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sayakpaul
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diffusers-qa-chatbot-artifacts

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Returns
~pipelines.stable_diffusion.AltDiffusionPipelineOutput or tuple
~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`.
Function invoked when calling the pipeline for generation.
Examples:
Copied
>>> import torch
>>> from diffusers import AltDiffusionPipeline
>>> pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> # "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"
>>> 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"
>>> image = pipe(prompt).images[0]
disable_vae_slicing
<
source
>
(
)
Disable sliced VAE decoding. If enable_vae_slicing was previously invoked, this method will go back to
computing decoding in one step.
disable_vae_tiling
<
source
>
(
)
Disable tiled VAE decoding. If enable_vae_tiling was previously invoked, this method will go back to
computing decoding in one step.
enable_model_cpu_offload
<
source
>
(
gpu_id = 0
)
Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
to enable_sequential_cpu_offload, this method moves one whole model at a time to the GPU when its forward
method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with
enable_sequential_cpu_offload, but performance is much better due to the iterative execution of the unet.
enable_sequential_cpu_offload
<
source
>
(
gpu_id = 0
)
Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
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.
enable_vae_slicing
<
source
>
(
)
Enable sliced VAE decoding.
When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several
steps. This is useful to save some memory and allow larger batch sizes.
enable_vae_tiling