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NilayYadavNilayYadav
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Added InstantID example (#85)
* added InstantID example * use volume
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‎image_generation/instant_id/.beamignore‎

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# Generated by Beam SDK
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.beamignore
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pyproject.toml
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.git
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.idea
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.python-version
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.vscode
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.venv
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venv
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__pycache__
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.DS_Store
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.config
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drive/MyDrive
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.coverage
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.pytest_cache
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.ipynb
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.ruff_cache
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.dockerignore
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.ipynb_checkpoints
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.env.local
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.envrc
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**/__pycache__/
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**/.pytest_cache/
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**/node_modules/
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**/.venv/
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*.pyc
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.next/
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.circleci

‎image_generation/instant_id/README.md‎

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# InstantID
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## Overview
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This repository contains the implementation of InstantID as a Beam endpoint.
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In your shell, start by uploading the model:
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`python upload.py`
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After that, run `beam deploy app.py:generate_image` to deploy the model
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To use this endpoint with curl:
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```bash
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curl -X POST 'https://your-beam-endpoint.app.beam.cloud' \
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-H 'Connection: keep-alive' \
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-H 'Content-Type: application/json' \
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-H 'Authorization: YOUR_BEAM_API_KEY' \
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-d '{ "image": "https://hips.hearstapps.com/hmg-prod/images/screen-shot-2024-05-22-at-3-00-35-pm-664e410d9114a.png",
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"prompt": "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette"
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}'
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```
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Response:
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```json
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{
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"output_url": "https://app.beam.cloud/output/id/f43f5411-d96b-48b1-bab6-28b2defb9b36"
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}
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```
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## Input Parameters
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| Parameter | Description | Default Value |
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| --------- | ----------------------------------- | -------------------- |
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| `image` | Input reference image | URL to sample image |
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| `prompt` | Text prompt describing output style | "film noir style..." |

‎image_generation/instant_id/app.py‎

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from beam import endpoint, Image, Output, Volume
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VOLUME_PATH = "/instant-id"
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image = (
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Image(python_version="python3.11")
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.add_commands(
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[
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"apt-get update",
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"apt-get install -y libgl1-mesa-glx libglib2.0-0 build-essential g++ python3-dev wget unzip",
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]
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)
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.add_python_packages(
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[
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"opencv-python==4.9.0.80",
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"transformers==4.37.0",
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"accelerate==0.26.1",
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"insightface==0.7.3",
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"diffusers==0.25.1",
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"onnxruntime==1.16.3",
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"omegaconf==2.3.0",
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"gradio==3.50.2",
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"peft==0.8.2",
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"controlnet-aux==0.0.7",
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"huggingface_hub==0.25.2",
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"gdown",
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]
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)
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.add_commands(
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[
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"git clone https://github.com/zsxkib/InstantID.git /instantid",
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]
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)
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)
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@endpoint(
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name="instant-id",
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cpu=12,
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memory="32Gi",
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gpu="A10G",
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image=image,
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volumes=[Volume(name="instant-id", mount_path=VOLUME_PATH)],
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)
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def generate_image(
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image: str = "https://live-production.wcms.abc-cdn.net.au/a241657894f4d79f0c3ea0705f0f1f07?impolicy=wcms_crop_resize&cropH=1989&cropW=2992&xPos=8&yPos=8&width=862&height=575",
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prompt: str = "film noir style, ink sketch|vector, male man, highly detailed, sharp focus, ultra sharpness, monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic",
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):
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import sys
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sys.path.append("/instantid")
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from diffusers.utils import load_image
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from diffusers.models import ControlNetModel
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import cv2
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import torch
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import numpy as np
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from insightface.app import FaceAnalysis
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import requests
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import uuid
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from pipeline_stable_diffusion_xl_instantid import (
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StableDiffusionXLInstantIDPipeline,
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draw_kps,
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)
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app = FaceAnalysis(
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name="antelopev2",
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root=VOLUME_PATH,
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providers=["CUDAExecutionProvider", "CPUExecutionProvider"],
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)
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app.prepare(ctx_id=0, det_size=(640, 640))
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face_adapter = f"{VOLUME_PATH}/checkpoints/ip-adapter.bin"
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controlnet_path = f"{VOLUME_PATH}/checkpoints/ControlNetModel/ControlNetModel"
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base_model = f"{VOLUME_PATH}/weights"
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controlnet = ControlNetModel.from_pretrained(
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controlnet_path, torch_dtype=torch.float16
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)
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pipe = StableDiffusionXLInstantIDPipeline.from_pretrained(
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base_model, controlnet=controlnet, torch_dtype=torch.float16
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)
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pipe.cuda()
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pipe.load_ip_adapter_instantid(face_adapter)
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response = requests.get(image)
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response.raise_for_status()
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img_path = "/tmp/" + str(uuid.uuid4()) + ".png"
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with open(img_path, "wb") as f:
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f.write(response.content)
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face_image = load_image(img_path)
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face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
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face_info = sorted(
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face_info,
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key=lambda x: (x["bbox"][2] - x["bbox"][0]) * (x["bbox"][3] - x["bbox"][1]),
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)[-1]
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face_emb = face_info["embedding"]
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face_kps = draw_kps(face_image, face_info["kps"])
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image = pipe(
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prompt,
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image_embeds=face_emb,
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image=face_kps,
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controlnet_conditioning_scale=0.8,
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ip_adapter_scale=0.8,
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).images[0]
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output_path = "/tmp/" + str(uuid.uuid4()) + ".png"
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image.save(output_path)
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output = Output(path=output_path)
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output.save()
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output_url = output.public_url()
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return {"output_url": output_url}

