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
HSToric
Historic
DiT
transformer
Instructions to use AlekseyCalvin/HyperHistoricColor_FluxDev_Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AlekseyCalvin/HyperHistoricColor_FluxDev_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/HyperHistoricColor_FluxDev_Diffusers", 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
- Draw Things
- DiffusionBee
Upload model_index.json with huggingface_hub
Browse files- model_index.json +32 -0
model_index.json
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{
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"_class_name": "FluxPipeline",
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"_diffusers_version": "0.30.0.dev0",
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"scheduler": [
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"diffusers",
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"FlowMatchEulerDiscreteScheduler"
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],
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"text_encoder": [
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"transformers",
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"CLIPTextModel"
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],
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"text_encoder_2": [
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"transformers",
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"T5EncoderModel"
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],
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"tokenizer": [
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"transformers",
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"CLIPTokenizer"
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],
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"tokenizer_2": [
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"transformers",
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"T5TokenizerFast"
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],
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"transformer": [
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"diffusers",
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"FluxTransformer2DModel"
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],
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"vae": [
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"diffusers",
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"AutoencoderKL"
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]
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}
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