Bouw een video!

Tijd om zelf een video te maken, helemaal vanuit een tekstprompt! Je gebruikt een CogVideoXPipeline-pipeline en de volgende prompt om de generatie te sturen:

Een robot die de robotdans doet. De dansvloer heeft kleurrijke vierkanten en een glitterbal.

Let op: inferentie met videogeneratiemodellen kan lang duren, dus we hebben de gegenereerde video alvast voor je geladen. Het uitvoeren van andere prompts genereert geen nieuwe video's.

De klasse CogVideoXPipeline is al voor je geïmporteerd.

Deze oefening maakt deel uit van de cursus

Multi-modale modellen met Hugging Face

Cursus bekijken

Oefeninstructies

Maak een CogVideoXPipeline vanuit het THUDM/CogVideoX-2b-checkpoint.
Voer de pipeline uit met de gegeven prompt, met het aantal inferentiestappen op 20, het aantal te genereren frames op 20, en de guidance scale op 6.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

prompt = "A robot doing the robot dance. The dance floor has colorful squares and a glitterball."

# Create a CogVideoXPipeline
pipe = ____(
    "____",
    torch_dtype=torch.float16
)

# Run the pipeline with the provided prompt
video = pipe(
    prompt=____,
    num_inference_steps=____,
    num_frames=____,
    guidance_scale=____
)
video = video.frames[0]

video_path = export_to_video(video, "output.mp4", fps=8)
video = VideoFileClip(video_path)
video.write_gif("video_ex.gif")

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Multi-modale modellen met Hugging Face

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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 Exercise 4: Preprocessing different modalities Exercise 5: Text tokenizing Exercise 6: Image preprocessing Exercise 7: Audio preprocessing Exercise 8: Pipeline tasks and evaluations Exercise 9: Pipeline caption generation Exercise 10: Passing keyword arguments Exercise 11: Model evaluation on a custom dataset

Learn to master individual modalities with state-of-the-art models. Dive into computer vision for image classification and segmentation, explore speech recognition and text-to-speech synthesis, and learn effective fine-tuning techniques. Build practical skills with pre-trained models from Hugging Face's transformers library.

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 met Vision Language Transformers (ViLTs)Exercise 3: Document-VQA met LayoutLM Exercise 4: Afbeeldingen bewerken met diffusiemodellen Exercise 5: Aangepaste bewerkingen op afbeeldingen Exercise 6: Image inpainting Exercise 7: Video genereren Exercise 8: Bouw een video!

Huidige oefening

Exercise 9: Prestaties van videogeneratie beoordelen Exercise 10: Gefeliciteerd!