Instructions to use Intel/Ovis-Image-7B-int4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Intel/Ovis-Image-7B-int4-AutoRound with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Intel/Ovis-Image-7B-int4-AutoRound", 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
File size: 746 Bytes
8ac85fc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"_class_name": "OvisImageTransformer2DModel",
"_diffusers_version": "0.36.0.dev0",
"_name_or_path": "/mnt/disk4/lvl/Ovis-Image-7B/transformer",
"attention_head_dim": 128,
"axes_dims_rope": [
16,
56,
56
],
"in_channels": 64,
"joint_attention_dim": 2048,
"num_attention_heads": 24,
"num_layers": 6,
"num_single_layers": 27,
"out_channels": null,
"patch_size": 1,
"quantization_config": {
"autoround_version": "0.13.0",
"batch_size": 1,
"bits": 4,
"block_name_to_quantize": "transformer_blocks,single_transformer_blocks",
"data_type": "int",
"group_size": 128,
"nsamples": 64,
"packing_format": "auto_round:auto_gptq",
"quant_method": "auto-round",
"sym": true
}
}
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