Instructions to use cuio/URSA-1.7B-IBQ1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cuio/URSA-1.7B-IBQ1024 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cuio/URSA-1.7B-IBQ1024", 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
| { | |
| "_class_name": "AutoencoderVQ", | |
| "_quantizer_name": "VQuantizer", | |
| "in_channels": 3, | |
| "latent_channels": 256, | |
| "layers_per_block": 4, | |
| "norm_num_groups": 32, | |
| "out_channels": 3, | |
| "sample_size": 1024, | |
| "num_vq_embeddings": 131072, | |
| "vq_embed_dim": 256, | |
| "temporal_stride": 1, | |
| "spatial_stride": 16, | |
| "attn_down_block": true, | |
| "attn_up_block": true, | |
| "decoder_dtype": "bfloat16", | |
| "block_out_channels": [ | |
| 256, | |
| 256, | |
| 512, | |
| 512, | |
| 1024 | |
| ] | |
| } | |