Text-to-Image
Diffusers
Safetensors
English
StableDiffusionPipeline
Base Model
Western Art
Inked
Arthemy
stable-diffusion
stable-diffusion-diffusers
Instructions to use Yntec/ArthemyComics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Yntec/ArthemyComics with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Yntec/ArthemyComics", 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
File size: 724 Bytes
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"_class_name": "DPMSolverMultistepScheduler",
"_diffusers_version": "0.18.0.dev0",
"algorithm_type": "dpmsolver++",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"clip_sample": false,
"clip_sample_range": 1.0,
"dynamic_thresholding_ratio": 0.995,
"lambda_min_clipped": -Infinity,
"lower_order_final": true,
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"rescale_betas_zero_snr": false,
"sample_max_value": 1.0,
"set_alpha_to_one": false,
"solver_order": 2,
"solver_type": "midpoint",
"steps_offset": 1,
"thresholding": false,
"timestep_spacing": "leading",
"trained_betas": null,
"use_karras_sigmas": true,
"variance_type": null
}
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