Unity pipeline scripts cleanup + enable dev build

Reviewed-on: https://127.0.0.1:3000/cgvr/DeltaVR/pulls/5

.gitattributes

@@ -1,8 +1,8 @@
 * text=auto
 # Unity files
 *.meta -text merge=unityyamlmerge diff
-*.unity -text merge=unityyamlmerge diff
-*.asset -text merge=unityyamlmerge diff
+*.unity filter=lfs diff=lfs merge=lfs -text
+*.asset filter=lfs diff=lfs merge=lfs -text
 *.prefab -text merge=unityyamlmerge diff
 *.mat -text merge=unityyamlmerge diff
 *.anim -text merge=unityyamlmerge diff
@@ -79,3 +79,24 @@
 *.tar filter=lfs diff=lfs merge=lfs -text
 *.exr filter=lfs diff=lfs merge=lfs -text
 *.hdr filter=lfs diff=lfs merge=lfs -text
+*.cubemap filter=lfs diff=lfs merge=lfs -text
+*.assets filter=lfs diff=lfs merge=lfs -text
+*.resS filter=lfs diff=lfs merge=lfs -text
+*.dylib filter=lfs diff=lfs merge=lfs -text
+*.tar.7z filter=lfs diff=lfs merge=lfs -text
+*.f8 filter=lfs diff=lfs merge=lfs -text
+Assets/_PROJECT/Components/Portals2/New[[:space:]]Cubemap.cubemap filter=lfs diff=lfs merge=lfs -text
+Assets/_PROJECT/Other/Delta[[:space:]](Toomas)[[:space:]]v3.blend filter=lfs diff=lfs merge=lfs -text
+Assets/_PROJECT/Other/Delta[[:space:]](Toomas)[[:space:]]v3.blend1 filter=lfs diff=lfs merge=lfs -text
+Assets/_PROJECT/Scenes/DeltaBuilding_base/LightingData.asset filter=lfs diff=lfs merge=lfs -text
+Build/DeltaVR_Data/level0 filter=lfs diff=lfs merge=lfs -text
+Build/DeltaVR_Data/sharedassets0.assets filter=lfs diff=lfs merge=lfs -text
+Build/DeltaVR_Data/sharedassets0.assets.resS filter=lfs diff=lfs merge=lfs -text
+Library/Artifacts/6e/6e8680668429d5a691eb5372cd18a3f8 filter=lfs diff=lfs merge=lfs -text
+Library/PackageCache/com.unity.burst@1.8.3/.Runtime/libburst-llvm-14.dylib filter=lfs diff=lfs merge=lfs -text
+Library/PackageCache/com.unity.toolchain.win-x86_64-linux-x86_64@2.0.4/data~/payload.tar.7z filter=lfs diff=lfs merge=lfs -text
+Library/PlayerDataCache/Win64/Data/level0 filter=lfs diff=lfs merge=lfs -text
+Library/PlayerDataCache/Win64/Data/sharedassets0.assets filter=lfs diff=lfs merge=lfs -text
+Library/PlayerDataCache/Win64/Data/sharedassets0.assets.resS filter=lfs diff=lfs merge=lfs -text
+Assets/_PROJECT/Scenes/DeltaBuilding_base.unity filter=lfs diff=lfs merge=lfs -text
+*.ext filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -73,3 +73,7 @@ crashlytics-build.properties
 /[Aa]ssets/[Ss]treamingAssets/aa.meta
 /[Aa]ssets/[Ss]treamingAssets/aa/*
 Recordings/
+
+# Paid assets
+/[Aa]ssets/Magic Light Probes/*
+/[Aa]ssets/[Bb]akery/*

.vsconfig

@@ -0,0 +1,6 @@
+{
+  "version": "1.0",
+  "components": [ 
+    "Microsoft.VisualStudio.Workload.ManagedGame"
+  ]
+} 

3d-generation-pipeline/.env.example

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

3d-generation-pipeline/.gitignore

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

3d-generation-pipeline/README.md

@@ -0,0 +1,6 @@
+### 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

@@ -0,0 +1,61 @@
+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

@@ -0,0 +1,489 @@
+# 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

@@ -0,0 +1,74 @@
+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

@@ -0,0 +1,153 @@
+{
+ "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

@@ -0,0 +1,122 @@
+{
+ "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

@@ -0,0 +1,152 @@
+{
+ "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

@@ -0,0 +1,165 @@
+{
+ "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

@@ -0,0 +1,7 @@
+#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

@@ -0,0 +1,62 @@
+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/ActivateTeleportationRay.cs

@@ -0,0 +1,45 @@
+using UnityEngine;
+using UnityEngine.InputSystem;
+using UnityEngine.XR.Interaction.Toolkit;
+
+public class ActivateTeleportationRay : MonoBehaviour
+{
+    public GameObject teleportRay;
+    public float rayHideDelay = 0.1f;
+
+    public InputActionProperty activateTeleportRay;
+    public TeleportationProvider teleportationProvider;
+
+    // Update is called once per frame
+    private void Start()
+    {
+        teleportRay.SetActive(false);
+        activateTeleportRay.action.performed += ShowRay;
+        activateTeleportRay.action.canceled += HideRayPerformed;
+        teleportationProvider.endLocomotion += HideRayPerformed;
+    }
+
+    private void HideRayPerformed(InputAction.CallbackContext obj)
+    {
+        if (!teleportRay.activeSelf) return;
+        if (IsInvoking(nameof(HideRay))) return;
+        Invoke(nameof(HideRay), rayHideDelay);
+    }
+
+    private void HideRayPerformed(LocomotionSystem obj)
+    {
+        if (!teleportRay.activeSelf) return;
+        if (IsInvoking(nameof(HideRay))) return;
+        Invoke(nameof(HideRay), rayHideDelay);
+    }
+
+    private void ShowRay(InputAction.CallbackContext obj)
+    {
+        teleportRay.SetActive(true);
+    }
+
+    private void HideRay()
+    {
+        teleportRay.SetActive(false);
+    }
+}

