type"]
-            }
-        }
-
-    # Build edges
-    edges = []
-
-    # Determine source for UNet and CLIP (model_loader or lora_loader)
-    unet_source = "lora_loader" if lora_key is not None else "model_loader"
-    clip_source = "lora_loader" if lora_key is not None else "model_loader"
-    # Determine source for VAE (vae_loader if specified, otherwise model_loader)
-    vae_source = "vae_loader" if vae_key is not None else "model_loader"
-
-    # If using LoRA, connect model_loader to lora_loader first
-    if lora_key is not None:
-        edges.extend([
-            {
-                "source": {"node_id": "model_loader", "field": "unet"},
-                "destination": {"node_id": "lora_loader", "field": "unet"}
-            },
-            {
-                "source": {"node_id": "model_loader", "field": "clip"},
-                "destination": {"node_id": "lora_loader", "field": "clip"}
-            }
-        ])
-        # Note: lora_loader doesn't have a clip2 field, so for SDXL we route clip2 directly from model_loader
-
-    # Connect UNet and CLIP to downstream nodes
-    edges.extend([
-        # Connect UNet to denoise
-        {
-            "source": {"node_id": unet_source, "field": "unet"},
-            "destination": {"node_id": "denoise", "field": "unet"}
-        },
-        # Connect CLIP to prompts
-        {
-            "source": {"node_id": clip_source, "field": "clip"},
-            "destination": {"node_id": "positive_prompt", "field": "clip"}
-        },
-        {
-            "source": {"node_id": clip_source, "field": "clip"},
-            "destination": {"node_id": "negative_prompt", "field": "clip"}
-        },
-    ])
-
-    # For SDXL models, also connect clip2
-    # Note: clip2 always comes from model_loader, even when using LoRA (lora_loader doesn't support clip2)
-    if is_sdxl:
-        edges.extend([
-            {
-                "source": {"node_id": "model_loader", "field": "clip2"},
-                "destination": {"node_id": "positive_prompt", "field": "clip2"}
-            },
-            {
-                "source": {"node_id": "model_loader", "field": "clip2"},
-                "destination": {"node_id": "negative_prompt", "field": "clip2"}
-            },
-        ])
-
-    edges.extend([
-
-        # Connect prompts to denoise
-        {
-            "source": {"node_id": "positive_prompt", "field": "conditioning"},
-            "destination": {"node_id": "denoise", "field": "positive_conditioning"}
-        },
-        {
-            "source": {"node_id": "negative_prompt", "field": "conditioning"},
-            "destination": {"node_id": "denoise", "field": "negative_conditioning"}
-        },
-
-        # Connect noise to denoise
-        {
-            "source": {"node_id": "noise", "field": "noise"},
-            "destination": {"node_id": "denoise", "field": "noise"}
-        },
-
-        # Connect denoise to latents_to_image
-        {
-            "source": {"node_id": "denoise", "field": "latents"},
-            "destination": {"node_id": "latents_to_image", "field": "latents"}
-        },
-        {
-            "source": {"node_id": vae_source, "field": "vae"},
-            "destination": {"node_id": "latents_to_image", "field": "vae"}
-        },
-
-        # Connect latents_to_image to save_image
-        {
-            "source": {"node_id": "latents_to_image", "field": "image"},
-            "destination": {"node_id": "save_image", "field": "image"}
-        }
-    ])
-
-    graph = {
-        "id": "text2img_graph",
-        "nodes": nodes,
-        "edges": edges
-    }
-
-    return graph

3d-generation-pipeline/generate_model_local.py

@@ -1,74 +0,0 @@
-import os
-import time
-import requests
-import base64
-
-from dotenv import load_dotenv
-
-load_dotenv()
-API_URL = os.environ["TRELLIS_URL"]
-
-
-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_trellis(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
-    

