Text-to-Image
Diffusers
Safetensors
MLX
cosmos3
quantization
int8
w8a16
sdnq
apple-silicon
cuda
8-bit precision
Instructions to use JuliaML/Cosmos3-Super-Text2Image-4Step-INT8-G64-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JuliaML/Cosmos3-Super-Text2Image-4Step-INT8-G64-BF16 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JuliaML/Cosmos3-Super-Text2Image-4Step-INT8-G64-BF16", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - MLX
How to use JuliaML/Cosmos3-Super-Text2Image-4Step-INT8-G64-BF16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Cosmos3-Super-Text2Image-4Step-INT8-G64-BF16 JuliaML/Cosmos3-Super-Text2Image-4Step-INT8-G64-BF16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Draw Things
- DiffusionBee
- Atomic Chat
| { | |
| "format": "cosmos3-sdnq-int8-verification-v1", | |
| "source": "models/Cosmos3-Super-Text2Image-4Step/transformer", | |
| "checkpoint": "checkpoints/Cosmos3-Super-Text2Image-4Step-SDNQ-INT8-G64-BF16IO/transformer", | |
| "selection_report": "benchmarks/results/mac-grid-fix-priority/w8-common-selection-verification.json", | |
| "weights_dtype": "int8", | |
| "group_size": 64, | |
| "selected_tensors": [ | |
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| "layers.13.self_attn.add_v_proj.weight", | |
| "layers.16.mlp_moe_gen.up_proj.weight", | |
| "layers.19.mlp.up_proj.weight", | |
| "layers.20.self_attn.to_v.weight", | |
| "layers.23.self_attn.to_add_out.weight", | |
| "layers.26.self_attn.add_k_proj.weight", | |
| "layers.29.mlp_moe_gen.down_proj.weight", | |
| "layers.31.mlp.down_proj.weight", | |
| "layers.33.self_attn.to_out.weight", | |
| "layers.36.self_attn.add_v_proj.weight", | |
| "layers.39.mlp_moe_gen.up_proj.weight", | |
| "layers.41.mlp.up_proj.weight", | |
| "layers.43.self_attn.to_v.weight", | |
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| "layers.49.self_attn.add_q_proj.weight", | |
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| "layers.59.self_attn.add_v_proj.weight", | |
| "layers.61.mlp_moe_gen.up_proj.weight", | |
| "layers.7.mlp.up_proj.weight", | |
| "layers.9.self_attn.to_v.weight", | |
| "proj_in.weight", | |
| "proj_out.weight" | |
| ], | |
| "packing_exact": true, | |
| "global_relative_mse": 3.8788798613407266e-05, | |
| "core_global_relative_mse": 3.8788798613407266e-05, | |
| "elapsed_seconds": 2.4776670410001316, | |
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| } | |