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Assets/Billboard.cs

@@ -0,0 +1,107 @@
+using TMPro;
+using UnityEngine;
+
+public class Billboard : MonoBehaviour
+{
+    private GameObject _parent;
+    public string defaultText = "Sample Text";
+    public TMP_Text tmpText;
+    public Canvas canvas;
+    public string prefix = "<mark=#000000BB>";
+    private Camera _camera;
+
+    public LineRenderer lineRenderer;
+    public Transform lineStart;
+    
+    private GameObject _lineEndObject;
+    private MeshFilter _lineEndMeshFilter;
+
+    private void Start()
+    {
+        _camera = Camera.main;
+        lineRenderer = GetComponent<LineRenderer>();
+        _parent = transform.parent.gameObject;
+        transform.SetParent(null);
+        tmpText.text = defaultText;
+    }
+
+    private void LateUpdate()
+    {
+        if (canvas.enabled) transform.position = _parent.transform.position + (_camera.transform.up * 0.1f);
+        if (!lineRenderer.enabled) return;
+        
+        lineRenderer.SetPosition(0, lineStart.position);
+        lineRenderer.SetPosition(1, CalculateLineEndPosition());
+    }
+
+    private void SetText(string txt)
+    {
+        tmpText.text = prefix + txt;
+    }
+
+
+    private void ShowCanvas()
+    {
+        canvas.enabled = true;
+        lineRenderer.enabled = true;
+    }
+
+    private void HideCanvas()
+    {
+        canvas.enabled = false;
+        lineRenderer.enabled = false;
+    }
+
+    public void ShowHint(string txt)
+    {
+        ShowCanvas();
+        SetText(txt);
+    }
+
+    public void HideHint()
+    {
+        HideCanvas();
+    }
+
+    public void SetLineTarget(GameObject newGM)
+    {
+        _lineEndObject = newGM;
+        _lineEndMeshFilter = _lineEndObject.GetComponent<MeshFilter>();
+    }
+
+    private Vector3 CalculateLineEndPosition()
+    {
+        if (_lineEndObject == null)
+        {
+            lineRenderer.enabled = false;
+            return Vector3.zero;
+        }
+        return _lineEndMeshFilter == null ? _lineEndObject.transform.position : FindClosestVertexToTargetPoint(lineStart.position, _lineEndMeshFilter);
+    }
+
+    private Vector3 FindClosestVertexToTargetPoint(Vector3 start, MeshFilter filter)
+    {
+        // Get the Mesh object from the MeshFilter component
+        Mesh mesh = filter.mesh;
+        // Get the world position of the point you want to find the closest point on the mesh to
+        // Transform the point into local space relative to the MeshFilter's transform
+        Vector3 localPoint = filter.transform.InverseTransformPoint(start);
+
+        // Find the closest vertex to the point
+        float closestDistance = Mathf.Infinity;
+        Vector3 closestVertex = Vector3.zero;
+        for (int i = 0; i < mesh.vertexCount; i++)
+        {
+            Vector3 vertex = mesh.vertices[i];
+            float distance = Vector3.Distance(localPoint, vertex);
+            if (distance < closestDistance)
+            {
+                closestDistance = distance;
+                closestVertex = vertex;
+            }
+        }
+
+        // Transform the closest vertex back into world space
+        return filter.transform.TransformPoint(closestVertex);
+    }
+}

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Assets/Fish-Networking-Discovery-main/.gitignore

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+# This .gitignore file should be placed at the root of your Unity project directory
+#
+# Get latest from https://github.com/github/gitignore/blob/master/Unity.gitignore
+#
+/[Ll]ibrary/
+/[Tt]emp/
+/[Oo]bj/
+/[Bb]uild/
+/[Bb]uilds/
+/[Ll]ogs/
+/[Mm]emoryCaptures/
+
+# Asset meta data should only be ignored when the corresponding asset is also ignored
+!/[Aa]ssets/**/*.meta
+
+# Uncomment this line if you wish to ignore the asset store tools plugin
+# /[Aa]ssets/AssetStoreTools*
+
+# Autogenerated Jetbrains Rider plugin
+[Aa]ssets/Plugins/Editor/JetBrains*
+
+# Visual Studio cache directory
+.vs/
+
+# Gradle cache directory
+.gradle/
+
+# Autogenerated VS/MD/Consulo solution and project files
+ExportedObj/
+.consulo/
+*.csproj
+*.unityproj
+*.sln
+*.suo
+*.tmp
+*.user
+*.userprefs
+*.pidb
+*.booproj
+*.svd
+*.pdb
+*.mdb
+*.opendb
+*.VC.db
+
+# Unity3D generated meta files
+*.pidb.meta
+*.pdb.meta
+*.mdb.meta
+*.meta
+
+# Unity3D generated file on crash reports
+sysinfo.txt
+
+# Builds
+*.apk
+*.unitypackage
+
+# Crashlytics generated file
+crashlytics-build.properties
+

Assets/Fish-Networking-Discovery-main/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2022 Winterbolt Games
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Assets/Fish-Networking-Discovery-main/NetworkDiscovery.cs