3d-generation-pipeline/notebooks/cloudflare_API_test.ipynb

@@ -1,153 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "1dc6faae",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import base64\n",
-    "import requests\n",
-    "from dotenv import load_dotenv"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "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: test_resources/resolution_test_1.jpg  (315728 bytes)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# https://developers.cloudflare.com/workers-ai/models/flux-1-schnell/\n",
-    "\n",
-    "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\": \"cyborg crocodile, realistic style, single object, front and side fully visible, plain neutral background, clear details, soft studio lighting, true-to-scale\",\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 = \"test_resources/resolution_test_1.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
-}

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
-}

3d-generation-pipeline/notebooks/local_model_generation_test.ipynb

@@ -1,152 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "d55eb3ce",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import requests\n",
-    "import base64\n",
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "77b23cd8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# API endpoint\n",
-    "BASE_URL = \"http://127.0.0.1:7960\"\n",
-    "\n",
-    "def generate_no_preview(image_base64: str):\n",
-    "    \"\"\"Generate 3D model from a single base64-encoded image without previews.\n",
-    "    \n",
-    "    Args:\n",
-    "        image_base64: Base64 string of the image (without 'data:image/...' prefix)\n",
-    "    \"\"\"\n",
-    "    try:\n",
-    "        # Set generation parameters\n",
-    "        params = {\n",
-    "            'image_base64': image_base64,\n",
-    "            'seed': 42,\n",
-    "            'ss_guidance_strength': 7.5,\n",
-    "            'ss_sampling_steps': 30,\n",
-    "            'slat_guidance_strength': 7.5,\n",
-    "            'slat_sampling_steps': 30,\n",
-    "            'mesh_simplify_ratio': 0.95,\n",
-    "            'texture_size': 1024,\n",
-    "            'output_format': 'glb'\n",
-    "        }\n",
-    "        \n",
-    "        # Start generation\n",
-    "        print(\"Starting generation...\")\n",
-    "        response = requests.post(f\"{BASE_URL}/generate_no_preview\", data=params)\n",
-    "        print(\"Response status:\", response.status_code)\n",
-    "        response.raise_for_status()\n",
-    "        \n",
-    "        # Poll status until complete\n",
-    "        while True:\n",
-    "            status = requests.get(f\"{BASE_URL}/status\").json()\n",
-    "            print(f\"Progress: {status['progress']}%\")\n",
-    "            \n",
-    "            if status['status'] == 'COMPLETE':\n",
-    "                break\n",
-    "            elif status['status'] == 'FAILED':\n",
-    "                raise Exception(f\"Generation failed: {status['message']}\")\n",
-    "            \n",
-    "            time.sleep(1)\n",
-    "        \n",
-    "        # Download the model\n",
-    "        print(\"Downloading model...\")\n",
-    "        response = requests.get(f\"{BASE_URL}/download/model\")\n",
-    "        response.raise_for_status()\n",
-    "        print(\"Model downloaded.\")\n",
-    "        \n",
-    "        return response.content\n",
-    "        \n",
-    "    except Exception as e:\n",
-    "        print(f\"Error: {str(e)}\")\n",
-    "        return None"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "eb122295",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def generate_model(image_path, output_path):\n",
-    "    with open(image_path, 'rb') as image_file:\n",
-    "        image_data = image_file.read()\n",
-    "\n",
-    "    base64_encoded = base64.b64encode(image_data).decode('utf-8')\n",
-    "    model = generate_no_preview(base64_encoded)\n",
-    "    \n",
-    "    with open(output_path, 'wb') as f:\n",
-    "        f.write(model)\n",
-    "    print(f\"Model saved to {output_path}\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "2ce7dfdf",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting generation...\n",
-      "Response status: 200\n",
-      "Progress: 100%\n",
-      "Downloading model...\n",
-      "Model downloaded.\n",
-      "Model saved to test_resources/style_test_3_model.glb\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "image_path = 'test_resources/style_test_3.jpg'\n",
-    "output_path = \"test_resources/style_test_3_model.glb\"\n",
-    "\n",
-    "generate_model(image_path, output_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a1224d13",
-   "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": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

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
-}

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

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_trellis
-
-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, front and side fully visible, realistic style, plain neutral background, clear details, soft studio lighting, 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_trellis(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())

