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A function that will be called every callback_steps steps during inference. The function will be |
called with the following arguments: callback(step: int, timestep: int, latents: torch.FloatTensor). |
callback_steps (int, optional, defaults to 1) — |
The frequency at which the callback function will be called. If not specified, the callback will be |
called at every step. |
clean_caption (bool, optional, defaults to True) — |
Whether or not to clean the caption before creating embeddings. Requires beautifulsoup4 and ftfy to |
be installed. If the dependencies are not installed, the embeddings will be created from the raw |
prompt. |
cross_attention_kwargs (dict, optional) — |
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under |
self.processor in |
diffusers.cross_attention. |
Returns |
~pipelines.stable_diffusion.IFPipelineOutput or tuple |
~pipelines.stable_diffusion.IFPipelineOutput 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) or watermarked ... |
Function invoked when calling the pipeline for generation. |
Examples: |
Copied |
>>> from diffusers import IFPipeline, IFSuperResolutionPipeline, DiffusionPipeline |
>>> from diffusers.utils import pt_to_pil |
>>> import torch |
>>> pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-IF-v1.0", variant="fp16", torch_dtype=torch.float16) |
>>> pipe.enable_model_cpu_offload() |
>>> prompt = 'a photo of a kangaroo wearing an orange hoodie and blue sunglasses standing in front of the eiffel tower holding a sign that says "very deep learning"' |
>>> prompt_embeds, negative_embeds = pipe.encode_prompt(prompt) |
>>> image = pipe(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt").images |
>>> # save intermediate image |
>>> pil_image = pt_to_pil(image) |
>>> pil_image[0].save("./if_stage_I.png") |
>>> super_res_1_pipe = IFSuperResolutionPipeline.from_pretrained( |
... "DeepFloyd/IF-II-L-v1.0", text_encoder=None, variant="fp16", torch_dtype=torch.float16 |
... ) |
>>> super_res_1_pipe.enable_model_cpu_offload() |
>>> image = super_res_1_pipe( |
... image=image, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_embeds, output_type="pt" |
... ).images |
>>> # save intermediate image |
>>> pil_image = pt_to_pil(image) |
>>> pil_image[0].save("./if_stage_I.png") |
>>> safety_modules = { |
... "feature_extractor": pipe.feature_extractor, |
... "safety_checker": pipe.safety_checker, |
... "watermarker": pipe.watermarker, |
... } |
>>> super_res_2_pipe = DiffusionPipeline.from_pretrained( |
... "stabilityai/stable-diffusion-x4-upscaler", **safety_modules, torch_dtype=torch.float16 |
... ) |
>>> super_res_2_pipe.enable_model_cpu_offload() |
>>> image = super_res_2_pipe( |
... prompt=prompt, |
... image=image, |
... ).images |
>>> image[0].save("./if_stage_II.png") |
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 |
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