@@ -0,0 +1,311 @@
+using FishNet.Managing;
+using FishNet.Managing.Logging;
+using FishNet.Transporting;
+using System;
+using System.Net;
+using System.Net.Sockets;
+using System.Text;
+using System.Threading.Tasks;
+using UnityEngine;
+
+namespace FishNet.Discovery
+{
+	/// <summary>
+	/// A component that advertises a server or searches for servers.
+	/// </summary>
+	public sealed class NetworkDiscovery : MonoBehaviour
+	{
+		/// <summary>
+		/// A string that differentiates your application/game from others.
+		/// <b>Must not</b> be null, empty, or blank.
+		/// </summary>
+		[SerializeField]
+		[Tooltip("A string that differentiates your application/game from others. Must not be null, empty, or blank.")]
+		private string secret;
+
+		/// <summary>
+		/// The port number used by this <see cref="NetworkDiscovery"/> component.
+		/// <b>Must</b> be different from the one used by the <seealso cref="Transport"/>.
+		/// </summary>
+		[SerializeField]
+		[Tooltip("The port number used by this NetworkDiscovery component. Must be different from the one used by the Transport.")]
+		private ushort port;
+
+		/// <summary>
+		/// How often does this <see cref="NetworkDiscovery"/> component advertises a server or searches for servers.
+		/// </summary>
+		[SerializeField]
+		[Tooltip("How often does this NetworkDiscovery component advertises a server or searches for servers.")]
+		private float discoveryInterval;
+
+		/// <summary>
+		/// Whether this <see cref="NetworkDiscovery"/> component will automatically start/stop? <b>Setting this to true is recommended.</b>
+		/// </summary>
+		[SerializeField]
+		[Tooltip("Whether this NetworkDiscovery component will automatically start/stop? Setting this to true is recommended.")]
+		private bool automatic;
+
+		/// <summary>
+		/// The <see cref="UdpClient"/> used to advertise the server.
+		/// </summary>
+		private UdpClient _serverUdpClient;
+
+		/// <summary>
+		/// The <see cref="UdpClient"/> used to search for servers.
+		/// </summary>
+		private UdpClient _clientUdpClient;
+
+		/// <summary>
+		/// Whether this <see cref="NetworkDiscovery"/> component is currently advertising a server or not.
+		/// </summary>
+		public bool IsAdvertising => _serverUdpClient != null;
+
+		/// <summary>
+		/// Whether this <see cref="NetworkDiscovery"/> component is currently searching for servers or not.
+		/// </summary>
+		public bool IsSearching => _clientUdpClient != null;
+
+		/// <summary>
+		/// An <see cref="Action"/> that is invoked by this <seealso cref="NetworkDiscovery"/> component whenever a server is found.
+		/// </summary>
+		public event Action<IPEndPoint> ServerFoundCallback;
+
+		private void Start()
+		{
+			if (automatic)
+			{
+				InstanceFinder.ServerManager.OnServerConnectionState += ServerConnectionStateChangedHandler;
+
+				InstanceFinder.ClientManager.OnClientConnectionState += ClientConnectionStateChangedHandler;
+
+				StartSearchingForServers();
+			}
+		}
+
+		private void OnDisable()
+		{
+			InstanceFinder.ServerManager.OnServerConnectionState -= ServerConnectionStateChangedHandler;
+
+			InstanceFinder.ClientManager.OnClientConnectionState -= ClientConnectionStateChangedHandler;
+
+			StopAdvertisingServer();
+
+			StopSearchingForServers();
+		}
+
+		private void OnDestroy()
+		{
+			InstanceFinder.ServerManager.OnServerConnectionState -= ServerConnectionStateChangedHandler;
+
+			InstanceFinder.ClientManager.OnClientConnectionState -= ClientConnectionStateChangedHandler;
+
+			StopAdvertisingServer();
+
+			StopSearchingForServers();
+		}
+
+		private void OnApplicationQuit()
+		{
+			InstanceFinder.ServerManager.OnServerConnectionState -= ServerConnectionStateChangedHandler;
+
+			InstanceFinder.ClientManager.OnClientConnectionState -= ClientConnectionStateChangedHandler;
+
+			StopAdvertisingServer();
+
+			StopSearchingForServers();
+		}
+
+		#region Connection State Handlers
+
+		private void ServerConnectionStateChangedHandler(ServerConnectionStateArgs args)
+		{
+			if (args.ConnectionState == LocalConnectionState.Starting)
+			{
+				StopSearchingForServers();
+			}
+			else if (args.ConnectionState == LocalConnectionState.Started)
+			{
+				StartAdvertisingServer();
+			}
+			else if (args.ConnectionState == LocalConnectionState.Stopping)
+			{
+				//StopAdvertisingServer();
+			}
+			else if (args.ConnectionState == LocalConnectionState.Stopped)
+			{
+				StartSearchingForServers();
+			}
+		}
+
+		private void ClientConnectionStateChangedHandler(ClientConnectionStateArgs args)
+		{
+			if (args.ConnectionState == LocalConnectionState.Starting)
+			{