Assets/StreamingAssets/Whisper.meta

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-folderAsset: yes
-DefaultImporter:
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-  userData: 
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Assets/StreamingAssets/Whisper/ggml-base.bin.meta

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-  userData: 
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Assets/_PROJECT/Components/Bolt/Lexus2016RXTireNormal.png.png.meta

@@ -7,7 +7,7 @@ TextureImporter:
   mipmaps:
     mipMapMode: 0
     enableMipMap: 1
-    sRGBTexture: 0
+    sRGBTexture: 1
     linearTexture: 0
     fadeOut: 0
     borderMipMap: 0
@@ -54,7 +54,7 @@ TextureImporter:
   alphaUsage: 1
   alphaIsTransparency: 0
   spriteTessellationDetail: -1
-  textureType: 1
+  textureType: 0
   textureShape: 1
   singleChannelComponent: 0
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Assets/_PROJECT/Components/Bow/Scripts/ArcheryRange.cs

@@ -1,11 +1,9 @@
-using _PROJECT.Scripts.Bow;
-using FishNet.Component.Transforming;
-using FishNet.Object;
-using FishNet.Object.Synchronizing;
 using System;
 using System.Collections.Generic;
 using System.Linq;
-using System.Threading.Tasks;
+using _PROJECT.Scripts.Bow;
+using FishNet.Object;
+using FishNet.Object.Synchronizing;
 using TMPro;
 using Unity.XR.CoreUtils;
 using UnityEngine;
@@ -22,14 +20,11 @@ public class ArcheryRange : NetworkBehaviour
     public GameObject targetPrefab;
     public StartTarget startTarget;
 
-    public GameObject archeryTargetPointsText;
-
     public Vector3 minRandomOffset = new Vector3(0f, -2f, -5f);
     public Vector3 maxRandomOffset = new Vector3(0f, 2f, 5f);
     public float roundLength = 60f;
     public float targetSpawnTime = 3f;
 
-    public ModelGenerationBox modelGenerationBox;
     public KeyboardManager keyboardManager;
 
     private List<ArcheryTarget> _targets;
@@ -117,56 +112,30 @@ public class ArcheryRange : NetworkBehaviour
         }
     }
 
-    async private void SpawnTarget()
+    private void SpawnTarget()
     {
         if (!IsServer) return;
         
         var randomPos = targetStartPosition.position + new Vector3(
             Random.Range(minRandomOffset.x, maxRandomOffset.x),
-            (float) Math.Round(Random.Range(minRandomOffset.y, maxRandomOffset.y)),
+            (float)Math.Round(Random.Range(minRandomOffset.y, maxRandomOffset.y)),
-            Random.Range(minRandomOffset.z, maxRandomOffset.z)
+            Random.Range(minRandomOffset.z, maxRandomOffset.z));
-        );
+
+        var target = SpawnTarget(randomPos);
 
-        var target = await SpawnTarget(randomPos);
         _targets.Add(target);
     }
 
-    async private Task<ArcheryTarget> SpawnTarget(Vector3 randomPos)
+    private ArcheryTarget SpawnTarget(Vector3 randomPos)
     {
-        GameObject targetObject;
+        var prefab = Instantiate(targetPrefab, randomPos, Quaternion.identity, null);
-        if (modelGenerationBox.LastModelPath == null)
+        ArcheryTarget target = prefab.GetComponent<ArcheryTarget>();
-        {
-            // spawn default UFO
-            targetObject = Instantiate(targetPrefab, randomPos, Quaternion.identity, null);
-        } else
-        {
-            // spawn generated model
-            targetObject = await PipelineManager.Instance.SpawnModel(modelGenerationBox.LastModelPath);
-            targetObject.transform.position = randomPos;
-            targetObject.transform.rotation = Quaternion.identity;
-            InitializeArcherytargetObject(targetObject);
-        }
-
-        ArcheryTarget target = targetObject.GetComponent<ArcheryTarget>();
         target.endPosition = targetEndPosition.position;
         target.addScore = AddScore;
-        Spawn(targetObject);
+        Spawn(prefab);
         return target;
     }
 
