Instructions to use MonsterMMORPG/VExpressQuick with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MonsterMMORPG/VExpressQuick with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MonsterMMORPG/VExpressQuick", 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
| unet_additional_kwargs: | |
| use_inflated_groupnorm: true | |
| unet_use_cross_frame_attention: false | |
| unet_use_temporal_attention: false | |
| use_motion_module: true | |
| motion_module_resolutions: | |
| - 1 | |
| - 2 | |
| - 4 | |
| - 8 | |
| motion_module_mid_block: true | |
| motion_module_decoder_only: false | |
| motion_module_type: Vanilla | |
| motion_module_kwargs: | |
| num_attention_heads: 8 | |
| num_transformer_block: 1 | |
| attention_block_types: | |
| - Temporal_Self | |
| - Temporal_Self | |
| temporal_position_encoding: true | |
| temporal_position_encoding_max_len: 32 | |
| temporal_attention_dim_div: 1 | |
| noise_scheduler_kwargs: | |
| beta_start: 0.00085 | |
| beta_end: 0.012 | |
| beta_schedule: "scaled_linear" | |
| clip_sample: false | |
| steps_offset: 1 | |
| ### Zero-SNR params | |
| prediction_type: "v_prediction" | |
| rescale_betas_zero_snr: True | |
| timestep_spacing: "trailing" | |
| sampler: DDIM | |