AI-Master-Book
  • about AI-Master-Book
  • AI Master Book
    • 이상치 탐지 with Python
    • 베이지안 뉴럴네트워크 (BNN) with Python
    • 그래프 뉴럴네트워크 (GNN) with Python
    • 데이터 마케팅 분석 with Python
  • LLM MASTER BOOK
    • OpenAI API 쿡북 with Python
    • 기초부터 심화까지 RAG 쿡북 with Python
  • LLMs
    • OpenAI API
      • 1️⃣ChatCompletion
      • 2️⃣DALL-E
      • 3️⃣Text to Speech
      • 4️⃣Video to Transcripts
      • 5️⃣Assistants API
      • 6️⃣Prompt Engineering
      • 7️⃣OpenAI New GPT-4o
    • LangChain
      • LangChain Basic
        • 1️⃣Basic Modules
        • 2️⃣Model I/O
        • 3️⃣Prompts
        • 4️⃣Chains
        • 5️⃣Agents
        • 6️⃣Tools
        • 7️⃣Memory
      • LangChain Intermediate
        • 1️⃣OpenAI LLM
        • 2️⃣Prompt Template
        • 3️⃣Retrieval
        • 4️⃣RAG ChatBot
        • 5️⃣RAG with Gemini
        • 6️⃣New Huggingface-LangChain
        • 7️⃣Huggingface Hub
        • 8️⃣SQL Agent & Chain
        • 9️⃣Expression Language(LCEL)
        • 🔟Llama3-8B with LangChain
      • LangChain Advanced
        • 1️⃣LLM Evaluation
        • 2️⃣RAG Evaluation with RAGAS
        • 3️⃣LangChain with RAGAS
        • 4️⃣RAG Paradigms
        • 5️⃣LangChain: Advance Techniques
        • 6️⃣LangChain with NeMo-Guardrails
        • 7️⃣LangChain vs. LlamaIndex
        • 8️⃣LangChain LCEL vs. LangGraph
    • LlamaIndex
      • LlamaIndex Basic
        • 1️⃣Introduction
        • 2️⃣Customization
        • 3️⃣Data Connectors
        • 4️⃣Documents & Nodes
        • 5️⃣Naive RAG
        • 6️⃣Advanced RAG
        • 7️⃣Llama3-8B with LlamaIndex
        • 8️⃣LlmaPack
      • LlamaIndex Intermediate
        • 1️⃣QueryEngine
        • 2️⃣Agent
        • 3️⃣Evaluation
        • 4️⃣Evaluation-Driven Development
        • 5️⃣Fine-tuning
        • 6️⃣Prompt Compression with LLMLingua
      • LlamaIndex Advanced
        • 1️⃣Agentic RAG: Router Engine
        • 2️⃣Agentic RAG: Tool Calling
        • 3️⃣Building Agent Reasoning Loop
        • 4️⃣Building Multi-document Agent
    • Hugging Face
      • Huggingface Basic
        • 1️⃣Datasets
        • 2️⃣Tokenizer
        • 3️⃣Sentence Embeddings
        • 4️⃣Transformers
        • 5️⃣Sentence Transformers
        • 6️⃣Evaluate
        • 7️⃣Diffusers
      • Huggingface Tasks
        • NLP
          • 1️⃣Sentiment Analysis
          • 2️⃣Zero-shot Classification
          • 3️⃣Aspect-Based Sentiment Analysis
          • 4️⃣Feature Extraction
          • 5️⃣Intent Classification
          • 6️⃣Topic Modeling: BERTopic
          • 7️⃣NER: Token Classification
          • 8️⃣Summarization
          • 9️⃣Translation
          • 🔟Text Generation
        • Audio & Tabular
          • 1️⃣Text-to-Speech: TTS
          • 2️⃣Speech Recognition: Whisper
          • 3️⃣Audio Classification
          • 4️⃣Tabular Qustaion & Answering
        • Vision & Multimodal
          • 1️⃣Image-to-Text
          • 2️⃣Text to Image
          • 3️⃣Image to Image
          • 4️⃣Text or Image-to-Video
          • 5️⃣Depth Estimation
          • 6️⃣Image Classification
          • 7️⃣Object Detection
          • 8️⃣Segmentatio
      • Huggingface Optimization
        • 1️⃣Accelerator
        • 2️⃣Bitsandbytes
        • 3️⃣Flash Attention
        • 4️⃣Quantization
        • 5️⃣Safetensors
        • 6️⃣Optimum-ONNX
        • 7️⃣Optimum-NVIDIA
        • 8️⃣Optimum-Intel
      • Huggingface Fine-tuning
        • 1️⃣Transformer Fine-tuning
        • 2️⃣PEFT Fine-tuning
        • 3️⃣PEFT: Fine-tuning with QLoRA
        • 4️⃣PEFT: Fine-tuning Phi-2 with QLoRA
        • 5️⃣Axoltl Fine-tuning with QLoRA
        • 6️⃣TRL: RLHF Alignment Fine-tuning
        • 7️⃣TRL: DPO Fine-tuning with Phi-3-4k-instruct
        • 8️⃣TRL: ORPO Fine-tuning with Llama3-8B
        • 9️⃣Convert GGUF gemma-2b with llama.cpp
        • 🔟Apple Silicon Fine-tuning Gemma-2B with MLX
        • 🔢LLM Mergekit
    • Agentic LLM
      • Agentic LLM
        • 1️⃣Basic Agentic LLM
        • 2️⃣Multi-agent with CrewAI
        • 3️⃣LangGraph: Multi-agent Basic
        • 4️⃣LangGraph: Agentic RAG with LangChain
        • 5️⃣LangGraph: Agentic RAG with Llama3-8B by Groq
      • Autonomous Agent
        • 1️⃣LLM Autonomous Agent?
        • 2️⃣AutoGPT: Worldcup Winner Search with LangChain
        • 3️⃣BabyAGI: Weather Report with LangChain
        • 4️⃣AutoGen: Writing Blog Post with LangChain
        • 5️⃣LangChain: Autonomous-agent Debates with Tools
        • 6️⃣CAMEL Role-playing Autonomous Cooperative Agents
        • 7️⃣LangChain: Two-player Harry Potter D&D based CAMEL
        • 8️⃣LangChain: Multi-agent Bid for K-Pop Debate
        • 9️⃣LangChain: Multi-agent Authoritarian Speaker Selection
        • 🔟LangChain: Multi-Agent Simulated Environment with PettingZoo
    • Multimodal
      • 1️⃣PaliGemma: Open Vision LLM
      • 2️⃣FLUX.1: Generative Image
    • Building LLM
      • 1️⃣DSPy
      • 2️⃣DSPy RAG
      • 3️⃣DSPy with LangChain
      • 4️⃣Mamba
      • 5️⃣Mamba RAG with LangChain
      • 7️⃣PostgreSQL VectorDB with pgvorco.rs
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  1. LLMs
  2. Hugging Face
  3. Huggingface Tasks
  4. Vision & Multimodal

