Instructions to use vidfom/Wav2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use vidfom/Wav2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("vidfom/Wav2", 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: 775 Bytes
79dc332 | 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 | import torch
from transformers import T5EncoderModel, T5Config
from .sd_text_encoder import SDTextEncoder
class FluxTextEncoder2(T5EncoderModel):
def __init__(self, config):
super().__init__(config)
self.eval()
def forward(self, input_ids):
outputs = super().forward(input_ids=input_ids)
prompt_emb = outputs.last_hidden_state
return prompt_emb
@staticmethod
def state_dict_converter():
return FluxTextEncoder2StateDictConverter()
class FluxTextEncoder2StateDictConverter():
def __init__(self):
pass
def from_diffusers(self, state_dict):
state_dict_ = state_dict
return state_dict_
def from_civitai(self, state_dict):
return self.from_diffusers(state_dict)
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