+h>SSKJrJrJrJrJrJrJr SSKrSSK J r J r SSK J r SSKJrJr SSKJr SSKJrJr S S KJrJrJr \"5(a SSKJs Jr S rOS rS r"SS\\5r g))AnyCallableDictListOptionalTupleUnionN)CLIPTextModelWithProjection CLIPTokenizer)VaeImageProcessor) UVit2DModelVQModel)AmusedScheduler)is_torch_xla_availablereplace_example_docstring)DeprecatedPipelineMixinDiffusionPipelineImagePipelineOutputTFa{ Examples: ```py >>> import torch >>> from diffusers import AmusedPipeline >>> pipe = AmusedPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16) >>> pipe = pipe.to("cuda") >>> prompt = "a photo of an astronaut riding a horse on mars" >>> image = pipe(prompt).images[0] ``` c/^\rSrSr%Sr\\S'\\S'\\S'\ \S'\ \S'\ \S'S r S\S\S\ S\ S\ 4 U4S jjr \R"5\"\5S"S \\\\\4S \\S\\S\S\S\\\\\4S\\S\\R0S\\R2S\\R4S\\R4S\\R4S\\R4S\S\\\\\R4/S 4S\S\\\\4S\S\\\4S\\\\\4\\44(S jj55r S!r!U=r"$)#AmusedPipeline2z0.33.1image_processorvqvae tokenizer text_encoder transformer schedulerz text_encoder->transformer->vqvaec >[TU]5 URUUUUUS9 [USS5(a/S[ UR R R5S- -OSUl[URSS9Ul g)N)rrrrrrrF)vae_scale_factor do_normalize) super__init__register_modulesgetattrlenrconfigblock_out_channelsr#r r)selfrrrrr __class__s d/home/james-whalen/.local/lib/python3.13/site-packages/diffusers/pipelines/amused/pipeline_amused.pyr&AmusedPipeline.__init__=s  %#  ELDRY[_D`D`A#djj''::;a? @fg  1$BWBWfklNpromptheightwidthnum_inference_stepsguidance_scalenegative_promptnum_images_per_prompt generatorlatents prompt_embedsencoder_hidden_statesnegative_prompt_embedsnegative_encoder_hidden_states return_dictcallbackcallback_stepscross_attention_kwargs"micro_conditioning_aesthetic_scoremicro_conditioning_crop_coord temperaturec  U bU bU cU b [S5eU bU bU cU b [S5eUcU bUbU b [S5e[U[5(aU/nUb [U5nOU RSnUU-nUc-UR R RUR-nUc-UR R RUR-nU cvURUSSSURRS 9RRUR5nURUSSS 9nURn UR S n U R#US 5n U R#US S 5n US :aU cUcS/[U5-n[U[5(aU/nURUSSSURRS 9RRUR5nURUSSS 9nURn UR S n U R#US 5n U R#US S 5n [$R&"X/5n [$R&"X/5n [$R("UUUSUS U/URU R*S9nUR-S5nUR/US :aSU-OUS5nUX R-X0R-4nU cM[$R0"UUR2R R4[$R6URS9n UR2R9UUUR5 [UR2R:5X@R2R<-- nUR?US9n[AUR2R:5GH:unnUS :a[$RB"U /S-5nOU nUR UUU U US9n US :aU RES5un!n"U!UU"U!- --n UR2RGU UU US9RHn U[UR2R:5S - :Xd)US -U:a`US -UR2R<-S:Xa@URK5 Ub-UU-S:Xa$U[MUR2SS 5-n#U"U#UU 5 [N(dGM%[PRR"5 GM= SSS5 US:XaU n$GOURTR*[$RV:H=(a URTR RXn%U%(aURTR[5 URTR]U SUX R-X0R-URTR R^4S9R`RcSS 5n$URdRgU$U5n$U%(aURTRi5 URk5 U(dU$4$[mU$5$!,(df  GNY=f)aw The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. height (`int`, *optional*, defaults to `self.transformer.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 16): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. guidance_scale (`float`, *optional*, defaults to 10.0): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.IntTensor`, *optional*): Pre-generated tokens representing latent vectors in `self.vqvae`, to be used as inputs for image generation. If not provided, the starting latents will be completely masked. prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. A single vector from the pooled and projected final hidden states. encoder_hidden_states (`torch.Tensor`, *optional*): Pre-generated penultimate hidden states from the text encoder providing additional text conditioning. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. negative_encoder_hidden_states (`torch.Tensor`, *optional*): Analogous to `encoder_hidden_states` for the positive prompt. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that calls every `callback_steps` steps during inference. The function is called with the following arguments: `callback(step: int, timestep: int, latents: torch.Tensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function is called. If not specified, the callback is called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). micro_conditioning_aesthetic_score (`int`, *optional*, defaults to 6): The targeted aesthetic score according to the laion aesthetic classifier. See https://laion.ai/blog/laion-aesthetics/ and the micro-conditioning section of https://huggingface.co/papers/2307.01952. micro_conditioning_crop_coord (`Tuple[int]`, *optional*, defaults to (0, 0)): The targeted height, width crop coordinates. See the micro-conditioning section of https://huggingface.co/papers/2307.01952. temperature (`Union[int, Tuple[int, int], List[int]]`, *optional*, defaults to (2, 0)): Configures the temperature scheduler on `self.scheduler` see `AmusedScheduler#set_timesteps`. Examples: Returns: [`~pipelines.pipeline_utils.ImagePipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.pipeline_utils.ImagePipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images. 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