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4
3d-generation-pipeline/.env.example
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4
3d-generation-pipeline/.env.example
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CLOUDFLARE_ACCOUNT_ID=
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CLOUDFLARE_API_TOKEN=
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PIPELINE_FOLDER=
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MODEL_FOLDER=
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4
3d-generation-pipeline/.gitignore
vendored
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4
3d-generation-pipeline/.gitignore
vendored
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.venv
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.env
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images/
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models/
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2
3d-generation-pipeline/README.md
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2
3d-generation-pipeline/README.md
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### TODO
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* Azure OpenAI API: https://wiki.ut.ee/spaces/AA/pages/218073589/Azure+OpenAI+API+teenus
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28
3d-generation-pipeline/generate_image_local.py
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28
3d-generation-pipeline/generate_image_local.py
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import torch
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from diffusers import StableDiffusionPipeline, StableDiffusion3Pipeline
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import time
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start_timestamp = time.time()
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#model = "stabilityai/stable-diffusion-3.5-medium" # generation time: 13 min
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model = "stabilityai/stable-diffusion-3-medium-diffusers" # generation time: 10 min
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#model = "stabilityai/stable-diffusion-2" # generation time: 4 sec
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pipe = StableDiffusion3Pipeline.from_pretrained(model, torch_dtype=torch.float16)
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#pipe = StableDiffusionPipeline.from_pretrained(model, torch_dtype=torch.float16)
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pipe = pipe.to("cuda")
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model_loaded_timestamp = time.time()
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model_load_time = model_loaded_timestamp - start_timestamp
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print(f"model load time: {round(model_load_time)} seconds")
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prompt = "A majestic broadsword with a golden pommel, no background"
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image = pipe(
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prompt,
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guidance_scale=3.0,
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).images[0]
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image_name = "image7.png"
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image.save(f"images/{image_name}")
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generation_time = time.time() - model_loaded_timestamp
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print(f"image generation time: {round(generation_time)} seconds")
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152
3d-generation-pipeline/notebooks/cloudflare_API_test.ipynb
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152
3d-generation-pipeline/notebooks/cloudflare_API_test.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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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": [
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"import os\n",
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"import base64\n",
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"import requests\n",
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"from dotenv import load_dotenv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b3107275",
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"metadata": {},
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"outputs": [],
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"source": [
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"load_dotenv()\n",
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"\n",
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"ACCOUNT_ID = os.environ[\"CLOUDFLARE_ACCOUNT_ID\"]\n",
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"API_TOKEN = os.environ[\"CLOUDFLARE_API_TOKEN\"]"
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]
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},
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{
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"cell_type": "markdown",
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"id": "999adf95",
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"metadata": {},
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"source": [
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"## Text to image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "40b35163",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Saved: output.jpg (263282 bytes)\n",
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"Saved: image9.jpg (263282 bytes)\n"
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]
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}
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],
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"source": [
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"MODEL = \"@cf/black-forest-labs/flux-1-schnell\"\n",
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"URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
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"\n",
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"payload = {\n",
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" \"prompt\": \"a slightly curved broadsword with a fancy golden crossguard\",\n",
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"}\n",
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"\n",
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"headers = {\n",
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" \"Authorization\": f\"Bearer {API_TOKEN}\",\n",
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" \"Content-Type\": \"application/json\",\n",
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"}\n",
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"\n",
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"resp = requests.post(URL, json=payload, headers=headers, timeout=60)\n",
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"resp.raise_for_status()\n",
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"\n",
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"data = resp.json()\n",
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"b64 = data[\"result\"][\"image\"]\n",