+				StopSearchingForServers();
+			}
+			else if (args.ConnectionState == LocalConnectionState.Stopped)
+			{
+				StartSearchingForServers();
+			}
+		}
+
+		#endregion
+
+		#region Server
+
+		/// <summary>
+		/// Makes this <see cref="NetworkDiscovery"/> component start advertising a server.
+		/// </summary>
+		public void StartAdvertisingServer()
+		{
+			//Debug.Log("NetworkDiscovery is advertizing on port " + port);
+			if (!InstanceFinder.IsServer)
+			{
+				if (NetworkManager.StaticCanLog(LoggingType.Warning)) Debug.LogWarning("Unable to start advertising server. Server is inactive.", this);
+
+				return;
+			}
+
+			if (_serverUdpClient != null)
+			{
+				if (NetworkManager.StaticCanLog(LoggingType.Common)) Debug.Log("Server is already being advertised.", this);
+
+				return;
+			}
+
+			if (port == InstanceFinder.TransportManager.Transport.GetPort())
+			{
+				if (NetworkManager.StaticCanLog(LoggingType.Warning)) Debug.LogWarning("Unable to start advertising server on the same port as the transport.", this);
+
+				return;
+			}
+
+			_serverUdpClient = new UdpClient(port)
+			{
+				EnableBroadcast = true,
+				MulticastLoopback = false,
+			};
+			//Debug.Log("UDP = " + _serverUdpClient.ToString());
+			Task.Run(AdvertiseServerAsync);
+
+			if (NetworkManager.StaticCanLog(LoggingType.Common)) Debug.Log("Started advertising server.", this);
+		}
+
+		/// <summary>
+		/// Makes this <see cref="NetworkDiscovery"/> component <i>immediately</i> stop advertising the server it is currently advertising.
+		/// </summary>
+		public void StopAdvertisingServer()
+		{
+			if (_serverUdpClient == null) return;
+
+			_serverUdpClient.Close();
+
+			_serverUdpClient = null;
+
+			if (NetworkManager.StaticCanLog(LoggingType.Common)) Debug.Log("Stopped advertising server.", this);
+		}
+
+		private async void AdvertiseServerAsync()
+		{
+			while (_serverUdpClient != null)
+			{
+				
+				await Task.Delay(TimeSpan.FromSeconds(discoveryInterval));
+				
+				UdpReceiveResult result = await _serverUdpClient.ReceiveAsync();
+				Debug.Log("Awaiting for server");
+				string receivedSecret = Encoding.UTF8.GetString(result.Buffer);
+				Debug.Log(receivedSecret);
+
+				if (receivedSecret == secret)
+				{
+					byte[] okBytes = BitConverter.GetBytes(true);
+
+					await _serverUdpClient.SendAsync(okBytes, okBytes.Length, result.RemoteEndPoint);
+				}
+			}
+			Debug.Log("_serverUdpClient == null");
+		}
+
+		#endregion
+
+		#region Client
+
+		/// <summary>
+		/// Makes this <see cref="NetworkDiscovery"/> component start searching for servers.
+		/// </summary>
+		public void StartSearchingForServers()
+		{
+			// if (InstanceFinder.IsServer)
+			// {
+			// 	if (NetworkManager.StaticCanLog(LoggingType.Warning)) Debug.LogWarning("Unable to start searching for servers. Server is active.", this);
+			//
+			// 	return;
+			// }
+			Debug.Log("NetworkDiscovery is searching");
+
+			if (InstanceFinder.IsClient)
+			{
+				if (NetworkManager.StaticCanLog(LoggingType.Warning)) Debug.LogWarning("Unable to start searching for servers. Client is active.", this);
+
+				return;
+			}
+
+			if (_clientUdpClient != null)
+			{
+				if (NetworkManager.StaticCanLog(LoggingType.Common)) Debug.Log("Already searching for servers.", this);
+
+				return;
+			}
+
+			_clientUdpClient = new UdpClient()
+			{
+				EnableBroadcast = true,
+				MulticastLoopback = false,
+			};
+
+			Task.Run(SearchForServersAsync);
+
+			if (NetworkManager.StaticCanLog(LoggingType.Common)) Debug.Log("Started searching for servers.", this);
+		}
+
+		/// <summary>
+		/// Makes this <see cref="NetworkDiscovery"/> component <i>immediately</i> stop searching for servers.
+		/// </summary>
+		public void StopSearchingForServers()
+		{
+			if (_clientUdpClient == null) return;
+
+			_clientUdpClient.Close();
+
+			_clientUdpClient = null;
+
+			if (NetworkManager.StaticCanLog(LoggingType.Common)) Debug.Log("Stopped searching for servers.", this);
+		}
+
+		private async void SearchForServersAsync()
+		{
+			byte[] secretBytes = Encoding.UTF8.GetBytes(secret);
+
+			IPEndPoint endPoint = new IPEndPoint(IPAddress.Broadcast, port);
+
+			while (_clientUdpClient != null)
+			{
+				await Task.Delay(TimeSpan.FromSeconds(discoveryInterval));
+
+				await _clientUdpClient.SendAsync(secretBytes, secretBytes.Length, endPoint);
+
+				UdpReceiveResult result = await _clientUdpClient.ReceiveAsync();
+
+				if (BitConverter.ToBoolean(result.Buffer, 0))
+				{
+					ServerFoundCallback?.Invoke(result.RemoteEndPoint);
+
+					StopSearchingForServers();
+				}
+			}
+		}
+
+		#endregion
+	}
+}

