Instructions to use marshallhamzah/EchoX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use marshallhamzah/EchoX with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("marshallhamzah/EchoX", 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
File size: 990 Bytes
2079d5c | 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 34 35 36 37 38 39 40 41 42 43 44 45 | {
"_class_name": "UNetSpatioTemporalConditionModel",
"_diffusers_version": "0.32.2",
"addition_time_embed_dim": 1,
"block_out_channels": [
128,
256,
256,
512
],
"cross_attention_dim": 1,
"decay": 0.9999,
"down_block_types": [
"CrossAttnDownBlockSpatioTemporal",
"CrossAttnDownBlockSpatioTemporal",
"CrossAttnDownBlockSpatioTemporal",
"DownBlockSpatioTemporal"
],
"in_channels": 8,
"inv_gamma": 1.0,
"layers_per_block": 2,
"min_decay": 0.0,
"num_attention_heads": [
8,
16,
16,
32
],
"num_frames": 64,
"optimization_step": 200000,
"out_channels": 4,
"power": 0.6666666666666666,
"projection_class_embeddings_input_dim": 1,
"sample_size": 28,
"transformer_layers_per_block": 1,
"up_block_types": [
"UpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal",
"CrossAttnUpBlockSpatioTemporal"
],
"update_after_step": 0,
"use_ema_warmup": false
}
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