├── .gitattributes ├── benchmarks ├── base_classes.py ├── benchmark_controlnet.py ├── benchmark_ip_adapters.py ├── benchmark_sd_img.py ├── benchmark_sd_inpainting.py ├── benchmark_t2i_adapter.py ├── benchmark_t2i_lcm_lora.py ├── benchmark_text_to_image.py ├── push_results.py ├── run_all.py └── utils.py ├── docker ├── diffusers-flax-cpu │ └── Dockerfile ├── diffusers-flax-tpu │ └── Dockerfile ├── diffusers-onnxruntime-cpu │ └── Dockerfile ├── diffusers-onnxruntime-cuda │ └── Dockerfile ├── diffusers-pytorch-compile-cuda │ └── Dockerfile ├── diffusers-pytorch-cpu │ └── Dockerfile ├── diffusers-pytorch-cuda │ └── Dockerfile └── diffusers-pytorch-xformers-cuda │ └── Dockerfile ├── docs ├── README.md ├── TRANSLATING.md └── source │ ├── _config.py │ ├── en │ ├── _toctree.yml │ ├── api │ │ ├── activations.md │ │ ├── attnprocessor.md │ │ ├── configuration.md │ │ ├── image_processor.md │ │ ├── internal_classes_overview.md │ │ ├── loaders │ │ │ ├── ip_adapter.md │ │ │ ├── lora.md │ │ │ ├── peft.md │ │ │ ├── single_file.md │ │ │ ├── textual_inversion.md │ │ │ └── unet.md │ │ ├── logging.md │ │ ├── models │ │ │ ├── asymmetricautoencoderkl.md │ │ │ ├── autoencoder_tiny.md │ │ │ ├── autoencoderkl.md │ │ │ ├── consistency_decoder_vae.md │ │ │ ├── controlnet.md │ │ │ ├── overview.md │ │ │ ├── prior_transformer.md │ │ │ ├── transformer2d.md │ │ │ ├── transformer_temporal.md │ │ │ ├── unet-motion.md │ │ │ ├── unet.md │ │ │ ├── unet2d-cond.md │ │ │ ├── unet2d.md │ │ │ ├── unet3d-cond.md │ │ │ ├── uvit2d.md │ │ │ └── vq.md │ │ ├── normalization.md │ │ ├── outputs.md │ │ ├── pipelines │ │ │ ├── amused.md │ │ │ ├── animatediff.md │ │ │ ├── attend_and_excite.md │ │ │ ├── audioldm.md │ │ │ ├── audioldm2.md │ │ │ ├── auto_pipeline.md │ │ │ ├── blip_diffusion.md │ │ │ ├── consistency_models.md │ │ │ ├── controlnet.md │ │ │ ├── controlnet_sdxl.md │ │ │ ├── dance_diffusion.md │ │ │ ├── ddim.md │ │ │ ├── ddpm.md │ │ │ ├── deepfloyd_if.md │ │ │ ├── diffedit.md │ │ │ ├── dit.md │ │ │ ├── i2vgenxl.md │ │ │ ├── kandinsky.md │ │ │ ├── kandinsky3.md │ │ │ ├── kandinsky_v22.md │ │ │ ├── latent_consistency_models.md │ │ │ ├── latent_diffusion.md │ │ │ ├── musicldm.md │ │ │ ├── overview.md │ │ │ ├── paint_by_example.md │ │ │ ├── panorama.md │ │ │ ├── pia.md │ │ │ ├── pix2pix.md │ │ │ ├── pixart.md │ │ │ ├── self_attention_guidance.md │ │ │ ├── semantic_stable_diffusion.md │ │ │ ├── shap_e.md │ │ │ ├── stable_cascade.md │ │ │ ├── stable_diffusion │ │ │ │ ├── adapter.md │ │ │ │ ├── depth2img.md │ │ │ │ ├── gligen.md │ │ │ │ ├── image_variation.md │ │ │ │ ├── img2img.md │ │ │ │ ├── inpaint.md │ │ │ │ ├── k_diffusion.md │ │ │ │ ├── latent_upscale.md │ │ │ │ ├── ldm3d_diffusion.md │ │ │ │ ├── overview.md │ │ │ │ ├── sdxl_turbo.md │ │ │ │ ├── stable_diffusion_2.md │ │ │ │ ├── stable_diffusion_safe.md │ │ │ │ ├── stable_diffusion_xl.md │ │ │ │ ├── svd.md │ │ │ │ ├── text2img.md │ │ │ │ └── upscale.md │ │ │ ├── stable_unclip.md │ │ │ ├── text_to_video.md │ │ │ ├── text_to_video_zero.md │ │ │ ├── unclip.md │ │ │ ├── unidiffuser.md │ │ │ ├── value_guided_sampling.md │ │ │ └── wuerstchen.md │ │ ├── schedulers │ │ │ ├── cm_stochastic_iterative.md │ │ │ ├── consistency_decoder.md │ │ │ ├── ddim.md │ │ │ ├── ddim_inverse.md │ │ │ ├── ddpm.md │ │ │ ├── deis.md │ │ │ ├── dpm_discrete.md │ │ │ ├── dpm_discrete_ancestral.md │ │ │ ├── dpm_sde.md │ │ │ ├── euler.md │ │ │ ├── euler_ancestral.md │ │ │ ├── heun.md │ │ │ ├── ipndm.md │ │ │ ├── lcm.md │ │ │ ├── lms_discrete.md │ │ │ ├── multistep_dpm_solver.md │ │ │ ├── multistep_dpm_solver_inverse.md │ │ │ ├── overview.md │ │ │ ├── pndm.md │ │ │ ├── repaint.md │ │ │ ├── score_sde_ve.md │ │ │ ├── score_sde_vp.md │ │ │ ├── singlestep_dpm_solver.md │ │ │ ├── stochastic_karras_ve.md │ │ │ ├── tcd.md │ │ │ ├── unipc.md │ │ │ └── vq_diffusion.md │ │ └── utilities.md │ ├── conceptual │ │ ├── contribution.md │ │ ├── ethical_guidelines.md │ │ ├── evaluation.md │ │ └── philosophy.md │ ├── imgs │ │ ├── access_request.png │ │ └── diffusers_library.jpg │ ├── index.md │ ├── installation.md │ ├── optimization │ │ ├── coreml.md │ │ ├── deepcache.md │ │ ├── fp16.md │ │ ├── habana.md │ │ ├── memory.md │ │ ├── mps.md │ │ ├── onnx.md │ │ ├── open_vino.md │ │ ├── opt_overview.md │ │ ├── tome.md │ │ ├── torch2.0.md │ │ └── xformers.md │ ├── quicktour.md │ ├── stable_diffusion.md │ ├── training │ │ ├── adapt_a_model.md │ │ ├── controlnet.md │ │ ├── create_dataset.md │ │ ├── custom_diffusion.md │ │ ├── ddpo.md │ │ ├── distributed_inference.md │ │ ├── dreambooth.md │ │ ├── instructpix2pix.md │ │ ├── kandinsky.md │ │ ├── lcm_distill.md │ │ ├── lora.md │ │ ├── overview.md │ │ ├── sdxl.md │ │ ├── t2i_adapters.md │ │ ├── text2image.md │ │ ├── text_inversion.md │ │ ├── unconditional_training.md │ │ └── wuerstchen.md │ ├── tutorials │ │ ├── autopipeline.md │ │ ├── basic_training.md │ │ ├── fast_diffusion.md │ │ ├── tutorial_overview.md │ │ └── using_peft_for_inference.md │ └── using-diffusers │ │ ├── callback.md │ │ ├── conditional_image_generation.md │ │ ├── contribute_pipeline.md │ │ ├── control_brightness.md │ │ ├── controlling_generation.md │ │ ├── controlnet.md │ │ ├── custom_pipeline_examples.md │ │ ├── custom_pipeline_overview.md │ │ ├── depth2img.md │ │ ├── diffedit.md │ │ ├── distilled_sd.md │ │ ├── freeu.md │ │ ├── img2img.md │ │ ├── inference_with_lcm.md │ │ ├── inference_with_lcm_lora.md │ │ ├── inpaint.md │ │ ├── ip_adapter.md │ │ ├── kandinsky.md │ │ ├── loading.md │ │ ├── loading_adapters.md │ │ ├── loading_overview.md │ │ ├── merge_loras.md │ │ ├── other-formats.md │ │ ├── other-modalities.md │ │ ├── pipeline_overview.md │ │ ├── push_to_hub.md │ │ ├── reproducibility.md │ │ ├── reusing_seeds.md │ │ ├── schedulers.md │ │ ├── sdxl.md │ │ ├── sdxl_turbo.md │ │ ├── shap-e.md │ │ ├── stable_diffusion_jax_how_to.md │ │ ├── svd.md │ │ ├── text-img2vid.md │ │ ├── textual_inversion_inference.md │ │ ├── unconditional_image_generation.md │ │ ├── using_safetensors.md │ │ ├── weighted_prompts.md │ │ └── write_own_pipeline.md │ ├── ja │ ├── _toctree.yml │ ├── index.md │ ├── installation.md │ ├── quicktour.md │ ├── stable_diffusion.md │ └── tutorials │ │ ├── autopipeline.md │ │ └── tutorial_overview.md │ ├── ko │ ├── _toctree.yml │ ├── api │ │ └── pipelines │ │ │ └── stable_diffusion │ │ │ └── stable_diffusion_xl.md │ ├── in_translation.md │ ├── index.md │ ├── installation.md │ ├── optimization │ │ ├── coreml.md │ │ ├── fp16.md │ │ ├── habana.md │ │ ├── mps.md │ │ ├── onnx.md │ │ ├── open_vino.md │ │ ├── opt_overview.md │ │ ├── tome.md │ │ ├── torch2.0.md │ │ └── xformers.md │ ├── quicktour.md │ ├── stable_diffusion.md │ ├── training │ │ ├── adapt_a_model.md │ │ ├── controlnet.md │ │ ├── create_dataset.md │ │ ├── custom_diffusion.md │ │ ├── distributed_inference.md │ │ ├── dreambooth.md │ │ ├── instructpix2pix.md │ │ ├── lora.md │ │ ├── overview.md │ │ ├── text2image.md │ │ ├── text_inversion.md │ │ └── unconditional_training.md │ ├── tutorials │ │ ├── basic_training.md │ │ └── tutorial_overview.md │ └── using-diffusers │ │ ├── conditional_image_generation.md │ │ ├── contribute_pipeline.md │ │ ├── control_brightness.md │ │ ├── controlling_generation.md │ │ ├── custom_pipeline_examples.md │ │ ├── custom_pipeline_overview.md │ │ ├── depth2img.md │ │ ├── img2img.md │ │ ├── inpaint.md │ │ ├── loading.md │ │ ├── loading_overview.md │ │ ├── other-formats.md │ │ ├── pipeline_overview.md │ │ ├── reproducibility.md │ │ ├── reusing_seeds.md │ │ ├── schedulers.md │ │ ├── stable_diffusion_jax_how_to.md │ │ ├── textual_inversion_inference.md │ │ ├── unconditional_image_generation.md │ │ ├── using_safetensors.md │ │ ├── weighted_prompts.md │ │ └── write_own_pipeline.md │ ├── pt │ ├── _toctree.yml │ ├── index.md │ ├── installation.md │ └── quicktour.md │ └── zh │ ├── _toctree.yml │ ├── index.md │ ├── installation.md │ ├── quicktour.md │ └── stable_diffusion.md ├── examples ├── README.md ├── advanced_diffusion_training │ ├── README.md │ ├── requirements.txt │ ├── train_dreambooth_lora_sd15_advanced.py │ └── train_dreambooth_lora_sdxl_advanced.py ├── amused │ ├── README.md │ └── train_amused.py ├── brushnet │ ├── app_brushnet.py │ ├── evaluate_brushnet.py │ ├── replacenet_output │ │ └── logs │ │ │ └── text2image-fine-tune │ │ │ ├── 1716375294.5988402 │ │ │ └── events.out.tfevents.1716375294.dgx056.scc.idea.1221950.1 │ │ │ ├── 1716375294.6155684 │ │ │ └── hparams.yml │ │ │ ├── 1716375346.5897996 │ │ │ └── events.out.tfevents.1716375346.dgx056.scc.idea.1223643.1 │ │ │ ├── 1716375346.6081643 │ │ │ └── hparams.yml │ │ │ ├── 1716375405.4539065 │ │ │ └── events.out.tfevents.1716375405.dgx056.scc.idea.1225197.1 │ │ │ ├── 1716375405.4716277 │ │ │ └── hparams.yml │ │ │ ├── 1716376045.8172774 │ │ │ └── events.out.tfevents.1716376045.dgx056.scc.idea.1243448.1 │ │ │ ├── 1716376045.8287854 │ │ │ └── hparams.yml │ │ │ ├── 1716376095.8429906 │ │ │ └── events.out.tfevents.1716376095.dgx056.scc.idea.1244910.1 │ │ │ ├── 1716376095.8575857 │ │ │ └── hparams.yml │ │ │ ├── 1716376722.8218124 │ │ │ └── events.out.tfevents.1716376722.dgx056.scc.idea.1263207.1 │ │ │ ├── 1716376722.8391442 │ │ │ └── hparams.yml │ │ │ ├── 1716379615.4063938 │ │ │ └── events.out.tfevents.1716379615.dgx056.scc.idea.1532364.1 │ │ │ ├── 1716379615.4356453 │ │ │ └── hparams.yml │ │ │ ├── 1716379659.525904 │ │ │ └── events.out.tfevents.1716379659.dgx056.scc.idea.1533848.1 │ │ │ ├── 1716379659.5451274 │ │ │ └── hparams.yml │ │ │ ├── 1716454088.9945462 │ │ │ └── events.out.tfevents.1716454088.dgx056.scc.idea.560908.1 │ │ │ ├── 1716454089.0188463 │ │ │ └── hparams.yml │ │ │ ├── 1716454925.647512 │ │ │ └── events.out.tfevents.1716454925.dgx056.scc.idea.586912.1 │ │ │ ├── 1716454925.6654825 │ │ │ └── hparams.yml │ │ │ ├── 1716463441.459813 │ │ │ └── events.out.tfevents.1716463441.dgx056.scc.idea.1451862.1 │ │ │ ├── 1716463441.5809672 │ │ │ └── hparams.yml │ │ │ ├── events.out.tfevents.1716375294.dgx056.scc.idea.1221950.0 │ │ │ ├── events.out.tfevents.1716375346.dgx056.scc.idea.1223643.0 │ │ │ ├── events.out.tfevents.1716375405.dgx056.scc.idea.1225197.0 │ │ │ ├── events.out.tfevents.1716376045.dgx056.scc.idea.1243448.0 │ │ │ ├── events.out.tfevents.1716376095.dgx056.scc.idea.1244910.0 │ │ │ ├── events.out.tfevents.1716376722.dgx056.scc.idea.1263207.0 │ │ │ ├── events.out.tfevents.1716379615.dgx056.scc.idea.1532364.0 │ │ │ ├── events.out.tfevents.1716379659.dgx056.scc.idea.1533848.0 │ │ │ ├── events.out.tfevents.1716454088.dgx056.scc.idea.560908.0 │ │ │ ├── events.out.tfevents.1716454925.dgx056.scc.idea.586912.0 │ │ │ └── events.out.tfevents.1716463441.dgx056.scc.idea.1451862.0 │ ├── replacenet_output_stage_2 │ │ └── logs │ │ │ └── text2image-fine-tune │ │ │ ├── 1716380414.881609 │ │ │ └── events.out.tfevents.1716380414.dgx056.scc.idea.1556298.1 │ │ │ ├── 1716380414.9005296 │ │ │ └── hparams.yml │ │ │ ├── 1716382912.728693 │ │ │ └── events.out.tfevents.1716382912.dgx056.scc.idea.1630624.1 │ │ │ ├── 1716382912.7458518 │ │ │ └── hparams.yml │ │ │ ├── 1716382976.0338347 │ │ │ └── events.out.tfevents.1716382976.dgx056.scc.idea.1632835.1 │ │ │ ├── 1716382976.0483525 │ │ │ └── hparams.yml │ │ │ ├── 1716383146.7434232 │ │ │ └── events.out.tfevents.1716383146.dgx056.scc.idea.1639132.1 │ │ │ ├── 1716383146.7570305 │ │ │ └── hparams.yml │ │ │ ├── 1716383549.148326 │ │ │ └── events.out.tfevents.1716383549.dgx056.scc.idea.1651826.1 │ │ │ ├── 1716383549.167656 │ │ │ └── hparams.yml │ │ │ ├── 1716383642.5208204 │ │ │ └── events.out.tfevents.1716383642.dgx056.scc.idea.1655652.1 │ │ │ ├── 1716383642.5471237 │ │ │ └── hparams.yml │ │ │ ├── 1716383979.3017948 │ │ │ └── events.out.tfevents.1716383979.dgx056.scc.idea.1666893.1 │ │ │ ├── 1716383979.3213682 │ │ │ └── hparams.yml │ │ │ ├── 1716384205.5351841 │ │ │ └── events.out.tfevents.1716384205.dgx056.scc.idea.1674951.1 │ │ │ ├── 1716384205.5546448 │ │ │ └── hparams.yml │ │ │ ├── 1716384592.557294 │ │ │ └── events.out.tfevents.1716384592.dgx056.scc.idea.1688794.1 │ │ │ ├── 1716384592.5924938 │ │ │ └── hparams.yml │ │ │ ├── 1716384719.065303 │ │ │ └── events.out.tfevents.1716384719.dgx056.scc.idea.1693459.1 │ │ │ ├── 1716384719.0841897 │ │ │ └── hparams.yml │ │ │ ├── 1716384962.168617 │ │ │ └── events.out.tfevents.1716384962.dgx056.scc.idea.1702110.1 │ │ │ ├── 1716384962.187086 │ │ │ └── hparams.yml │ │ │ ├── 1716385418.3680806 │ │ │ └── events.out.tfevents.1716385418.dgx056.scc.idea.1717742.1 │ │ │ ├── 1716385418.3887773 │ │ │ └── hparams.yml │ │ │ ├── 1716389442.93385 │ │ │ └── events.out.tfevents.1716389442.dgx056.scc.idea.1846021.1 │ │ │ ├── 1716389442.9745367 │ │ │ └── hparams.yml │ │ │ ├── 1716389606.3728828 │ │ │ └── events.out.tfevents.1716389606.dgx056.scc.idea.1850296.1 │ │ │ ├── 1716389606.3870578 │ │ │ └── hparams.yml │ │ │ ├── 1716389751.5613987 │ │ │ └── events.out.tfevents.1716389751.dgx056.scc.idea.1855394.1 │ │ │ ├── 1716389751.576123 │ │ │ └── hparams.yml │ │ │ ├── 1716389795.4144542 │ │ │ └── events.out.tfevents.1716389795.dgx056.scc.idea.1856667.1 │ │ │ ├── 1716389795.4301991 │ │ │ └── hparams.yml │ │ │ ├── 1716389872.815018 │ │ │ └── events.out.tfevents.1716389872.dgx056.scc.idea.1858780.1 │ │ │ ├── 1716389872.829187 │ │ │ └── hparams.yml │ │ │ ├── 1716390289.8250098 │ │ │ └── events.out.tfevents.1716390289.dgx056.scc.idea.1872589.1 │ │ │ ├── 1716390289.8443682 │ │ │ └── hparams.yml │ │ │ ├── 1716390452.1294196 │ │ │ └── events.out.tfevents.1716390452.dgx056.scc.idea.1877684.1 │ │ │ ├── 1716390452.1440647 │ │ │ └── hparams.yml │ │ │ ├── 1716390594.6717284 │ │ │ └── events.out.tfevents.1716390594.dgx056.scc.idea.1883258.1 │ │ │ ├── 1716390594.6858609 │ │ │ └── hparams.yml │ │ │ ├── 1716390847.4026217 │ │ │ └── events.out.tfevents.1716390847.dgx056.scc.idea.1890025.1 │ │ │ ├── 1716390847.4179935 │ │ │ └── hparams.yml │ │ │ ├── 1716391855.5047703 │ │ │ └── events.out.tfevents.1716391855.dgx056.scc.idea.1919260.1 │ │ │ ├── 1716391855.519122 │ │ │ └── hparams.yml │ │ │ ├── 1716392030.6080308 │ │ │ └── events.out.tfevents.1716392030.dgx056.scc.idea.1923965.1 │ │ │ ├── 1716392030.6205738 │ │ │ └── hparams.yml │ │ │ ├── 1716392838.037567 │ │ │ └── events.out.tfevents.1716392838.dgx056.scc.idea.1947082.1 │ │ │ ├── 1716392838.056221 │ │ │ └── hparams.yml │ │ │ ├── 1716393409.7737563 │ │ │ └── events.out.tfevents.1716393409.dgx056.scc.idea.1963472.1 │ │ │ ├── 1716393409.8056242 │ │ │ └── hparams.yml │ │ │ ├── 1716393453.972932 │ │ │ └── events.out.tfevents.1716393453.dgx056.scc.idea.1965065.1 │ │ │ ├── 1716393454.0748668 │ │ │ └── hparams.yml │ │ │ ├── 1716393854.5048451 │ │ │ └── events.out.tfevents.1716393854.dgx056.scc.idea.1976313.1 │ │ │ ├── 1716393854.5600033 │ │ │ └── hparams.yml │ │ │ ├── 1716394216.0182827 │ │ │ └── events.out.tfevents.1716394216.dgx056.scc.idea.1987161.1 │ │ │ ├── 1716394216.102231 │ │ │ └── hparams.yml │ │ │ ├── 1716394357.5716872 │ │ │ └── events.out.tfevents.1716394357.dgx056.scc.idea.1991013.1 │ │ │ ├── 1716394357.603195 │ │ │ └── hparams.yml │ │ │ ├── 1716394420.3644235 │ │ │ └── events.out.tfevents.1716394420.dgx056.scc.idea.1992842.1 │ │ │ ├── 1716394420.5073915 │ │ │ └── hparams.yml │ │ │ ├── 1716394550.4763336 │ │ │ └── events.out.tfevents.1716394550.dgx056.scc.idea.1996949.1 │ │ │ ├── 1716394550.4946039 │ │ │ └── hparams.yml │ │ │ ├── 1716394729.5474215 │ │ │ └── events.out.tfevents.1716394729.dgx056.scc.idea.2001610.1 │ │ │ ├── 1716394729.6445727 │ │ │ └── hparams.yml │ │ │ ├── 1716394955.3286421 │ │ │ └── events.out.tfevents.1716394955.dgx056.scc.idea.2008487.1 │ │ │ ├── 1716394955.4327264 │ │ │ └── hparams.yml │ │ │ ├── 1716395113.6585987 │ │ │ └── events.out.tfevents.1716395113.dgx056.scc.idea.2013286.1 │ │ │ ├── 1716395113.6895995 │ │ │ └── hparams.yml │ │ │ ├── 1716395469.480092 │ │ │ └── events.out.tfevents.1716395469.dgx056.scc.idea.2023302.1 │ │ │ ├── 1716395469.5646172 │ │ │ └── hparams.yml │ │ │ ├── 1716395619.8812966 │ │ │ └── events.out.tfevents.1716395619.dgx056.scc.idea.2027258.1 │ │ │ ├── 1716395619.8999808 │ │ │ └── hparams.yml │ │ │ ├── 