o pidI@sddlmZmZmZmZmZmZmZddlZddl m Z m Z ddl m Z mZddlmZmZddlmZddlmZd d lmZmZd ZGd d d eZdS))AnyCallableDictListOptionalTupleUnionN)CLIPTextModelWithProjection CLIPTokenizer)PipelineImageInputVaeImageProcessor) UVit2DModelVQModel)AmusedScheduler)replace_example_docstring)DiffusionPipelineImagePipelineOutputa Examples: ```py >>> import torch >>> from diffusers import AmusedInpaintPipeline >>> from diffusers.utils import load_image >>> pipe = AmusedInpaintPipeline.from_pretrained( ... "amused/amused-512", variant="fp16", torch_dtype=torch.float16 ... ) >>> pipe = pipe.to("cuda") >>> prompt = "fall mountains" >>> input_image = ( ... load_image( ... "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1.jpg" ... ) ... .resize((512, 512)) ... .convert("RGB") ... ) >>> mask = ( ... load_image( ... "https://huggingface.co/datasets/diffusers/docs-images/resolve/main/open_muse/mountains_1_mask.png" ... ) ... .resize((512, 512)) ... .convert("L") ... ) >>> pipe(prompt, input_image, mask).images[0].save("out.png") ``` c/seZdZUeed<eed<eed<eed<eed<e ed<dZ dgZ dedededede f fdd Z e ee       d*deeeeefdedededededeeeeefdeedee jdee jdee jdee jd ee jd!ed"eeeee jgd fd#ed$eeeefd%ed&eeefd'eeeeefeeff(d(d)ZZ S)+AmusedInpaintPipelineimage_processorvqvae tokenizer text_encoder transformer schedulerz text_encoder->transformer->vqvaecsnt|j|||||ddt|jjjd|_t|jdd|_ t|jddddd|_ |j j dd dS) N)rrrrrrF)vae_scale_factor do_normalizeT)rr do_binarizedo_convert_grayscale do_resizelinear)masking_schedule) super__init__register_moduleslenrconfigblock_out_channelsrr rmask_processorrregister_to_config)selfrrrrr __class__p/home/ubuntu/SoloSpeech/.venv/lib/python3.10/site-packages/diffusers/pipelines/amused/pipeline_amused_inpaint.pyr%Ks$ zAmusedInpaintPipeline.__init__N? $@rpilTrrrrpromptimage mask_imagestrengthnum_inference_stepsguidance_scalenegative_promptnum_images_per_prompt generator prompt_embedsencoder_hidden_statesnegative_prompt_embedsnegative_encoder_hidden_states return_dictcallbackcallback_stepscross_attention_kwargs"micro_conditioning_aesthetic_scoremicro_conditioning_crop_coord temperaturec. Cs| dur| dus| dur| durtd| dur| dus$| 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. 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 truncationrM)rEoutput_hidden_statesrr1)devicedtyper)rUrT)total) micro_condspooled_text_embrBrH) model_outputtimestepsampler@starting_mask_ratioorderlatent)force_not_quantizeshape)< ValueError isinstancestrr'rarmodel_max_length input_idsto_execution_devicer text_embeds hidden_statesrepeattorchconcatr preprocesstensorrU unsqueezeexpandr set_timestepsint timestepsrfloat16r( force_upcastfloatencodelatentsquantizereshaper*rboolrT mask_token_idsumnumel progress_barrangecatrchunkstep prev_sampler^updategetattrdecodelatent_channelsr\clip postprocesshalfmaybe_free_model_hooksr).r,r8r9r:r;r<r=r>r?r@rArBrCrD output_typerErFrGrHrIrJrK batch_sizerfoutputsheightwidthrXstart_timestep_idxneeds_upcastingry latents_bszchannelslatents_height latents_widthmaskr]rir[ model_inputrZ uncond_logits cond_logitsstep_idxoutputr/r/r0__call__gsl                  ,  #   zAmusedInpaintPipeline.__call__)NNNr1r2r3NrNNNNNr4TNrNr5r6r7)!__name__ __module__ __qualname__r __annotations__rr r rrmodel_cpu_offload_seq_exclude_from_cpu_offloadr%rlno_gradrEXAMPLE_DOC_STRINGrrrrdr rwrs GeneratorTensorr|rrrrr __classcell__r/r/r-r0r<s       r)typingrrrrrrrrl transformersr r rr r modelsrr schedulersrutilsrpipeline_utilsrrrrr/r/r/r0s$