Instructions to use stillerman/poke-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stillerman/poke-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("stillerman/poke-lora") 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
| from dataclasses import dataclass | |
| from enum import Enum | |
| from typing import List, Optional, Union | |
| import numpy as np | |
| import PIL | |
| from PIL import Image | |
| from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available | |
| class SafetyConfig(object): | |
| WEAK = { | |
| "sld_warmup_steps": 15, | |
| "sld_guidance_scale": 20, | |
| "sld_threshold": 0.0, | |
| "sld_momentum_scale": 0.0, | |
| "sld_mom_beta": 0.0, | |
| } | |
| MEDIUM = { | |
| "sld_warmup_steps": 10, | |
| "sld_guidance_scale": 1000, | |
| "sld_threshold": 0.01, | |
| "sld_momentum_scale": 0.3, | |
| "sld_mom_beta": 0.4, | |
| } | |
| STRONG = { | |
| "sld_warmup_steps": 7, | |
| "sld_guidance_scale": 2000, | |
| "sld_threshold": 0.025, | |
| "sld_momentum_scale": 0.5, | |
| "sld_mom_beta": 0.7, | |
| } | |
| MAX = { | |
| "sld_warmup_steps": 0, | |
| "sld_guidance_scale": 5000, | |
| "sld_threshold": 1.0, | |
| "sld_momentum_scale": 0.5, | |
| "sld_mom_beta": 0.7, | |
| } | |
| class StableDiffusionSafePipelineOutput(BaseOutput): | |
| """ | |
| Output class for Safe Stable Diffusion pipelines. | |
| Args: | |
| images (`List[PIL.Image.Image]` or `np.ndarray`) | |
| List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width, | |
| num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline. | |
| nsfw_content_detected (`List[bool]`) | |
| List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work" | |
| (nsfw) content, or `None` if safety checking could not be performed. | |
| images (`List[PIL.Image.Image]` or `np.ndarray`) | |
| List of denoised PIL images that were flagged by the safety checker any may contain "not-safe-for-work" | |
| (nsfw) content, or `None` if no safety check was performed or no images were flagged. | |
| applied_safety_concept (`str`) | |
| The safety concept that was applied for safety guidance, or `None` if safety guidance was disabled | |
| """ | |
| images: Union[List[PIL.Image.Image], np.ndarray] | |
| nsfw_content_detected: Optional[List[bool]] | |
| unsafe_images: Optional[Union[List[PIL.Image.Image], np.ndarray]] | |
| applied_safety_concept: Optional[str] | |
| try: | |
| if not (is_transformers_available() and is_torch_available()): | |
| raise OptionalDependencyNotAvailable() | |
| except OptionalDependencyNotAvailable: | |
| from ...utils.dummy_torch_and_transformers_objects import * | |
| else: | |
| from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe | |
| from .safety_checker import SafeStableDiffusionSafetyChecker | |