Instructions to use hf-internal-testing/tiny-pika-h3-flex-modular-pipe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hf-internal-testing/tiny-pika-h3-flex-modular-pipe with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-pika-h3-flex-modular-pipe", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Commit History
7 days ago
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Use MiniMaxH3Scheduler with stochastic sampling for the two schedulers
Verified signaturePika H3 Flex's sampler is MiniMaxH3Scheduler with `stochastic_sampling` and `shift_provided_sigmas`; the six-step schedule moves to the pipeline config.7c41d9c -
Name MiniMaxH3Transformer3DModel in the index and transformer configs (#1)
- Name MiniMaxH3Transformer3DModel in the index and transformer configs (f5632f59b4846711a72730e708aa791c00bb73c4)db9a5ed -
Add tiny Pika H3 Flex modular pipeline test checkpoint
Verified signature80ed144 -
initial commit
Verified signature6c58566