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"if not b64:\n",
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" raise RuntimeError(f\"Unexpected response structure: {data}\")\n",
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"\n",
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"img_bytes = base64.b64decode(b64)\n",
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"\n",
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"out_path = \"output.jpg\"\n",
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"with open(out_path, \"wb\") as f:\n",
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" f.write(img_bytes)\n",
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"\n",
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"print(f\"Saved: {out_path} ({len(img_bytes)} bytes)\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "14a874c4",
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"metadata": {},
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"source": [
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"## Text prompt refinement"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "485f6f46",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\"dark wooden battleaxe with bronze blade\"\n"
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]
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}
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],
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"source": [
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"MODEL = \"@cf/meta/llama-3.2-3b-instruct\"\n",
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"URL = f\"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/{MODEL}\"\n",
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"\n",
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"instructions = \"\"\"\n",
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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.\n",
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"Example:\n",
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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.'\n",
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"You: 'a slightly curved sword with bright orange crossguard'\n",
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"\"\"\"\n",
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"prompt = \"Umm, alright, can you please give me an epic battleaxe? It should have a dark wooden shaft and bronze blade.\"\n",
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"\n",
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"response = requests.post(URL,\n",
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" headers={\"Authorization\": f\"Bearer {API_TOKEN}\"},\n",
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" json={\n",
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" \"messages\": [\n",
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" {\"role\": \"system\", \"content\": instructions},\n",
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" {\"role\": \"user\", \"content\": prompt}\n",
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" ]\n",
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" }\n",
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")\n",
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"data = response.json()\n",
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"result_text = data[\"result\"][\"response\"]\n",
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"print(result_text)"
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]
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}
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||||
],
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"metadata": {
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"kernelspec": {
|
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
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"name": "ipython",
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"version": 3
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},
|
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"file_extension": ".py",
|
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"mimetype": "text/x-python",
|
||||
"name": "python",
|
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"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
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||||
"nbformat_minor": 5
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||||
}
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105
3d-generation-pipeline/notebooks/local_model_test.ipynb
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105
3d-generation-pipeline/notebooks/local_model_test.ipynb
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{
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||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
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||||
"id": "2c0da293",
|
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"metadata": {},
|
||||
"outputs": [
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{
|
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"name": "stderr",
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"output_type": "stream",
|
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"text": [
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"c:\\Users\\henrisel.DOMENIS\\DeltaVR3DModelGeneration\\3d-generation-pipeline\\.venv\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
|
||||
]
|
||||
}
|
||||
],
|
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"source": [
|
||||
"import torch\n",
|
||||
"from diffusers import FluxPipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "51879ff1",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
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||||
"output_type": "stream",
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||||
"text": [
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"Fetching 23 files: 0%| | 0/23 [00:00<?, ?it/s]"
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||||
]
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||||
}
|
||||
],
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"source": [
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||||
"model_name = \"black-forest-labs/FLUX.1-schnell\"\n",
|
||||
"\n",
|
||||
"pipe = FluxPipeline.from_pretrained(model_name, torch_dtype=torch.bfloat16)\n",
|
||||
"#pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "50a38bf4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"prompt = \"slightly curved sword, one side blue and other side green\"\n",
|
||||
"pipe_result = pipe(\n",
|
||||
" prompt,\n",
|
||||
" guidance_scale=0.0,\n",
|
||||
" num_inference_steps=4,\n",
|
||||
" max_sequence_length=256,\n",
|
||||
" generator=torch.Generator(\"gpu\")\n",
|
||||
")\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b692177a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pipe_result"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d55eb3ce",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"image = pipe_result[\"images\"][0]\n",
|
||||
"image.save(\"flux-schnell.png\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
165
3d-generation-pipeline/notebooks/subprocess_test.ipynb
