Instructions to use nilor-corp/zenerator-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nilor-corp/zenerator-models with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nilor-corp/zenerator-models", 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
Commit History
-
vae, clip, diffusion models for wan-smooth-travel
404S06fad6c
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more Florence2 LLM models, not sure which are necessary
0b41267 -
Florence2 for "Motion Directed LTXV" workflow
e5e1897 -
models for "Flux with Loras" workflow
79d17d7 -
google_t5-v1_1-xxl_encoderonly-fp8_e4m3fn
16d8090 -
the three flux loras that were referenced in Zenerator
02a814a -
CogVideoX
2b435fb -
LTXV 2b v0.9.1
83fba9d
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removed problematic ultralytics SEGM model that was found to be unsafe
1ae48ce
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added all models required by ImageTravelRework Zenerator workflow
2df1491 -
added tensorrt engine model folder to .gitignore
2f75205
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models revised to match minimal requirements to run zenerator general, upscale, and upscale TensorRT
5e41e56
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a few controlnet 1.5 models and add_detail lora
f220bcb
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add 32frame animdiff lora
9c109c2 -
added juggernaut_reborn checkpoint and sams, bbox, segm models
2b026f4
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4xLexica
41db125 -
controlnet v11f1e
46d91a9 -
WAS26 ip_image_encoder vae-840000
48837d9 -
photonLCM_v10
a0d37e6
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RealESRGAN_x4
0427c36 -
ipadapter and clip models
d454365
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motuion controlnet checkpoint
98350db -
add lcm lora sd15
4c2f0b3 -
add photon_v1
8ebf5c9 -
initial commit
Verified signature11166b3