Instructions to use kimi000/cedar-cloud-83 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kimi000/cedar-cloud-83 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kimi000/cedar-cloud-83", 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: 665 Bytes
b69ce6f | 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 | {
"_class_name": "Flux2KleinPipeline",
"_diffusers_version": "0.39.0.dev0",
"_name_or_path": "/lustre/fsw/portfolios/nvr/projects/nvr_elm_llm/users/hcai/workspace/code/.cache/huggingface/hub/models--black-forest-labs--FLUX.2-klein-base-4B/snapshots/a3b4f4849157f664bdbc776fd7453c2783562f4d",
"is_distilled": false,
"scheduler": [
"diffusers",
"FlowMatchEulerDiscreteScheduler"
],
"text_encoder": [
"transformers",
"Qwen3ForCausalLM"
],
"tokenizer": [
"transformers",
"Qwen2TokenizerFast"
],
"transformer": [
"diffusers",
"Flux2Transformer2DModel"
],
"vae": [
"diffusers",
"AutoencoderKLFlux2"
]
}
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