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165
3d-generation-pipeline/notebooks/subprocess_test.ipynb
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@ -0,0 +1,165 @@
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||||
{
|
||||
"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",
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||||
" 0%| | 0/1 [00:00<?, ?it/s]\n",
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||||
" 0%| | 0/1 [00:00<?, ?it/s]\n",
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\run.py\", line 122, in <module>\n",
|
||||
" mesh, glob_dict = model.run_image(\n",
|
||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\sf3d\\system.py\", line 286, in run_image\n",
|
||||
" meshes, global_dict = self.generate_mesh(\n",
|
||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\sf3d\\system.py\", line 369, in generate_mesh\n",
|
||||
" rast = self.baker.rasterize(\n",
|
||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\texture_baker\\baker.py\", line 28, in rasterize\n",
|
||||
" return torch.ops.texture_baker_cpp.rasterize(\n",
|
||||
" File \"D:\\users\\henrisel\\stable-fast-3d\\.venv\\lib\\site-packages\\torch\\_ops.py\", line 1243, in __call__\n",
|
||||
" return self._op(*args, **kwargs)\n",
|
||||
"NotImplementedError: Could not run 'texture_baker_cpp::rasterize' with arguments from the 'CUDA' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'texture_baker_cpp::rasterize' is only available for these backends: [CPU, Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMTIA, AutogradMAIA, AutogradMeta, Tracer, AutocastCPU, AutocastMTIA, AutocastMAIA, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n",
|
||||
"\n",
|
||||
"CPU: registered at texture_baker\\csrc\\baker.cpp:543 [kernel]\n",
|
||||
"Meta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\MetaFallbackKernel.cpp:23 [backend fallback]\n",
|
||||
"BackendSelect: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\n",
|
||||
"Python: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:194 [backend fallback]\n",
|
||||
"FuncTorchDynamicLayerBackMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:479 [backend fallback]\n",
|
||||
"Functionalize: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:375 [backend fallback]\n",
|
||||
"Named: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\n",
|
||||
"Conjugate: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\n",
|
||||
"Negative: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\native\\NegateFallback.cpp:18 [backend fallback]\n",
|
||||
"ZeroTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\n",
|
||||
"ADInplaceOrView: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:104 [backend fallback]\n",
|
||||
"AutogradOther: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:63 [backend fallback]\n",
|
||||
"AutogradCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:67 [backend fallback]\n",
|
||||
"AutogradCUDA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:75 [backend fallback]\n",
|
||||
"AutogradXLA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:87 [backend fallback]\n",
|
||||
"AutogradMPS: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:95 [backend fallback]\n",
|
||||
"AutogradXPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:71 [backend fallback]\n",
|
||||
"AutogradHPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:108 [backend fallback]\n",
|
||||
"AutogradLazy: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:91 [backend fallback]\n",
|
||||
"AutogradMTIA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:79 [backend fallback]\n",
|
||||
"AutogradMAIA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:83 [backend fallback]\n",
|
||||
"AutogradMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:99 [backend fallback]\n",
|
||||
"Tracer: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\torch\\csrc\\autograd\\TraceTypeManual.cpp:294 [backend fallback]\n",
|
||||
"AutocastCPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:322 [backend fallback]\n",
|
||||
"AutocastMTIA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:466 [backend fallback]\n",
|
||||
"AutocastMAIA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:504 [backend fallback]\n",
|
||||
"AutocastXPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:542 [backend fallback]\n",
|
||||
"AutocastMPS: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:209 [backend fallback]\n",
|
||||
"AutocastCUDA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:165 [backend fallback]\n",
|
||||
"FuncTorchBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:731 [backend fallback]\n",
|
||||
"BatchedNestedTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:758 [backend fallback]\n",
|
||||
"FuncTorchVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:27 [backend fallback]\n",
|
||||
"Batched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\n",
|
||||
"VmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\n",
|
||||
"FuncTorchGradWrapper: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:210 [backend fallback]\n",
|
||||
"PythonTLSSnapshot: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:202 [backend fallback]\n",
|
||||
"FuncTorchDynamicLayerFrontMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:475 [backend fallback]\n",
|
||||
"PreDispatch: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:206 [backend fallback]\n",
|
||||
"PythonDispatcher: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:198 [backend fallback]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"Return Code: 1\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import subprocess\n",
|
||||
"\n",
|
||||
"MODEL_FOLDER = r\"D:\\users\\henrisel\\stable-fast-3d\"\n",
|
||||
"PROJECT_FOLDER = r\"D:\\users\\henrisel\\DeltaVR3DModelGeneration\\3d-generation-pipeline\"\n",
|
||||
"\n",
|
||||
"# Path to the Python interpreter in the other virtual environment\n",
|
||||
"venv_python = MODEL_FOLDER + r\"\\.venv\\Scripts\\python.exe\"\n",
|
||||
"\n",
|
||||
"# Path to the .py file you want to run\n",
|
||||
"script_path = MODEL_FOLDER + r\"\\run.py\"\n",
|
||||
"\n",
|
||||
"# Optional: arguments to pass to the script\n",
|
||||
"args = [MODEL_FOLDER + r\"\\demo_files\\examples\\chair1.png\", \"--output-dir\", PROJECT_FOLDER + r\"\\images\"]\n",
|
||||
"\n",
|
||||
"# Build the command\n",
|
||||
"command = [venv_python, script_path] + args\n",
|
||||
"\n",
|
||||
"try:\n",
|
||||
" # Run the subprocess\n",
|
||||
" result = subprocess.run(command, capture_output=True, text=True)\n",
|
||||
"\n",
|
||||
" # Print output and errors\n",
|
||||
" print(\"STDOUT:\\n\", result.stdout)\n",
|
||||
" print(\"STDERR:\\n\", result.stderr)\n",
|
||||
" print(\"Return Code:\", result.returncode)\n",
|
||||
"\n",
|
||||
"except Exception as e:\n",
|
||||
" print(f\"Error occurred: {e}\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ee480ba6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
7
3d-generation-pipeline/requirements.txt
Normal file
7
3d-generation-pipeline/requirements.txt
Normal file
@ -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
|
||||
111
3d-generation-pipeline/start_pipeline.py
Normal file
111
3d-generation-pipeline/start_pipeline.py
Normal file
@ -0,0 +1,111 @@
|
||||
import os
|
||||
import base64