-    private void InitializeArcherytargetObject(GameObject targetObject)
-    {
-        ArcheryTarget archeryTarget = targetObject.AddComponent<ArcheryTarget>();
-        archeryTarget.pointsText = archeryTargetPointsText;
-
-        Rigidbody rigidbody = targetObject.AddComponent<Rigidbody>();
-        rigidbody.useGravity = false;
-        rigidbody.isKinematic = true;
-
-        targetObject.AddComponent<NetworkObject>();
-        targetObject.AddComponent<NetworkTransform>();
-    }
-
 
     public void ResetRange()
     {
@@ -179,7 +148,7 @@ public class ArcheryRange : NetworkBehaviour
         _targets = new List<ArcheryTarget>();
         if (_maxScore < _score) _maxScore = _score;
 
-        if (_presentPlayers.Count != 0) // If there are players in the area.
+        if(_presentPlayers.Count != 0) // If there are players in the area.
         {
             // Gives the score to the player longest-lasting in the area. It would be better to give it to the player that fired the starting arrow, but I'm not spending 10 hours on this. 
 
@@ -209,7 +178,6 @@ public class ArcheryRange : NetworkBehaviour
     public void StartRound()
     {
         if (!IsServer) return;
-
         _roundEndTime = Time.time + roundLength;
         _nextTargetTime = Time.time;
         _roundActive = true;

Assets/_PROJECT/Components/Bow/Scripts/ArcheryTarget.cs

@@ -1,7 +1,10 @@
 using System;
+using _PROJECT.Scripts.Bow;
+using _PROJECT.Scripts.Bow.Extra;
 using FishNet.Object;
 using FishNet.Object.Synchronizing;
 using UnityEngine;
+using Random = UnityEngine.Random;
 
 public class ArcheryTarget : NetworkBehaviour, IArrowHittable
 {
@@ -11,9 +14,11 @@ public class ArcheryTarget : NetworkBehaviour, IArrowHittable
     public float forwardSpeed = 2f;
     public Action<float> addScore;
     
+    private bool _flipDirection;
+
     private void Awake()
     {
-        
+        _flipDirection = Random.value > 0.5f;
     }
 
     // Update is called once per frame
@@ -23,12 +28,11 @@ public class ArcheryTarget : NetworkBehaviour, IArrowHittable
         float step = forwardSpeed * Time.deltaTime;
         var position = transform.position;
 
-        if (Math.Abs(position.x - endPosition.x) < 0.1)
+        if (Math.Abs(position.x - endPosition.x) < 0.1) Destroy(gameObject);
-        {
-            Destroy(gameObject);
-        }
 
-        transform.position = Vector3.MoveTowards(position, new Vector3(endPosition.x, position.y, position.z), step);
+        
+        transform.position = Vector3.MoveTowards(position,
+            new Vector3(endPosition.x, position.y, position.z), step);
     }
 
     public void Hit(Arrow arrow)

Assets/_PROJECT/Components/Bow/Scripts/Arrow.cs

@@ -1,4 +1,7 @@
-using _PROJECT.Multiplayer.NewBow;
+using System.Collections;
+using _PROJECT.Multiplayer.NewBow;
+using _PROJECT.Scripts.Bow;
+using FishNet.Object;
 using UnityEngine;
 using UnityEngine.XR.Interaction.Toolkit;
 

Assets/_PROJECT/Components/NewHandPresence/Prefabs/Scripts/Continuos locomotion Conf.cs

@@ -17,7 +17,6 @@ public class ContinuoslocomotionConfigurator : MonoBehaviour
         turnOnButton.onClick.AddListener(enableLocomotion);
         turnOffButton.onClick.AddListener(disableLocomotion);
         turnOffButton.gameObject.SetActive(false); // off by default
-        enableLocomotion();
     }
     public void UpdateSpeed(float speed)
     {