1716395701.114961 │ │ │ └── events.out.tfevents.1716395701.dgx056.scc.idea.2029821.1 │ │ │ ├── 1716395701.2720144 │ │ │ └── hparams.yml │ │ │ ├── 1716395815.2675571 │ │ │ └── events.out.tfevents.1716395815.dgx056.scc.idea.2032962.1 │ │ │ ├── 1716395815.2842898 │ │ │ └── hparams.yml │ │ │ ├── 1716447210.594312 │ │ │ └── events.out.tfevents.1716447210.dgx056.scc.idea.46863.1 │ │ │ ├── 1716447210.6052012 │ │ │ └── hparams.yml │ │ │ ├── 1716447256.6749153 │ │ │ └── events.out.tfevents.1716447256.dgx056.scc.idea.48445.1 │ │ │ ├── 1716447256.6895304 │ │ │ └── hparams.yml │ │ │ ├── 1716453954.3739483 │ │ │ └── events.out.tfevents.1716453954.dgx056.scc.idea.557035.1 │ │ │ ├── 1716453954.3891187 │ │ │ └── hparams.yml │ │ │ ├── 1716454048.2474556 │ │ │ └── events.out.tfevents.1716454048.dgx056.scc.idea.559429.1 │ │ │ ├── 1716454048.272137 │ │ │ └── hparams.yml │ │ │ ├── 1716454239.3573139 │ │ │ └── events.out.tfevents.1716454239.dgx056.scc.idea.565281.1 │ │ │ ├── 1716454239.374592 │ │ │ └── hparams.yml │ │ │ ├── 1716454307.4448683 │ │ │ └── events.out.tfevents.1716454307.dgx056.scc.idea.567039.1 │ │ │ ├── 1716454307.4665065 │ │ │ └── hparams.yml │ │ │ ├── 1716454372.5334616 │ │ │ └── events.out.tfevents.1716454372.dgx056.scc.idea.568628.1 │ │ │ ├── 1716454372.5479605 │ │ │ └── hparams.yml │ │ │ ├── 1716454584.5205529 │ │ │ └── events.out.tfevents.1716454584.dgx056.scc.idea.576838.1 │ │ │ ├── 1716454584.5359795 │ │ │ └── hparams.yml │ │ │ ├── 1716454646.5674164 │ │ │ └── events.out.tfevents.1716454646.dgx056.scc.idea.578514.1 │ │ │ ├── 1716454646.6019485 │ │ │ └── hparams.yml │ │ │ ├── 1716454882.6566896 │ │ │ └── events.out.tfevents.1716454882.dgx056.scc.idea.585491.1 │ │ │ ├── 1716454882.669038 │ │ │ └── hparams.yml │ │ │ ├── 1716454964.981315 │ │ │ └── events.out.tfevents.1716454964.dgx056.scc.idea.587776.1 │ │ │ ├── 1716454964.9986336 │ │ │ └── hparams.yml │ │ │ ├── 1716455145.7484126 │ │ │ └── events.out.tfevents.1716455145.dgx056.scc.idea.592867.1 │ │ │ ├── 1716455145.7713232 │ │ │ └── hparams.yml │ │ │ ├── 1716455243.066775 │ │ │ └── events.out.tfevents.1716455243.dgx056.scc.idea.595439.1 │ │ │ ├── 1716455243.0853422 │ │ │ └── hparams.yml │ │ │ ├── 1716455329.2774801 │ │ │ └── events.out.tfevents.1716455329.dgx056.scc.idea.597520.1 │ │ │ ├── 1716455329.2904289 │ │ │ └── hparams.yml │ │ │ ├── 1716455423.0167484 │ │ │ └── events.out.tfevents.1716455423.dgx056.scc.idea.600702.1 │ │ │ ├── 1716455423.0347612 │ │ │ └── hparams.yml │ │ │ ├── 1716455604.6522143 │ │ │ └── events.out.tfevents.1716455604.dgx056.scc.idea.605811.1 │ │ │ ├── 1716455604.6736226 │ │ │ └── hparams.yml │ │ │ ├── 1716455912.5640883 │ │ │ └── events.out.tfevents.1716455912.dgx056.scc.idea.615207.1 │ │ │ ├── 1716455912.5798695 │ │ │ └── hparams.yml │ │ │ ├── 1716456069.7793915 │ │ │ └── events.out.tfevents.1716456069.dgx056.scc.idea.619719.1 │ │ │ ├── 1716456069.7933674 │ │ │ └── hparams.yml │ │ │ ├── 1716456276.0083725 │ │ │ └── events.out.tfevents.1716456276.dgx056.scc.idea.625825.1 │ │ │ ├── 1716456276.0207222 │ │ │ └── hparams.yml │ │ │ ├── 1716462681.7574172 │ │ │ └── events.out.tfevents.1716462681.dgx056.scc.idea.663900.1 │ │ │ ├── 1716462681.812348 │ │ │ └── hparams.yml │ │ │ ├── 1716463024.3948033 │ │ │ └── events.out.tfevents.1716463024.dgx056.scc.idea.1439290.1 │ │ │ ├── 1716463024.4373188 │ │ │ └── hparams.yml │ │ │ ├── 1716463181.6175842 │ │ │ └── events.out.tfevents.1716463181.dgx056.scc.idea.1444470.1 │ │ │ ├── 1716463181.6436818 │ │ │ └── hparams.yml │ │ │ ├── 1716463302.4339528 │ │ │ └── events.out.tfevents.1716463302.dgx056.scc.idea.1448110.1 │ │ │ ├── 1716463302.5249066 │ │ │ └── hparams.yml │ │ │ ├── 1716463486.7039838 │ │ │ └── events.out.tfevents.1716463486.dgx056.scc.idea.1452747.1 │ │ │ ├── 1716463486.732592 │ │ │ └── hparams.yml │ │ │ ├── events.out.tfevents.1716380414.dgx056.scc.idea.1556298.0 │ │ │ ├── events.out.tfevents.1716382912.dgx056.scc.idea.1630624.0 │ │ │ ├── events.out.tfevents.1716382976.dgx056.scc.idea.1632835.0 │ │ │ ├── events.out.tfevents.1716383146.dgx056.scc.idea.1639132.0 │ │ │ ├── events.out.tfevents.1716383549.dgx056.scc.idea.1651826.0 │ │ │ ├── events.out.tfevents.1716383642.dgx056.scc.idea.1655652.0 │ │ │ ├── events.out.tfevents.1716383979.dgx056.scc.idea.1666893.0 │ │ │ ├── events.out.tfevents.1716384205.dgx056.scc.idea.1674951.0 │ │ │ ├── events.out.tfevents.1716384592.dgx056.scc.idea.1688794.0 │ │ │ ├── events.out.tfevents.1716384719.dgx056.scc.idea.1693459.0 │ │ │ ├── events.out.tfevents.1716384962.dgx056.scc.idea.1702110.0 │ │ │ ├── events.out.tfevents.1716385418.dgx056.scc.idea.1717742.0 │ │ │ ├── events.out.tfevents.1716389442.dgx056.scc.idea.1846021.0 │ │ │ ├── events.out.tfevents.1716389606.dgx056.scc.idea.1850296.0 │ │ │ ├── events.out.tfevents.1716389751.dgx056.scc.idea.1855394.0 │ │ │ ├── events.out.tfevents.1716389795.dgx056.scc.idea.1856667.0 │ │ │ ├── events.out.tfevents.1716389872.dgx056.scc.idea.1858780.0 │ │ │ ├── events.out.tfevents.1716390289.dgx056.scc.idea.1872589.0 │ │ │ ├── events.out.tfevents.1716390452.dgx056.scc.idea.1877684.0 │ │ │ ├── events.out.tfevents.1716390594.dgx056.scc.idea.1883258.0 │ │ │ ├── events.out.tfevents.1716390847.dgx056.scc.idea.1890025.0 │ │ │ ├── events.out.tfevents.1716391855.dgx056.scc.idea.1919260.0 │ │ │ ├── events.out.tfevents.1716392030.dgx056.scc.idea.1923965.0 │ │ │ ├── events.out.tfevents.1716392837.dgx056.scc.idea.1947082.0 │ │ │ ├── events.out.tfevents.1716393409.dgx056.scc.idea.1963472.0 │ │ │ ├── events.out.tfevents.1716393453.dgx056.scc.idea.1965065.0 │ │ │ ├── events.out.tfevents.1716393854.dgx056.scc.idea.1976313.0 │ │ │ ├── events.out.tfevents.1716394215.dgx056.scc.idea.1987161.0 │ │ │ ├── events.out.tfevents.1716394357.dgx056.scc.idea.1991013.0 │ │ │ ├── events.out.tfevents.1716394420.dgx056.scc.idea.1992842.0 │ │ │ ├── events.out.tfevents.1716394550.dgx056.scc.idea.1996949.0 │ │ │ ├── events.out.tfevents.1716394729.dgx056.scc.idea.2001610.0 │ │ │ ├── events.out.tfevents.1716394955.dgx056.scc.idea.2008487.0 │ │ │ ├── events.out.tfevents.1716395113.dgx056.scc.idea.2013286.0 │ │ │ ├── events.out.tfevents.1716395469.dgx056.scc.idea.2023302.0 │ │ │ ├── events.out.tfevents.1716395619.dgx056.scc.idea.2027258.0 │ │ │ ├── events.out.tfevents.1716395701.dgx056.scc.idea.2029821.0 │ │ │ ├── events.out.tfevents.1716395815.dgx056.scc.idea.2032962.0 │ │ │ ├── events.out.tfevents.1716447210.dgx056.scc.idea.46863.0 │ │ │ ├── events.out.tfevents.1716447256.dgx056.scc.idea.48445.0 │ │ │ ├── events.out.tfevents.1716453954.dgx056.scc.idea.557035.0 │ │ │ ├── events.out.tfevents.1716454048.dgx056.scc.idea.559429.0 │ │ │ ├── events.out.tfevents.1716454239.dgx056.scc.idea.565281.0 │ │ │ ├── events.out.tfevents.1716454307.dgx056.scc.idea.567039.0 │ │ │ ├── events.out.tfevents.1716454372.dgx056.scc.idea.568628.0 │ │ │ ├── events.out.tfevents.1716454584.dgx056.scc.idea.576838.0 │ │ │ ├── events.out.tfevents.1716454646.dgx056.scc.idea.578514.0 │ │ │ ├── events.out.tfevents.1716454882.dgx056.scc.idea.585491.0 │ │ │ ├── events.out.tfevents.1716454964.dgx056.scc.idea.587776.0 │ │ │ ├── events.out.tfevents.1716455145.dgx056.scc.idea.592867.0 │ │ │ ├── events.out.tfevents.1716455243.dgx056.scc.idea.595439.0 │ │ │ ├── events.out.tfevents.1716455329.dgx056.scc.idea.597520.0 │ │ │ ├── events.out.tfevents.1716455422.dgx056.scc.idea.600702.0 │ │ │ ├── events.out.tfevents.1716455604.dgx056.scc.idea.605811.0 │ │ │ ├── events.out.tfevents.1716455912.dgx056.scc.idea.615207.0 │ │ │ ├── events.out.tfevents.1716456069.dgx056.scc.idea.619719.0 │ │ │ ├── events.out.tfevents.1716456275.dgx056.scc.idea.625825.0 │ │ │ ├── events.out.tfevents.1716462681.dgx056.scc.idea.663900.0 │ │ │ ├── events.out.tfevents.1716463024.dgx056.scc.idea.1439290.0 │ │ │ ├── events.out.tfevents.1716463181.dgx056.scc.idea.1444470.0 │ │ │ ├── events.out.tfevents.1716463302.dgx056.scc.idea.1448110.0 │ │ │ └── events.out.tfevents.1716463486.dgx056.scc.idea.1452747.0 │ ├── requirements.txt │ ├── src │ │ ├── example_1.jpg │ │ ├── example_1_mask.jpg │ │ ├── example_1_result.png │ │ ├── example_2.jpg │ │ ├── example_2_mask.jpg │ │ ├── example_2_result.png │ │ ├── example_3.jpg │ │ ├── example_3_mask.jpg │ │ ├── example_3_result.png │ │ ├── example_4.jpeg │ │ ├── example_4_mask.jpg │ │ ├── example_4_result.png │ │ ├── example_5.jpg │ │ ├── example_5_mask.jpg │ │ ├── example_5_result.png │ │ ├── model.png │ │ ├── test_image.jpg │ │ ├── test_mask.jpg │ │ └── test_result.png │ ├── test_brushnet.py │ ├── test_brushnet_sdxl.py │ ├── train_brushnet.py │ ├── train_brushnet_sdxl.py │ ├── train_replacenet_stage_1.py │ ├── train_replacenet_stage_2.py │ ├── train_stage_1.sh │ └── train_stage_2.sh ├── community │ ├── README.md │ ├── bit_diffusion.py │ ├── checkpoint_merger.py │ ├── clip_guided_images_mixing_stable_diffusion.py │ ├── clip_guided_stable_diffusion.py │ ├── clip_guided_stable_diffusion_img2img.py │ ├── composable_stable_diffusion.py │ ├── ddim_noise_comparative_analysis.py │ ├── dps_pipeline.py │ ├── edict_pipeline.py │ ├── gluegen.py │ ├── iadb.py │ ├── imagic_stable_diffusion.py │ ├── img2img_inpainting.py │ ├── instaflow_one_step.py │ ├── interpolate_stable_diffusion.py │ ├── ip_adapter_face_id.py │ ├── latent_consistency_img2img.py │ ├── latent_consistency_interpolate.py │ ├── latent_consistency_txt2img.py │ ├── llm_grounded_diffusion.py │ ├── lpw_stable_diffusion.py │ ├── lpw_stable_diffusion_onnx.py │ ├── lpw_stable_diffusion_xl.py │ ├── magic_mix.py │ ├── marigold_depth_estimation.py │ ├── masked_stable_diffusion_img2img.py │ ├── mixture_canvas.py │ ├── mixture_tiling.py │ ├── multilingual_stable_diffusion.py │ ├── one_step_unet.py │ ├── pipeline_animatediff_controlnet.py │ ├── pipeline_animatediff_img2video.py │ ├── pipeline_demofusion_sdxl.py │ ├── pipeline_fabric.py │ ├── pipeline_null_text_inversion.py │ ├── pipeline_prompt2prompt.py │ ├── pipeline_sdxl_style_aligned.py │ ├── pipeline_stable_diffusion_upscale_ldm3d.py │ ├── pipeline_stable_diffusion_xl_controlnet_adapter.py │ ├── pipeline_stable_diffusion_xl_controlnet_adapter_inpaint.py │ ├── pipeline_stable_diffusion_xl_instantid.py │ ├── pipeline_stable_diffusion_xl_ipex.py │ ├── pipeline_zero1to3.py │ ├── regional_prompting_stable_diffusion.py │ ├── rerender_a_video.py │ ├── run_onnx_controlnet.py │ ├── run_tensorrt_controlnet.py │ ├── scheduling_ufogen.py │ ├── sd_text2img_k_diffusion.py │ ├── sde_drag.py │ ├── seed_resize_stable_diffusion.py │ ├── speech_to_image_diffusion.py │ ├── stable_diffusion_comparison.py │ ├── stable_diffusion_controlnet_img2img.py │ ├── stable_diffusion_controlnet_inpaint.py │ ├── stable_diffusion_controlnet_inpaint_img2img.py │ ├── stable_diffusion_controlnet_reference.py │ ├── stable_diffusion_ipex.py │ ├── stable_diffusion_mega.py │ ├── stable_diffusion_reference.py │ ├── stable_diffusion_repaint.py │ ├── stable_diffusion_tensorrt_img2img.py │ ├── stable_diffusion_tensorrt_inpaint.py │ ├── stable_diffusion_tensorrt_txt2img.py │ ├── stable_diffusion_xl_reference.py │ ├── stable_unclip.py │ ├── text_inpainting.py │ ├── tiled_upscaling.py │ ├── unclip_image_interpolation.py │ ├── unclip_text_interpolation.py │ └── wildcard_stable_diffusion.py ├── conftest.py ├── consistency_distillation │ ├── README.md │ ├── README_sdxl.md │ ├── requirements.txt │ ├── test_lcm_lora.py │ ├── train_lcm_distill_lora_sd_wds.py │ ├── train_lcm_distill_lora_sdxl.py │ ├── train_lcm_distill_lora_sdxl_wds.py │ ├── train_lcm_distill_sd_wds.py │ └── train_lcm_distill_sdxl_wds.py ├── controlnet │ ├── README.md │ ├── README_sdxl.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── requirements_sdxl.txt │ ├── test_controlnet.py │ ├── train_controlnet.py │ ├── train_controlnet_flax.py │ └── train_controlnet_sdxl.py ├── custom_diffusion │ ├── README.md │ ├── requirements.txt │ ├── retrieve.py │ ├── test_custom_diffusion.py │ └── train_custom_diffusion.py ├── dreambooth │ ├── README.md │ ├── README_sdxl.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── requirements_sdxl.txt │ ├── test_dreambooth.py │ ├── test_dreambooth_lora.py │ ├── test_dreambooth_lora_edm.py │ ├── train_dreambooth.py │ ├── train_dreambooth_flax.py │ ├── train_dreambooth_lora.py │ └── train_dreambooth_lora_sdxl.py ├── inference │ ├── README.md │ ├── image_to_image.py │ └── inpainting.py ├── instruct_pix2pix │ ├── README.md │ ├── README_sdxl.md │ ├── requirements.txt │ ├── test_instruct_pix2pix.py │ ├── train_instruct_pix2pix.py │ └── train_instruct_pix2pix_sdxl.py ├── kandinsky2_2 │ └── text_to_image │ │ ├── README.md │ │ ├── requirements.txt │ │ ├── train_text_to_image_decoder.py │ │ ├── train_text_to_image_lora_decoder.py │ │ ├── train_text_to_image_lora_prior.py │ │ └── train_text_to_image_prior.py ├── reinforcement_learning │ ├── README.md │ └── run_diffuser_locomotion.py ├── replace_anything │ ├── replacenet_output_stage_1 │ │ └── logs │ │ │ └── text2image-fine-tune │ │ │ ├── 1716463909.9798012 │ │ │ └── events.out.tfevents.1716463909.dgx056.scc.idea.1464817.1 │ │ │ ├── 1716463910.147478 │ │ │ └── hparams.yml │ │ │ ├── 1716464804.7347267 │ │ │ └── events.out.tfevents.1716464804.dgx056.scc.idea.1493358.1 │ │ │ ├── 1716464804.7564914 │ │ │ └── hparams.yml │ │ │ ├── events.out.tfevents.1716463909.dgx056.scc.idea.1464817.0 │ │ │ └── events.out.tfevents.1716464804.dgx056.scc.idea.1493358.0 │ ├── replacenet_output_stage_2 │ │ └── logs │ │ │ └── text2image-fine-tune │ │ │ ├── 1716464059.8612726 │ │ │ └── events.out.tfevents.1716464059.dgx056.scc.idea.1467870.1 │ │ │ ├── 1716464059.9250808 │ │ │ └── hparams.yml │ │ │ ├── 1716464638.7547264 │ │ │ └── events.out.tfevents.1716464638.dgx056.scc.idea.1487026.1 │ │ │ ├── 1716464638.8163958 │ │ │ └── hparams.yml │ │ │ ├── events.out.tfevents.1716464059.dgx056.scc.idea.1467870.0 │ │ │ └── events.out.tfevents.1716464638.dgx056.scc.idea.1487026.0 │ ├── requirements.txt │ ├── train_replacenet_stage_1.py │ ├── train_replacenet_stage_2.py │ ├── train_stage_1.sh │ ├── train_stage_2.sh │ └── training_data │ │ ├── 000001.json │ │ ├── 000001.png │ │ ├── 000001_canny.png │ │ ├── 000001_dino.pt │ │ ├── 000001_object.png │ │ └── 000001_pose.png ├── research_projects │ ├── README.md │ ├── colossalai │ │ ├── README.md │ │ ├── inference.py │ │ ├── requirement.txt │ │ └── train_dreambooth_colossalai.py │ ├── consistency_training │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_cm_ct_unconditional.py │ ├── controlnet │ │ └── train_controlnet_webdataset.py │ ├── controlnetxs │ │ ├── README.md │ │ ├── README_sdxl.md │ │ ├── controlnetxs.py │ │ ├── infer_sd_controlnetxs.py │ │ ├── infer_sdxl_controlnetxs.py │ │ ├── pipeline_controlnet_xs.py │ │ └── pipeline_controlnet_xs_sd_xl.py │ ├── diffusion_dpo │ │ ├── README.md │ │ ├── requirements.txt │ │ ├── train_diffusion_dpo.py │ │ └── train_diffusion_dpo_sdxl.py │ ├── dreambooth_inpaint │ │ ├── README.md │ │ ├── requirements.txt │ │ ├── train_dreambooth_inpaint.py │ │ └── train_dreambooth_inpaint_lora.py │ ├── instructpix2pix_lora │ │ ├── README.md │ │ └── train_instruct_pix2pix_lora.py │ ├── intel_opts │ │ ├── README.md │ │ ├── inference_bf16.py │ │ ├── textual_inversion │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ └── textual_inversion_bf16.py │ │ └── textual_inversion_dfq │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ ├── text2images.py │ │ │ └── textual_inversion.py │ ├── lora │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_text_to_image_lora.py │ ├── multi_subject_dreambooth │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_multi_subject_dreambooth.py │ ├── multi_subject_dreambooth_inpainting │ │ ├── README.md │ │ ├── requirements.txt │ │ └── train_multi_subject_dreambooth_inpainting.py │ ├── multi_token_textual_inversion │ │ ├── README.md │ │ ├── multi_token_clip.py │ │ ├── requirements.txt │ │ ├── requirements_flax.txt │ │ ├── textual_inversion.py │ │ └── textual_inversion_flax.py │ ├── onnxruntime │ │ ├── README.md │ │ ├── text_to_image │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ └── train_text_to_image.py │ │ ├── textual_inversion │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ └── textual_inversion.py │ │ └── unconditional_image_generation │ │ │ ├── README.md │ │ │ ├── requirements.txt │ │ │ └── train_unconditional.py │ ├── promptdiffusion │ │ ├── README.md │ │ ├── convert_original_promptdiffusion_to_diffusers.py │ │ ├── pipeline_prompt_diffusion.py │ │ └── promptdiffusioncontrolnet.py │ ├── rdm │ │ ├── README.md │ │ ├── pipeline_rdm.py │ │ └── retriever.py │ ├── realfill │ │ ├── README.md │ │ ├── infer.py │ │ ├── requirements.txt │ │ └── train_realfill.py │ └── sdxl_flax │ │ ├── README.md │ │ ├── sdxl_single.py │ │ └── sdxl_single_aot.py ├── t2i_adapter │ ├── README.md │ ├── README_sdxl.md │ ├── requirements.txt │ ├── test_t2i_adapter.py │ └── train_t2i_adapter_sdxl.py ├── test_examples_utils.py ├── text_to_image │ ├── README.md │ ├── README_sdxl.