Text-to-Image
Diffusers
English
StableDiffusionPipeline
stable-diffusion
lora
template:diffusion-lora
Instructions to use Jonny001/R.I.O.W with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jonny001/R.I.O.W with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Jonny001/R.I.O.W") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Create model_index.json
Browse files- model_index.json +14 -0
model_index.json
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{
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"_class_name": "StableDiffusionPipeline",
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"_diffusers_version": "0.27.0",
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"architectures": ["StableDiffusionPipeline"],
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"feature_extractor": ["transformers.CLIPImageProcessor"],
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"image_encoder": ["diffusers.CLIPVisionModelWithProjection"],
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"requires_safety_checker": true,
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"safety_checker": ["stable_diffusion.safety_checker.FlakeSafetyChecker"],
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"scheduler": ["diffusers.DPMSolverMultistepScheduler"],
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"text_encoder": ["transformers.CLIPTextModel"],
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"tokenizer": ["transformers.CLIPTokenizer"],
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"unet": ["diffusers.UNet2DConditionModel"],
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"vae": ["diffusers.AutoencoderKL"]
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}
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