o pi=@sddlmZmZmZmZmZmZmZddlZddl m Z m Z ddl m Z ddlmZmZddlmZddlmZd d lmZmZd ZGd d d eZdS))AnyCallableDictListOptionalTupleUnionN)CLIPTextModelWithProjection CLIPTokenizer)VaeImageProcessor) UVit2DModelVQModel)AmusedScheduler)replace_example_docstring)DiffusionPipelineImagePipelineOutputa{ 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/seZdZUeed<eed<eed<eed<eed<e ed<dZ dedededede f fdd Z e ee     d)deeeeefdeedeedededeeeeefdeedee jdee jdee jdee jdee jdee jd ed!eeeee jgd fd"ed#eeeefd$ed%eeefd&eeeeefeeff(d'd(ZZS)*AmusedPipelineimage_processorvqvae tokenizer text_encoder transformer schedulerz text_encoder->transformer->vqvaecsJt|j|||||ddt|jjjd|_t|jdd|_ dS)N)rrrrrrF)vae_scale_factor do_normalize) super__init__register_moduleslenrconfigblock_out_channelsrr r)selfrrrrr __class__h/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/diffusers/pipelines/amused/pipeline_amused.pyr4s zAmusedPipeline.__init__N $@rpilTrrrrpromptheightwidthnum_inference_stepsguidance_scalenegative_promptnum_images_per_prompt generatorlatents prompt_embedsencoder_hidden_statesnegative_prompt_embedsnegative_encoder_hidden_states return_dictcallbackcallback_stepscross_attention_kwargs"micro_conditioning_aesthetic_scoremicro_conditioning_crop_coord temperaturec& Cs|| dur| dus| dur| durtd| dur| dus$| dur(| dur(td|dur0| dus8|dur<| dur 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 gneration. 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://arxiv.org/abs/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://arxiv.org/abs/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. NzGpass either both `prompt_embeds` and `encoder_hidden_states` or neitherzXpass either both `negatve_prompt_embeds` and `negative_encoder_hidden_states` or neitherz,pass only one of `prompt` or `prompt_embeds`rpt max_lengthT)return_tensorspadding truncationrD)r<output_hidden_statesrg?)devicedtyper)rLrK)total) micro_condspooled_text_embr9r?) model_outputtimestepsampler6orderlatent)force_not_quantizeshape)4 ValueError isinstancestrr!rWrr" sample_sizerrmodel_max_length input_idsto_execution_devicer text_embeds hidden_statesrepeattorchconcattensorrL unsqueezeexpandfullr mask_token_idlong set_timesteps timestepsrT progress_bar enumeratecatchunkstep prev_sampleupdategetattrrfloat16 force_upcastfloatdecodelatent_channelsrSclipr postprocesshalfmaybe_free_model_hooksr)&r$r/r0r1r2r3r4r5r6r7r8r9r:r; output_typer<r=r>r?r@rArB batch_sizer]outputsrOrWnum_warmup_stepsrmirR model_inputrQ uncond_logits cond_logitsstep_idxoutputneeds_upcastingr'r'r(__call__Hs_               $  "    zAmusedPipeline.__call__)NNNr)r*NrNNNNNNr+TNrNr,r-r.) __name__ __module__ __qualname__r __annotations__rr r r rmodel_cpu_offload_seqrrcno_gradrEXAMPLE_DOC_STRINGrrrrZintrw Generator IntTensorTensorboolrrrrr __classcell__r'r'r%r(r*s       r)typingrrrrrrrrc transformersr r rr modelsr r schedulersrutilsrpipeline_utilsrrrrr'r'r'r(s$