forked from cgvr/DeltaVR
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12 Commits
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@ -1,5 +0,0 @@
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3D_GENERATION_URL=
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MODEL_FOLDER=
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CLOUDFLARE_ACCOUNT_ID=
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CLOUDFLARE_API_TOKEN=
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6
3d-generation-pipeline/.gitignore
vendored
6
3d-generation-pipeline/.gitignore
vendored
@ -1,6 +0,0 @@
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.venv
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.env
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__pycache__
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images/
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models/
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logs/
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@ -1,6 +0,0 @@
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### TODO
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* Artikkel text-to-3d prompt engineeringu kohta: "Sel3DCraft: Interactive Visual Prompts for User-Friendly Text-to-3D Generation"
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* TRELLIS: postprocessing_utils: texture baking mode: 'opt' vs 'fast' - hardcoded 'opt', kui võimaldada 'fast' siis tuleb error
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### Notes
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* 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)
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@ -1,61 +0,0 @@
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import base64
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import requests
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import os
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from dotenv import load_dotenv
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load_dotenv()
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ACCOUNT_ID = os.environ["CLOUDFLARE_ACCOUNT_ID"]
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API_TOKEN = os.environ["CLOUDFLARE_API_TOKEN"]
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def text_to_image_cloudflare(prompt, output_path):
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MODEL = "@cf/black-forest-labs/flux-1-schnell"
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URL = f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}"
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payload = {
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"prompt": prompt,
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}
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headers = {
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"Authorization": f"Bearer {API_TOKEN}",
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"Content-Type": "application/json",
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}
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resp = requests.post(URL, json=payload, headers=headers, timeout=60)
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resp.raise_for_status()
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data = resp.json()
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b64 = data["result"]["image"]
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if not b64:
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raise RuntimeError(f"Unexpected response structure: {data}")
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img_bytes = base64.b64decode(b64)
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with open(output_path, "wb") as f:
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f.write(img_bytes)
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return True
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def refine_text_prompt(prompt):
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MODEL = "@cf/meta/llama-3.2-3b-instruct"
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URL = f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}"
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instructions = """
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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.
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Example:
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User: 'Umm, okay, I would like a really cool sword, with for example a bright orange crossguard. And also it should be slightly curved.'
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You: 'a slightly curved sword with bright orange crossguard'
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"""
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response = requests.post(URL,
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headers={"Authorization": f"Bearer {API_TOKEN}"},
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json={
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"messages": [
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{"role": "system", "content": instructions},
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{"role": "user", "content": prompt}
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]
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}
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)
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data = response.json()
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return data["result"]["response"]
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@ -1,86 +0,0 @@
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import requests
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from invokeai_mcp_server import create_text2img_graph, enqueue_graph, wait_for_completion, get_image_url
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from urllib.parse import urljoin
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INVOKEAI_BASE_URL = "http://127.0.0.1:9090"
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async def generate_image(arguments: dict):
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# Extract parameters
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prompt = arguments["prompt"]
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negative_prompt = arguments.get("negative_prompt", "")
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width = arguments.get("width", 512)
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height = arguments.get("height", 512)
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steps = arguments.get("steps", 30)
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cfg_scale = arguments.get("cfg_scale", 7.5)
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scheduler = arguments.get("scheduler", "euler")
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seed = arguments.get("seed")
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model_key = arguments.get("model_key")
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lora_key = arguments.get("lora_key")
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lora_weight = arguments.get("lora_weight", 1.0)
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vae_key = arguments.get("vae_key")
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print(f"Generating image with prompt: {prompt[:50]}...")
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# Create graph
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graph = await create_text2img_graph(
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prompt=prompt,
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negative_prompt=negative_prompt,
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model_key=model_key,
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lora_key=lora_key,
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lora_weight=lora_weight,
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vae_key=vae_key,
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width=width,
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height=height,
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steps=steps,
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cfg_scale=cfg_scale,
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scheduler=scheduler,
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seed=seed
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)
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# Enqueue and wait for completion
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result = await enqueue_graph(graph)
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batch_id = result["batch"]["batch_id"]
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print(f"Enqueued batch {batch_id}, waiting for completion...")
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completed = await wait_for_completion(batch_id)
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# Extract image name from result
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if "result" in completed and "outputs" in completed["result"]:
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outputs = completed["result"]["outputs"]
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# Find the image output
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for node_id, output in outputs.items():
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if output.get("type") == "image_output":
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image_name = output["image"]["image_name"]
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image_url = await get_image_url(image_name)
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return urljoin(INVOKEAI_BASE_URL, image_url)
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raise RuntimeError("Failed to generate image!")