Assets/Fish-Networking-Discovery-main/NetworkDiscoveryCanvasUI.cs

@@ -0,0 +1,257 @@
+using _PROJECT.NewHandPresence;
+using FishNet;
+using FishNet.Discovery;
+using FishNet.Managing.Scened;
+using FishNet.Object;
+using System.Collections;
+using System.Collections.Concurrent;
+using System.Collections.Generic;
+using System.Net;
+using TMPro;
+using Unity.XR.CoreUtils;
+using UnityEngine;
+using UnityEngine.UI;
+
+public class NetworkMenuUI : MonoBehaviour
+{
+
+
+    private ConcurrentQueue<System.Action> mainThreadQueue = new();
+    [Header("UI References")]
+    public Toggle VRToggle;
+   
+    public Button startPlayingButton;
+    public Button reloadButton;
+    public Button joinMultiplayerButton;
+    public Transform serverListContainer;
+    public GameObject serverListItemPrefab;
+    public TMP_Text statusText;
+    public Button quitButton;
+
+    [Header("Networking")]
+    public NetworkDiscovery networkDiscovery;
+    public Camera uiCamera;
+    public AudioListener placeholderAudioListener;
+    private bool _useVR;
+    
+    //public Image coverImage;
+
+    private readonly List<IPEndPoint> foundServers = new();
+
+    private void Start()
+    {
+
+        if (networkDiscovery == null)
+            networkDiscovery = FindObjectOfType<NetworkDiscovery>();
+
+        _useVR = PlayerPrefs.GetInt("UseVR", 0) == 1;
+        VRToggle.isOn = _useVR;
+
+        // React to UI toggle changes
+        VRToggle.onValueChanged.AddListener((isOn) =>
+        {
+            _useVR = isOn;
+            PlayerPrefs.SetInt("UseVR", _useVR ? 1 : 0);
+            PlayerPrefs.Save();
+            Debug.Log($"UseVR set to {_useVR}");
+        });
+
+        startPlayingButton.onClick.AddListener(OnStartPlaying);
+        reloadButton.onClick.AddListener(OnReload);
+        joinMultiplayerButton.onClick.AddListener(OnJoinMultiplayer);
+        quitButton.onClick.AddListener(OnQuit);
+
+        networkDiscovery.ServerFoundCallback += (endPoint) =>
+        {
+            Debug.Log("Found a server");
+            // Only queue the endpoint for main-thread processing
+            mainThreadQueue.Enqueue(() =>
+            {
+                StartCoroutine(ProcessServerFound(endPoint));
+            });
+        };
+    }
+    private void Update()
+    {
+        while (mainThreadQueue.TryDequeue(out var action))
+        {
+            action.Invoke(); // safely runs on main thread
+        }
+    }
+    private void OnStartPlaying()
+    {
+        statusText.text = "Starting host...";
+        StartCoroutine(HostAndSearchRoutine());
+    }
+
+
+    private void OnReload()
+    {
+        /*if (!InstanceFinder.IsClient)
+        {
+            Debug.LogWarning("Reload can only be called from a client!");
+            return;
+        }
+
+        // Use the ClientManager.Objects dictionary
+        foreach (NetworkObject obj in InstanceFinder.ClientManager.Objects.)
+        {
+            if (obj.IsOwner) // This ensures it's YOUR local player
+            {
+                var xr = obj.GetComponentInChildren<XROrigin>();
+                if (xr != null)
+                {
+                    Debug.Log("Found my XR Origin player.");
+
+                    TutorialController tutorial = xr.GetComponent<TutorialController>();
+                    if (tutorial != null)
+                    {
+                        tutorial._state = TutorialController.TutorialState.Initializing;
+                    }
+                }
+                else
+                {
+                    Debug.Log("My local player is not an XR Origin.");
+                }
+            }
+        }*/
+    }
+
+    private void OnJoinMultiplayer()
+    {
+        statusText.text = "Searching for servers...";
+        networkDiscovery.StopSearchingForServers();
+        ClearServerList();
+        //Debug.Log(foundServers.Count);
+        foundServers.Clear();
+        //Debug.Log(foundServers.Count);
+        networkDiscovery.StartSearchingForServers();
+    }
+
+    private void OnQuit()
+    {
+        Debug.Log("Quitting application");
+        Application.Quit();
+    }
+
+    private IEnumerator ProcessServerFound(IPEndPoint endPoint)
+    {
+        Debug.Log("server found");
+        //Debug.Log(endPoint.Address.ToString());
+        //Debug.Log(foundServers.Count);
+        if (foundServers.Contains(endPoint)) yield return null;
+        foundServers.Add(endPoint);
+
+            Debug.Log($"Server found {endPoint}");
+            // Auto-join if started as host
+            if (InstanceFinder.IsServer)
+            {
+                Debug.Log("Server is local");
+                //networkDiscovery.StopAdvertisingServer();
+                networkDiscovery.StopSearchingForServers();
+
+                uiCamera.enabled = false;
+                Debug.Log("Disabled placeholder audio source");
+                placeholderAudioListener.enabled = false;
+                //coverImage.gameObject.SetActive(false);
+
+                InstanceFinder.ClientManager.StartConnection(endPoint.Address.ToString());
+                statusText.text = $"Joined server: {endPoint.Address}";
+                yield return null;
+            }
+            
+            Debug.Log("Server is foregin");
+            GameObject item = Instantiate(serverListItemPrefab, serverListContainer);
+
+            
+
+            TMP_Text label = item.GetComponentInChildren<TMP_Text>();
+            if (label != null)
+                label.text = endPoint.Address.ToString();
+
+            Button btn = item.GetComponent<Button>();
+            btn.onClick.RemoveAllListeners(); // clear any old listener
+            btn.onClick.AddListener(() =>
+            {
+                networkDiscovery.StopSearchingForServers();
+                if (uiCamera != null) uiCamera.enabled = false;
+                if (placeholderAudioListener != null)
+                {
+                    Debug.Log("Disabled placeholder audio source");
+                    placeholderAudioListener.enabled = false;
+                }
+                //coverImage.gameObject.SetActive(false);
+                InstanceFinder.ClientManager.StartConnection(endPoint.Address.ToString());
+                statusText.text = $"Joined server: {endPoint.Address}";
+            });
+        
+    }
+    private Coroutine joinRoutine;
+
+    private IEnumerator HostAndSearchRoutine()
+    {
+        // Step 1: Start hosting
+        InstanceFinder.ServerManager.StartConnection();
+
+        // Step 2: Start advertising
+        yield return new WaitForSeconds(0.5f); // Let the server settle
+        networkDiscovery.StartAdvertisingServer();
+
+        // Step 3: Begin searching
+        yield return new WaitForSeconds(0.5f); // Let advertisement initialize
+        networkDiscovery.StartSearchingForServers();
+
+        // Step 4: Try to find and join a better server
+        yield return new WaitForSeconds(0.5f); // Give discovery a moment
+        StartJoinRoutine();
+    }
+
+
+    public void StartJoinRoutine()
+    {
+        if (joinRoutine != null) StopCoroutine(joinRoutine);
+        joinRoutine = StartCoroutine(TryJoinServerRepeatedly());
+    }
+
+    private IEnumerator TryJoinServerRepeatedly()
+    {
+        float timeout = 10f;
+        float timer = 0f;
+
+        while (timer < timeout)
+        {
+            if (foundServers.Count > 0)
+            {
+                var firstServer = foundServers[0];
+                //networkDiscovery.StopAdvertisingServer();
+                if (uiCamera != null) uiCamera.enabled = false;
+                if (placeholderAudioListener != null)
+                {
+                    Debug.Log("Disabled placeholder audio source");
+                    placeholderAudioListener.enabled = false;
+                }
+                networkDiscovery.StopSearchingForServers();
+                InstanceFinder.ClientManager.StartConnection(firstServer.Address.ToString());
+                statusText.text = $"Joined server: {firstServer.Address}";
+                //coverImage.gameObject.SetActive(false);
+                yield break;
+            }
+
+            statusText.text = $"Searching for servers... ({Mathf.FloorToInt(timer)}s)";
+            yield return new WaitForSeconds(1f);
+            timer += 1f;
+        }
+
+
+        statusText.text = "No servers found. Acting as host.";
+        // You can optionally start your own server here
+    }
+
+    private void ClearServerList()
+    {
+        foreach (Transform child in serverListContainer)
+        {
+            Destroy(child.gameObject);
+        }
+    }
+}

