port InvokeAI API client to Unity, use it in ImageGenerationBox

Just a small reference change

.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 +1,6 @@
+INVOKEAI_URL=
+INVOKEAI_MODEL_KEY=
+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 +1,492 @@
+# 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"]
+INVOKEAI_MODEL_KEY = os.environ["INVOKEAI_MODEL_KEY"]
+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 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)
+
+
+
+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
+
+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!")
+
+
+async def text_to_image_invoke_ai(prompt, output_path):
+    # see available model keys via GET http://INVOKEAI_BASE_URL:9090/api/v2/models/?model_type=main
+    args = {
+        "prompt": prompt,
+        "width": 512,
+        "height": 512,
+        "model_key": INVOKEAI_MODEL_KEY
+    }
+    image_url = await generate_image(args)
+    print("got image url: ", image_url)
+    download_file(image_url, output_path)

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",
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+      "BatchedNestedTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:758 [backend fallback]\n",
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+      "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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+        <name>DOTweenEditor</name>
+    </assembly>
+    <members>
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+            <summary>
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+            </summary>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorCompatibilityUtils.FindObjectOfType``1(System.Boolean)">
+            <summary>
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+            </summary>
+        </member>
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+            </summary>
+        </member>
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+            <summary>
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+            </summary>
+        </member>
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+            </summary>
+        </member>
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+            <summary>
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+            </summary>
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+        </member>
+        <member name="M:DG.DOTweenEditor.DOTweenEditorPreview.Stop(System.Boolean,System.Boolean)">
+            <summary>
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+            </summary>
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+            Note that this works by calling Rewind on all tweens, so it will work correctly
+            only if you have a single tween type per object and it wasn't killed</param>
+            <param name="clearTweens">If TRUE also kills any cached tween</param>
+        </member>
+        <member name="M:DG.DOTweenEditor.DOTweenEditorPreview.PrepareTweenForPreview(DG.Tweening.Tween,System.Boolean,System.Boolean,System.Boolean)">
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+            </summary>
+            <param name="t">The tween to ready</param>
+            <param name="clearCallbacks">If TRUE (recommended) removes all callbacks (OnComplete/Rewind/etc)</param>
+            <param name="preventAutoKill">If TRUE prevents the tween from being auto-killed at completion</param>
+            <param name="andPlay">If TRUE starts playing the tween immediately</param>
+        </member>
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+            <summary>Full major version + first minor version (ex: 2018.1f)</summary>
+        </member>
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+            <summary>Major version</summary>
+        </member>
+        <member name="F:DG.DOTweenEditor.EditorVersion.MinorVersion">
+            <summary>First minor version (ex: in 2018.1 it would be 1)</summary>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorUtils.SetEditorTexture(UnityEngine.Texture2D,UnityEngine.FilterMode,System.Int32)">
+            <summary>
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+            and applies them if they're incorrect.
+            </summary>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorUtils.DOTweenSetupRequired">
+            <summary>
+            Returns TRUE if setup is required
+            </summary>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorUtils.AssetExists(System.String)">
+            <summary>
+            Returns TRUE if the file/directory at the given path exists.
+            </summary>
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+            <returns></returns>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorUtils.ADBPathToFullPath(System.String)">
+            <summary>
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+            with backward (\) slashes).
+            </summary>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorUtils.FullPathToADBPath(System.String)">
+            <summary>
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+            (relative to Unity's project folder, and with the correct Unity forward (/) slashes).
+            </summary>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorUtils.ConnectToSourceAsset``1(System.String,System.Boolean)">
+            <summary>
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+            If the asset already exists at the given path, loads it and returns it.
+            Otherwise, either returns NULL or automatically creates it before loading and returning it
+            (depending on the given parameters).
+            </summary>
+            <typeparam name="T">Asset type</typeparam>
+            <param name="adbFilePath">File path (relative to Unity's project folder)</param>
+            <param name="createIfMissing">If TRUE and the requested asset doesn't exist, forces its creation</param>
+        </member>
+        <member name="M:DG.DOTweenEditor.EditorUtils.GetAssemblyFilePath(System.Reflection.Assembly)">
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+            Adds the given global define if it's not already present
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+            <summary>
+            Removes the given global define if it's present
+            </summary>
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+            <summary>
+            Returns TRUE if the given global define is present in all the <see cref="T:UnityEditor.BuildTargetGroup"/>
+            or only in the given <see cref="T:UnityEditor.BuildTargetGroup"/>, depending on passed parameters.<para/>
+            </summary>
+            <param name="id"></param>
+            <param name="buildTargetGroup"><see cref="T:UnityEditor.BuildTargetGroup"/>to use. Leave NULL to check in all of them.</param>
+        </member>
+        <member name="T:DG.DOTweenEditor.DOTweenDefines">
+            <summary>
+            Not used as menu item anymore, but as a utility function
+            </summary>
+        </member>
+        <member name="F:DG.DOTweenEditor.UnityEditorVersion.Version">
+            <summary>Full major version + first minor version (ex: 2018.1f)</summary>
+        </member>
+        <member name="F:DG.DOTweenEditor.UnityEditorVersion.MajorVersion">
+            <summary>Major version</summary>
+        </member>
+        <member name="F:DG.DOTweenEditor.UnityEditorVersion.MinorVersion">
+            <summary>First minor version (ex: in 2018.1 it would be 1)</summary>
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