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def download_file(url, filepath):
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response = requests.get(url)
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if response.status_code == 200:
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with open(filepath, "wb") as file:
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file.write(response.content)
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else:
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raise RuntimeError(f"Failed to download image. Status code: {response.status_code}")
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async def text_to_image_invoke_ai(prompt, output_path):
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# see available model keys via GET http://127.0.0.1:9090/api/v2/models/?model_type=main
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args = {
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"prompt": prompt,
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"width": 512,
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"height": 512,
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"model_key": "79401292-0a6b-428d-b7d7-f1e86caeba2b" # Juggernaut XL v9
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#"model_key": "735f6485-6703-498f-929e-07cf0bbbd179" # Dreamshaper 8
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}
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image_url = await generate_image(args)
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print("got image url: ", image_url)
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download_file(image_url, output_path)
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@ -1,97 +0,0 @@
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import subprocess
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import os
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import time
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import requests
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import base64
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from dotenv import load_dotenv
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load_dotenv()
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MODEL_FOLDER = os.environ["MODEL_FOLDER"]
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API_URL = os.environ["3D_GENERATION_URL"]
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def image_to_3d_subprocess(image_path, output_path):
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venv_python = MODEL_FOLDER + r"\.venv\Scripts\python.exe"
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script_path = MODEL_FOLDER + r"\run.py"
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args = [image_path, "--output-dir", output_path]
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command = [venv_python, script_path] + args
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try:
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# Run the subprocess
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result = subprocess.run(command, capture_output=True, text=True)
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# Print output and errors
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print("STDOUT:\n", result.stdout)
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print("STDERR:\n", result.stderr)
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print("Return Code:", result.returncode)
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except Exception as e:
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||||||
print(f"Error occurred: {e}")
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||||||
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||||||
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def generate_no_preview(image_base64: str):
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"""Generate 3D model from a single base64-encoded image without previews.
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Args:
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image_base64: Base64 string of the image (without 'data:image/...' prefix)
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"""
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try:
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# Set generation parameters
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params = {
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'image_base64': image_base64,
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'seed': 42,
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'ss_guidance_strength': 7.5,
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'ss_sampling_steps': 10,
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'slat_guidance_strength': 7.5,
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'slat_sampling_steps': 10,
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'mesh_simplify_ratio': 0.99,
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'texture_size': 1024,
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#'texture_baking_mode': 'opt',
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'texture_opt_total_steps': 1000,
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'output_format': 'glb'
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}
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# Start generation
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print("Starting generation...")
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||||||
response = requests.post(f"{API_URL}/generate_no_preview", data=params)
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response.raise_for_status()
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# Poll status until complete
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while True:
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||||||
status = requests.get(f"{API_URL}/status").json()
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||||||
print(f"Progress: {status['progress']}%")
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if status['status'] == 'COMPLETE':
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||||||
break
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|
||||||
elif status['status'] == 'FAILED':
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|
||||||
raise Exception(f"Generation failed: {status['message']}")
