Finding the most popular text-to-image model

Time to refine your search to find and load the most popular text-to-image Stable Diffusion model from CompVis on the Hugging Face Hub.

Photo of a labrador chasing a ball

The Hugging Face Hub API has been loaded (api), as has the StableDiffusionPipeline module from the diffusers library. With Stable Diffusion models you can generate an image from any prompt you like, e.g., "a black labrador chasing a tennis ball".

Este ejercicio forma parte del curso

Multi-Modal Models with Hugging Face

Instrucciones del ejercicio

Use only models that are for text-to-image tasks.
Use an appropriate tag to ensure the model can be loaded by the StableDiffusionPipeline class from the diffusers library.
Sort the results according to the number of "likes".
Load the most popular model from models using its ID.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

models = api.list_models(
    # Filter for text-to-image tasks
    task="____",
    author="CompVis",
    # Filter for models that can be loaded by the diffusers library
    tags="diffusers:____",
    # Sort according to the most popular
    sort="____"
)

models = list(models)

# Load the most popular model from models
pipe = StableDiffusionPipeline.from_pretrained(models[0].____)

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Este ejercicio forma parte del curso

Multi-Modal Models with Hugging Face

IntermedioNivel de habilidad

4.8+

Comienza el curso gratis

Navigate the Hugging Face model hub, transform raw text, audio, and visual data into AI-friendly formats. Learn how to find the latest most popular models for tasks such as text generation and harness the power of pre-built pipelines.

Exercise 1: Hugging Face model navigation Exercise 2: How many models!?Exercise 3: Finding the most popular text-to-image model

Ejercicio actual

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Exercise 1: Computer vision Exercise 2: Image classification Exercise 3: Object detection Exercise 4: Image background removal Exercise 5: Fine-tuning computer vision models Exercise 6: CV fine-tuning: dataset prep Exercise 7: CV fine-tuning: model classes Exercise 8: CV fine-tuning: trainer configuration Exercise 9: Speech recognition and audio generation Exercise 10: Automatic speech recognition Exercise 11: Creating speech embeddings Exercise 12: Audio denoising Exercise 13: Fine-tuning text-to-speech models Exercise 14: Fine-tuning a text-to-speech model Exercise 15: Generating new speech

Learn to fuse visual, textual, and audio information for richer AI applications. Master techniques like CLIP for zero-shot classification, build sentiment analyzers that see and read, and create emotion detectors that combine facial expressions with voice. Take your AI models beyond single-modality thinking.

Exercise 1: Zero-shot image classification Exercise 2: Zero-shot learning with CLIP Exercise 3: Automated caption quality assessment Exercise 4: Multi-modal sentiment analysis Exercise 5: Prompting Vision Language Models (VLMs)Exercise 6: Multi-modal sentiment classification with Qwen Exercise 7: Zero-shot video classification Exercise 8: Video audio splitting Exercise 9: Video sentiment analysis with CLIP CLAP

Transform ideas into reality! Master cutting-edge AI techniques to generate and manipulate visual content using text prompts. Create stunning images, edit photos intelligently, and build powerful question-answering systems for images and documents. Turn your creative vision into digital reality with multi-modal AI.

Exercise 1: Visual question-answering (VQA)Exercise 2: VQA with Vision Language Transformers (ViLTs)Exercise 3: Document VQA with LayoutLM Exercise 4: Image editing with diffusion models Exercise 5: Custom image editing Exercise 6: Image inpainting Exercise 7: Video generation Exercise 8: Build a video!Exercise 9: Assessing video generation performance Exercise 10: Congratulations!