Text-to-Image
Diffusers
Safetensors
FluxPipeline
FluxPipeline
FLUXv1-schnell
image-generation
flux-diffusers
art
realism
photography
illustration
anime
full finetune
trained
finetune
trainable
full-finetune
checkpoint
text2image
Schnell
Flux
DiT
transformer
Instructions to use AlekseyCalvin/GENOVA_APEX_flux_byDNA_1_618_Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlekseyCalvin/GENOVA_APEX_flux_byDNA_1_618_Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("AlekseyCalvin/GENOVA_APEX_flux_byDNA_1_618_Diffusers", torch_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 Settings
- Draw Things
- DiffusionBee
File size: 568 Bytes
624ae55 | 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 | {
"_class_name": "FluxPipeline",
"_diffusers_version": "0.30.0.dev0",
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"CLIPTextModel"
],
"text_encoder_2": [
"transformers",
"T5EncoderModel"
],
"tokenizer": [
"transformers",
"CLIPTokenizer"
],
"tokenizer_2": [
"transformers",
"T5TokenizerFast"
],
"transformer": [
"diffusers",
"FluxTransformer2DModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
|