Assets/_PROJECT/Components/NewHandPresence/Prefabs/XR Origin.prefab

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Assets/_PROJECT/Components/Portals2/TargetUFO Variant.prefab

@@ -47,42 +47,32 @@ PrefabInstance:
         type: 3}
       propertyPath: _networkBehaviours.Array.data[0]
       value: 
-      objectReference: {fileID: 0}
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         type: 3}
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         type: 3}
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         type: 3}
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+      objectReference: {fileID: 427371208704957824}
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         type: 3}
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       value: 
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-        type: 3}
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         type: 3}
       propertyPath: _componentIndexCache
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       value: 1
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-      value: TargetUFO Variant
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         type: 3}
       propertyPath: _componentIndexCache
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Assets/_PROJECT/Scripts/ModeGeneration.meta

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Assets/_PROJECT/Scripts/ModeGeneration/ModelGenerationBox.cs

@@ -1,60 +0,0 @@
-using Unity.XR.CoreUtils;
-using UnityEngine;
-
-public class ModelGenerationBox : MonoBehaviour
-{
-    public Material inactiveMaterial;
-    public Material loadingMaterial;
-
-    public Transform modelSpawnPoint;
-    public VoiceTranscriptionBox voiceTranscriptionTestBox;
-
-    private MeshRenderer meshRenderer;
-    private bool isLoading;
-
-    private string lastModelPath;
-    public string LastModelPath
-    {
-        get {
-            return lastModelPath;
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-    }
-
-    // Start is called before the first frame update
-    void Start()
-    {
-        meshRenderer = GetComponent<MeshRenderer>();
-    }
-
-    // Update is called once per frame
-    void Update()
-    {
-        
-    }
-
-    async void OnTriggerEnter(Collider other)
-    {
-        if (isLoading) return;
-
-        KbmController controller = other.GetComponent<KbmController>();
-        XROrigin playerOrigin = other.GetComponent<XROrigin>();
-        if (controller != null || playerOrigin != null)
-        {
-            string inputPrompt = voiceTranscriptionTestBox.LastTextOutput;
-
-            isLoading = true;
-            meshRenderer.material = loadingMaterial;
-
-            string modelPath = await PipelineManager.Instance.GenerateModelAsync(inputPrompt);
-            lastModelPath = modelPath;
-
-            GameObject spawnedObject = await PipelineManager.Instance.SpawnModel(modelPath);
-            spawnedObject.AddComponent<Rigidbody>();
-            spawnedObject.transform.parent = modelSpawnPoint;
-            spawnedObject.transform.position = modelSpawnPoint.position;
-
-            isLoading = false;
-            meshRenderer.material = inactiveMaterial;
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-    }
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Assets/_PROJECT/Scripts/ModeGeneration/ModelGenerationBox.cs.meta

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Assets/_PROJECT/Scripts/ModeGeneration/PipelineManager.cs

@@ -1,105 +0,0 @@
-using GLTFast;
-using System.Diagnostics;
-using System.IO;
-using System.Threading.Tasks;
-using UnityEngine;
-
-public class PipelineManager : MonoBehaviour
-{
-    public static PipelineManager Instance { get; private set; }
-
-    private void Awake()
-    {
-        Instance = this;
-    }
-
-    // Start is called before the first frame update
-    void Start()
-    {
-        
-    }
-
-    // Update is called once per frame
-    void Update()
-    {
-        
-    }
-
-    public async Task<string> GenerateModelAsync(string inputPrompt)
-    {
-        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();
-                    }
-                }
-
-                throw new System.Exception("Failed to generate 3D model!");
-            }
-        });
-    }
-
-    public async Task<GameObject> SpawnModel(string modelPath)
-    {
-        var gltf = new GltfImport();
-        bool loadSuccess = await gltf.Load(modelPath);
-        if (loadSuccess)
-        {
-            string objectName = Path.GetFileName(modelPath);
-            GameObject spawningParent = new GameObject("Parent-" + objectName);
-
-            bool spawnSuccess = await gltf.InstantiateMainSceneAsync(spawningParent.transform);
-            if (spawnSuccess)
-            {
-                Transform spawnedObjectWorldTransform = spawningParent.transform.GetChild(0).transform;
-                GameObject spawnedObjectBody = spawnedObjectWorldTransform.GetChild(0).transform.gameObject;
-                MeshCollider collider = spawnedObjectBody.AddComponent<MeshCollider>();
-                collider.convex = true;
-                MeshRenderer renderer = spawnedObjectBody.GetComponent<MeshRenderer>();
-                renderer.material.SetFloat("metallicFactor", 0);
-
-                spawnedObjectBody.name = objectName;
-                return spawnedObjectBody;
-            }
-        }
-
-        throw new System.Exception("Failed to spawn GameObject from model" + modelPath);
-    }
-}