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── requirements_sdxl.txt │ ├── test_text_to_image.py │ ├── test_text_to_image_lora.py │ ├── train_text_to_image.py │ ├── train_text_to_image_flax.py │ ├── train_text_to_image_lora.py │ ├── train_text_to_image_lora_sdxl.py │ └── train_text_to_image_sdxl.py ├── textual_inversion │ ├── README.md │ ├── README_sdxl.md │ ├── requirements.txt │ ├── requirements_flax.txt │ ├── test_textual_inversion.py │ ├── test_textual_inversion_sdxl.py │ ├── textual_inversion.py │ ├── textual_inversion_flax.py │ └── textual_inversion_sdxl.py ├── unconditional_image_generation │ ├── README.md │ ├── requirements.txt │ ├── test_unconditional.py │ └── train_unconditional.py └── wuerstchen │ └── text_to_image │ ├── README.md │ ├── __init__.py │ ├── modeling_efficient_net_encoder.py │ ├── requirements.txt │ ├── train_text_to_image_lora_prior.py │ └── train_text_to_image_prior.py ├── install.sh ├── install.sh.baiduyun.uploading.cfg ├── pyproject.toml ├── scripts ├── __init__.py ├── change_naming_configs_and_checkpoints.py ├── conversion_ldm_uncond.py ├── convert_amused.py ├── convert_animatediff_motion_lora_to_diffusers.py ├── convert_animatediff_motion_module_to_diffusers.py ├── convert_asymmetric_vqgan_to_diffusers.py ├── convert_blipdiffusion_to_diffusers.py ├── convert_consistency_decoder.py ├── convert_consistency_to_diffusers.py ├── convert_dance_diffusion_to_diffusers.py ├── convert_ddpm_original_checkpoint_to_diffusers.py ├── convert_diffusers_sdxl_lora_to_webui.py ├── convert_diffusers_to_original_sdxl.py ├── convert_diffusers_to_original_stable_diffusion.py ├── convert_dit_to_diffusers.py ├── convert_gligen_to_diffusers.py ├── convert_i2vgen_to_diffusers.py ├── convert_if.py ├── convert_k_upscaler_to_diffusers.py ├── convert_kakao_brain_unclip_to_diffusers.py ├── convert_kandinsky3_unet.py ├── convert_kandinsky_to_diffusers.py ├── convert_ldm_original_checkpoint_to_diffusers.py ├── convert_lora_safetensor_to_diffusers.py ├── convert_models_diffuser_to_diffusers.py ├── convert_ms_text_to_video_to_diffusers.py ├── convert_music_spectrogram_to_diffusers.py ├── convert_ncsnpp_original_checkpoint_to_diffusers.py ├── convert_original_audioldm2_to_diffusers.py ├── convert_original_audioldm_to_diffusers.py ├── convert_original_controlnet_to_diffusers.py ├── convert_original_musicldm_to_diffusers.py ├── convert_original_stable_diffusion_to_diffusers.py ├── convert_original_t2i_adapter.py ├── convert_pixart_alpha_to_diffusers.py ├── convert_shap_e_to_diffusers.py ├── convert_stable_cascade.py ├── convert_stable_diffusion_checkpoint_to_onnx.py ├── convert_stable_diffusion_controlnet_to_onnx.py ├── convert_stable_diffusion_controlnet_to_tensorrt.py ├── convert_svd_to_diffusers.py ├── convert_tiny_autoencoder_to_diffusers.py ├── convert_unclip_txt2img_to_image_variation.py ├── convert_unidiffuser_to_diffusers.py ├── convert_vae_diff_to_onnx.py ├── convert_vae_pt_to_diffusers.py ├── convert_versatile_diffusion_to_diffusers.py ├── convert_vq_diffusion_to_diffusers.py ├── convert_wuerstchen.py ├── convert_zero123_to_diffusers.py └── generate_logits.py ├── setup.py ├── src ├── diffusers.egg-info │ ├── PKG-INFO │ ├── SOURCES.txt │ ├── dependency_links.txt │ ├── entry_points.txt │ ├── requires.txt │ └── top_level.txt └── diffusers │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-39.pyc │ ├── configuration_utils.cpython-39.pyc │ ├── dependency_versions_check.cpython-39.pyc │ ├── dependency_versions_table.cpython-39.pyc │ ├── image_processor.cpython-39.pyc │ ├── optimization.cpython-39.pyc │ └── training_utils.cpython-39.pyc │ ├── commands │ ├── __init__.py │ ├── diffusers_cli.py │ ├── env.py │ └── fp16_safetensors.py │ ├── configuration_utils.py │ ├── dependency_versions_check.py │ ├── dependency_versions_table.py │ ├── experimental │ ├── README.md │ ├── __init__.py │ └── rl │ │ ├── __init__.py │ │ └── value_guided_sampling.py │ ├── image_processor.py │ ├── loaders │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-39.pyc │ │ ├── autoencoder.cpython-39.pyc │ │ ├── ip_adapter.cpython-39.pyc │ │ ├── lora.cpython-39.pyc │ │ ├── lora_conversion_utils.cpython-39.pyc │ │ ├── peft.cpython-39.pyc │ │ ├── single_file.cpython-39.pyc │ │ ├── single_file_utils.cpython-39.pyc │ │ ├── textual_inversion.cpython-39.pyc │ │ ├── unet.cpython-39.pyc │ │ └── utils.cpython-39.pyc │ ├── autoencoder.py │ ├── controlnet.py │ ├── ip_adapter.py │ ├── lora.py │ ├── lora_conversion_utils.py │ ├── peft.py │ ├── single_file.py │ ├── single_file_utils.py │ ├── textual_inversion.py │ ├── unet.py │ └── utils.py │ ├── models │ ├── README.md │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-39.pyc │ │ ├── activations.cpython-39.pyc │ │ ├── attention.cpython-39.pyc │ │ ├── attention_processor.cpython-39.pyc │ │ ├── brushnet.cpython-39.pyc │ │ ├── downsampling.cpython-39.pyc │ │ ├── embeddings.cpython-39.pyc │ │ ├── lora.cpython-39.pyc │ │ ├── modeling_outputs.cpython-39.pyc │ │ ├── modeling_utils.cpython-39.pyc │ │ ├── normalization.cpython-39.pyc │ │ ├── replace_anything.cpython-39.pyc │ │ ├── resnet.cpython-39.pyc │ │ └── upsampling.cpython-39.pyc │ ├── activations.py │ ├── adapter.py │ ├── attention.py │ ├── attention_flax.py │ ├── attention_processor.py │ ├── autoencoders │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── autoencoder_asym_kl.cpython-39.pyc │ │ │ ├── autoencoder_kl.cpython-39.pyc │ │ │ ├── autoencoder_kl_temporal_decoder.cpython-39.pyc │ │ │ ├── autoencoder_tiny.cpython-39.pyc │ │ │ ├── consistency_decoder_vae.cpython-39.pyc │ │ │ └── vae.cpython-39.pyc │ │ ├── autoencoder_asym_kl.py │ │ ├── autoencoder_kl.py │ │ ├── autoencoder_kl_temporal_decoder.py │ │ ├── autoencoder_tiny.py │ │ ├── consistency_decoder_vae.py │ │ └── vae.py │ ├── brushnet.py │ ├── controlnet.py │ ├── controlnet_flax.py │ ├── downsampling.py │ ├── dual_transformer_2d.py │ ├── embeddings.py │ ├── embeddings_flax.py │ ├── lora.py │ ├── modeling_flax_pytorch_utils.py │ ├── modeling_flax_utils.py │ ├── modeling_outputs.py │ ├── modeling_pytorch_flax_utils.py │ ├── modeling_utils.py │ ├── normalization.py │ ├── prior_transformer.py │ ├── replace_anything.py │ ├── resnet.py │ ├── resnet_flax.py │ ├── t5_film_transformer.py │ ├── transformer_2d.py │ ├── transformer_temporal.py │ ├── transformers │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── dual_transformer_2d.cpython-39.pyc │ │ │ ├── prior_transformer.cpython-39.pyc │ │ │ ├── t5_film_transformer.cpython-39.pyc │ │ │ ├── transformer_2d.cpython-39.pyc │ │ │ └── transformer_temporal.cpython-39.pyc │ │ ├── dual_transformer_2d.py │ │ ├── prior_transformer.py │ │ ├── t5_film_transformer.py │ │ ├── transformer_2d.py │ │ └── transformer_temporal.py │ ├── unet_1d.py │ ├── unet_1d_blocks.py │ ├── unet_2d.py │ ├── unet_2d_blocks.py │ ├── unet_2d_condition.py │ ├── unets │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── unet_1d.cpython-39.pyc │ │ │ ├── unet_1d_blocks.cpython-39.pyc │ │ │ ├── unet_2d.cpython-39.pyc │ │ │ ├── unet_2d_blocks.cpython-39.pyc │ │ │ ├── unet_2d_condition.cpython-39.pyc │ │ │ ├── unet_3d_blocks.cpython-39.pyc │ │ │ ├── unet_3d_condition.cpython-39.pyc │ │ │ ├── unet_i2vgen_xl.cpython-39.pyc │ │ │ ├── unet_kandinsky3.cpython-39.pyc │ │ │ ├── unet_motion_model.cpython-39.pyc │ │ │ ├── unet_spatio_temporal_condition.cpython-39.pyc │ │ │ ├── unet_stable_cascade.cpython-39.pyc │ │ │ └── uvit_2d.cpython-39.pyc │ │ ├── unet_1d.py │ │ ├── unet_1d_blocks.py │ │ ├── unet_2d.py │ │ ├── unet_2d_blocks.py │ │ ├── unet_2d_blocks_flax.py │ │ ├── unet_2d_condition.py │ │ ├── unet_2d_condition_flax.py │ │ ├── unet_3d_blocks.py │ │ ├── unet_3d_condition.py │ │ ├── unet_i2vgen_xl.py │ │ ├── unet_kandinsky3.py │ │ ├── unet_motion_model.py │ │ ├── unet_spatio_temporal_condition.py │ │ ├── unet_stable_cascade.py │ │ └── uvit_2d.py │ ├── upsampling.py │ ├── vae_flax.py │ └── vq_model.py │ ├── optimization.py │ ├── pipelines │ ├── README.md │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-39.pyc │ │ ├── pipeline_loading_utils.cpython-39.pyc │ │ └── pipeline_utils.cpython-39.pyc │ ├── amused │ │ ├── __init__.py │ │ ├── pipeline_amused.py │ │ ├── pipeline_amused_img2img.py │ │ └── pipeline_amused_inpaint.py │ ├── animatediff │ │ ├── __init__.py │ │ ├── pipeline_animatediff.py │ │ ├── pipeline_animatediff_video2video.py │ │ └── pipeline_output.py │ ├── audioldm │ │ ├── __init__.py │ │ └── pipeline_audioldm.py │ ├── audioldm2 │ │ ├── __init__.py │ │ ├── modeling_audioldm2.py │ │ └── pipeline_audioldm2.py │ ├── auto_pipeline.py │ ├── blip_diffusion │ │ ├── __init__.py │ │ ├── blip_image_processing.py │ │ ├── modeling_blip2.py │ │ ├── modeling_ctx_clip.py │ │ └── pipeline_blip_diffusion.py │ ├── brushnet │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ └── pipeline_brushnet.cpython-39.pyc │ │ ├── pipeline_brushnet.py │ │ └── pipeline_brushnet_sd_xl.py │ ├── consistency_models │ │ ├── __init__.py │ │ └── pipeline_consistency_models.py │ ├── controlnet │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ └── pipeline_controlnet.cpython-39.pyc │ │ ├── multicontrolnet.py │ │ ├── pipeline_controlnet.py │ │ ├── pipeline_controlnet_blip_diffusion.py │ │ ├── pipeline_controlnet_img2img.py │ │ ├── pipeline_controlnet_inpaint.py │ │ ├── pipeline_controlnet_inpaint_sd_xl.py │ │ ├── pipeline_controlnet_sd_xl.py │ │ ├── pipeline_controlnet_sd_xl_img2img.py │ │ └── pipeline_flax_controlnet.py │ ├── dance_diffusion │ │ ├── __init__.py │ │ └── pipeline_dance_diffusion.py │ ├── ddim │ │ ├── __init__.py │ │ └── pipeline_ddim.py │ ├── ddpm │ │ ├── __init__.py │ │ └── pipeline_ddpm.py │ ├── deepfloyd_if │ │ ├── __init__.py │ │ ├── pipeline_if.py │ │ ├── pipeline_if_img2img.py │ │ ├── pipeline_if_img2img_superresolution.py │ │ ├── pipeline_if_inpainting.py │ │ ├── pipeline_if_inpainting_superresolution.py │ │ ├── pipeline_if_superresolution.py │ │ ├── pipeline_output.py │ │ ├── safety_checker.py │ │ ├── timesteps.py │ │ └── watermark.py │ ├── deprecated │ │ ├── README.md │ │ ├── __init__.py │ │ ├── alt_diffusion │ │ │ ├── __init__.py │ │ │ ├── modeling_roberta_series.py │ │ │ ├── pipeline_alt_diffusion.py │ │ │ ├── pipeline_alt_diffusion_img2img.py │ │ │ └── pipeline_output.py │ │ ├── audio_diffusion │ │ │ ├── __init__.py │ │ │ ├── mel.py │ │ │ └── pipeline_audio_diffusion.py │ │ ├── latent_diffusion_uncond │ │ │ ├── __init__.py │ │ │ └── pipeline_latent_diffusion_uncond.py │ │ ├── pndm │ │ │ ├── __init__.py │ │ │ └── pipeline_pndm.py │ │ ├── repaint │ │ │ ├── __init__.py │ │ │ └── pipeline_repaint.py │ │ ├── score_sde_ve │ │ │ ├── __init__.py │ │ │ └── pipeline_score_sde_ve.py │ │ ├── spectrogram_diffusion │ │ │ ├── __init__.py │ │ │ ├── continuous_encoder.py │ │ │ ├── midi_utils.py │ │ │ ├── notes_encoder.py │ │ │ └── pipeline_spectrogram_diffusion.py │ │ ├── stable_diffusion_variants │ │ │ ├── __init__.py │ │ │ ├── pipeline_cycle_diffusion.py │ │ │ ├── pipeline_onnx_stable_diffusion_inpaint_legacy.py │ │ │ ├── pipeline_stable_diffusion_inpaint_legacy.py │ │ │ ├── pipeline_stable_diffusion_model_editing.py │ │ │ ├── pipeline_stable_diffusion_paradigms.py │ │ │ └── pipeline_stable_diffusion_pix2pix_zero.py │ │ ├── stochastic_karras_ve │ │ │ ├── __init__.py │ │ │ └── pipeline_stochastic_karras_ve.py │ │ ├── versatile_diffusion │ │ │ ├── __init__.py │ │ │ ├── modeling_text_unet.py │ │ │ ├── pipeline_versatile_diffusion.py │ │ │ ├── pipeline_versatile_diffusion_dual_guided.py │ │ │ ├── pipeline_versatile_diffusion_image_variation.py │ │ │ └── pipeline_versatile_diffusion_text_to_image.py │ │ └── vq_diffusion │ │ │ ├── __init__.py │ │ │ └── pipeline_vq_diffusion.py │ ├── dit │ │ ├── __init__.py │ │ └── pipeline_dit.py │ ├── free_init_utils.py │ ├── i2vgen_xl │ │ ├── __init__.py │ │ └── pipeline_i2vgen_xl.py │ ├── kandinsky │ │ ├── __init__.py │ │ ├── pipeline_kandinsky.py │ │ ├── pipeline_kandinsky_combined.py │ │ ├── pipeline_kandinsky_img2img.py │ │ ├── pipeline_kandinsky_inpaint.py │ │ ├── pipeline_kandinsky_prior.py │ │ └── text_encoder.py │ ├── kandinsky2_2 │ │ ├── __init__.py │ │ ├── pipeline_kandinsky2_2.py │ │ ├── pipeline_kandinsky2_2_combined.py │ │ ├── pipeline_kandinsky2_2_controlnet.py │ │ ├── pipeline_kandinsky2_2_controlnet_img2img.py │ │ ├── pipeline_kandinsky2_2_img2img.py │ │ ├── pipeline_kandinsky2_2_inpainting.py │ │ ├── pipeline_kandinsky2_2_prior.py │ │ └── pipeline_kandinsky2_2_prior_emb2emb.py │ ├── kandinsky3 │ │ ├── __init__.py │ │ ├── convert_kandinsky3_unet.py │ │ ├── pipeline_kandinsky3.py │ │ └── pipeline_kandinsky3_img2img.py │ ├── latent_consistency_models │ │ ├── __init__.py │ │ ├── pipeline_latent_consistency_img2img.py │ │ └── pipeline_latent_consistency_text2img.py │ ├── latent_diffusion │ │ ├── __init__.py │ │ ├── pipeline_latent_diffusion.py │ │ └── pipeline_latent_diffusion_superresolution.py │ ├── musicldm │ │ ├── __init__.py │ │ └── pipeline_musicldm.py │ ├── onnx_utils.py │ ├── paint_by_example │ │ ├── __init__.py │ │ ├── image_encoder.py │ │ └── pipeline_paint_by_example.py │ ├── pia │ │ ├── __init__.py │ │ └── pipeline_pia.py │ ├── pipeline_flax_utils.py │ ├── pipeline_loading_utils.py │ ├── pipeline_utils.py │ ├── pixart_alpha │ │ ├── __init__.py │ │ └── pipeline_pixart_alpha.py │ ├── semantic_stable_diffusion │ │ ├── __init__.py │ │ ├── pipeline_output.py │ │ └── pipeline_semantic_stable_diffusion.py │ ├── shap_e │ │ ├── __init__.py │ │ ├── camera.py │ │ ├── pipeline_shap_e.py │ │ ├── pipeline_shap_e_img2img.py │ │ └── renderer.py │ ├── stable_cascade │ │ ├── __init__.py │ │ ├── pipeline_stable_cascade.py │ │ ├── pipeline_stable_cascade_combined.py │ │ └── pipeline_stable_cascade_prior.py │ ├── stable_diffusion │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-39.pyc │ │ │ ├── pipeline_output.cpython-39.pyc │ │ │ ├── pipeline_stable_diffusion.cpython-39.pyc │ │ │ └── safety_checker.cpython-39.pyc │ │ ├── clip_image_project_model.py │ │ ├── convert_from_ckpt.py │ │ ├── pipeline_flax_stable_diffusion.py │ │ ├── pipeline_flax_stable_diffusion_img2img.py │ │ ├── pipeline_flax_stable_diffusion_inpaint.py │ │ ├── pipeline_onnx_stable_diffusion.py │ │ ├── pipeline_onnx_stable_diffusion_img2img.py │ │ ├── pipeline_onnx_stable_diffusion_inpaint.py │ │ ├── pipeline_onnx_stable_diffusion_upscale.py │ │ ├── pipeline_output.py │ │ ├── pipeline_stable_diffusion.py │ │ ├── pipeline_stable_diffusion_depth2img.py │ │ ├── pipeline_stable_diffusion_image_variation.py │ │ ├── pipeline_stable_diffusion_img2img.py │ │ ├── pipeline_stable_diffusion_inpaint.py │ │ ├── pipeline_stable_diffusion_instruct_pix2pix.py │ │ ├── pipeline_stable_diffusion_latent_upscale.py │ │ ├── pipeline_stable_diffusion_upscale.py │ │ ├── pipeline_stable_unclip.py │ │ ├── pipeline_stable_unclip_img2img.py │ │ ├── safety_checker.py │ │ ├── safety_checker_flax.py │ │ └── stable_unclip_image_normalizer.py │ ├── stable_diffusion_attend_and_excite │ │ ├── __init__.py │ │ └── pipeline_stable_diffusion_attend_and_excite.py │ ├── stable_diffusion_diffedit │ │ ├── __init__.py │ │ └── pipeline_stable_diffusion_diffedit.py │ ├── stable_diffusion_gligen │ │ ├── __init__.py │ │ ├── pipeline_stable_diffusion_gligen.py │ │ └── pipeline_stable_diffusion_gligen_text_image.py │ ├── stable_diffusion_k_diffusion │ │ ├── __init__.py │ │ ├── pipeline_stable_diffusion_k_diffusion.py │ │ └── pipeline_stable_diffusion_xl_k_diffusion.py │ ├── stable_diffusion_ldm3d │ │ ├── __init__.py │ │ └── pipeline_stable_diffusion_ldm3d.py │ ├── stable_diffusion_panorama │ │ ├── __init__.py │ │ └── pipeline_stable_diffusion_panorama.py │ ├── stable_diffusion_safe │ │ ├── __init__.py │ │ ├── pipeline_output.py │ │ ├── pipeline_stable_diffusion_safe.py │ │ └── safety_checker.py │ ├── stable_diffusion_sag │ │ ├── __init__.py │ │ └── pipeline_stable_diffusion_sag.py │ ├── stable_diffusion_xl │ │ ├── __init__.py │ │ ├── pipeline_flax_stable_diffusion_xl.py │ │ ├── pipeline_output.py │ │ ├── pipeline_stable_diffusion_xl.py │ │ ├── pipeline_stable_diffusion_xl_img2img.py │ │ ├── pipeline_stable_diffusion_xl_inpaint.py │ │ ├── pipeline_stable_diffusion_xl_instruct_pix2pix.py │ │ └── watermark.py │ ├── stable_video_diffusion │ │ ├── __init__.py │ │ └── pipeline_stable_video_diffusion.py │ ├── t2i_adapter │ │ ├── __init__.py │ │ ├── pipeline_stable_diffusion_adapter.py │ │ └── pipeline_stable_diffusion_xl_adapter.py │ ├── text_to_video_synthesis │ │ ├── __init__.py │ │ ├── pipeline_output.py │ │ ├── pipeline_text_to_video_synth.py │ │ ├── pipeline_text_to_video_synth_img2img.py │ │ ├── pipeline_text_to_video_zero.py │ │ └── pipeline_text_to_video_zero_sdxl.py │ ├── unclip │ │ ├── __init__.py │ │ ├── pipeline_unclip.py │ │ ├── pipeline_unclip_image_variation.py │ │ └── text_proj.py │ ├── unidiffuser │ │ ├── __init__.py │ │ ├── modeling_text_decoder.py │ │ ├── modeling_uvit.py │ │ └── pipeline_unidiffuser.py │ └── wuerstchen │ │ ├── __init__.py │ │ ├── modeling_paella_vq_model.py │ │ ├── modeling_wuerstchen_common.py │ │ ├── modeling_wuerstchen_diffnext.py │ │ ├── modeling_wuerstchen_prior.py │ │ ├── pipeline_wuerstchen.py │ │ ├── pipeline_wuerstchen_combined.py │ │ └── pipeline_wuerstchen_prior.py │ ├── py.typed │ ├── schedulers │ ├── README.md │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-39.pyc │ │ ├── scheduling_consistency_decoder.cpython-39.pyc │ │ ├── scheduling_ddim.cpython-39.pyc │ │ ├── scheduling_ddpm.cpython-39.pyc │ │ ├── scheduling_deis_multistep.cpython-39.pyc │ │ ├── scheduling_dpmsolver_multistep.cpython-39.pyc │ │ ├── scheduling_dpmsolver_singlestep.cpython-39.pyc │ │ ├── scheduling_edm_dpmsolver_multistep.cpython-39.pyc │ │ ├── scheduling_edm_euler.cpython-39.pyc │ │ ├── scheduling_euler_ancestral_discrete.cpython-39.pyc │ │ ├── scheduling_euler_discrete.cpython-39.pyc │ │ ├── scheduling_heun_discrete.cpython-39.pyc │ │ ├── scheduling_k_dpm_2_ancestral_discrete.cpython-39.pyc │ │ ├── scheduling_k_dpm_2_discrete.cpython-39.pyc │ │ ├── scheduling_lms_discrete.cpython-39.pyc │ │ ├── scheduling_pndm.cpython-39.pyc │ │ ├── scheduling_unipc_multistep.cpython-39.pyc │ │ └── scheduling_utils.cpython-39.pyc │ ├── deprecated │ │ ├── __init__.py │ │ ├── scheduling_karras_ve.py │ │ └── scheduling_sde_vp.py │ ├── scheduling_amused.py │ ├── scheduling_consistency_decoder.py │ ├── scheduling_consistency_models.py │ ├── scheduling_ddim.py │ ├── scheduling_ddim_flax.py │ ├── scheduling_ddim_inverse.py │ ├── scheduling_ddim_parallel.py │ ├── scheduling_ddpm.py │ ├── scheduling_ddpm_flax.py │ ├── scheduling_ddpm_parallel.py │ ├── scheduling_ddpm_wuerstchen.py │ ├── scheduling_deis_multistep.py │ ├── scheduling_dpmsolver_multistep.py │ ├── scheduling_dpmsolver_multistep_flax.py │ ├── scheduling_dpmsolver_multistep_inverse.py │ ├── scheduling_dpmsolver_sde.py │ ├── scheduling_dpmsolver_singlestep.py │ ├── scheduling_edm_dpmsolver_multistep.py │ ├── scheduling_edm_euler.py │ ├── scheduling_euler_ancestral_discrete.py │ ├── scheduling_euler_discrete.py │ ├── scheduling_euler_discrete_flax.py │ ├── scheduling_heun_discrete.py │ ├── scheduling_ipndm.py │ ├── scheduling_k_dpm_2_ancestral_discrete.py │ ├── scheduling_k_dpm_2_discrete.py │ ├── scheduling_karras_ve_flax.py │ ├── scheduling_lcm.py │ ├── scheduling_lms_discrete.py │ ├── scheduling_lms_discrete_flax.py │ ├── scheduling_pndm.py │ ├── scheduling_pndm_flax.py │ ├── scheduling_repaint.py │ ├── scheduling_sasolver.py │ ├── scheduling_sde_ve.py │ ├── scheduling_sde_ve_flax.py │ ├── scheduling_tcd.py │ ├── scheduling_unclip.py │ ├── scheduling_unipc_multistep.py │ ├── scheduling_utils.py │ ├── scheduling_utils_flax.py │ └── scheduling_vq_diffusion.py │ ├── training_utils.py │ └── utils │ ├── __init__.py │ ├── __pycache__ │ ├── __init__.cpython-39.pyc │ ├── accelerate_utils.cpython-39.pyc │ ├── constants.cpython-39.pyc │ ├── deprecation_utils.cpython-39.pyc │ ├── doc_utils.cpython-39.pyc │ ├── dummy_flax_and_transformers_objects.cpython-39.pyc │ ├── dummy_flax_objects.cpython-39.pyc │ ├── dummy_note_seq_objects.cpython-39.pyc │ ├── dummy_onnx_objects.cpython-39.pyc │ ├── dummy_torch_and_librosa_objects.cpython-39.pyc │ ├── dummy_torch_and_torchsde_objects.cpython-39.pyc │ ├── dummy_torch_and_transformers_and_k_diffusion_objects.cpython-39.pyc │ ├── dummy_torch_and_transformers_and_onnx_objects.cpython-39.pyc │ ├── dummy_transformers_and_torch_and_note_seq_objects.cpython-39.pyc │ ├── dynamic_modules_utils.cpython-39.pyc │ ├── export_utils.cpython-39.pyc │ ├── hub_utils.cpython-39.pyc │ ├── import_utils.cpython-39.pyc │ ├── loading_utils.cpython-39.pyc │ ├── logging.cpython-39.pyc │ ├── outputs.cpython-39.pyc │ ├── peft_utils.cpython-39.pyc │ ├── pil_utils.cpython-39.pyc │ ├── state_dict_utils.cpython-39.pyc │ ├── torch_utils.cpython-39.pyc │ └── versions.cpython-39.pyc │ ├── accelerate_utils.py │ ├── constants.py │ ├── deprecation_utils.py │ ├── doc_utils.py │ ├── dummy_flax_and_transformers_objects.py │ ├── dummy_flax_objects.py │ ├── dummy_note_seq_objects.py │ ├── dummy_onnx_objects.py │ ├── dummy_pt_objects.py │ ├── dummy_torch_and_librosa_objects.py │ ├── dummy_torch_and_scipy_objects.py │ ├── dummy_torch_and_torchsde_objects.py │ ├── dummy_torch_and_transformers_and_k_diffusion_objects.py │ ├── dummy_torch_and_transformers_and_onnx_objects.py │ ├── dummy_torch_and_transformers_objects.py │ ├── dummy_transformers_and_torch_and_note_seq_objects.py │ ├── dynamic_modules_utils.py │ ├── export_utils.py │ ├── hub_utils.py │ ├── import_utils.py │ ├── loading_utils.py │ ├── logging.py │ ├── model_card_template.md │ ├── outputs.py │ ├── peft_utils.py │ ├── pil_utils.py │ ├── state_dict_utils.py │ ├── testing_utils.py │ ├── torch_utils.py │ └── versions.py ├── tests ├── __init__.py ├── conftest.py ├── fixtures │ ├── custom_pipeline │ │ ├── pipeline.py │ │ └── what_ever.py │ └── elise_format0.mid ├── lora │ └── test_lora_layers_peft.py ├── models │ ├── __init__.py │ ├── autoencoders │ │ ├── __init__.py │ │ ├── test_models_vae.py │ │ ├── test_models_vae_flax.py │ │ └── test_models_vq.py │ ├── test_activations.py │ ├── test_attention_processor.py │ ├── test_layers_utils.py │ ├── test_modeling_common.py │ ├── test_modeling_common_flax.py │ ├── transformers │ │ ├── __init__.py │ │ └── test_models_prior.py │ └── unets │ │ ├── __init__.py │ │ ├── test_models_unet_1d.py │ │ ├── test_models_unet_2d.py │ │ ├── test_models_unet_2d_condition.py │ │ ├── test_models_unet_2d_flax.py │ │ ├── test_models_unet_3d_condition.py │ │ ├── test_models_unet_motion.py │ │ ├── test_models_unet_spatiotemporal.py │ │ ├── test_unet_2d_blocks.py │ │ └── test_unet_blocks_common.py ├── others │ ├── test_check_copies.py │ ├── test_check_dummies.py │ ├── test_config.py │ ├── test_dependencies.py │ ├── test_ema.py │ ├── test_hub_utils.py │ ├── test_image_processor.py │ ├── test_outputs.py │ ├── test_training.py │ └── test_utils.py ├── pipelines │ ├── __init__.py │ ├── amused │ │ ├── __init__.py │ │ ├── test_amused.py │ │ ├── test_amused_img2img.py │ │ └── test_amused_inpaint.py │ ├── animatediff │ │ ├── __init__.py │ │ ├── test_animatediff.py │ │ └── test_animatediff_video2video.py │ ├── audioldm │ │ ├── __init__.py │ │ └── test_audioldm.py │ ├── audioldm2 │ │ ├── __init__.py │ │ └── test_audioldm2.py │ ├── blipdiffusion │ │ ├── __init__.py │ │ └── test_blipdiffusion.py │ ├── consistency_models │ │ ├── __init__.py │ │ └── test_consistency_models.py │ ├── controlnet │ │ ├── __init__.py │ │ ├── test_controlnet.py │ │ ├── test_controlnet_blip_diffusion.py │ │ ├── test_controlnet_img2img.py │ │ ├── test_controlnet_inpaint.py │ │ ├── test_controlnet_inpaint_sdxl.py │ │ ├── test_controlnet_sdxl.py │ │ ├── test_controlnet_sdxl_img2img.py │ │ └── test_flax_controlnet.py │ ├── dance_diffusion │ │ ├── __init__.py │ │ └── test_dance_diffusion.py │ ├── ddim │ │ ├── __init__.py │ │ └── test_ddim.py │ ├── ddpm │ │ ├── __init__.py │ │ └── test_ddpm.py │ ├── deepfloyd_if │ │ ├── __init__.py │ │ ├── test_if.py │ │ ├── test_if_img2img.py │ │ ├── test_if_img2img_superresolution.py │ │ ├── test_if_inpainting.py │ │ ├── test_if_inpainting_superresolution.py │ │ └── test_if_superresolution.py │ ├── dit │ │ ├── __init__.py │ │ └── test_dit.py │ ├── i2vgen_xl │ │ ├── __init__.py │ │ └── test_i2vgenxl.py │ ├── ip_adapters │ │ └── test_ip_adapter_stable_diffusion.py │ ├── kandinsky │ │ ├── __init__.py │ │ ├── test_kandinsky.py │ │ ├── test_kandinsky_combined.py │ │ ├── test_kandinsky_img2img.py │ │ ├── test_kandinsky_inpaint.py │ │ └── test_kandinsky_prior.py │ ├── kandinsky2_2 │ │ ├── __init__.py │ │ ├── test_kandinsky.py │ │ ├── test_kandinsky_combined.py │ │ ├── test_kandinsky_controlnet.py │ │ ├── test_kandinsky_controlnet_img2img.py │ │ ├── test_kandinsky_img2img.py │ │ ├── test_kandinsky_inpaint.py │ │ ├── test_kandinsky_prior.py │ │ └── test_kandinsky_prior_emb2emb.py │ ├── kandinsky3 │ │ ├── __init__.py │ │ ├── test_kandinsky3.py │ │ └── test_kandinsky3_img2img.py │ ├── latent_consistency_models │ │ ├── __init__.py │ │ ├── test_latent_consistency_models.py │ │ └── test_latent_consistency_models_img2img.py │ ├── latent_diffusion │ │ ├── __init__.py │ │ ├── test_latent_diffusion.py │ │ └── test_latent_diffusion_superresolution.py │ ├── musicldm │ │ ├── __init__.py │ │ └── test_musicldm.py │ ├── paint_by_example │ │ ├── __init__.py │ │ └── test_paint_by_example.py │ ├── pia │ │ ├── __init__.py │ │ └── test_pia.py │ ├── pipeline_params.py │ ├── pixart_alpha │ │ ├── __init__.py │ │ └── test_pixart.py │ ├── pndm │ │ ├── __init__.py │ │ └── test_pndm.py │ ├── semantic_stable_diffusion │ │ ├── __init__.py │ │ └── test_semantic_diffusion.py │ ├── shap_e │ │ ├── __init__.py │ │ ├── test_shap_e.py │ │ └── test_shap_e_img2img.py │ ├── stable_cascade │ │ ├── __init__.py │ │ ├── test_stable_cascade_combined.py │ │ ├── test_stable_cascade_decoder.py │ │ └── test_stable_cascade_prior.py │ ├── stable_diffusion │ │ ├── __init__.py │ │ ├── test_onnx_stable_diffusion.py │ │ ├── test_onnx_stable_diffusion_img2img.py │ │ ├── test_onnx_stable_diffusion_inpaint.py │ │ ├── test_onnx_stable_diffusion_upscale.py │ │ ├── test_stable_diffusion.py │ │ ├── test_stable_diffusion_img2img.py │ │ ├── test_stable_diffusion_inpaint.py │ │ └── test_stable_diffusion_instruction_pix2pix.py │ ├── stable_diffusion_2 │ │ ├── __init__.py │ │ ├── test_stable_diffusion.py │ │ ├── test_stable_diffusion_attend_and_excite.py │ │ ├── test_stable_diffusion_depth.py │ │ ├── test_stable_diffusion_diffedit.py │ │ ├── test_stable_diffusion_flax.py │ │ ├── test_stable_diffusion_flax_inpaint.py │ │ ├── test_stable_diffusion_inpaint.py │ │ ├── test_stable_diffusion_latent_upscale.py │ │ ├── test_stable_diffusion_upscale.py │ │ └── test_stable_diffusion_v_pred.py │ ├── stable_diffusion_adapter │ │ ├── __init__.py │ │ └── test_stable_diffusion_adapter.py │ ├── stable_diffusion_gligen │ │ ├── __init__.py │ │ └── test_stable_diffusion_gligen.py │ ├── stable_diffusion_gligen_text_image │ │ ├── __init__.py │ │ └── test_stable_diffusion_gligen_text_image.py │ ├── stable_diffusion_image_variation │ │ ├── __init__.py │ │ └── test_stable_diffusion_image_variation.py │ ├── stable_diffusion_k_diffusion │ │ ├── __init__.py │ │ └── test_stable_diffusion_k_diffusion.py │ ├── stable_diffusion_ldm3d │ │ ├── __init__.py │ │ └── test_stable_diffusion_ldm3d.py │ ├── stable_diffusion_panorama │ │ ├── __init__.py │ │ └── test_stable_diffusion_panorama.py │ ├── stable_diffusion_safe │ │ ├── __init__.py │ │ └── test_safe_diffusion.py │ ├── stable_diffusion_sag │ │ ├── __init__.py │ │ └── test_stable_diffusion_sag.py │ ├── stable_diffusion_xl │ │ ├── __init__.py │ │ ├── test_stable_diffusion_xl.py │ │ ├── test_stable_diffusion_xl_adapter.py │ │ ├── test_stable_diffusion_xl_img2img.py │ │ ├── test_stable_diffusion_xl_inpaint.py │ │ ├── test_stable_diffusion_xl_instruction_pix2pix.py │ │ └── test_stable_diffusion_xl_k_diffusion.py │ ├── stable_unclip │ │ ├── __init__.py │ │ ├── test_stable_unclip.py │ │ └── test_stable_unclip_img2img.py │ ├── stable_video_diffusion │ │ ├── __init__.py │ │ └── test_stable_video_diffusion.py │ ├── test_pipeline_utils.py │ ├── test_pipelines.py │ ├── test_pipelines_auto.py │ ├── test_pipelines_combined.py │ ├── test_pipelines_common.py │ ├── test_pipelines_flax.py │ ├── test_pipelines_onnx_common.py │ ├── text_to_video_synthesis │ │ ├── __init__.py │ │ ├── test_text_to_video.py │ │ ├── test_text_to_video_zero.py │ │ ├── test_text_to_video_zero_sdxl.py │ │ └── test_video_to_video.py │ ├── unclip │ │ ├── __init__.py │ │ ├── test_unclip.py │ │ └── test_unclip_image_variation.py │ ├── unidiffuser │ │ ├── __init__.py │ │ └── test_unidiffuser.py │ └── wuerstchen │ │ ├── __init__.py │ │ ├── test_wuerstchen_combined.py │ │ ├── test_wuerstchen_decoder.py │ │ └── test_wuerstchen_prior.py └── schedulers │ ├── __init__.py │ ├── test_scheduler_consistency_model.py │ ├── test_scheduler_ddim.py │ ├── test_scheduler_ddim_inverse.py │ ├── test_scheduler_ddim_parallel.py │ ├── test_scheduler_ddpm.py │ ├── test_scheduler_ddpm_parallel.py │ ├── test_scheduler_deis.py │ ├── test_scheduler_dpm_multi.py │ ├── test_scheduler_dpm_multi_inverse.py │ ├── test_scheduler_dpm_sde.py │ ├── test_scheduler_dpm_single.py │ ├── test_scheduler_edm_dpmsolver_multistep.py │ ├── test_scheduler_edm_euler.py │ ├── test_scheduler_euler.py │ ├── test_scheduler_euler_ancestral.py │ ├── test_scheduler_flax.py │ ├── test_scheduler_heun.py │ ├── test_scheduler_ipndm.py │ ├── test_scheduler_kdpm2_ancestral.py │ ├── test_scheduler_kdpm2_discrete.py │ ├── test_scheduler_lcm.py │ ├── test_scheduler_lms.py │ ├── test_scheduler_pndm.py │ ├── test_scheduler_sasolver.py │ ├── test_scheduler_score_sde_ve.py │ ├── test_scheduler_tcd.py │ ├── test_scheduler_unclip.py │ ├── test_scheduler_unipc.py │ ├── test_scheduler_vq_diffusion.py │ └── test_schedulers.py └── utils ├── check_config_docstrings.py ├── check_copies.py ├── check_doc_toc.py ├── check_dummies.py ├── check_inits.py ├── check_repo.py ├── check_table.py ├── custom_init_isort.py ├── fetch_torch_cuda_pipeline_test_matrix.py ├── get_modified_files.py ├── overwrite_expected_slice.py ├── print_env.py ├── release.py ├── stale.py └── tests_fetcher.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /benchmarks/benchmark_controlnet.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | 4 | 5 | sys.path.append(".") 6 | from base_classes import ControlNetBenchmark, ControlNetSDXLBenchmark # noqa: E402 7 | 8 | 9 | if __name__ == "__main__": 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument( 12 | "--ckpt", 13 | type=str, 14 | default="lllyasviel/sd-controlnet-canny", 15 | choices=["lllyasviel/sd-controlnet-canny", "diffusers/controlnet-canny-sdxl-1.0"], 16 | ) 17 | parser.add_argument("--batch_size", type=int, default=1) 18 | parser.add_argument("--num_inference_steps", type=int, default=50) 19 | parser.add_argument("--model_cpu_offload", action="store_true") 20 | parser.add_argument("--run_compile", action="store_true") 21 | args = parser.parse_args() 22 | 23 | benchmark_pipe = ( 24 | ControlNetBenchmark(args) if args.ckpt == "lllyasviel/sd-controlnet-canny" else ControlNetSDXLBenchmark(args) 25 | ) 26 | benchmark_pipe.benchmark(args) 27 | -------------------------------------------------------------------------------- /benchmarks/benchmark_ip_adapters.