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time.sleep(1)
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|
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# Download the model
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|
||||||
print("Downloading model...")
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|
||||||
response = requests.get(f"{API_URL}/download/model")
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response.raise_for_status()
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return response.content
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|
||||||
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|
||||||
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|
||||||
except Exception as e:
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|
||||||
print(f"Error: {str(e)}")
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|
||||||
return None
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|
||||||
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|
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def image_to_3d_api(image_path, output_path):
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|
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with open(image_path, 'rb') as image_file:
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|
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image_data = image_file.read()
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|
||||||
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|
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base64_encoded = base64.b64encode(image_data).decode('utf-8')
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|
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model_binary = generate_no_preview(base64_encoded)
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|
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|
||||||
output_file = f"{output_path}.glb"
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|
||||||
with open(output_file, 'wb') as f:
|
|
||||||
f.write(model_binary)
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||||||
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|
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return output_file
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@ -1,152 +0,0 @@
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{
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"cells": [
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{
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|
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"cell_type": "code",
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|
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"execution_count": null,
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||||||
"id": "1dc6faae",
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||||||
"metadata": {},
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|
||||||
"outputs": [],
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|
||||||
"source": [
|
|
||||||
"import os\n",
|
|
||||||
"import base64\n",
|
|
||||||
"import requests\n",
|
|
||||||
"from dotenv import load_dotenv"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "b3107275",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
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|
||||||
"source": [
|
|
||||||
"load_dotenv()\n",
|
|
||||||
"\n",
|
|
||||||
"ACCOUNT_ID = os.environ[\"CLOUDFLARE_ACCOUNT_ID\"]\n",
|
|
||||||
"API_TOKEN = os.environ[\"CLOUDFLARE_API_TOKEN\"]"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "999adf95",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Text to image"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "40b35163",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"Saved: output.jpg (263282 bytes)\n",
|
|
||||||
"Saved: image9.jpg (263282 bytes)\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"MODEL = \"@cf/black-forest-labs/flux-1-schnell\"\n",
|
|
||||||
"URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
|
|
||||||
"\n",
|
|
||||||
"payload = {\n",
|
|
||||||
" \"prompt\": \"a slightly curved broadsword with a fancy golden crossguard\",\n",
|
|
||||||
"}\n",
|
|
||||||
"\n",
|
|
||||||
"headers = {\n",
|
|
||||||
" \"Authorization\": f\"Bearer {API_TOKEN}\",\n",
|
|
||||||
" \"Content-Type\": \"application/json\",\n",
|
|
||||||
"}\n",
|
|
||||||
"\n",
|
|
||||||
"resp = requests.post(URL, json=payload, headers=headers, timeout=60)\n",
|
|
||||||
"resp.raise_for_status()\n",
|
|
||||||
"\n",
|
|
||||||
"data = resp.json()\n",
|
|
||||||
"b64 = data[\"result\"][\"image\"]\n",
|
|
||||||
"if not b64:\n",
|
|
||||||
" raise RuntimeError(f\"Unexpected response structure: {data}\")\n",
|
|
||||||
"\n",
|
|
||||||
"img_bytes = base64.b64decode(b64)\n",
|
|
||||||
"\n",
|
|
||||||
"out_path = \"output.jpg\"\n",
|
|
||||||
"with open(out_path, \"wb\") as f:\n",
|
|
||||||
" f.write(img_bytes)\n",
|
|
||||||
"\n",
|
|
||||||
"print(f\"Saved: {out_path} ({len(img_bytes)} bytes)\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "14a874c4",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"## Text prompt refinement"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 13,
|
|
||||||
"id": "485f6f46",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\"dark wooden battleaxe with bronze blade\"\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"MODEL = \"@cf/meta/llama-3.2-3b-instruct\"\n",
|
|
||||||
"URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
|
|
||||||
"\n",
|
|
||||||
"instructions = \"\"\"\n",
|
|
||||||
"User is talking about some object. Your task is to generate a short and concise description of it. Use only user's own words, keep it as short as possible.\n",
|
|
||||||
"Example:\n",
|
|
||||||
"User: 'Umm, okay, I would like a really cool sword, with for example a bright orange crossguard. And also it should be slightly curved.'\n",
|
|
||||||
"You: 'a slightly curved sword with bright orange crossguard'\n",
|
|
||||||
"\"\"\"\n",
|
|
||||||
"prompt = \"Umm, alright, can you please give me an epic battleaxe? It should have a dark wooden shaft and bronze blade.\"\n",
|
|
||||||
"\n",
|
|
||||||
"response = requests.post(URL,\n",
|
|
||||||
" headers={\"Authorization\": f\"Bearer {API_TOKEN}\"},\n",
|
|
||||||
" json={\n",
|
|
||||||
" \"messages\": [\n",
|
|
||||||
" {\"role\": \"system\", \"content\": instructions},\n",
|
|
||||||
" {\"role\": \"user\", \"content\": prompt}\n",
|
|
||||||
" ]\n",
|
|
||||||