Assets/Fish-Networking-Discovery-main/NetworkDiscoveryHUD.cs

@@ -0,0 +1,117 @@
+using System.Collections.Generic;
+using System.Net;
+using UnityEngine;
+
+namespace FishNet.Discovery
+{
+	public sealed class NetworkDiscoveryHUD : MonoBehaviour
+	{
+		[SerializeField]
+		private NetworkDiscovery networkDiscovery;
+
+		private Camera _camera;
+
+		private readonly List<IPEndPoint> _endPoints = new List<IPEndPoint>();
+
+		private Vector2 _serversListScrollVector;
+
+		private bool _useVR;
+
+		private void Start()
+		{
+			if (networkDiscovery == null) networkDiscovery = FindObjectOfType<NetworkDiscovery>();
+
+			_camera = GetComponentInChildren<Camera>();
+			
+			_useVR = PlayerPrefs.GetInt("UseVR", 0) == 1;
+			
+			networkDiscovery.ServerFoundCallback += (endPoint) =>
+			{
+				if (!_endPoints.Contains(endPoint)) _endPoints.Add(endPoint);
+			};
+		}
+
+		private void OnGUI()
+		{
+			GUILayoutOption buttonHeight = GUILayout.Height(30.0f);
+
+			using (new GUILayout.AreaScope(new Rect(Screen.width - 240.0f - 10.0f, 10.0f, 240.0f, Screen.height - 20.0f)))
+			{
+				GUILayout.Box("Settings");
+				
+				using (new GUILayout.HorizontalScope())
+				{
+					bool useVRNew = GUILayout.Toggle(_useVR, "Use VR");
+					
+					if (useVRNew != _useVR)
+					{
+						_useVR = useVRNew;
+						PlayerPrefs.SetInt("UseVR", _useVR ? 1 : 0);
+						Debug.Log($"UseVR set to {_useVR}");
+					}
+				}
+				
+				GUILayout.Box("Server");
+
+				using (new GUILayout.HorizontalScope())
+				{
+					if (GUILayout.Button("Start", buttonHeight)) InstanceFinder.ServerManager.StartConnection();
+
+					if (GUILayout.Button("Stop", buttonHeight)) InstanceFinder.ServerManager.StopConnection(true);
+				}
+
+				GUILayout.Box("Advertising");
+
+				using (new GUILayout.HorizontalScope())
+				{
+					if (networkDiscovery.IsAdvertising)
+					{
+						if (GUILayout.Button("Stop", buttonHeight)) networkDiscovery.StopAdvertisingServer();
+					}
+					else
+					{
+						if (GUILayout.Button("Start", buttonHeight)) networkDiscovery.StartAdvertisingServer();
+					}
+				}
+
+				GUILayout.Box("Searching");
+
+				using (new GUILayout.HorizontalScope())
+				{
+					if (networkDiscovery.IsSearching)
+					{
+						if (GUILayout.Button("Stop", buttonHeight)) networkDiscovery.StopSearchingForServers();
+					}
+					else
+					{
+						if (GUILayout.Button("Start", buttonHeight)) networkDiscovery.StartSearchingForServers();
+					}
+				}
+
+				if (_endPoints.Count > 0)
+				{
+					GUILayout.Box("Servers");
+
+					using (new GUILayout.ScrollViewScope(_serversListScrollVector))
+					{
+						for (int i = 0; i < _endPoints.Count; i++)
+						{
+							string ipAddress = _endPoints[i].Address.ToString();
+
+							if (GUILayout.Button(ipAddress))
+							{
+								//networkDiscovery.StopAdvertisingServer();
+
+								networkDiscovery.StopSearchingForServers();
+								
+								_camera.enabled = false;
+
+								InstanceFinder.ClientManager.StartConnection(ipAddress);
+							}
+						}
+					}
+				}
+			}
+		}
+	}
+}

Assets/Fish-Networking-Discovery-main/README.md

@@ -0,0 +1,48 @@
+# Fish-Networking-Discovery
+
+A very simple LAN network discovery component for Fish-Networking ([Asset Store](https://assetstore.unity.com/packages/tools/network/fish-net-networking-evolved-207815) | [GitHub](https://github.com/FirstGearGames/FishNet))
+
+### Getting Started (GUI)
+
+1. Download the code in this repo as a zip
+2. Extract the code inside your project folder **(FISH-NETWORKING MUST BE ALREADY INSTALLED)**
+3. Create an empty game object
+4. Add a `NetworkManager` component to the game object you just created
+5. Add a `NetworkDiscovery` component to the game object you just created
+6. Set the `secret`, `port`, and `discoveryInterval` fields
+7. Add a `NetworkDiscoveryHUD` component
+8. Enter play mode
+	- If you want to begin advertising a server
+		1. Press "Start" under the "Server" group
+		2. Press "Start" under the "Advertising" group
+	- If you want to stop advertising a server
+    	- Press "Stop" under the "Advertising" group
+    - If you want to begin searching for servers
+    	- Press "Start" under the "Searching" group
+	- If you want to stop searching for servers
+    	- Press "Stop" under the "Searching" group
+
+### Getting Started (Code)
+
+1. Download the code in this repo as a zip
+2. Extract the code inside your project folder **(FISH-NETWORKING MUST BE ALREADY INSTALLED)**
+3. Create an empty game object
+4. Add a `NetworkManager` component to the game object you just created
+5. Add a `NetworkDiscovery` component to the game object you just created
+6. Set the `secret`, `port`, and `discoveryInterval` fields
+7. Enter play mode
+	- If you want begin advertising a server
+		1. Call `InstanceFinder.ServerManager.StartConnection()`
+		2. Call `FindObjectOfType<NetworkDiscovery>().StartAdvertisingServer()`
+  	- If you want to stop advertising a server
+    	- Call `FindObjectOfType<NetworkDiscovery>().StopAdvertisingServer()`
+  	- If you want to start searching for servers
+    	- Call `FindObjectOfType<NetworkDiscovery>().StartSearchingForServers()`
+  	- If you want to stop seaching for servers
+    	- Call `FindObjectOfType<NetworkDiscovery>().StopSearchingForServers()`
+
+### Planned Features
+
+- [x] Automatically start/stop advertising server
+- [ ] Automatically remove servers that are no longer alive
+- [ ] Introduce Unity coroutines for all `NetworkDiscovery` methods

Assets/Fish-Networking-Discovery-main/Server List item prefab.prefab

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Assets/FishNet/CodeGenerating/Extension.meta

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Assets/FishNet/CodeGenerating/Extension/ILProcessorExtensions.cs

@@ -0,0 +1,20 @@
+using FishNet.CodeGenerating.Helping.Extension;
+using MonoFN.Cecil.Cil;
+
+namespace FishNet.CodeGenerating.Extension
+{
+
+
+    internal static class ILProcessorExtensions
+    {
+        /// <summary>
+        /// Creates a variable type within the body and returns it's VariableDef.
+        /// </summary>
+        internal static VariableDefinition CreateVariable(this ILProcessor processor, CodegenSession session, System.Type variableType)
+        {
+            return processor.Body.Method.CreateVariable(session, variableType);
+        }
+    }
+
+
+}

Assets/FishNet/CodeGenerating/Extension/ILProcessorExtensions.cs.meta

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Assets/FishNet/CodeGenerating/Extension/MethodDefinitionExtensions.cs

