Text-to-Image
Diffusers
Safetensors
English
Pipeline
Non-Autoregressive
Masked-Generative-Transformer
Instructions to use MeissonFlow/Meissonic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MeissonFlow/Meissonic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MeissonFlow/Meissonic", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download model_index.json from MeissonFlow/Meissonic: direct link, hf CLI and curl.
- Browser
- Download file 379 Bytes
-
https://huggingface.co/MeissonFlow/Meissonic/resolve/main/model_index.json
- Command line
-
hf download hf://MeissonFlow/Meissonic/model_index.json
-
curl -L -o model_index.json https://huggingface.co/MeissonFlow/Meissonic/resolve/main/model_index.json
379 Bytes
| { | |
| "_class_name": "Pipeline", | |
| "_diffusers_version": "0.30.2", | |
| "scheduler": [ | |
| "scheduler", | |
| "Scheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ], | |
| "transformer": [ | |
| "transformer", | |
| "Transformer2DModel" | |
| ], | |
| "vqvae": [ | |
| "diffusers", | |
| "VQModel" | |
| ] | |
| } | |