‎image_generation/instant_id/upload.py‎

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from beam import function, Volume, Image
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VOLUME_PATH = "./instant-id"
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@function(
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image=Image()
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.add_python_packages(
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["huggingface_hub", "datasets", "huggingface_hub[hf-transfer]", "gdown"]
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)
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.add_commands(
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[
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"apt-get update",
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"apt-get install -y unzip",
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]
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)
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.with_envs("HF_HUB_ENABLE_HF_TRANSFER=1"),
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memory="32Gi",
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cpu=4,
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secrets=["HF_TOKEN"],
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volumes=[Volume(name="instant-id", mount_path=VOLUME_PATH)],
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)
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def upload():
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from huggingface_hub import hf_hub_download, snapshot_download
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import gdown
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import os
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snapshot_download(
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repo_id="wangqixun/YamerMIX_v8",
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local_dir=f"{VOLUME_PATH}/weights",
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)
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controlnet_dir = f"{VOLUME_PATH}/checkpoints/ControlNetModel"
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os.makedirs(controlnet_dir, exist_ok=True)
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hf_hub_download(
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repo_id="InstantX/InstantID",
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filename="ControlNetModel/config.json",
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local_dir=controlnet_dir,
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)
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hf_hub_download(
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repo_id="InstantX/InstantID",
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filename="ControlNetModel/diffusion_pytorch_model.safetensors",
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local_dir=controlnet_dir,
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)
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hf_hub_download(
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repo_id="InstantX/InstantID",
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filename="ip-adapter.bin",
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local_dir=f"{VOLUME_PATH}/checkpoints",
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)
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os.makedirs(f"{VOLUME_PATH}/models", exist_ok=True)
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gdown.download(
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url="https://drive.google.com/uc?id=18wEUfMNohBJ4K3Ly5wpTejPfDzp-8fI8",
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output=f"{VOLUME_PATH}/models/antelopev2.zip",
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quiet=False,
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fuzzy=True,
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)
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os.system(f"unzip {VOLUME_PATH}/models/antelopev2.zip -d {VOLUME_PATH}/models/")
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if __name__ == "__main__":
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upload()

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