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | 4 | 5 | sys.path.append(".") 6 | from base_classes import IPAdapterTextToImageBenchmark # noqa: E402 7 | 8 | 9 | IP_ADAPTER_CKPTS = { 10 | "runwayml/stable-diffusion-v1-5": ("h94/IP-Adapter", "ip-adapter_sd15.bin"), 11 | "stabilityai/stable-diffusion-xl-base-1.0": ("h94/IP-Adapter", "ip-adapter_sdxl.bin"), 12 | } 13 | 14 | 15 | if __name__ == "__main__": 16 | parser = argparse.ArgumentParser() 17 | parser.add_argument( 18 | "--ckpt", 19 | type=str, 20 | default="runwayml/stable-diffusion-v1-5", 21 | choices=list(IP_ADAPTER_CKPTS.keys()), 22 | ) 23 | parser.add_argument("--batch_size", type=int, default=1) 24 | parser.add_argument("--num_inference_steps", type=int, default=50) 25 | parser.add_argument("--model_cpu_offload", action="store_true") 26 | parser.add_argument("--run_compile", action="store_true") 27 | args = parser.parse_args() 28 | 29 | args.ip_adapter_id = IP_ADAPTER_CKPTS[args.ckpt] 30 | benchmark_pipe = IPAdapterTextToImageBenchmark(args) 31 | args.ckpt = f"{args.ckpt} (IP-Adapter)" 32 | benchmark_pipe.benchmark(args) 33 | -------------------------------------------------------------------------------- /benchmarks/benchmark_sd_img.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | 4 | 5 | sys.path.append(".") 6 | from base_classes import ImageToImageBenchmark, TurboImageToImageBenchmark # noqa: E402 7 | 8 | 9 | if __name__ == "__main__": 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument( 12 | "--ckpt", 13 | type=str, 14 | default="runwayml/stable-diffusion-v1-5", 15 | choices=[ 16 | "runwayml/stable-diffusion-v1-5", 17 | "stabilityai/stable-diffusion-2-1", 18 | "stabilityai/stable-diffusion-xl-refiner-1.0", 19 | "stabilityai/sdxl-turbo", 20 | ], 21 | ) 22 | parser.add_argument("--batch_size", type=int, default=1) 23 | parser.add_argument("--num_inference_steps", type=int, default=50) 24 | parser.add_argument("--model_cpu_offload", action="store_true") 25 | parser.add_argument("--run_compile", action="store_true") 26 | args = parser.parse_args() 27 | 28 | benchmark_pipe = ImageToImageBenchmark(args) if "turbo" not in args.ckpt else TurboImageToImageBenchmark(args) 29 | benchmark_pipe.benchmark(args) 30 | -------------------------------------------------------------------------------- /benchmarks/benchmark_sd_inpainting.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | 4 | 5 | sys.path.append(".") 6 | from base_classes import InpaintingBenchmark # noqa: E402 7 | 8 | 9 | if __name__ == "__main__": 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument( 12 | "--ckpt", 13 | type=str, 14 | default="runwayml/stable-diffusion-v1-5", 15 | choices=[ 16 | "runwayml/stable-diffusion-v1-5", 17 | "stabilityai/stable-diffusion-2-1", 18 | "stabilityai/stable-diffusion-xl-base-1.0", 19 | ], 20 | ) 21 | parser.add_argument("--batch_size", type=int, default=1) 22 | parser.add_argument("--num_inference_steps", type=int, default=50) 23 | parser.add_argument("--model_cpu_offload", action="store_true") 24 | parser.add_argument("--run_compile", action="store_true") 25 | args = parser.parse_args() 26 | 27 | benchmark_pipe = InpaintingBenchmark(args) 28 | benchmark_pipe.benchmark(args) 29 | -------------------------------------------------------------------------------- /benchmarks/benchmark_t2i_adapter.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | 4 | 5 | sys.path.append(".") 6 | from base_classes import T2IAdapterBenchmark, T2IAdapterSDXLBenchmark # noqa: E402 7 | 8 | 9 | if __name__ == "__main__": 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument( 12 | "--ckpt", 13 | type=str, 14 | default="TencentARC/t2iadapter_canny_sd14v1", 15 | choices=["TencentARC/t2iadapter_canny_sd14v1", "TencentARC/t2i-adapter-canny-sdxl-1.0"], 16 | ) 17 | parser.add_argument("--batch_size", type=int, default=1) 18 | parser.add_argument("--num_inference_steps", type=int, default=50) 19 | parser.add_argument("--model_cpu_offload", action="store_true") 20 | parser.add_argument("--run_compile", action="store_true") 21 | args = parser.parse_args() 22 | 23 | benchmark_pipe = ( 24 | T2IAdapterBenchmark(args) 25 | if args.ckpt == "TencentARC/t2iadapter_canny_sd14v1" 26 | else T2IAdapterSDXLBenchmark(args) 27 | ) 28 | benchmark_pipe.benchmark(args) 29 | -------------------------------------------------------------------------------- /benchmarks/benchmark_t2i_lcm_lora.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import sys 3 | 4 | 5 | sys.path.append(".") 6 | from base_classes import LCMLoRATextToImageBenchmark # noqa: E402 7 | 8 | 9 | if __name__ == "__main__": 10 | parser = argparse.ArgumentParser() 11 | parser.add_argument( 12 | "--ckpt", 13 | type=str, 14 | default="stabilityai/stable-diffusion-xl-base-1.0", 15 | ) 16 | parser.add_argument("--batch_size", type=int, default=1) 17 | parser.add_argument("--num_inference_steps", type=int, default=4) 18 | parser.add_argument("--model_cpu_offload", action="store_true") 19 | parser.add_argument("--run_compile", action="store_true") 20 | args = parser.parse_args() 21 | 22 | benchmark_pipe = LCMLoRATextToImageBenchmark(args) 23 | benchmark_pipe.benchmark(args) 24 | -------------------------------------------------------------------------------- /docker/diffusers-pytorch-cuda/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM nvidia/cuda:12.1.0-runtime-ubuntu20.04 2 | LABEL maintainer="Hugging Face" 3 | LABEL repository="diffusers" 4 | 5 | ENV DEBIAN_FRONTEND=noninteractive 6 | 7 | RUN apt update && \ 8 | apt install -y bash \ 9 | build-essential \ 10 | git \ 11 | git-lfs \ 12 | curl \ 13 | ca-certificates \ 14 | libsndfile1-dev \ 15 | libgl1 \ 16 | python3.8 \ 17 | python3-pip \ 18 | python3.8-venv && \ 19 | rm -rf /var/lib/apt/lists 20 | 21 | # make sure to use venv 22 | RUN python3 -m venv /opt/venv 23 | ENV PATH="/opt/venv/bin:$PATH" 24 | 25 | # pre-install the heavy dependencies (these can later be overridden by the deps from setup.py) 26 | RUN python3 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \ 27 | python3 -m uv pip install --no-cache-dir \ 28 | torch \ 29 | torchvision \ 30 | torchaudio \ 31 | invisible_watermark && \ 32 | python3 -m pip install --no-cache-dir \ 33 | accelerate \ 34 | datasets \ 35 | hf-doc-builder \ 36 | huggingface-hub \ 37 | Jinja2 \ 38 | librosa \ 39 | numpy \ 40 | scipy \ 41 | tensorboard \ 42 | transformers \ 43 | pytorch-lightning 44 | 45 | CMD ["/bin/bash"] 46 | -------------------------------------------------------------------------------- /docs/source/_config.py: -------------------------------------------------------------------------------- 1 | # docstyle-ignore 2 | INSTALL_CONTENT = """ 3 | # Diffusers installation 4 | ! pip install diffusers transformers datasets accelerate 5 | # To install from source instead of the last release, comment the command above and uncomment the following one. 6 | # ! pip install git+https://github.com/huggingface/diffusers.git 7 | """ 8 | 9 | notebook_first_cells = [{"type": "code", "content": INSTALL_CONTENT}] 10 | -------------------------------------------------------------------------------- /docs/source/en/api/activations.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # Activation functions 14 | 15 | Customized activation functions for supporting various models in 🤗 Diffusers. 16 | 17 | ## GELU 18 | 19 | [[autodoc]] models.activations.GELU 20 | 21 | ## GEGLU 22 | 23 | [[autodoc]] models.activations.GEGLU 24 | 25 | ## ApproximateGELU 26 | 27 | [[autodoc]] models.activations.ApproximateGELU 28 | -------------------------------------------------------------------------------- /docs/source/en/api/internal_classes_overview.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # Overview 14 | 15 | The APIs in this section are more experimental and prone to breaking changes. Most of them are used internally for development, but they may also be useful to you if you're interested in building a diffusion model with some custom parts or if you're interested in some of our helper utilities for working with 🤗 Diffusers. 16 | -------------------------------------------------------------------------------- /docs/source/en/api/models/transformer_temporal.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # Transformer Temporal 14 | 15 | A Transformer model for video-like data. 16 | 17 | ## TransformerTemporalModel 18 | 19 | [[autodoc]] models.transformers.transformer_temporal.TransformerTemporalModel 20 | 21 | ## TransformerTemporalModelOutput 22 | 23 | [[autodoc]] models.transformers.transformer_temporal.TransformerTemporalModelOutput 24 | -------------------------------------------------------------------------------- /docs/source/en/api/normalization.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # Normalization layers 14 | 15 | Customized normalization layers for supporting various models in 🤗 Diffusers. 16 | 17 | ## AdaLayerNorm 18 | 19 | [[autodoc]] models.normalization.AdaLayerNorm 20 | 21 | ## AdaLayerNormZero 22 | 23 | [[autodoc]] models.normalization.AdaLayerNormZero 24 | 25 | ## AdaLayerNormSingle 26 | 27 | [[autodoc]] models.normalization.AdaLayerNormSingle 28 | 29 | ## AdaGroupNorm 30 | 31 | [[autodoc]] models.normalization.AdaGroupNorm 32 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/consistency_decoder.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # ConsistencyDecoderScheduler 14 | 15 | This scheduler is a part of the [`ConsistencyDecoderPipeline`] and was introduced in [DALL-E 3](https://openai.com/dall-e-3). 16 | 17 | The original codebase can be found at [openai/consistency_models](https://github.com/openai/consistency_models). 18 | 19 | 20 | ## ConsistencyDecoderScheduler 21 | [[autodoc]] schedulers.scheduling_consistency_decoder.ConsistencyDecoderScheduler 22 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/ddim_inverse.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # DDIMInverseScheduler 14 | 15 | `DDIMInverseScheduler` is the inverted scheduler from [Denoising Diffusion Implicit Models](https://huggingface.co/papers/2010.02502) (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. 16 | The implementation is mostly based on the DDIM inversion definition from [Null-text Inversion for Editing Real Images using Guided Diffusion Models](https://huggingface.co/papers/2211.09794). 17 | 18 | ## DDIMInverseScheduler 19 | [[autodoc]] DDIMInverseScheduler 20 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/dpm_sde.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # DPMSolverSDEScheduler 14 | 15 | The `DPMSolverSDEScheduler` is inspired by the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/). 16 | 17 | ## DPMSolverSDEScheduler 18 | [[autodoc]] DPMSolverSDEScheduler 19 | 20 | ## SchedulerOutput 21 | [[autodoc]] schedulers.scheduling_utils.SchedulerOutput 22 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/heun.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # HeunDiscreteScheduler 14 | 15 | The Heun scheduler (Algorithm 1) is from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper by Karras et al. The scheduler is ported from the [k-diffusion](https://github.com/crowsonkb/k-diffusion) library and created by [Katherine Crowson](https://github.com/crowsonkb/). 16 | 17 | ## HeunDiscreteScheduler 18 | [[autodoc]] HeunDiscreteScheduler 19 | 20 | ## SchedulerOutput 21 | [[autodoc]] schedulers.scheduling_utils.SchedulerOutput 22 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/ipndm.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # IPNDMScheduler 14 | 15 | `IPNDMScheduler` is a fourth-order Improved Pseudo Linear Multistep scheduler. The original implementation can be found at [crowsonkb/v-diffusion-pytorch](https://github.com/crowsonkb/v-diffusion-pytorch/blob/987f8985e38208345c1959b0ea767a625831cc9b/diffusion/sampling.py#L296). 16 | 17 | ## IPNDMScheduler 18 | [[autodoc]] IPNDMScheduler 19 | 20 | ## SchedulerOutput 21 | [[autodoc]] schedulers.scheduling_utils.SchedulerOutput 22 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/lcm.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # Latent Consistency Model Multistep Scheduler 14 | 15 | ## Overview 16 | 17 | Multistep and onestep scheduler (Algorithm 3) introduced alongside latent consistency models in the paper [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) by Simian Luo, Yiqin Tan, Longbo Huang, Jian Li, and Hang Zhao. 18 | This scheduler should be able to generate good samples from [`LatentConsistencyModelPipeline`] in 1-8 steps. 19 | 20 | ## LCMScheduler 21 | [[autodoc]] LCMScheduler 22 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/pndm.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # PNDMScheduler 14 | 15 | `PNDMScheduler`, or pseudo numerical methods for diffusion models, uses more advanced ODE integration techniques like the Runge-Kutta and linear multi-step method. The original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181). 16 | 17 | ## PNDMScheduler 18 | [[autodoc]] PNDMScheduler 19 | 20 | ## SchedulerOutput 21 | [[autodoc]] schedulers.scheduling_utils.SchedulerOutput 22 | -------------------------------------------------------------------------------- /docs/source/en/api/schedulers/stochastic_karras_ve.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # KarrasVeScheduler 14 | 15 | `KarrasVeScheduler` is a stochastic sampler tailored to variance-expanding (VE) models. It is based on the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) and [Score-based generative modeling through stochastic differential equations](https://huggingface.co/papers/2011.13456) papers. 16 | 17 | ## KarrasVeScheduler 18 | [[autodoc]] KarrasVeScheduler 19 | 20 | ## KarrasVeOutput 21 | [[autodoc]] schedulers.deprecated.scheduling_karras_ve.KarrasVeOutput -------------------------------------------------------------------------------- /docs/source/en/api/utilities.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # Utilities 14 | 15 | Utility and helper functions for working with 🤗 Diffusers. 16 | 17 | ## numpy_to_pil 18 | 19 | [[autodoc]] utils.numpy_to_pil 20 | 21 | ## pt_to_pil 22 | 23 | [[autodoc]] utils.pt_to_pil 24 | 25 | ## load_image 26 | 27 | [[autodoc]] utils.load_image 28 | 29 | ## export_to_gif 30 | 31 | [[autodoc]] utils.export_to_gif 32 | 33 | ## export_to_video 34 | 35 | [[autodoc]] utils.export_to_video 36 | 37 | ## make_image_grid 38 | 39 | [[autodoc]] utils.make_image_grid 40 | -------------------------------------------------------------------------------- /docs/source/en/imgs/access_request.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/docs/source/en/imgs/access_request.png -------------------------------------------------------------------------------- /docs/source/en/imgs/diffusers_library.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/docs/source/en/imgs/diffusers_library.jpg -------------------------------------------------------------------------------- /docs/source/ja/_toctree.yml: -------------------------------------------------------------------------------- 1 | - sections: 2 | - local: index 3 | title: 🧨 Diffusers 4 | - local: quicktour 5 | title: クイックツアー 6 | - local: stable_diffusion 7 | title: 有効で効率の良い拡散モデル 8 | - local: installation 9 | title: インストール 10 | title: はじめに 11 | - sections: 12 | - local: tutorials/tutorial_overview 13 | title: 概要 14 | - local: tutorials/autopipeline 15 | title: AutoPipeline 16 | title: チュートリアル -------------------------------------------------------------------------------- /docs/source/ko/in_translation.md: -------------------------------------------------------------------------------- 1 | 12 | 13 | # 번역중 14 | 15 | 열심히 번역을 진행중입니다. 조금만 기다려주세요. 