@@ -0,0 +1,131 @@
+using FishNet.CodeGenerating.Helping.Extension;
+using MonoFN.Cecil;
+using MonoFN.Cecil.Rocks;
+using MonoFN.Collections.Generic;
+using System.Collections.Generic;
+
+namespace FishNet.CodeGenerating.Extension
+{
+
+
+    internal static class MethodDefinitionExtensions
+    {
+        /// <summary>
+        /// Returns the proper OpCode to use for call methods.
+        /// </summary>
+        public static MonoFN.Cecil.Cil.OpCode GetCallOpCode(this MethodDefinition md)
+        {
+            if (md.Attributes.HasFlag(MethodAttributes.Virtual))
+                return MonoFN.Cecil.Cil.OpCodes.Callvirt;
+            else
+                return MonoFN.Cecil.Cil.OpCodes.Call;
+        }
+        /// <summary>
+        /// Returns the proper OpCode to use for call methods.
+        /// </summary>
+        public static MonoFN.Cecil.Cil.OpCode GetCallOpCode(this MethodReference mr, CodegenSession session)
+        {
+            return mr.CachedResolve(session).GetCallOpCode();
+        }
+
+        /// <summary>
+        /// Adds otherMd parameters to thisMR and returns added parameters.
+        /// </summary>
+        public static List<ParameterDefinition> CreateParameters(this MethodReference thisMr, CodegenSession session, MethodDefinition otherMd)
+        {
+            return thisMr.CachedResolve(session).CreateParameters(session, otherMd);
+        }
+        /// <summary>
+        /// Adds otherMr parameters to thisMR and returns added parameters.
+        /// </summary>
+        public static List<ParameterDefinition> CreateParameters(this MethodReference thisMr, CodegenSession session, MethodReference otherMr)
+        {
+            return thisMr.CachedResolve(session).CreateParameters(session, otherMr.CachedResolve(session));
+        }
+
+        /// <summary>
+        /// Adds otherMd parameters to thisMd and returns added parameters.
+        /// </summary>
+        public static List<ParameterDefinition> CreateParameters(this MethodDefinition thisMd, CodegenSession session, MethodDefinition otherMd)
+        {
+            List<ParameterDefinition> results = new List<ParameterDefinition>();
+
+            foreach (ParameterDefinition pd in otherMd.Parameters)
+            {
+                session.ImportReference(pd.ParameterType);
+                int currentCount = thisMd.Parameters.Count;
+                string name = (pd.Name + currentCount);
+                ParameterDefinition parameterDef = new ParameterDefinition(name, pd.Attributes, pd.ParameterType);
+                //Set any default values.
+                parameterDef.Constant = pd.Constant;
+                parameterDef.IsReturnValue = pd.IsReturnValue;
+                parameterDef.IsOut = pd.IsOut;
+                foreach (CustomAttribute item in pd.CustomAttributes)
+                    parameterDef.CustomAttributes.Add(item);
+                parameterDef.HasConstant = pd.HasConstant;
+                parameterDef.HasDefault = pd.HasDefault;
+                
+                thisMd.Parameters.Add(parameterDef);
+
+                results.Add(parameterDef);
+            }
+
+            return results;
+        }
+
+        /// <summary>
+        /// Returns a method reference while considering if declaring type is generic.
+        /// </summary>
+        public static MethodReference GetMethodReference(this MethodDefinition md, CodegenSession session)
+        {
+            MethodReference methodRef = session.ImportReference(md);
+
+            //Is generic.
+            if (md.DeclaringType.HasGenericParameters)
+            {
+                GenericInstanceType git = methodRef.DeclaringType.MakeGenericInstanceType();
+                MethodReference result = new MethodReference(md.Name, md.ReturnType)
+                {
+                    HasThis = md.HasThis,
+                    ExplicitThis = md.ExplicitThis,
+                    DeclaringType = git,
+                    CallingConvention = md.CallingConvention,
+                };
+                foreach (ParameterDefinition pd in md.Parameters)
+                {
+                    session.ImportReference(pd.ParameterType);
+                    result.Parameters.Add(pd);
+                }
+                return result;
+            }
+            else
+            {
+                return methodRef;
+            }
+        }
+
+
+        /// <summary>
+        /// Returns a method reference for a generic method.
+        /// </summary>
+        public static MethodReference GetMethodReference(this MethodDefinition md, CodegenSession session, TypeReference typeReference)
+        {
+            MethodReference methodRef = session.ImportReference(md);
+            return methodRef.GetMethodReference(session, typeReference);
+        }
+
+
+        /// <summary>
+        /// Returns a method reference for a generic method.
+        /// </summary>
+        public static MethodReference GetMethodReference(this MethodDefinition md, CodegenSession session, TypeReference[] typeReferences)
+        {
+            MethodReference methodRef = session.ImportReference(md);
+            return methodRef.GetMethodReference(session, typeReferences);
+        }
+
+
+    }
+
+
+}

Assets/FishNet/CodeGenerating/Extension/MethodDefinitionExtensions.cs.meta

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Assets/FishNet/CodeGenerating/Extension/TypeDefinitionExtensions.cs

