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Unconditional Image Generation |
semantic_stable_diffusion |
Semantic Guidance |
Text-Guided Generation |
stable_diffusion_text2img |
Stable Diffusion |
Text-to-Image Generation |
stable_diffusion_img2img |
Stable Diffusion |
Image-to-Image Text-Guided Generation |
stable_diffusion_inpaint |
Stable Diffusion |
Text-Guided Image Inpainting |
stable_diffusion_panorama |
MultiDiffusion |
Text-to-Panorama Generation |
stable_diffusion_pix2pix |
InstructPix2Pix |
Text-Guided Image Editing |
stable_diffusion_pix2pix_zero |
Zero-shot Image-to-Image Translation |
Text-Guided Image Editing |
stable_diffusion_attend_and_excite |
Attend and Excite for Stable Diffusion |
Text-to-Image Generation |
stable_diffusion_self_attention_guidance |
Self-Attention Guidance |
Text-to-Image Generation |
stable_diffusion_image_variation |
Stable Diffusion Image Variations |
Image-to-Image Generation |
stable_diffusion_latent_upscale |
Stable Diffusion Latent Upscaler |
Text-Guided Super Resolution Image-to-Image |
stable_diffusion_2 |
Stable Diffusion 2 |
Text-to-Image Generation |
stable_diffusion_2 |
Stable Diffusion 2 |
Text-Guided Image Inpainting |
stable_diffusion_2 |
Depth-Conditional Stable Diffusion |
Depth-to-Image Generation |
stable_diffusion_2 |
Stable Diffusion 2 |
Text-Guided Super Resolution Image-to-Image |
stable_diffusion_safe |
Safe Stable Diffusion |
Text-Guided Generation |
stable_unclip |
Stable unCLIP |
Text-to-Image Generation |
stable_unclip |
Stable unCLIP |
Image-to-Image Text-Guided Generation |
stochastic_karras_ve |
Elucidating the Design Space of Diffusion-Based Generative Models |
Unconditional Image Generation |
unclip |
Hierarchical Text-Conditional Image Generation with CLIP Latents |
Text-to-Image Generation |
versatile_diffusion |
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model |
Text-to-Image Generation |
versatile_diffusion |
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model |
Image Variations Generation |
versatile_diffusion |
Versatile Diffusion: Text, Images and Variations All in One Diffusion Model |
Dual Image and Text Guided Generation |
vq_diffusion |
Vector Quantized Diffusion Model for Text-to-Image Synthesis |
Text-to-Image Generation |
Note: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. |
Latent upscaler The Stable Diffusion latent upscaler model was created by Katherine Crowson in collaboration with Stability AI. It is used to enhance the output image resolution by a factor of 2 (see this demo notebook for a demonstration of the original implementation). Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! If you’re interested in using one of the official checkpoints for a task, explore the CompVis, Runway, and Stability AI Hub organizations! StableDiffusionLatentUpscalePipeline class diffusers.StableDiffusionLatentUpscalePipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: EulerDiscreteScheduler ) Parameters vae (AutoencoderKL) — |
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