Instructions to use wfen/Cosmos3-Nano-FP8-Blockwise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wfen/Cosmos3-Nano-FP8-Blockwise with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wfen/Cosmos3-Nano-FP8-Blockwise", 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
| { | |
| "n_weight_quantized": 216, | |
| "expected_quantized_count": 216, | |
| "mixed_spec_diff_empty": true, | |
| "unexpectedly_quantized": [], | |
| "unexpectedly_bf16": [], | |
| "wrong_mode": [], | |
| "dropped_action_keys": [], | |
| "note": "P6-S5: appended 5 BF16 action tensors + restored BF16 lm_head from the BF16 source; quantized set is now mlp.*/mlp_moe_gen.* only (INV-7 restored).", | |
| "appended_action_keys": [ | |
| "action_modality_embed", | |
| "action_proj_in.bias.weight", | |
| "action_proj_in.fc.weight", | |
| "action_proj_out.bias.weight", | |
| "action_proj_out.fc.weight" | |
| ], | |
| "lm_head_restored_bf16": true | |
| } | |