16 | 감사합니다! -------------------------------------------------------------------------------- /docs/source/pt/_toctree.yml: -------------------------------------------------------------------------------- 1 | - sections: 2 | - local: index 3 | title: 🧨 Diffusers 4 | - local: quicktour 5 | title: Tour rápido 6 | - local: installation 7 | title: Instalação 8 | title: Primeiros passos 9 | -------------------------------------------------------------------------------- /docs/source/zh/_toctree.yml: -------------------------------------------------------------------------------- 1 | - sections: 2 | - local: index 3 | title: 🧨 Diffusers 4 | - local: quicktour 5 | title: 快速入门 6 | - local: stable_diffusion 7 | title: 有效和高效的扩散 8 | - local: installation 9 | title: 安装 10 | title: 开始 11 | -------------------------------------------------------------------------------- /examples/advanced_diffusion_training/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | peft==0.7.0 -------------------------------------------------------------------------------- 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--resolution=512 \ 6 | --train_batch_size=1 \ 7 | --gradient_checkpointing \ 8 | --max_train_steps=15000 \ 9 | --learning_rate=1e-05 \ 10 | --output_dir="/cto_studio/vistring/yuedongxu/BrushNet/examples/brushnet/replacenet_output" \ 11 | -------------------------------------------------------------------------------- /examples/brushnet/train_stage_2.sh: -------------------------------------------------------------------------------- 1 | export MODEL_NAME="/cto_studio/vistring/yuedongxu/pretrained/sd_pretrained/sd_15" 2 | 3 | accelerate launch --mixed_precision="fp16" train_replacenet_stage_2.py \ 4 | --pretrained_model_name_or_path=$MODEL_NAME \ 5 | --resolution=512 \ 6 | --train_batch_size=1 \ 7 | --gradient_checkpointing \ 8 | --max_train_steps=15000 \ 9 | --learning_rate=1e-05 \ 10 | --output_dir="/cto_studio/vistring/yuedongxu/BrushNet/examples/brushnet/replacenet_output_stage_2" \ 11 | -------------------------------------------------------------------------------- /examples/community/one_step_unet.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | import torch 3 | 4 | from diffusers import DiffusionPipeline 5 | 6 | 7 | class UnetSchedulerOneForwardPipeline(DiffusionPipeline): 8 | def __init__(self, unet, scheduler): 9 | super().__init__() 10 | 11 | self.register_modules(unet=unet, scheduler=scheduler) 12 | 13 | def __call__(self): 14 | image = torch.randn( 15 | (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size), 16 | ) 17 | timestep = 1 18 | 19 | model_output = self.unet(image, timestep).sample 20 | scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample 21 | 22 | result = scheduler_output - scheduler_output + torch.ones_like(scheduler_output) 23 | 24 | return result 25 | -------------------------------------------------------------------------------- /examples/consistency_distillation/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | webdataset -------------------------------------------------------------------------------- /examples/controlnet/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | datasets 7 | -------------------------------------------------------------------------------- /examples/controlnet/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | datasets 3 | flax 4 | optax 5 | torch 6 | torchvision 7 | ftfy 8 | tensorboard 9 | Jinja2 10 | -------------------------------------------------------------------------------- /examples/controlnet/requirements_sdxl.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | datasets 8 | wandb 9 | -------------------------------------------------------------------------------- /examples/custom_diffusion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | -------------------------------------------------------------------------------- /examples/dreambooth/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | peft==0.7.0 -------------------------------------------------------------------------------- /examples/dreambooth/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | flax 3 | optax 4 | torch 5 | torchvision 6 | ftfy 7 | tensorboard 8 | Jinja2 9 | -------------------------------------------------------------------------------- /examples/dreambooth/requirements_sdxl.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | peft==0.7.0 -------------------------------------------------------------------------------- /examples/inference/README.md: -------------------------------------------------------------------------------- 1 | # Inference Examples 2 | 3 | **The inference examples folder is deprecated and will be removed in a future version**. 4 | **Officially supported inference examples can be found in the [Pipelines folder](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines)**. 5 | 6 | - For `Image-to-Image text-guided generation with Stable Diffusion`, please have a look at the official [Pipeline examples](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines#examples) 7 | - For `In-painting using Stable Diffusion`, please have a look at the official [Pipeline examples](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines#examples) 8 | - For `Tweak prompts reusing seeds and latents`, please have a look at the official [Pipeline examples](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines#examples) 9 | -------------------------------------------------------------------------------- /examples/inference/image_to_image.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | from diffusers import StableDiffusionImg2ImgPipeline # noqa F401 4 | 5 | 6 | warnings.warn( 7 | "The `image_to_image.py` script is outdated. Please use directly `from diffusers import" 8 | " StableDiffusionImg2ImgPipeline` instead." 9 | ) 10 | -------------------------------------------------------------------------------- /examples/inference/inpainting.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | 3 | from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401 4 | 5 | 6 | warnings.warn( 7 | "The `inpainting.py` script is outdated. Please use directly `from diffusers import" 8 | " StableDiffusionInpaintPipeline` instead." 9 | ) 10 | -------------------------------------------------------------------------------- /examples/instruct_pix2pix/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard -------------------------------------------------------------------------------- /examples/kandinsky2_2/text_to_image/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard 7 | Jinja2 8 | -------------------------------------------------------------------------------- /examples/reinforcement_learning/README.md: -------------------------------------------------------------------------------- 1 | # Overview 2 | 3 | These examples show how to run [Diffuser](https://arxiv.org/abs/2205.09991) in Diffusers. 4 | There are two ways to use the script, `run_diffuser_locomotion.py`. 5 | 6 | The key option is a change of the variable `n_guide_steps`. 7 | When `n_guide_steps=0`, the trajectories are sampled from the diffusion model, but not fine-tuned to maximize reward in the environment. 8 | By default, `n_guide_steps=2` to match the original implementation. 9 | 10 | 11 | You will need some RL specific requirements to run the examples: 12 | 13 | ``` 14 | pip install -f https://download.pytorch.org/whl/torch_stable.html \ 15 | free-mujoco-py \ 16 | einops \ 17 | gym==0.24.1 \ 18 | protobuf==3.20.1 \ 19 | git+https://github.com/rail-berkeley/d4rl.git \ 20 | mediapy \ 21 | Pillow==9.0.0 22 | ``` 23 | -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /examples/replace_anything/requirements.txt: -------------------------------------------------------------------------------- 1 | torchvision 2 | transformers>=4.25.1 3 | ftfy 4 | tensorboard 5 | datasets 6 | Pillow==9.5.0 7 | opencv-python 8 | imgaug 9 | accelerate==0.20.3 10 | image-reward 11 | hpsv2 12 | torchmetrics 13 | open-clip-torch 14 | clip 15 | gradio==3.50.0 16 | segment_anything -------------------------------------------------------------------------------- /examples/replace_anything/train_stage_1.sh: -------------------------------------------------------------------------------- 1 | export MODEL_NAME="runwayml/stable-diffusion-v1-5" 2 | 3 | accelerate launch --mixed_precision="fp16" train_replacenet_stage_1.py \ 4 | --pretrained_model_name_or_path=$MODEL_NAME \ 5 | --resolution=512 \ 6 | --train_batch_size=1 \ 7 | --gradient_checkpointing \ 8 | --max_train_steps=15000 \ 9 | --learning_rate=1e-05 \ 10 | --data_root_path="./training_data" \ 11 | --output_dir="./replacenet_output_stage_1" \ 12 | -------------------------------------------------------------------------------- /examples/replace_anything/train_stage_2.sh: -------------------------------------------------------------------------------- 1 | export MODEL_NAME="runwayml/stable-diffusion-v1-5" 2 | 3 | accelerate launch --mixed_precision="fp16" train_replacenet_stage_2.py \ 4 | --pretrained_model_name_or_path=$MODEL_NAME \ 5 | --resolution=512 \ 6 | --train_batch_size=1 \ 7 | --gradient_checkpointing \ 8 | --max_train_steps=15000 \ 9 | --learning_rate=1e-05 \ 10 | --data_root_path="./training_data" \ 11 | --output_dir="./replacenet_output_stage_2" \ 12 | -------------------------------------------------------------------------------- /examples/replace_anything/training_data/000001.json: -------------------------------------------------------------------------------- 1 | [{"image_id": "000001", "caption": "The image features a woman wearing a yellow shirt, holding up a smartphone in her hand. She is pointing at the screen of the phone, possibly showing something interesting or important to the viewer. The woman appears to be the main focus of the scene, and the phone is held up prominently in the foreground."}] -------------------------------------------------------------------------------- /examples/replace_anything/training_data/000001.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/examples/replace_anything/training_data/000001.png -------------------------------------------------------------------------------- /examples/replace_anything/training_data/000001_canny.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/examples/replace_anything/training_data/000001_canny.png -------------------------------------------------------------------------------- /examples/replace_anything/training_data/000001_dino.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/examples/replace_anything/training_data/000001_dino.pt -------------------------------------------------------------------------------- /examples/replace_anything/training_data/000001_object.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/examples/replace_anything/training_data/000001_object.png -------------------------------------------------------------------------------- /examples/replace_anything/training_data/000001_pose.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/examples/replace_anything/training_data/000001_pose.png -------------------------------------------------------------------------------- /examples/research_projects/README.md: -------------------------------------------------------------------------------- 1 | # Research projects 2 | 3 | This folder contains various research projects using 🧨 Diffusers. 4 | They are not really maintained by the core maintainers of this library and often require a specific version of Diffusers that is indicated in the requirements file of each folder. 5 | Updating them to the most recent version of the library will require some work. 6 | 7 | To use any of them, just run the command 8 | 9 | ``` 10 | pip install -r requirements.txt 11 | ``` 12 | inside the folder of your choice. 13 | 14 | If you need help with any of those, please open an issue where you directly ping the author(s), as indicated at the top of the README of each folder. 15 | -------------------------------------------------------------------------------- /examples/research_projects/colossalai/inference.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from diffusers import StableDiffusionPipeline 4 | 5 | 6 | model_id = "path-to-your-trained-model" 7 | pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") 8 | 9 | prompt = "A photo of sks dog in a bucket" 10 | image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0] 11 | 12 | image.save("dog-bucket.png") 13 | -------------------------------------------------------------------------------- /examples/research_projects/colossalai/requirement.txt: -------------------------------------------------------------------------------- 1 | diffusers 2 | torch 3 | torchvision 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | transformers -------------------------------------------------------------------------------- /examples/research_projects/consistency_training/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 -------------------------------------------------------------------------------- /examples/research_projects/diffusion_dpo/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | peft 8 | wandb -------------------------------------------------------------------------------- /examples/research_projects/dreambooth_inpaint/requirements.txt: -------------------------------------------------------------------------------- 1 | diffusers==0.9.0 2 | accelerate>=0.16.0 3 | torchvision 4 | transformers>=4.21.0 5 | ftfy 6 | tensorboard 7 | Jinja2 8 | -------------------------------------------------------------------------------- /examples/research_projects/intel_opts/textual_inversion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.21.0 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | intel_extension_for_pytorch>=1.13 8 | -------------------------------------------------------------------------------- /examples/research_projects/intel_opts/textual_inversion_dfq/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate 2 | torchvision 3 | transformers>=4.25.0 4 | ftfy 5 | tensorboard 6 | modelcards 7 | neural-compressor -------------------------------------------------------------------------------- /examples/research_projects/lora/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard 7 | Jinja2 8 | git+https://github.com/huggingface/peft.git -------------------------------------------------------------------------------- /examples/research_projects/multi_subject_dreambooth/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 -------------------------------------------------------------------------------- /examples/research_projects/multi_subject_dreambooth_inpainting/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | datasets>=2.16.0 5 | wandb>=0.16.1 6 | ftfy 7 | tensorboard 8 | Jinja2 -------------------------------------------------------------------------------- /examples/research_projects/multi_token_textual_inversion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | -------------------------------------------------------------------------------- /examples/research_projects/multi_token_textual_inversion/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | flax 3 | optax 4 | torch 5 | torchvision 6 | ftfy 7 | tensorboard 8 | Jinja2 9 | -------------------------------------------------------------------------------- /examples/research_projects/onnxruntime/README.md: -------------------------------------------------------------------------------- 1 | ## Diffusers examples with ONNXRuntime optimizations 2 | 3 | **This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Prathik Rao (prathikr), Sunghoon Choi (hanbitmyths), Ashwini Khade (askhade), or Peng Wang (pengwa) on github with any questions.** 4 | 5 | This aims to provide diffusers examples with ONNXRuntime optimizations for training/fine-tuning unconditional image generation, text to image, and textual inversion. Please see individual directories for more details on how to run each task using ONNXRuntime. 