Image to Image

Image to Image

Image to Image는 입력 이미지를 가져와 출력 이미지로 변환하는 작업입니다.

이러한 유형의 작업은 스타일 전송, 색상화, 초고해상도 등과 같은 다양한 상황에 적용할 수 있습니다.

!wget https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Everest_North_Face_toward_Base_Camp_Tibet_Luca_Galuzzi_2006.jpg/660px-Everest_North_Face_toward_Base_Camp_Tibet_Luca_Galuzzi_2006.jpg -O ./dataset/mountain.jpg
--2024-05-19 17:10:06--  https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/Everest_North_Face_toward_Base_Camp_Tibet_Luca_Galuzzi_2006.jpg/660px-Everest_North_Face_toward_Base_Camp_Tibet_Luca_Galuzzi_2006.jpg
Resolving upload.wikimedia.org (upload.wikimedia.org)... 103.102.166.240, 2001:df2:e500:ed1a::2:b
Connecting to upload.wikimedia.org (upload.wikimedia.org)|103.102.166.240|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 98683 (96K) [image/jpeg]
Saving to: ‘./dataset/mountain.jpg’

./dataset/mountain. 100%[===================>]  96.37K   493KB/s    in 0.2s    

2024-05-19 17:10:07 (493 KB/s) - ‘./dataset/mountain.jpg’ saved [98683/98683]
import torch
from PIL import Image
from diffusers import StableDiffusionImg2ImgPipeline

model_id_or_path = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    model_id_or_path, 
    torch_dtype=torch.float32
)
pipe = pipe.to("cuda")

init_image = Image.open("dataset/mountain.jpg").convert("RGB").resize((768, 512))
init_image
/home/kubwa/anaconda3/envs/pytorch/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(



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  • prompt=prompt: 변환을 안내하는 텍스트 프롬프트입니다.

  • image=init_imag: 변환할 초기 이미지입니다. 이전 코드 스니펫에서 로드되고 처리된 이미지입니다.

  • strength=0.7: 이 매개변수는 프롬프트에서 생성된 측면으로 원본 이미지가 변경되는 정도를 제어합니다. 값이 0.75이면 원본 이미지의 일부 요소는 그대로 유지하면서 상당한 변형이 이루어짐을 나타냅니다.

  • guidance_scale=7.: 생성된 이미지가 프롬프트와 얼마나 밀접하게 일치해야 하는지에 영향을 주는 요소입니다. 값이 클수록 텍스트 프롬프트를 더 강하게 준수하게 되므로 결과물의 창의성이나 다양성이 저하될 위험이 있습니다.

prompt = "A fantasy landscape, trending on artstation"

images = pipe(
    prompt=prompt, 
    image=init_image, 
    strength=0.75, 
    guidance_scale=7.5).images
images[0]
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PreviousText to ImageNextText or Image-to-Video

Last updated 11 months ago

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