o GiJ@sddlmZmZddlZddlmZmZddlmZm Z ddl m Z m Z ddl mZddlmZmZd d lmZmZmZerKddlmmZd Znd Zd ZGdddeeZdS))AnyCallableN)CLIPTextModelWithProjection CLIPTokenizer)PipelineImageInputVaeImageProcessor) UVit2DModelVQModel)AmusedScheduler)is_torch_xla_availablereplace_example_docstring)DeprecatedPipelineMixinDiffusionPipelineImagePipelineOutputTFa 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.s~eZdZUdZeed<eed<eed<eed<e ed<e ed<dZ dgZ dededede de f fd d Z eee        d+deeeBd BdedededededeeeBd Bded Bdejd Bdejd Bdejd Bd ejd Bd!ejd Bd"ed#eeeejgd fd Bd$ed%eeefd Bd&ed'eeefd(eeeefBeeBf(d)d*ZZS),AmusedInpaintPipelinez0.33.1image_processorvqvae tokenizer text_encoder transformer schedulerz text_encoder->transformer->vqvaecs~t|j|||||dt|ddr dt|jjjdnd|_t |jdd|_ t |jddddd |_ |j j d d dS) N)rrrrrrrF)vae_scale_factor do_normalizeT)rr do_binarizedo_convert_grayscale do_resizelinear)masking_schedule)super__init__register_modulesgetattrlenrconfigblock_out_channelsrrrmask_processorrregister_to_config)selfrrrrr __class__f/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/amused/pipeline_amused_inpaint.pyr#Ts& $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|-S)a 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`. image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `list[torch.Tensor]`, `list[PIL.Image.Image]`, or `list[np.ndarray]`): `Image`, numpy array or tensor representing an image batch to be used as the starting point. For both numpy array and pytorch tensor, the expected value range is between `[0, 1]` If it's a tensor or a list or tensors, the expected shape should be `(B, C, H, W)` or `(C, H, W)`. If it is a numpy array or a list of arrays, the expected shape should be `(B, H, W, C)` or `(H, W, C)` It can also accept image latents as `image`, but if passing latents directly it is not encoded again. mask_image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `list[torch.Tensor]`, `list[PIL.Image.Image]`, or `list[np.ndarray]`): `Image`, numpy array or tensor representing an image batch to mask `image`. White pixels in the mask are repainted while black pixels are preserved. If `mask_image` is a PIL image, it is converted to a single channel (luminance) before use. If it's a numpy array or pytorch tensor, it should contain one color channel (L) instead of 3, so the expected shape for pytorch tensor would be `(B, 1, H, W)`, `(B, H, W)`, `(1, H, W)`, `(H, W)`. And for numpy array would be for `(B, H, W, 1)`, `(B, H, W)`, `(H, W, 1)`, or `(H, W)`. strength (`float`, *optional*, defaults to 1.0): Indicates extent to transform the reference `image`. Must be between 0 and 1. `image` is used as a starting point and more noise is added the higher the `strength`. The number of denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise is maximum and the denoising process runs for the full number of iterations specified in `num_inference_steps`. A value of 1 essentially ignores `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. 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 (`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 truncationrL)rDoutput_hidden_statesrr0)devicedtyper)rTrS)total) micro_condspooled_text_embrArG) model_outputtimestepsampler?starting_mask_ratioorderlatent)force_not_quantizeshape)? ValueError isinstancestrr&r`rmodel_max_length input_idsto_execution_devicer text_embeds hidden_statesrepeattorchconcatr preprocesstensorrT unsqueezeexpandr set_timestepsint timestepsrfloat16r' force_upcastfloatencodelatentsquantizereshaper)rboolrS mask_token_idsumnumel progress_barrangecatrchunkstep prev_sampler]updater% XLA_AVAILABLExm mark_stepdecodelatent_channelsr[clip postprocesshalfmaybe_free_model_hooksr).r+r7r8r9r:r;r<r=r>r?r@rArBrC output_typerDrErFrGrHrIrJ batch_sizereoutputsheightwidthrWstart_timestep_idxneeds_upcastingrx latents_bszchannelslatents_height latents_widthmaskr\rirZ model_inputrY uncond_logits cond_logitsstep_idxoutputr.r.r/__call__rsl                  ,  &   zAmusedInpaintPipeline.__call__)NNNr0r1r2NrNNNNNr3TNrNr4r5r6) __name__ __module__ __qualname___last_supported_versionr__annotations__r rrr r model_cpu_offload_seq_exclude_from_cpu_offloadr#rkno_gradr EXAMPLE_DOC_STRINGlistrcrrvrr GeneratorTensorr{rdictrtupler __classcell__r.r.r,r/rDs       r)typingrrrk transformersrrrrrmodelsr r schedulersr utilsr r pipeline_utilsrrrtorch_xla.core.xla_modelcore xla_modelrrrrr.r.r.r/s