" }\n",
|
|
||||||
")\n",
|
|
||||||
"data = response.json()\n",
|
|
||||||
"result_text = data[\"result\"][\"response\"]\n",
|
|
||||||
"print(result_text)"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": ".venv",
|
|
||||||
"language": "python",
|
|
||||||
"name": "python3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.10.11"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
@ -1,122 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "50e24baa",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from invokeai_mcp_server import create_text2img_graph, enqueue_graph, wait_for_completion, get_image_url\n",
|
|
||||||
"from urllib.parse import urljoin\n",
|
|
||||||
"\n",
|
|
||||||
"import asyncio"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "0407cd9a",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"INVOKEAI_BASE_URL = \"http://127.0.0.1:9090\"\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"async def generate_image(arguments: dict):\n",
|
|
||||||
"\n",
|
|
||||||
" # Extract parameters\n",
|
|
||||||
" prompt = arguments[\"prompt\"]\n",
|
|
||||||
" negative_prompt = arguments.get(\"negative_prompt\", \"\")\n",
|
|
||||||
" width = arguments.get(\"width\", 512)\n",
|
|
||||||
" height = arguments.get(\"height\", 512)\n",
|
|
||||||
" steps = arguments.get(\"steps\", 30)\n",
|
|
||||||
" cfg_scale = arguments.get(\"cfg_scale\", 7.5)\n",
|
|
||||||
" scheduler = arguments.get(\"scheduler\", \"euler\")\n",
|
|
||||||
" seed = arguments.get(\"seed\")\n",
|
|
||||||
" model_key = arguments.get(\"model_key\")\n",
|
|
||||||
" lora_key = arguments.get(\"lora_key\")\n",
|
|
||||||
" lora_weight = arguments.get(\"lora_weight\", 1.0)\n",
|
|
||||||
" vae_key = arguments.get(\"vae_key\")\n",
|
|
||||||
"\n",
|
|
||||||
" #logger.info(f\"Generating image with prompt: {prompt[:50]}...\")\n",
|
|
||||||
"\n",
|
|
||||||
" # Create graph\n",
|
|
||||||
" graph = await create_text2img_graph(\n",
|
|
||||||
" prompt=prompt,\n",
|
|
||||||
" negative_prompt=negative_prompt,\n",
|
|
||||||
" model_key=model_key,\n",
|
|
||||||
" lora_key=lora_key,\n",
|
|
||||||
" lora_weight=lora_weight,\n",
|
|
||||||
" vae_key=vae_key,\n",
|
|
||||||
" width=width,\n",
|
|
||||||
" height=height,\n",
|
|
||||||
" steps=steps,\n",
|
|
||||||
" cfg_scale=cfg_scale,\n",
|
|
||||||
" scheduler=scheduler,\n",
|
|
||||||
" seed=seed\n",
|
|
||||||
" )\n",
|
|
||||||
"\n",
|
|
||||||
" # Enqueue and wait for completion\n",
|
|
||||||
" result = await enqueue_graph(graph)\n",
|
|
||||||
" batch_id = result[\"batch\"][\"batch_id\"]\n",
|
|
||||||
"\n",
|
|
||||||
" #logger.info(f\"Enqueued batch {batch_id}, waiting for completion...\")\n",
|
|
||||||
"\n",
|
|
||||||
" completed = await wait_for_completion(batch_id)\n",
|
|
||||||
"\n",
|
|
||||||
" # Extract image name from result\n",
|
|
||||||
" if \"result\" in completed and \"outputs\" in completed[\"result\"]:\n",
|
|
||||||
" outputs = completed[\"result\"][\"outputs\"]\n",
|
|
||||||
" # Find the image output\n",
|
|
||||||
" for node_id, output in outputs.items():\n",
|
|
||||||
" if output.get(\"type\") == \"image_output\":\n",
|
|
||||||
" image_name = output[\"image\"][\"image_name\"]\n",
|
|
||||||
" image_url = await get_image_url(image_name)\n",
|
|
||||||
"\n",
|
|
||||||
" text=f\"Image generated successfully!\\n\\nImage Name: {image_name}\\nImage URL: {image_url}\\n\\nYou can view the image at: {urljoin(INVOKEAI_BASE_URL, f'/api/v1/images/i/{image_name}/full')}\"\n",
|
|
||||||
" print(text)\n",
|
|
||||||
"\n",
|
|
||||||
" # Fallback if we couldn't find image output\n",
|
|
||||||
" #text=f\"Image generation completed but output format was unexpected. Batch ID: {batch_id}\\n\\nResult: {json.dumps(completed, indent=2)}\""
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": null,
|
|
||||||
"id": "6cf9d879",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"async def main():\n",
|
|
||||||
" args = {\n",
|
|
||||||
" \"prompt\": \"a golden katana with a fancy pommel\"\n",
|
|
||||||
" }\n",
|
|
||||||
" await generate_image(args)\n",
|
|
||||||
"\n",
|
|
||||||
"asyncio.run(main())"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": ".venv",
|
|
||||||
"language": "python",
|
|
||||||
"name": "python3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.10.11"
|
|
||||||
}
|
|
||||||
},
|
|
||||||
"nbformat": 4,
|
|
||||||
"nbformat_minor": 5
|
|
||||||
}
|
|
||||||
File diff suppressed because one or more lines are too long
@ -1,165 +0,0 @@
|
|||||||
{
|
|
||||||
"cells": [
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "4826c91d",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"data": {
|
|
||||||
"text/plain": [
|
|
||||||
"'2025-10-18-16-35-47'"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
"execution_count": 1,
|
|
||||||
"metadata": {},
|
|
||||||
"output_type": "execute_result"
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"from datetime import datetime\n",
|
|
||||||
"\n",
|
|
||||||
"datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 4,
|
|
||||||
"id": "9419e692",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"STDOUT:\n",
|
|
||||||
" Device used: cuda\n",
|
|
||||||
"After Remesh 9998 19996\n",
|
|
||||||
"\n",
|
|
||||||
"STDERR:\n",
|
|
||||||
" D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\timm\\models\\layers\\__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers\n",
|
|
||||||
" warnings.warn(f\"Importing from {__name__} is deprecated, please import via timm.layers\", FutureWarning)\n",
|
|
||||||
"\n",
|
|
||||||
" 0%| | 0/1 [00:00<?, ?it/s]\n",
|
|
||||||
" 0%| | 0/1 [00:00<?, ?it/s]\n",
|
|
||||||
"Traceback (most recent call last):\n",
|
|
||||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\run.py\", line 122, in <module>\n",
|
|
||||||
" mesh, glob_dict = model.run_image(\n",
|
|
||||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\sf3d\\system.py\", line 286, in run_image\n",
|
|
||||||
" meshes, global_dict = self.generate_mesh(\n",
|
|
||||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\sf3d\\system.py\", line 369, in generate_mesh\n",
|
|
||||||
" rast = self.baker.rasterize(\n",
|
|
||||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\texture_baker\\baker.py\", line 28, in rasterize\n",
|
|
||||||
" return torch.ops.texture_baker_cpp.rasterize(\n",
|
|
||||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\torch\\_ops.py\", line 1243, in __call__\n",
|
|
||||||
" return self._op(*args, **kwargs)\n",
|
|
||||||