6 | -------------------------------------------------------------------------------- /examples/research_projects/onnxruntime/text_to_image/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard 7 | modelcards 8 | -------------------------------------------------------------------------------- /examples/research_projects/onnxruntime/textual_inversion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | modelcards 7 | -------------------------------------------------------------------------------- /examples/research_projects/onnxruntime/unconditional_image_generation/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | datasets 4 | tensorboard -------------------------------------------------------------------------------- /examples/research_projects/rdm/README.md: -------------------------------------------------------------------------------- 1 | ## Diffusers examples with ONNXRuntime optimizations 2 | 3 | **This research project is not actively maintained by the diffusers team. For any questions or comments, please contact Isamu Isozaki(isamu-isozaki) on github with any questions.** 4 | 5 | The aim of this project is to provide retrieval augmented diffusion models to diffusers! -------------------------------------------------------------------------------- /examples/research_projects/realfill/requirements.txt: -------------------------------------------------------------------------------- 1 | diffusers==0.20.1 2 | accelerate==0.23.0 3 | transformers==4.36.0 4 | peft==0.5.0 5 | torch==2.0.1 6 | torchvision>=0.16 7 | ftfy==6.1.1 8 | tensorboard==2.14.0 9 | Jinja2==3.1.3 10 | -------------------------------------------------------------------------------- /examples/t2i_adapter/README.md: -------------------------------------------------------------------------------- 1 | We don't yet support training T2I-Adapters on Stable Diffusion yet. For training T2I-Adapters on Stable Diffusion XL, refer [here](./README_sdxl.md). -------------------------------------------------------------------------------- /examples/t2i_adapter/requirements.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | accelerate>=0.16.0 3 | safetensors 4 | datasets 5 | torchvision 6 | ftfy 7 | tensorboard 8 | wandb -------------------------------------------------------------------------------- /examples/text_to_image/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | datasets 5 | ftfy 6 | tensorboard 7 | Jinja2 8 | peft==0.7.0 -------------------------------------------------------------------------------- /examples/text_to_image/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | datasets 3 | flax 4 | optax 5 | torch 6 | torchvision 7 | ftfy 8 | tensorboard 9 | Jinja2 10 | -------------------------------------------------------------------------------- /examples/text_to_image/requirements_sdxl.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.22.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | datasets 8 | peft==0.7.0 -------------------------------------------------------------------------------- /examples/textual_inversion/README_sdxl.md: -------------------------------------------------------------------------------- 1 | ## Textual Inversion fine-tuning example for SDXL 2 | 3 | ``` 4 | export MODEL_NAME="stabilityai/stable-diffusion-xl-base-1.0" 5 | export DATA_DIR="./cat" 6 | 7 | accelerate launch textual_inversion_sdxl.py \ 8 | --pretrained_model_name_or_path=$MODEL_NAME \ 9 | --train_data_dir=$DATA_DIR \ 10 | --learnable_property="object" \ 11 | --placeholder_token="" \ 12 | --initializer_token="toy" \ 13 | --mixed_precision="bf16" \ 14 | --resolution=768 \ 15 | --train_batch_size=1 \ 16 | --gradient_accumulation_steps=4 \ 17 | --max_train_steps=500 \ 18 | --learning_rate=5.0e-04 \ 19 | --scale_lr \ 20 | --lr_scheduler="constant" \ 21 | --lr_warmup_steps=0 \ 22 | --save_as_full_pipeline \ 23 | --output_dir="./textual_inversion_cat_sdxl" 24 | ``` 25 | 26 | For now, only training of the first text encoder is supported. -------------------------------------------------------------------------------- /examples/textual_inversion/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | ftfy 5 | tensorboard 6 | Jinja2 7 | -------------------------------------------------------------------------------- /examples/textual_inversion/requirements_flax.txt: -------------------------------------------------------------------------------- 1 | transformers>=4.25.1 2 | flax 3 | optax 4 | torch 5 | torchvision 6 | ftfy 7 | tensorboard 8 | Jinja2 9 | -------------------------------------------------------------------------------- /examples/unconditional_image_generation/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | datasets 4 | -------------------------------------------------------------------------------- /examples/wuerstchen/text_to_image/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/examples/wuerstchen/text_to_image/__init__.py -------------------------------------------------------------------------------- /examples/wuerstchen/text_to_image/modeling_efficient_net_encoder.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | from torchvision.models import efficientnet_v2_l, efficientnet_v2_s 3 | 4 | from diffusers.configuration_utils import ConfigMixin, register_to_config 5 | from diffusers.models.modeling_utils import ModelMixin 6 | 7 | 8 | class EfficientNetEncoder(ModelMixin, ConfigMixin): 9 | @register_to_config 10 | def __init__(self, c_latent=16, c_cond=1280, effnet="efficientnet_v2_s"): 11 | super().__init__() 12 | 13 | if effnet == "efficientnet_v2_s": 14 | self.backbone = efficientnet_v2_s(weights="DEFAULT").features 15 | else: 16 | self.backbone = efficientnet_v2_l(weights="DEFAULT").features 17 | self.mapper = nn.Sequential( 18 | nn.Conv2d(c_cond, c_latent, kernel_size=1, bias=False), 19 | nn.BatchNorm2d(c_latent), # then normalize them to have mean 0 and std 1 20 | ) 21 | 22 | def forward(self, x): 23 | return self.mapper(self.backbone(x)) 24 | -------------------------------------------------------------------------------- /examples/wuerstchen/text_to_image/requirements.txt: -------------------------------------------------------------------------------- 1 | accelerate>=0.16.0 2 | torchvision 3 | transformers>=4.25.1 4 | wandb 5 | bitsandbytes 6 | deepspeed 7 | peft>=0.6.0 8 | -------------------------------------------------------------------------------- /install.sh: -------------------------------------------------------------------------------- 1 | python -m pip install --upgrade pip 2 | pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116 3 | pip install -e . 4 | cd examples/brushnet/ 5 | pip install -r requirements.txt 6 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.ruff] 2 | # Never enforce `E501` (line length violations). 3 | ignore = ["C901", "E501", "E741", "F402", "F823"] 4 | select = ["C", "E", "F", "I", "W"] 5 | line-length = 119 6 | 7 | # Ignore import violations in all `__init__.py` files. 8 | [tool.ruff.per-file-ignores] 9 | "__init__.py" = ["E402", "F401", "F403", "F811"] 10 | "src/diffusers/utils/dummy_*.py" = ["F401"] 11 | 12 | [tool.ruff.isort] 13 | lines-after-imports = 2 14 | known-first-party = ["diffusers"] 15 | 16 | [tool.ruff.format] 17 | # Like Black, use double quotes for strings. 18 | quote-style = "double" 19 | 20 | # Like Black, indent with spaces, rather than tabs. 21 | indent-style = "space" 22 | 23 | # Like Black, respect magic trailing commas. 24 | skip-magic-trailing-comma = false 25 | 26 | # Like Black, automatically detect the appropriate line ending. 27 | line-ending = "auto" 28 | -------------------------------------------------------------------------------- /scripts/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/scripts/__init__.py -------------------------------------------------------------------------------- /src/diffusers.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /src/diffusers.egg-info/entry_points.txt: -------------------------------------------------------------------------------- 1 | [console_scripts] 2 | diffusers-cli = diffusers.commands.diffusers_cli:main 3 | -------------------------------------------------------------------------------- /src/diffusers.egg-info/top_level.txt: -------------------------------------------------------------------------------- 1 | diffusers 2 | -------------------------------------------------------------------------------- /src/diffusers/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/__pycache__/configuration_utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/__pycache__/configuration_utils.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/__pycache__/dependency_versions_check.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/__pycache__/dependency_versions_check.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/__pycache__/dependency_versions_table.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/__pycache__/dependency_versions_table.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/__pycache__/image_processor.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/__pycache__/image_processor.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/__pycache__/optimization.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/__pycache__/optimization.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/__pycache__/training_utils.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/__pycache__/training_utils.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/commands/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright 2024 The HuggingFace Team. All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from abc import ABC, abstractmethod 16 | from argparse import ArgumentParser 17 | 18 | 19 | class BaseDiffusersCLICommand(ABC): 20 | @staticmethod 21 | @abstractmethod 22 | def register_subcommand(parser: ArgumentParser): 23 | raise NotImplementedError() 24 | 25 | @abstractmethod 26 | def run(self): 27 | raise NotImplementedError() 28 | -------------------------------------------------------------------------------- /src/diffusers/experimental/README.md: -------------------------------------------------------------------------------- 1 | # 🧨 Diffusers Experimental 2 | 3 | We are adding experimental code to support novel applications and usages of the Diffusers library. 4 | Currently, the following experiments are supported: 5 | * Reinforcement learning via an implementation of the [Diffuser](https://arxiv.org/abs/2205.09991) model. -------------------------------------------------------------------------------- /src/diffusers/experimental/__init__.py: -------------------------------------------------------------------------------- 1 | from .rl import ValueGuidedRLPipeline 2 | -------------------------------------------------------------------------------- /src/diffusers/experimental/rl/__init__.py: -------------------------------------------------------------------------------- 1 | from .value_guided_sampling import 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All rights reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | from ..utils import deprecate 15 | from .transformers.dual_transformer_2d import DualTransformer2DModel 16 | 17 | 18 | class DualTransformer2DModel(DualTransformer2DModel): 19 | deprecation_message = "Importing `DualTransformer2DModel` from `diffusers.models.dual_transformer_2d` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel`, instead." 20 | deprecate("DualTransformer2DModel", "0.29", deprecation_message) 21 | -------------------------------------------------------------------------------- /src/diffusers/models/modeling_outputs.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | 3 | from ..utils import BaseOutput 4 | 5 | 6 | @dataclass 7 | class AutoencoderKLOutput(BaseOutput): 8 | """ 9 | Output of AutoencoderKL encoding method. 10 | 11 | Args: 12 | latent_dist (`DiagonalGaussianDistribution`): 13 | Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`. 14 | `DiagonalGaussianDistribution` allows for sampling latents from the distribution. 15 | """ 16 | 17 | latent_dist: "DiagonalGaussianDistribution" # noqa: F821 18 | -------------------------------------------------------------------------------- /src/diffusers/models/prior_transformer.py: -------------------------------------------------------------------------------- 1 | from ..utils import deprecate 2 | from .transformers.prior_transformer import PriorTransformer, PriorTransformerOutput 3 | 4 | 5 | class PriorTransformerOutput(PriorTransformerOutput): 6 | deprecation_message = "Importing `PriorTransformerOutput` from `diffusers.models.prior_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.prior_transformer import PriorTransformerOutput`, instead." 7 | deprecate("PriorTransformerOutput", "0.29", deprecation_message) 8 | 9 | 10 | class PriorTransformer(PriorTransformer): 11 | deprecation_message = "Importing `PriorTransformer` from `diffusers.models.prior_transformer` is deprecated and this will be removed in a future version. Please use `from diffusers.models.transformers.prior_transformer import PriorTransformer`, instead." 12 | deprecate("PriorTransformer", "0.29", deprecation_message) 13 | -------------------------------------------------------------------------------- /src/diffusers/models/transformers/__init__.py: -------------------------------------------------------------------------------- 1 | from ...utils import is_torch_available 2 | 3 | 4 | if is_torch_available(): 5 | from .dual_transformer_2d import DualTransformer2DModel 6 | from .prior_transformer import PriorTransformer 7 | from .t5_film_transformer import T5FilmDecoder 8 | from .transformer_2d import Transformer2DModel 9 | from .transformer_temporal import TransformerTemporalModel 10 | -------------------------------------------------------------------------------- /src/diffusers/models/transformers/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- 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from .unet_2d_condition import UNet2DConditionModel 8 | from .unet_3d_condition import UNet3DConditionModel 9 | from .unet_i2vgen_xl import I2VGenXLUNet 10 | from .unet_kandinsky3 import Kandinsky3UNet 11 | from .unet_motion_model import MotionAdapter, UNetMotionModel 12 | from .unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel 13 | from .unet_stable_cascade import StableCascadeUNet 14 | from .uvit_2d import UVit2DModel 15 | 16 | 17 | if is_flax_available(): 18 | from .unet_2d_condition_flax import FlaxUNet2DConditionModel 19 | -------------------------------------------------------------------------------- /src/diffusers/models/unets/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/models/unets/__pycache__/__init__.cpython-39.pyc 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-------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | from typing import List, Union 3 | 4 | import numpy as np 5 | import PIL.Image 6 | import torch 7 | 8 | from ...utils import BaseOutput 9 | 10 | 11 | @dataclass 12 | class AnimateDiffPipelineOutput(BaseOutput): 13 | r""" 14 | Output class for AnimateDiff pipelines. 