Instructions to use fusing/karlo-image-variations-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fusing/karlo-image-variations-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("fusing/karlo-image-variations-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
| { | |
| "_class_name": "UnCLIPImageVariationPipeline", | |
| "_diffusers_version": "0.12.0.dev0", | |
| "decoder": [ | |
| "diffusers", | |
| "UNet2DConditionModel" | |
| ], | |
| "decoder_scheduler": [ | |
| "diffusers", | |
| "UnCLIPScheduler" | |
| ], | |
| "feature_extractor": [ | |
| "transformers", | |
| "CLIPImageProcessor" | |
| ], | |
| "image_encoder": [ | |
| "transformers", | |
| "CLIPVisionModelWithProjection" | |
| ], | |
| "super_res_first": [ | |
| "diffusers", | |
| "UNet2DModel" | |
| ], | |
| "super_res_last": [ | |
| "diffusers", | |
| "UNet2DModel" | |
| ], | |
| "super_res_scheduler": [ | |
| "diffusers", | |
| "UnCLIPScheduler" | |
| ], | |
| "text_encoder": [ | |
| "transformers", | |
| "CLIPTextModelWithProjection" | |
| ], | |
| "text_proj": [ | |
| "unclip", | |
| "UnCLIPTextProjModel" | |
| ], | |
| "tokenizer": [ | |
| "transformers", | |
| "CLIPTokenizer" | |
| ] | |
| } | |