"NotImplementedError: Could not run 'texture_baker_cpp::rasterize' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'texture_baker_cpp::rasterize' is only available for these backends: [CPU, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMTIA, AutogradMAIA, AutogradMeta, Tracer, AutocastCPU, AutocastMTIA, AutocastMAIA, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n",
|
|
||||||
"\n",
|
|
||||||
"CPU: registered at texture_baker\\csrc\\baker.cpp:543 [kernel]\n",
|
|
||||||
"Meta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\MetaFallbackKernel.cpp:23 [backend fallback]\n",
|
|
||||||
"BackendSelect: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\n",
|
|
||||||
"Python: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:194 [backend fallback]\n",
|
|
||||||
"FuncTorchDynamicLayerBackMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:479 [backend fallback]\n",
|
|
||||||
"Functionalize: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:375 [backend fallback]\n",
|
|
||||||
"Named: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\n",
|
|
||||||
"Conjugate: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\n",
|
|
||||||
"Negative: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\native\\NegateFallback.cpp:18 [backend fallback]\n",
|
|
||||||
"ZeroTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\n",
|
|
||||||
"ADInplaceOrView: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:104 [backend fallback]\n",
|
|
||||||
"AutogradOther: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:63 [backend fallback]\n",
|
|
||||||
"AutogradCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:67 [backend fallback]\n",
|
|
||||||
"AutogradCUDA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:75 [backend fallback]\n",
|
|
||||||
"AutogradXLA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:87 [backend fallback]\n",
|
|
||||||
"AutogradMPS: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:95 [backend fallback]\n",
|
|
||||||
"AutogradXPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:71 [backend fallback]\n",
|
|
||||||
"AutogradHPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:108 [backend fallback]\n",
|
|
||||||
"AutogradLazy: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:91 [backend fallback]\n",
|
|
||||||
"AutogradMTIA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:79 [backend fallback]\n",
|
|
||||||
"AutogradMAIA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:83 [backend fallback]\n",
|
|
||||||
"AutogradMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:99 [backend fallback]\n",
|
|
||||||
"Tracer: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\torch\\csrc\\autograd\\TraceTypeManual.cpp:294 [backend fallback]\n",
|
|
||||||
"AutocastCPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:322 [backend fallback]\n",
|
|
||||||
"AutocastMTIA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:466 [backend fallback]\n",
|
|
||||||
"AutocastMAIA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:504 [backend fallback]\n",
|
|
||||||
"AutocastXPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:542 [backend fallback]\n",
|
|
||||||
"AutocastMPS: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:209 [backend fallback]\n",
|
|
||||||
"AutocastCUDA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:165 [backend fallback]\n",
|
|
||||||
"FuncTorchBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:731 [backend fallback]\n",
|
|
||||||
"BatchedNestedTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:758 [backend fallback]\n",
|
|
||||||
"FuncTorchVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:27 [backend fallback]\n",
|
|
||||||
"Batched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\n",
|
|
||||||
"VmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\n",
|
|
||||||
"FuncTorchGradWrapper: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:210 [backend fallback]\n",
|
|
||||||
"PythonTLSSnapshot: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:202 [backend fallback]\n",
|
|
||||||
"FuncTorchDynamicLayerFrontMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:475 [backend fallback]\n",
|
|
||||||
"PreDispatch: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:206 [backend fallback]\n",
|
|
||||||
"PythonDispatcher: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:198 [backend fallback]\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"Return Code: 1\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"import subprocess\n",
|
|
||||||
"\n",
|
|
||||||
"MODEL_FOLDER = r\"D:\\users\\henrisel\\stable-fast-3d\"\n",
|
|
||||||
"PROJECT_FOLDER = r\"D:\\users\\henrisel\\DeltaVR3DModelGeneration\\3d-generation-pipeline\"\n",
|
|
||||||
"\n",
|
|
||||||
"# Path to the Python interpreter in the other virtual environment\n",
|
|
||||||
"venv_python = MODEL_FOLDER + r\"\\.venv\\Scripts\\python.exe\"\n",
|
|
||||||
"\n",
|
|
||||||
"# Path to the .py file you want to run\n",
|
|
||||||
"script_path = MODEL_FOLDER + r\"\\run.py\"\n",
|
|
||||||
"\n",
|
|
||||||
"# Optional: arguments to pass to the script\n",
|
|
||||||
"args = [MODEL_FOLDER + r\"\\demo_files\\examples\\chair1.png\", \"--output-dir\", PROJECT_FOLDER + r\"\\images\"]\n",
|
|
||||||
"\n",
|
|
||||||
"# Build the command\n",
|
|
||||||
"command = [venv_python, script_path] + args\n",
|
|
||||||
"\n",
|
|
||||||
"try:\n",
|
|
||||||
" # Run the subprocess\n",
|
|
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" 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"
|
|
||||||
]
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},
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{
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}
|
|
||||||
@ -1,7 +0,0 @@
|
|||||||
#torch==2.8.0+cu129 https://pytorch.org/get-started/previous-versions/
|
|
||||||
transformers==4.57.0
|
|
||||||
git+https://github.com/huggingface/diffusers.git
|
|
||||||
accelerate==1.10.1
|
|
||||||
huggingface_hub[hf_xet]==1.1.10
|
|
||||||
sentencepiece==0.2.1
|
|
||||||
protobuf==6.32.1
|
|
||||||
@ -1,62 +0,0 @@
|
|||||||
import argparse
|
|
||||||
import asyncio
|
|
||||||
import logging
|
|
||||||
import time
|
|
||||||
|
|
||||||
from pathlib import Path
|
|
||||||
from datetime import datetime
|
|
||||||
from dotenv import load_dotenv
|
|
||||||
|
|
||||||
from generate_image_local import text_to_image_invoke_ai
|
|
||||||
from generate_model_local import image_to_3d_api
|
|
||||||
|
|
||||||
load_dotenv()
|
|
||||||
|
|
||||||
|
|
||||||
def get_timestamp():
|
|
||||||
return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