15 | 16 | Args: 17 | frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): 18 | List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised 19 | PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape 20 | `(batch_size, num_frames, channels, height, width)` 21 | """ 22 | 23 | frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]] 24 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/blip_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | from typing import List, Optional, Union 3 | 4 | import numpy as np 5 | import PIL 6 | from PIL import Image 7 | 8 | from ...utils import OptionalDependencyNotAvailable, is_torch_available, is_transformers_available 9 | 10 | 11 | try: 12 | if not (is_transformers_available() and is_torch_available()): 13 | raise OptionalDependencyNotAvailable() 14 | except OptionalDependencyNotAvailable: 15 | from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline 16 | else: 17 | from .blip_image_processing import BlipImageProcessor 18 | from .modeling_blip2 import Blip2QFormerModel 19 | from .modeling_ctx_clip import ContextCLIPTextModel 20 | from .pipeline_blip_diffusion import BlipDiffusionPipeline 21 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/brushnet/__pycache__/__init__.cpython-39.pyc: 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_LazyModule, 6 | ) 7 | 8 | 9 | _import_structure = { 10 | "pipeline_consistency_models": ["ConsistencyModelPipeline"], 11 | } 12 | 13 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 14 | from .pipeline_consistency_models import ConsistencyModelPipeline 15 | 16 | else: 17 | import sys 18 | 19 | sys.modules[__name__] = _LazyModule( 20 | __name__, 21 | globals()["__file__"], 22 | _import_structure, 23 | module_spec=__spec__, 24 | ) 25 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/controlnet/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/pipelines/controlnet/__pycache__/__init__.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/pipelines/controlnet/__pycache__/pipeline_controlnet.cpython-39.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/pipelines/controlnet/__pycache__/pipeline_controlnet.cpython-39.pyc -------------------------------------------------------------------------------- /src/diffusers/pipelines/dance_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_dance_diffusion": ["DanceDiffusionPipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_dance_diffusion import DanceDiffusionPipeline 10 | else: 11 | import sys 12 | 13 | sys.modules[__name__] = _LazyModule( 14 | __name__, 15 | globals()["__file__"], 16 | _import_structure, 17 | module_spec=__spec__, 18 | ) 19 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/ddim/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_ddim": ["DDIMPipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_ddim import DDIMPipeline 10 | else: 11 | import sys 12 | 13 | sys.modules[__name__] = _LazyModule( 14 | __name__, 15 | globals()["__file__"], 16 | _import_structure, 17 | module_spec=__spec__, 18 | ) 19 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/ddpm/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ...utils import ( 4 | DIFFUSERS_SLOW_IMPORT, 5 | _LazyModule, 6 | ) 7 | 8 | 9 | _import_structure = {"pipeline_ddpm": ["DDPMPipeline"]} 10 | 11 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 12 | from .pipeline_ddpm import DDPMPipeline 13 | 14 | else: 15 | import sys 16 | 17 | sys.modules[__name__] = _LazyModule( 18 | __name__, 19 | globals()["__file__"], 20 | _import_structure, 21 | module_spec=__spec__, 22 | ) 23 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/README.md: -------------------------------------------------------------------------------- 1 | # Deprecated Pipelines 2 | 3 | This folder contains pipelines that have very low usage as measured by model downloads, issues and PRs. While you can still use the pipelines just as before, we will stop testing the pipelines and will not accept any changes to existing files. -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/alt_diffusion/pipeline_output.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | from typing import List, Optional, Union 3 | 4 | import numpy as np 5 | import PIL.Image 6 | 7 | from ....utils import ( 8 | BaseOutput, 9 | ) 10 | 11 | 12 | @dataclass 13 | # Copied from diffusers.pipelines.stable_diffusion.pipeline_output.StableDiffusionPipelineOutput with Stable->Alt 14 | class AltDiffusionPipelineOutput(BaseOutput): 15 | """ 16 | Output class for Alt Diffusion pipelines. 17 | 18 | Args: 19 | images (`List[PIL.Image.Image]` or `np.ndarray`) 20 | List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, 21 | num_channels)`. 22 | nsfw_content_detected (`List[bool]`) 23 | List indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content or 24 | `None` if safety checking could not be performed. 25 | """ 26 | 27 | images: Union[List[PIL.Image.Image], np.ndarray] 28 | nsfw_content_detected: Optional[List[bool]] 29 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/audio_diffusion/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = { 7 | "mel": ["Mel"], 8 | "pipeline_audio_diffusion": ["AudioDiffusionPipeline"], 9 | } 10 | 11 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 12 | from .mel import Mel 13 | from .pipeline_audio_diffusion import AudioDiffusionPipeline 14 | 15 | else: 16 | import sys 17 | 18 | sys.modules[__name__] = _LazyModule( 19 | __name__, 20 | globals()["__file__"], 21 | _import_structure, 22 | module_spec=__spec__, 23 | ) 24 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/latent_diffusion_uncond/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_latent_diffusion_uncond": ["LDMPipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_latent_diffusion_uncond import LDMPipeline 10 | else: 11 | import sys 12 | 13 | sys.modules[__name__] = _LazyModule( 14 | __name__, 15 | globals()["__file__"], 16 | _import_structure, 17 | module_spec=__spec__, 18 | ) 19 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/pndm/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_pndm": ["PNDMPipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_pndm import PNDMPipeline 10 | else: 11 | import sys 12 | 13 | sys.modules[__name__] = _LazyModule( 14 | __name__, 15 | globals()["__file__"], 16 | _import_structure, 17 | module_spec=__spec__, 18 | ) 19 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/repaint/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_repaint": ["RePaintPipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_repaint import RePaintPipeline 10 | 11 | else: 12 | import sys 13 | 14 | sys.modules[__name__] = _LazyModule( 15 | __name__, 16 | globals()["__file__"], 17 | _import_structure, 18 | module_spec=__spec__, 19 | ) 20 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/score_sde_ve/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_score_sde_ve": ["ScoreSdeVePipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_score_sde_ve import ScoreSdeVePipeline 10 | 11 | else: 12 | import sys 13 | 14 | sys.modules[__name__] = _LazyModule( 15 | __name__, 16 | globals()["__file__"], 17 | _import_structure, 18 | module_spec=__spec__, 19 | ) 20 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/deprecated/stochastic_karras_ve/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ....utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_stochastic_karras_ve": ["KarrasVePipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_stochastic_karras_ve import KarrasVePipeline 10 | 11 | else: 12 | import sys 13 | 14 | sys.modules[__name__] = _LazyModule( 15 | __name__, 16 | globals()["__file__"], 17 | _import_structure, 18 | module_spec=__spec__, 19 | ) 20 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/dit/__init__.py: -------------------------------------------------------------------------------- 1 | from typing import TYPE_CHECKING 2 | 3 | from ...utils import DIFFUSERS_SLOW_IMPORT, _LazyModule 4 | 5 | 6 | _import_structure = {"pipeline_dit": ["DiTPipeline"]} 7 | 8 | if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: 9 | from .pipeline_dit import DiTPipeline 10 | 11 | else: 12 | import sys 13 | 14 | sys.modules[__name__] = _LazyModule( 15 | __name__, 16 | globals()["__file__"], 17 | _import_structure, 18 | module_spec=__spec__, 19 | ) 20 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/kandinsky/text_encoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel 3 | 4 | 5 | class MCLIPConfig(XLMRobertaConfig): 6 | model_type = "M-CLIP" 7 | 8 | def __init__(self, transformerDimSize=1024, imageDimSize=768, **kwargs): 9 | self.transformerDimensions = transformerDimSize 10 | self.numDims = imageDimSize 11 | super().__init__(**kwargs) 12 | 13 | 14 | class MultilingualCLIP(PreTrainedModel): 15 | config_class = MCLIPConfig 16 | 17 | def __init__(self, config, *args, **kwargs): 18 | super().__init__(config, *args, **kwargs) 19 | self.transformer = XLMRobertaModel(config) 20 | self.LinearTransformation = torch.nn.Linear( 21 | in_features=config.transformerDimensions, out_features=config.numDims 22 | ) 23 | 24 | def forward(self, input_ids, attention_mask): 25 | embs = self.transformer(input_ids=input_ids, attention_mask=attention_mask)[0] 26 | embs2 = (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None] 27 | return self.LinearTransformation(embs2), embs 28 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/semantic_stable_diffusion/pipeline_output.py: -------------------------------------------------------------------------------- 1 | from dataclasses import dataclass 2 | from typing import List, Optional, Union 3 | 4 | import numpy as np 5 | import PIL.Image 6 | 7 | from ...utils import BaseOutput 8 | 9 | 10 | @dataclass 11 | class SemanticStableDiffusionPipelineOutput(BaseOutput): 12 | """ 13 | Output class for Stable Diffusion pipelines. 14 | 15 | Args: 16 | images (`List[PIL.Image.Image]` or `np.ndarray`) 17 | List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, 18 | num_channels)`. 19 | nsfw_content_detected (`List[bool]`) 20 | List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or 21 | `None` if safety checking could not be performed. 22 | """ 23 | 24 | images: Union[List[PIL.Image.Image], np.ndarray] 25 | nsfw_content_detected: Optional[List[bool]] 26 | -------------------------------------------------------------------------------- /src/diffusers/pipelines/stable_diffusion/__pycache__/__init__.cpython-39.pyc: -------------------------------------------------------------------------------- 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| 8 | from ...utils import ( 9 | BaseOutput, 10 | ) 11 | 12 | 13 | @dataclass 14 | class TextToVideoSDPipelineOutput(BaseOutput): 15 | """ 16 | Output class for text-to-video pipelines. 17 | 18 | Args: 19 | frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]): 20 | List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised 21 | PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape 22 | `(batch_size, num_frames, channels, height, width)` 23 | """ 24 | 25 | frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]] 26 | -------------------------------------------------------------------------------- /src/diffusers/py.typed: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/src/diffusers/py.typed 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MidiProcessor(metaclass=DummyObject): 6 | _backends = ["note_seq"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["note_seq"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["note_seq"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["note_seq"]) 18 | -------------------------------------------------------------------------------- /src/diffusers/utils/dummy_onnx_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class OnnxRuntimeModel(metaclass=DummyObject): 6 | _backends = ["onnx"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["onnx"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["onnx"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["onnx"]) 18 | -------------------------------------------------------------------------------- /src/diffusers/utils/dummy_torch_and_librosa_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class AudioDiffusionPipeline(metaclass=DummyObject): 6 | _backends = ["torch", "librosa"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["torch", "librosa"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["torch", "librosa"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["torch", "librosa"]) 18 | 19 | 20 | class Mel(metaclass=DummyObject): 21 | _backends = ["torch", "librosa"] 22 | 23 | def __init__(self, *args, **kwargs): 24 | requires_backends(self, ["torch", "librosa"]) 25 | 26 | @classmethod 27 | def from_config(cls, *args, **kwargs): 28 | requires_backends(cls, ["torch", "librosa"]) 29 | 30 | @classmethod 31 | def from_pretrained(cls, *args, **kwargs): 32 | requires_backends(cls, ["torch", "librosa"]) 33 | -------------------------------------------------------------------------------- /src/diffusers/utils/dummy_torch_and_scipy_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class LMSDiscreteScheduler(metaclass=DummyObject): 6 | _backends = ["torch", "scipy"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["torch", "scipy"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["torch", "scipy"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["torch", "scipy"]) 18 | -------------------------------------------------------------------------------- /src/diffusers/utils/dummy_torch_and_torchsde_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class DPMSolverSDEScheduler(metaclass=DummyObject): 6 | _backends = ["torch", "torchsde"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["torch", "torchsde"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["torch", "torchsde"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["torch", "torchsde"]) 18 | -------------------------------------------------------------------------------- /src/diffusers/utils/dummy_transformers_and_torch_and_note_seq_objects.py: -------------------------------------------------------------------------------- 1 | # This file is autogenerated by the command `make fix-copies`, do not edit. 2 | from ..utils import DummyObject, requires_backends 3 | 4 | 5 | class SpectrogramDiffusionPipeline(metaclass=DummyObject): 6 | _backends = ["transformers", "torch", "note_seq"] 7 | 8 | def __init__(self, *args, **kwargs): 9 | requires_backends(self, ["transformers", "torch", "note_seq"]) 10 | 11 | @classmethod 12 | def from_config(cls, *args, **kwargs): 13 | requires_backends(cls, ["transformers", "torch", "note_seq"]) 14 | 15 | @classmethod 16 | def from_pretrained(cls, *args, **kwargs): 17 | requires_backends(cls, ["transformers", "torch", "note_seq"]) 18 | -------------------------------------------------------------------------------- /src/diffusers/utils/model_card_template.md: -------------------------------------------------------------------------------- 1 | --- 2 | {{ card_data }} 3 | --- 4 | 5 | 7 | 8 | {{ model_description }} 9 | 10 | ## Intended uses & limitations 11 | 12 | #### How to use 13 | 14 | ```python 15 | # TODO: add an example code snippet for running this diffusion pipeline 16 | ``` 17 | 18 | #### Limitations and bias 19 | 20 | [TODO: provide examples of latent issues and potential remediations] 21 | 22 | ## Training details 23 | 24 | [TODO: describe the data used to train the model] 25 | -------------------------------------------------------------------------------- /tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/tests/__init__.py -------------------------------------------------------------------------------- /tests/fixtures/elise_format0.mid: 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/tests/pipelines/test_pipelines_onnx_common.py: -------------------------------------------------------------------------------- 1 | from diffusers.utils.testing_utils import require_onnxruntime 2 | 3 | 4 | @require_onnxruntime 5 | class OnnxPipelineTesterMixin: 6 | """ 7 | This mixin is designed to be used with unittest.TestCase classes. 8 | It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline, 9 | equivalence of dict and tuple outputs, etc. 10 | """ 11 | 12 | pass 13 | -------------------------------------------------------------------------------- /tests/pipelines/text_to_video_synthesis/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/dongxuyue/Open-ReplaceAnything/83f0ae85ca216b5538377fb9bb89423c2aa7db46/tests/pipelines/text_to_video_synthesis/__init__.py -------------------------------------------------------------------------------- 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