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pip install pruna1from pruna import PrunaModel
2
3loaded_model = PrunaModel.from_pretrained(
4 "pruna-test/test-save-tiny-stable-diffusion-pipe-smashed"
5)
6# we can then run inference using the methods supported by the base modelsmash_config.json file, which describes the optimization methods that were applied to the model.1{
2 "awq": false,
3 "c_generate": false,
4 "c_translate": false,
5 "c_whisper": false,
6 "deepcache": false,
7 "diffusers_int8": false,
8 "fastercache": false,
9 "flash_attn3": false,
10 "fora": false,
11 "gptq": false,
12 "half": false,
13 "hqq": false,
14 "hqq_diffusers": false,
15 "hyper": false,
16 "ifw": false,
17 "img2img_denoise": false,
18 "kvpress": false,
19 "llama_cpp": false,
20 "llm_int8": false,
21 "moe_kernel_tuner": false,
22 "pab": false,
23 "padding_pruning": false,
24 "qkv_diffusers": false,
25 "quanto": false,
26 "realesrgan_upscale": false,
27 "reduce_noe": false,
28 "ring_attn": false,
29 "sage_attn": false,
30 "stable_fast": false,
31 "text_to_image_distillation_inplace_perp": false,
32 "text_to_image_distillation_lora": false,
33 "text_to_image_distillation_perp": false,
34 "text_to_image_inplace_perp": false,
35 "text_to_image_lora": false,
36 "text_to_image_perp": false,
37 "text_to_text_inplace_perp": false,
38 "text_to_text_lora": false,
39 "text_to_text_perp": false,
40 "token_merging": false,
41 "torch_compile": false,
42 "torch_dynamic": false,
43 "torch_structured": false,
44 "torch_unstructured": false,
45 "torchao": false,
46 "x_fast": false,
47 "zipar": false,
48 "batch_size": 1,
49 "device": "cpu",
50 "device_map": null,
51 "save_fns": [],
52 "save_artifacts_fns": [],
53 "load_fns": [
54 "diffusers"
55 ],
56 "load_artifacts_fns": [],
57 "reapply_after_load": {}
58}