|
|
||||||
|
|
||||||
def setup_logger(base_folder, timestamp):
|
|
||||||
log_dir = base_folder / Path("logs")
|
|
||||||
log_dir.mkdir(parents=True, exist_ok=True)
|
|
||||||
logging.basicConfig(
|
|
||||||
filename=log_dir / f"{timestamp}.log",
|
|
||||||
level=logging.INFO,
|
|
||||||
force=True
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
async def main():
|
|
||||||
parser = argparse.ArgumentParser(description="Text to 3D model pipeline")
|
|
||||||
parser.add_argument("--prompt", type=str, required=True, help="User text prompt")
|
|
||||||
args = parser.parse_args()
|
|
||||||
|
|
||||||
input_prompt = args.prompt
|
|
||||||
print(f"Input prompt: {input_prompt}")
|
|
||||||
image_generation_prompt = input_prompt + ", single object, fully visible, plain neutral background, high-resolution, sharp details, soft studio lighting, front and side visible, realistic style, true-to-scale"
|
|
||||||
|
|
||||||
pipeline_folder = Path(__file__).resolve().parent
|
|
||||||
timestamp = get_timestamp()
|
|
||||||
setup_logger(pipeline_folder, timestamp)
|
|
||||||
time_checkpoint = time.time()
|
|
||||||
|
|
||||||
image_path = pipeline_folder / "images" / f"{timestamp}.jpg"
|
|
||||||
# TODO: use Invoke AI or Cloudflare, depending on env var
|
|
||||||
#text_to_image_cloudflare(image_generation_prompt, image_path)
|
|
||||||
await text_to_image_invoke_ai(image_generation_prompt, image_path)
|
|
||||||
|
|
||||||
image_generation_time = time.time() - time_checkpoint
|
|
||||||
time_checkpoint = time.time()
|
|
||||||
logging.info(f"Image generation time: {round(image_generation_time, 1)} s")
|
|
||||||
print(f"Generated image file: {image_path}")
|
|
||||||
|
|
||||||
model_path = pipeline_folder / "models" / timestamp
|
|
||||||
model_file = image_to_3d_api(image_path, model_path)
|
|
||||||
|
|
||||||
model_generation_time = time.time() - time_checkpoint
|
|
||||||
logging.info(f"Model generation time: {round(model_generation_time, 1)} s")
|
|
||||||
print(f"Generated 3D model file: {model_file}")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
asyncio.run(main())
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m_Scale: {x: 1, y: 1}
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|
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- _EnvironmentReflections: 1
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- _GlossMapScale: 0
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|
||||||
- _Glossiness: 0
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- _GlossyReflections: 0
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- _OcclusionStrength: 1
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|
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- _Parallax: 0.005
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|
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- _QueueOffset: 0
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- _ReceiveShadows: 1
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|
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- _WorkflowMode: 1
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m_Colors:
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- _BaseColor: {r: 1, g: 0, b: 0, a: 1}
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- _Color: {r: 1, g: 0, b: 0, a: 1}
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m_BuildTextureStacks: []
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fileFormatVersion: 2
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externalObjects: {}
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assetBundleVariant:
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BIN
Assets/_PROJECT/Scenes/DeltaBuilding_base.unity
(Stored with Git LFS)
BIN
Assets/_PROJECT/Scenes/DeltaBuilding_base.unity
(Stored with Git LFS)
Binary file not shown.
@ -1,8 +0,0 @@
|
|||||||
fileFormatVersion: 2
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guid: 0ec3982ba49c4b84ea95332cb090e115
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folderAsset: yes
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externalObjects: {}
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userData:
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assetBundleName:
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assetBundleVariant:
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|
||||||
@ -1,144 +0,0 @@
|
|||||||
using UnityEngine;
|
|
||||||
using System.Diagnostics;
|
|
||||||
using System.Threading.Tasks;
|
|
||||||
using GLTFast;
|
|
||||||
|
|
||||||
public class ModelGenerationPipelineStarter : MonoBehaviour
|
|
||||||
{
|
|
||||||
public Material activeMaterial;
|
|
||||||
public Material inactiveMaterial;
|
|
||||||
public Transform modelSpawnPoint;
|
|
||||||
|
|
||||||
private MeshRenderer meshRenderer;
|
|
||||||
|
|
||||||
public string inputPrompt;
|
|
||||||
|
|
||||||
// Start is called before the first frame update
|
|
||||||
void Start()
|
|
||||||
{
|
|
||||||
meshRenderer = GetComponent<MeshRenderer>();
|
|
||||||
}
|
|
||||||
|
|
||||||
// Update is called once per frame
|
|
||||||
void Update()
|
|
||||||
{
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
void OnTriggerEnter(Collider other)
|
|
||||||
{
|
|
||||||
KbmController controller = other.GetComponent<KbmController>();
|
|
||||||
if (controller != null)
|
|
||||||
{
|
|
||||||
meshRenderer.material = activeMaterial;
|
|
||||||
|
|
||||||
StartModeGenerationPipeline();
|
|
||||||
//LoadModel("D:\\henrisel\\DeltaVR3DModelGeneration\\3d-generation-pipeline\\models\\2025-11-17-16-13-33\\mesh.glb");
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
private void OnTriggerExit(Collider other)
|
|
||||||
{
|
|
||||||
KbmController controller = other.GetComponent<KbmController>();
|
|
||||||
if (controller != null)
|
|
||||||
{
|
|
||||||
meshRenderer.material = inactiveMaterial;
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
private async Task<string> GenerateModelAsync()
|
|
||||||
{
|
|
||||||
return await Task.Run(() =>
|
|
||||||
{
|
|
||||||
|
|
||||||
|
|
||||||
// Path to your virtual environment's python.exe
|
|
||||||
string pythonExe = @"D:\users\henrisel\DeltaVR3DModelGeneration\3d-generation-pipeline\.venv\Scripts\python.exe";
|
|
||||||
|
|
||||||
// Path to your Python script
|
|
||||||
string scriptPath = @"D:\users\henrisel\DeltaVR3DModelGeneration\3d-generation-pipeline\start_pipeline.py";
|
|
||||||
|
|
||||||
// Arguments to pass to the script
|
|
||||||
string arguments = $"{scriptPath} --prompt \"{inputPrompt}\"";
