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Returns
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~pipelines.stable_diffusion.AltDiffusionPipelineOutput or tuple
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~pipelines.stable_diffusion.AltDiffusionPipelineOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of bools denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the safety_checker`.
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Function invoked when calling the pipeline for generation.
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Examples:
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Copied
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>>> import torch
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>>> from diffusers import AltDiffusionPipeline
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>>> pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.float16)
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>>> pipe = pipe.to("cuda")
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>>> # "dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap"
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>>> prompt = "黑暗精灵公主，非常详细，幻想，非常详细，数字绘画，概念艺术，敏锐的焦点，插图"
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>>> image = pipe(prompt).images[0]
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disable_vae_slicing
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<
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source
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>
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(
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)
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Disable sliced VAE decoding. If enable_vae_slicing was previously invoked, this method will go back to
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computing decoding in one step.
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disable_vae_tiling
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<
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source
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>
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(
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)
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Disable tiled VAE decoding. If enable_vae_tiling was previously invoked, this method will go back to
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computing decoding in one step.
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enable_model_cpu_offload
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<
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source
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>
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(
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gpu_id = 0
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)
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Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
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to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward
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method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
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enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet.
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enable_sequential_cpu_offload
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<
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source
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>
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(
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gpu_id = 0
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)
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Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
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text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
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torch.device('meta') and loaded to GPU only when their specific submodule has its forwardmethod called. Note that offloading happens on a submodule basis. Memory savings are higher than withenable_model_cpu_offload`, but performance is lower.
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enable_vae_slicing
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<
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source
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>
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(
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)
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Enable sliced VAE decoding.
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When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
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steps. This is useful to save some memory and allow larger batch sizes.
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enable_vae_tiling
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