Instructions to use d-wang26/mk1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use d-wang26/mk1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("d-wang26/mk1", 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
- amused
- animatediff
- audioldm
- audioldm2
- blipdiffusion
- consistency_models
- controlnet
- dance_diffusion
- ddim
- ddpm
- deepfloyd_if
- dit
- i2vgen_xl
- ip_adapters
- kandinsky
- kandinsky2_2
- kandinsky3
- latent_consistency_models
- latent_diffusion
- ledits_pp
- musicldm
- paint_by_example
- pia
- pixart_alpha
- pndm
- semantic_stable_diffusion
- shap_e
- stable_cascade
- stable_diffusion
- stable_diffusion_2
- stable_diffusion_adapter
- stable_diffusion_gligen
- stable_diffusion_gligen_text_image
- stable_diffusion_image_variation
- stable_diffusion_k_diffusion
- stable_diffusion_ldm3d
- stable_diffusion_panorama
- stable_diffusion_safe
- stable_diffusion_sag
- stable_diffusion_xl
- stable_unclip
- stable_video_diffusion
- text_to_video_synthesis
- unclip
- unidiffuser
- wuerstchen
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