|
|
||||||
|
|
||||||
ProcessStartInfo psi = new ProcessStartInfo
|
|
||||||
{
|
|
||||||
FileName = pythonExe,
|
|
||||||
Arguments = arguments,
|
|
||||||
UseShellExecute = false,
|
|
||||||
RedirectStandardOutput = true,
|
|
||||||
RedirectStandardError = true,
|
|
||||||
CreateNoWindow = true
|
|
||||||
};
|
|
||||||
|
|
||||||
using (Process process = new Process())
|
|
||||||
{
|
|
||||||
process.StartInfo = psi;
|
|
||||||
process.OutputDataReceived += (sender, e) => UnityEngine.Debug.Log(e.Data);
|
|
||||||
process.ErrorDataReceived += (sender, e) => UnityEngine.Debug.LogError(e.Data);
|
|
||||||
|
|
||||||
process.Start();
|
|
||||||
|
|
||||||
string output = process.StandardOutput.ReadToEnd();
|
|
||||||
string error = process.StandardError.ReadToEnd();
|
|
||||||
|
|
||||||
process.WaitForExit();
|
|
||||||
|
|
||||||
|
|
||||||
// Extract model path from output
|
|
||||||
foreach (string line in output.Split('\n'))
|
|
||||||
{
|
|
||||||
if (line.StartsWith("Generated 3D model file: "))
|
|
||||||
{
|
|
||||||
return line.Replace("Generated 3D model file: ", "").Trim();
|
|
||||||
}
|
|
||||||
}
|
|
||||||
|
|
||||||
return null;
|
|
||||||
}
|
|
||||||
});
|
|
||||||
}
|
|
||||||
|
|
||||||
private async void StartModeGenerationPipeline()
|
|
||||||
{
|
|
||||||
string modelPath = await GenerateModelAsync();
|
|
||||||
|
|
||||||
if (!string.IsNullOrEmpty(modelPath))
|
|
||||||
{
|
|
||||||
UnityEngine.Debug.Log("Got generated model path: " + modelPath);
|
|
||||||
await LoadModel(modelPath);
|
|
||||||
}
|
|
||||||
else
|
|
||||||
{
|
|
||||||
UnityEngine.Debug.LogError("Model path not found in Python output.");
|
|
||||||
}
|
|
||||||
|
|
||||||
}
|
|
||||||
|
|
||||||
private async Task LoadModel(string modelPath)
|
|
||||||
{
|
|
||||||
var gltf = new GltfImport();
|
|
||||||
bool loadSuccess = await gltf.Load(modelPath);
|
|
||||||
UnityEngine.Debug.Log("Load model success: " + loadSuccess);
|
|
||||||
if (loadSuccess)
|
|
||||||
{
|
|
||||||
GameObject spawnedObject = new GameObject("spawned model");
|
|
||||||
spawnedObject.transform.parent = modelSpawnPoint;
|
|
||||||
spawnedObject.transform.position = modelSpawnPoint.position;
|
|
||||||
|
|
||||||
bool spawnSuccess = await gltf.InstantiateMainSceneAsync(spawnedObject.transform);
|
|
||||||
UnityEngine.Debug.Log("Spawn model success: " + spawnSuccess);
|
|
||||||
|
|
||||||
|
|
||||||
Transform spawnedObjectMainTransform = spawnedObject.transform.GetChild(0).transform;
|
|
||||||
GameObject spawnedObjectBody = spawnedObjectMainTransform.GetChild(0).transform.gameObject;
|
|
||||||
MeshCollider collider = spawnedObjectBody.AddComponent<MeshCollider>();
|
|
||||||
collider.convex = true;
|
|
||||||
MeshRenderer renderer = spawnedObjectBody.GetComponent<MeshRenderer>();
|
|
||||||
renderer.material.SetFloat("metallicFactor", 0);
|
|
||||||
|
|
||||||
spawnedObjectMainTransform.gameObject.AddComponent<Rigidbody>();
|
|
||||||
}
|
|
||||||
|
|
||||||
}
|
|
||||||
}
|
|
||||||
@ -1,11 +0,0 @@
|
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fileFormatVersion: 2
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guid: 46e67223dce9b7a4783ed36b8ed65f19
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MonoImporter:
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externalObjects: {}
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serializedVersion: 2
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|
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defaultReferences: []
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executionOrder: 0
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icon: {instanceID: 0}
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userData:
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assetBundleName:
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assetBundleVariant:
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Doc/clips/Bolt-Car-Network-Problem-Clip.gif
(Stored with Git LFS)
Normal file
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Doc/clips/Bolt-Car-Network-Problem-Clip.gif
(Stored with Git LFS)
Normal file
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Doc/clips/Bolt-Car-Network-Results-Clip.gif
(Stored with Git LFS)
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Doc/clips/Bolt-Car-Network-Results-Clip.gif
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Doc/clips/Camera-Collider-Clip.gif
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Doc/clips/Camera-Collider-Clip.gif
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Doc/clips/Elevator-network-problem-Clip.gif
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Doc/clips/Elevator-network-problem-Clip.gif
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Doc/clips/Elevator-network-results-Clip.gif
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Doc/clips/Elevator-network-results-Clip.gif
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Doc/clips/Hand-Collider-Prototype-Clip.gif
(Stored with Git LFS)
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Doc/clips/Hand-Collider-Prototype-Clip.gif
(Stored with Git LFS)
Normal file
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Doc/clips/Player-Hand-Collision-Complete-Clip.gif
(Stored with Git LFS)
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Doc/clips/Player-Hand-Collision-Complete-Clip.gif
(Stored with Git LFS)
Normal file
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BIN
Doc/clips/VR-Elevator-Example-Clip.gif
(Stored with Git LFS)
Normal file
BIN
Doc/clips/VR-Elevator-Example-Clip.gif
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
Doc/designs/Elevator-buttons-inside.png
(Stored with Git LFS)
Normal file
BIN
Doc/designs/Elevator-buttons-inside.png
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
Doc/designs/Model-Collider-Rework-Concept.png
(Stored with Git LFS)
Normal file
BIN
Doc/designs/Model-Collider-Rework-Concept.png
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
Doc/designs/Moder-Collider-Rework-Results.png
(Stored with Git LFS)
Normal file
BIN
Doc/designs/Moder-Collider-Rework-Results.png
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
Doc/designs/Network-Player-mirror.png
(Stored with Git LFS)
Normal file
BIN
Doc/designs/Network-Player-mirror.png
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
Doc/designs/Original-elevator-state-diagram.png
(Stored with Git LFS)
Normal file
BIN
Doc/designs/Original-elevator-state-diagram.png
(Stored with Git LFS)
Normal file
Binary file not shown.