Assets/_PROJECT/Scripts/ModeGeneration/PipelineManager.cs.meta

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Assets/_PROJECT/Scripts/ModeGeneration/VoiceTranscriptionBox.cs

@@ -1,112 +0,0 @@
-using System.Diagnostics;
-using TMPro;
-using Unity.XR.CoreUtils;
-using UnityEngine;
-using Whisper;
-using Whisper.Utils;
-
-public class VoiceTranscriptionBox : MonoBehaviour
-{
-    public Material activeMaterial;
-    public Material inactiveMaterial;
-    public Material loadingMaterial;
-
-    private MeshRenderer meshRenderer;
-    private bool isLoading;
-
-
-    public WhisperManager whisper;
-    public MicrophoneRecord microphoneRecord;
-    public TextMeshProUGUI outputText;
-
-    private string _buffer;
-
-    private string lastTextOutput;
-    public string LastTextOutput
-    {
-        get
-        {
-            return lastTextOutput;
-        }
-    }
-
-    private void Awake()
-    {
-        isLoading = false;
-
-        whisper.OnNewSegment += OnNewSegment;
-
-        microphoneRecord.OnRecordStop += OnRecordStop;
-    }
-
-    // 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)
-    {
-        if (isLoading)
-        {
-            return;
-        }
-
-        KbmController controller = other.GetComponent<KbmController>();
-        XROrigin playerOrigin = other.GetComponent<XROrigin>();
-        if (controller != null || playerOrigin != null)
-        {
-            meshRenderer.material = activeMaterial;
-            microphoneRecord.StartRecord();
-        }
-    }
-
-    private void OnTriggerExit(Collider other)
-    {
-        KbmController controller = other.GetComponent<KbmController>();
-        XROrigin playerOrigin = other.GetComponent<XROrigin>();
-        if (controller != null | playerOrigin != null)
-        {
-            microphoneRecord.StopRecord();
-            meshRenderer.material = loadingMaterial;
-            isLoading = true;
-        }
-    }
-
-
-    private async void OnRecordStop(AudioChunk recordedAudio)
-    {
-        _buffer = "";
-
-        var sw = new Stopwatch();
-        sw.Start();
-
-        var res = await whisper.GetTextAsync(recordedAudio.Data, recordedAudio.Frequency, recordedAudio.Channels);
-        if (res == null)
-            return;
-
-        var time = sw.ElapsedMilliseconds;
-        var rate = recordedAudio.Length / (time * 0.001f);
-        UnityEngine.Debug.Log($"Time: {time} ms\nRate: {rate:F1}x");
-
-        var text = res.Result;
-
-        lastTextOutput = text;
-        outputText.text = text;
-
-        meshRenderer.material = inactiveMaterial;
-        isLoading = false;
-    }
-
-    private void OnNewSegment(WhisperSegment segment)
-    {
-        _buffer += segment.Text;
-        UnityEngine.Debug.Log(_buffer + "...");
-    }
-}

