Text-to-Image
Diffusers
Safetensors
English
StableDiffusionXLPipeline
stable-diffusion
stable-diffusion-xl
Instructions to use drawhisper/animagine-xl-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use drawhisper/animagine-xl-v4 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("drawhisper/animagine-xl-v4", dtype=torch.bfloat16, device_map="cuda") prompt = "1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, night, turtleneck, masterpiece, high score, great score, absurdres" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
File size: 613 Bytes
3ebc830 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"_class_name": "EulerDiscreteScheduler",
"_diffusers_version": "0.29.0",
"beta_end": 0.012,
"beta_schedule": "scaled_linear",
"beta_start": 0.00085,
"clip_sample": false,
"final_sigmas_type": "zero",
"interpolation_type": "linear",
"num_train_timesteps": 1000,
"prediction_type": "epsilon",
"rescale_betas_zero_snr": false,
"sample_max_value": 1.0,
"set_alpha_to_one": false,
"sigma_max": null,
"sigma_min": null,
"skip_prk_steps": true,
"steps_offset": 1,
"timestep_spacing": "leading",
"timestep_type": "discrete",
"trained_betas": null,
"use_karras_sigmas": false
}
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