|
||||
import requests
|
||||
import argparse
|
||||
import subprocess
|
||||
from pathlib import Path
|
||||
from datetime import datetime
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
ACCOUNT_ID = os.environ["CLOUDFLARE_ACCOUNT_ID"]
|
||||
API_TOKEN = os.environ["CLOUDFLARE_API_TOKEN"]
|
||||
PIPELINE_FOLDER = os.environ["PIPELINE_FOLDER"]
|
||||
MODEL_FOLDER = os.environ["MODEL_FOLDER"]
|
||||
|
||||
|
||||
def get_timestamp():
|
||||
return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
|
||||
|
||||
|
||||
def text_to_image(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)
|
||||
|
||||
|
||||
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"]
|
||||
|
||||
def image_to_3d(image_path, output_path):
|
||||
venv_python = MODEL_FOLDER + r"\.venv\Scripts\python.exe"
|
||||
script_path = MODEL_FOLDER + r"\run.py"
|
||||
|
||||
args = [image_path, "--output-dir", output_path]
|
||||
command = [venv_python, script_path] + args
|
||||
|
||||
try:
|
||||
# Run the subprocess
|
||||
result = subprocess.run(command, capture_output=True, text=True)
|
||||
|
||||
# Print output and errors
|
||||
print("STDOUT:\n", result.stdout)
|
||||
print("STDERR:\n", result.stderr)
|
||||
print("Return Code:", result.returncode)
|
||||
|
||||
except Exception as e:
|
||||
print(f"Error occurred: {e}")
|
||||
|
||||
def 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()
|
||||
|
||||
user_prompt = args.prompt
|
||||
print(f"User prompt: {user_prompt}")
|
||||
refined_prompt = refine_text_prompt(user_prompt)
|
||||
print(f"Refined prompt: {refined_prompt}")
|
||||
timestamp = get_timestamp()
|
||||
pipeline_folder = Path(PIPELINE_FOLDER)
|
||||
image_path = pipeline_folder / "images" / f"{timestamp}.jpg"
|
||||
text_to_image(refined_prompt, image_path)
|
||||
print(f"Generated image file: {image_path}")
|
||||
model_path = pipeline_folder / "models" / timestamp
|
||||
image_to_3d(image_path, model_path)
|
||||
print(f"Generated 3D model file: {model_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
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(Stored with Git LFS)
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Binary file not shown.
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@ -0,0 +1,86 @@
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using System.Collections;
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using System.Collections.Generic;
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using UnityEngine;
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using System.Diagnostics;
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using System.Threading.Tasks;
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public class ModelGenerationPipelineStarter : MonoBehaviour
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{
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public Material activeMaterial;
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public Material inactiveMaterial;
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private MeshRenderer meshRenderer;
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void Start()
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{
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meshRenderer = GetComponent<MeshRenderer>();
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}
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void Update()
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{
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}
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{
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if (controller != null)
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{
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StartModeGenerationPipeline();
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}
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}
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private void OnTriggerExit(Collider other)
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{
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KbmController controller = other.GetComponent<KbmController>();
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if (controller != null)
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}
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private async void StartModeGenerationPipeline()
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{
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await Task.Run(() =>
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{
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string inputPrompt = "Uhm I want I think an epic broadsword with a fancy golden pommel";
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// Path to your virtual environment's python.exe
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string pythonExe = @"D:\users\henrisel\DeltaVR3DModelGeneration\3d-generation-pipeline\.venv\Scripts\python.exe";
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// Path to your Python script
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string scriptPath = @"D:\users\henrisel\DeltaVR3DModelGeneration\3d-generation-pipeline\start_pipeline.py";
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// Arguments to pass to the script
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string arguments = $"{scriptPath} --prompt \"{inputPrompt}\"";
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ProcessStartInfo psi = new ProcessStartInfo
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{
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FileName = pythonExe,
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Arguments = arguments,
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UseShellExecute = false,
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RedirectStandardOutput = true,
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RedirectStandardError = true,
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CreateNoWindow = true
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};
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using (Process process = new Process())
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{
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process.StartInfo = psi;
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process.OutputDataReceived += (sender, e) => UnityEngine.Debug.Log(e.Data);
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process.ErrorDataReceived += (sender, e) => UnityEngine.Debug.LogError(e.Data);
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process.Start();
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process.BeginOutputReadLine();
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process.BeginErrorReadLine();
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process.WaitForExit();
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}
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});
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UnityEngine.Debug.Log("Python script finished!");
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}
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}
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Loading…
x
Reference in New Issue
Block a user