Diffusers
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human-animation
talking-head
video-generation
nf4
quantized
echomimic
Instructions to use siyah1/EchoMimicV3-NF4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use siyah1/EchoMimicV3-NF4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("siyah1/EchoMimicV3-NF4", 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
EchoMimicV3 โ NF4 Quantized
This is a NF4 (NormalFloat4) quantized version of EchoMimicV3.
Quantization Details
| Parameter | Value |
|---|---|
| Quantization Type | NF4 (NormalFloat4) |
| Double Quantization | Yes |
| Compute Dtype | float16 |
| Library | bitsandbytes |
| VRAM Reduction | ~65% (5 GB โ ~1.7 GB) |
Usage
from transformers import BitsAndBytesConfig
import torch
nf4_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.float16
)
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Model tree for siyah1/EchoMimicV3-NF4
Base model
BadToBest/EchoMimicV3