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", torch_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
File size: 597 Bytes
9bf5d6a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | {
"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
}
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