BIN
Doc/designs/Player-Hand-Colliders.png
(Stored with Git LFS)
Normal file
BIN
Doc/designs/Player-Hand-Colliders.png
(Stored with Git LFS)
Normal file
Binary file not shown.
@ -4,7 +4,6 @@
|
|||||||
"com.unity.2d.tilemap": "1.0.0",
|
"com.unity.2d.tilemap": "1.0.0",
|
||||||
"com.unity.ai.navigation": "1.1.1",
|
"com.unity.ai.navigation": "1.1.1",
|
||||||
"com.unity.cinemachine": "2.9.5",
|
"com.unity.cinemachine": "2.9.5",
|
||||||
"com.unity.cloud.gltfast": "6.14.1",
|
|
||||||
"com.unity.collab-proxy": "2.0.1",
|
"com.unity.collab-proxy": "2.0.1",
|
||||||
"com.unity.ext.nunit": "1.0.6",
|
"com.unity.ext.nunit": "1.0.6",
|
||||||
"com.unity.feature.vr": "1.0.0",
|
"com.unity.feature.vr": "1.0.0",
|
||||||
|
|||||||
@ -31,12 +31,11 @@
|
|||||||
"url": "https://packages.unity.com"
|
"url": "https://packages.unity.com"
|
||||||
},
|
},
|
||||||
"com.unity.burst": {
|
"com.unity.burst": {
|
||||||
"version": "1.8.24",
|
"version": "1.8.3",
|
||||||
"depth": 1,
|
"depth": 1,
|
||||||
"source": "registry",
|
"source": "registry",
|
||||||
"dependencies": {
|
"dependencies": {
|
||||||
"com.unity.mathematics": "1.2.1",
|
"com.unity.mathematics": "1.2.1"
|
||||||
"com.unity.modules.jsonserialize": "1.0.0"
|
|
||||||
},
|
},
|
||||||
"url": "https://packages.unity.com"
|
"url": "https://packages.unity.com"
|
||||||
},
|
},
|
||||||
@ -49,19 +48,6 @@
|
|||||||
},
|
},
|
||||||
"url": "https://packages.unity.com"
|
"url": "https://packages.unity.com"
|
||||||
},
|
},
|
||||||
"com.unity.cloud.gltfast": {
|
|
||||||
"version": "6.14.1",
|
|
||||||
"depth": 0,
|
|
||||||
"source": "registry",
|
|
||||||
"dependencies": {
|
|
||||||
"com.unity.burst": "1.8.24",
|
|
||||||
"com.unity.collections": "1.2.4",
|
|
||||||
"com.unity.mathematics": "1.2.6",
|
|
||||||
"com.unity.modules.jsonserialize": "1.0.0",
|
|
||||||
"com.unity.modules.unitywebrequest": "1.0.0"
|
|
||||||
},
|
|
||||||
"url": "https://packages.unity.com"
|
|
||||||
},
|
|
||||||
"com.unity.collab-proxy": {
|
"com.unity.collab-proxy": {
|
||||||
"version": "2.0.1",
|
"version": "2.0.1",
|
||||||
"depth": 0,
|
"depth": 0,
|
||||||
@ -69,16 +55,6 @@
|
|||||||
"dependencies": {},
|
"dependencies": {},
|
||||||
"url": "https://packages.unity.com"
|
"url": "https://packages.unity.com"
|
||||||
},
|
},
|
||||||
"com.unity.collections": {
|
|
||||||
"version": "1.2.4",
|
|
||||||
"depth": 1,
|
|
||||||
"source": "registry",
|
|
||||||
"dependencies": {
|
|
||||||
"com.unity.burst": "1.6.6",
|
|
||||||
"com.unity.test-framework": "1.1.31"
|
|
||||||
},
|
|
||||||
"url": "https://packages.unity.com"
|
|
||||||
},
|
|
||||||
"com.unity.editorcoroutines": {
|
"com.unity.editorcoroutines": {
|
||||||
"version": "1.0.0",
|
"version": "1.0.0",
|
||||||
"depth": 1,
|
"depth": 1,
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user