Assets/_PROJECT/Scripts/ModeGeneration/VoiceTranscriptionBox.cs.meta

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Packages/manifest.json

@@ -4,7 +4,6 @@
     "com.unity.2d.tilemap": "1.0.0",
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     "com.unity.cinemachine": "2.9.5",
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@@ -28,7 +27,6 @@
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     "com.unity.xr.oculus": "3.2.3",
     "com.unity.xr.openxr": "1.7.0",
-    "com.whisper.unity": "https://github.com/Macoron/whisper.unity.git?path=/Packages/com.whisper.unity",
     "com.unity.modules.ai": "1.0.0",
     "com.unity.modules.androidjni": "1.0.0",
     "com.unity.modules.animation": "1.0.0",

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",
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-        "com.unity.mathematics": "1.2.1",
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-        "com.unity.modules.jsonserialize": "1.0.0"
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       "url": "https://packages.unity.com"
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@@ -49,19 +48,6 @@
       },
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-      },
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@@ -69,16 +55,6 @@
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-      "url": "https://packages.unity.com"
-    },
     "com.unity.editorcoroutines": {
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       "depth": 1,
@@ -388,13 +364,6 @@
       },
       "url": "https://packages.unity.com"
     },
-    "com.whisper.unity": {
-      "version": "https://github.com/Macoron/whisper.unity.git?path=/Packages/com.whisper.unity",
-      "depth": 0,
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README.md

@@ -66,23 +66,17 @@ Multiplayer and cross-play functionality. [Bachelor's Thesis](https://comserv.cs
 **Raimond Tunnel**<br/>
 Project management, visual design.
 
+**Timur Nizamov**<br/>
+Technical sound design.
+
 Developed in the [Computer Graphcis and Virtual Reality Study Lab](https://cgvr.cs.ut.ee/) of the [Institute of Computer Science, University of Tartu](https://cs.ut.ee).
 
 ### Used Attributions
 
 | Description                                         | License                                      | Source                                                                                      | Author           |
 |-----------------------------------------------------|----------------------------------------------|---------------------------------------------------------------------------------------------|------------------|
-| Bold's car driving sound                            | Attribution NonCommercial 3.0                | [Link](https://freesound.org/people/Pfujimoto/sounds/14371/)                               | Pfujimoto        |
-| Bold's car braking sound                            | Attribution 3.0                              | [Link](https://freesound.org/people/200154michaela/sounds/542448/)                         | 200154michaela   |
-| Bold's car horn sound                               | Attribution 4.0                              | [Link](https://freesound.org/people/ceberation/sounds/235506/)                             | ceberation       |
 | Server rack model                                   | Royalty Free, No AI License                  | [Link](https://www.cgtrader.com/free-3d-models/electronics/computer/simple-server-model)   | anymelok         |
-| Server rack humming sound                           | Attribution 4.0                              | [Link](https://freesound.org/people/jameswrowles/sounds/248217/)                           | jameswrowles     |
-| Fire suppression button press sound                 | Creative Commons 0                           | [Link](https://freesound.org/people/LamaMakesMusic/sounds/403556/)                         | LamaMakesMusic   |
-| Fire suppression alarm sound                        | Attribution 3.0                              | [Link](https://freesound.org/people/jobro/sounds/33737/)                                   | jobro            |
-| Fire-suppressing gas release sound                  | Creative Commons 0                           | [Link](https://freesound.org/people/mrmccormack/sounds/182359/)                            | mrmccormack      |
-| Coughing sound in response to fire-suppressing gas  | Attribution 4.0                              | [Link](https://freesound.org/people/qubodup/sounds/739416/)                                | qubodup          |
 | Robot movement sound                                | Creative Commons 0                           | [Link](https://freesound.org/people/Brazilio123/sounds/661435/)                            | Brazilio123      |
-| Portal humming sound                                | Attribution 4.0                              | [Link](https://freesound.org/people/zimbot/sounds/122972/)                                 | zimbot           |
 | Spacewalk UFO sound                                 | Attribution NonCommercial 4.0                           | [Link](https://freesound.org/people/Speedenza/sounds/209366/)                           | Speedenza     |
 | Keyboard icons                                      | Creative Commons Attribution-NoDerivs 3.0    | [Link](https://icons8.com/)                                                                 | icons8           |