Text-to-Image
Diffusers
Safetensors
ErnieImagePipeline
ernie-image
sdnq
quantized
uint4
static
quantized-matmul
Instructions to use WaveCut/ERNIE-Image-Turbo-SDNQ-uint4-static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use WaveCut/ERNIE-Image-Turbo-SDNQ-uint4-static with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WaveCut/ERNIE-Image-Turbo-SDNQ-uint4-static", 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
- Draw Things
- DiffusionBee
File size: 997 Bytes
b292728 | 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 32 33 | {
"runtime": {
"recommended_torch_cuda_alloc_conf": null,
"avoid_torch_cuda_alloc_conf": [
"expandable_segments:True,max_split_size_mb:32"
],
"keep_model_resident": true,
"avoid_empty_cache_between_generations": true,
"use_pe_for_image_benchmarks": false
},
"sdnq": {
"requires_explicit_apply_quantized_matmul": true,
"apply_quantized_matmul_components": [
"pe",
"text_encoder",
"transformer"
],
"apply_quantized_matmul_function": "sdnq.loader.apply_sdnq_options_to_model(component, use_quantized_matmul=True)"
},
"validated": {
"device": "NVIDIA RTX 6000 Ada Generation",
"torch": "2.8.0+cu128",
"sdnq": "0.1.9",
"num_inference_steps": 8,
"guidance_scale": 1.0,
"use_pe": false
},
"metrics": {
"explicit_quantized_matmul_default_allocator": "metrics/ernie_uint4_qmm_explicit_default_allocator_8step_metrics.json",
"allocator_debug": "metrics/runtime_allocator_debug_metrics.json"
}
}
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