@@ -0,0 +1,254 @@
+using FishNet.CodeGenerating.Helping.Extension;
+using MonoFN.Cecil;
+
+namespace FishNet.CodeGenerating.Extension
+{
+
+
+    internal static class TypeDefinitionExtensions
+    {
+
+
+        /// <summary>
+        /// Returns if a TypeDefinition is nullable.
+        /// </summary>
+        public static bool IsNullable(this TypeDefinition td)
+        {
+            return (td.Name == typeof(System.Nullable<>).Name);
+        }
+
+
+        public static MethodReference GetMethodReferenceInBase(this TypeDefinition td, CodegenSession session, string methodName)
+        {
+            MethodDefinition baseMd = td.GetMethodDefinitionInBase(session, methodName);
+            if (baseMd == null)
+                return null;
+
+
+            MethodReference baseMr;
+            TypeReference baseTr = td.BaseType;
+            if (baseTr.CachedResolve(session).HasGenericParameters)
+            {
+                GenericInstanceType git = (GenericInstanceType)baseTr;
+                baseMr = new MethodReference(baseMd.Name, baseMd.ReturnType, git)
+                {
+                    HasThis = baseMd.HasThis,
+                    CallingConvention = baseMd.CallingConvention,
+                    ExplicitThis = baseMd.ExplicitThis,
+                };
+                foreach (ParameterDefinition pd in baseMd.Parameters)
+                {
+                    session.ImportReference(pd.ParameterType);
+                    baseMr.Parameters.Add(pd);
+                }
+            }
+            else
+            {
+                baseMr = session.ImportReference(baseMd);
+            }
+
+            return baseMr;
+        }
+        /// <summary>
+        /// Returns a method in the next base class.
+        /// </summary>
+        public static MethodDefinition GetMethodDefinitionInBase(this TypeDefinition td, CodegenSession session, string methodName)
+        {
+            if (td.BaseType == null)
+            {
+                session.LogError($"BaseType for {td.FullName} is null.");
+                return null;
+            }
+
+            TypeDefinition baseTd = td.BaseType.CachedResolve(session);
+            return baseTd.GetMethod(methodName);
+        }
+
+
+        /// <summary>
+        /// Returns a method in the next base class.
+        /// </summary>
+        public static MethodReference GetMethodReference(this TypeDefinition td, CodegenSession session, string methodName)
+        {
+            MethodDefinition md = td.GetMethod(methodName);
+            //Not found.
+            if (md == null)
+                return null;
+
+            return md.GetMethodReference(session);
+        }
+
+        /// <summary>
+        /// Gets a MethodReference or creates one if missing.
+        /// </summary>
+        public static MethodReference GetOrCreateMethodReference(this TypeDefinition td, CodegenSession session, string methodName, MethodAttributes attributes, TypeReference returnType, out bool created)
+        {
+            MethodDefinition md = td.GetMethod(methodName);
+            //Not found.
+            if (md == null)
+            {
+                md = new MethodDefinition(methodName, attributes, returnType);
+                td.Methods.Add(md);
+                created = true;
+            }
+            else
+            {
+                created = false;
+            }
+
+            return md.GetMethodReference(session);
+        }
+
+
+        /// <summary>
+        /// Gets a MethodDefinition or creates one if missing.
+        /// </summary>
+        public static MethodDefinition GetOrCreateMethodDefinition(this TypeDefinition td, CodegenSession session, string methodName, MethodAttributes attributes, TypeReference returnType, out bool created)
+        {
+            MethodDefinition md = td.GetMethod(methodName);
+            //Not found.
+            if (md == null)
+            {
+                md = new MethodDefinition(methodName, attributes, returnType);
+                td.Methods.Add(md);
+                created = true;
+            }
+            else
+            {
+                created = false;
+            }
+
+            return md;
+        }
+
+        /// <summary>
+        /// Gets a MethodDefinition or creates one if missing.
+        /// </summary>
+        public static MethodDefinition GetOrCreateMethodDefinition(this TypeDefinition td, CodegenSession session, string methodName, MethodDefinition methodTemplate, bool copyParameters, out bool created)
+        {
+            MethodDefinition md = td.GetMethod(methodName);
+            //Not found.
+            if (md == null)
+            {
+                TypeReference returnType = session.ImportReference(methodTemplate.ReturnType);
+                md = new MethodDefinition(methodName, methodTemplate.Attributes, returnType)
+                {
+                    ExplicitThis = methodTemplate.ExplicitThis,
+                    AggressiveInlining = methodTemplate.AggressiveInlining,
+                    Attributes = methodTemplate.Attributes,
+                    CallingConvention = methodTemplate.CallingConvention,
+                    HasThis = methodTemplate.HasThis,
+                };
+                md.Body.InitLocals = methodTemplate.Body.InitLocals;
+
+                if (copyParameters)
+                {
+                    foreach (ParameterDefinition pd in methodTemplate.Parameters)
+                        md.Parameters.Add(pd);
+                }
+
+                td.Methods.Add(md);
+                created = true;
+            }
+            else
+            {
+                created = false;
+            }
+
+            return md;
+        }
+
+
+
+        /// <summary>
+        /// Returns a method in any inherited classes. The first found method is returned.
+        /// </summary>
+        public static MethodDefinition GetMethodDefinitionInAnyBase(this TypeDefinition td, CodegenSession session, string methodName)
+        {
+            while (td != null)
+            {
+                foreach (MethodDefinition md in td.Methods)
+                {
+                    if (md.Name == methodName)
+                        return md;
+                }
+
+                try
+                {
+                    td = td.GetNextBaseTypeDefinition(session);
+                }
+                catch
+                {
+                    return null;
+                }
+            }
+
+            return null;
+        }
+
+        /// <summary>
+        /// Returns the next base type.
+        /// </summary>
+        public static TypeDefinition GetNextBaseTypeDefinition(this TypeDefinition typeDef, CodegenSession session)
+        {
+            return (typeDef.BaseType == null) ? null : typeDef.BaseType.CachedResolve(session);
+        }
+
+        /// <summary>
+        /// Creates a FieldReference.
+        /// </summary>
+        public static FieldReference CreateFieldReference(this FieldDefinition fd, CodegenSession session)
+        {
+            FieldReference fr;
+            TypeDefinition declaringType = fd.DeclaringType;
+            //Is generic.
+            if (declaringType.HasGenericParameters)
+            {
+                GenericInstanceType git = new GenericInstanceType(declaringType);
+                foreach (GenericParameter item in declaringType.GenericParameters)
+                    git.GenericArguments.Add(item);
+                fr = new FieldReference(fd.Name, fd.FieldType, git);
+                return fr;
+            }
+            //Not generic.
+            else
+            {
+                return session.ImportReference(fd);
+            }
+        }
+
+        /// <summary>
+        /// Gets a FieldReference or creates it if missing.
+        /// </summary>
+        public static FieldReference GetOrCreateFieldReference(this TypeDefinition td, CodegenSession session, string fieldName, FieldAttributes attributes, TypeReference fieldTypeRef, out bool created)
+        {
+            FieldReference fr = td.GetFieldReference(fieldName, session);
+            if (fr == null)
+            {
+                fr = td.CreateFieldDefinition(session, fieldName, attributes, fieldTypeRef);
+                created = true;
+            }
+            else
+            {
+                created = false;
+            }
+
+            return fr;
+        }
+
+        /// <summary>
+        /// Creates a FieldReference.
+        /// </summary>
+        public static FieldReference CreateFieldDefinition(this TypeDefinition td, CodegenSession session, string fieldName, FieldAttributes attributes, TypeReference fieldTypeRef)
+        {
+            FieldDefinition fd = new FieldDefinition(fieldName, attributes, fieldTypeRef);
+            td.Fields.Add(fd);
+            return fd.CreateFieldReference(session);
+        }
+
+
+
+    }
+
+
+}

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