bi>ddlmZmZmZmZmZmZmZddlZddl m Z m Z ddl m Z ddlmZmZddlmZddlmZmZd d lmZmZmZer ddlmcmZd Znd Zd ZGddeeZ y))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/veZdZUdZeed<eed<eed<eed<e ed<e ed<dZ dededede de f fd Z e jee d d eeeeefd eed eedededeeeeefdeedee j.dee j0dee j2dee j2dee j2dee j2dedeeeee j2gd fdedeeeefdedeeefdeeeeefeeff(dZxZ S)!AmusedPipelinez0.33.1image_processorvqvae tokenizer text_encoder transformer schedulerz text_encoder->transformer->vqvaec t||j|||||t|ddr/dt |j j jdz znd|_t|jd|_ y)N)rrrrrrrF)vae_scale_factor do_normalize) super__init__register_modulesgetattrlenrconfigblock_out_channelsr"r r)selfrrrrr __class__s e/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/pipelines/amused/pipeline_amused.pyr%zAmusedPipeline.__init__=s  %#  ELDRY[_D`A#djj''::;a? @fg  1$BWBWfklNpromptheightwidthnum_inference_stepsguidance_scalenegative_promptnum_images_per_prompt generatorlatents prompt_embedsencoder_hidden_statesnegative_prompt_embedsnegative_encoder_hidden_states return_dictcallbackcallback_stepscross_attention_kwargs"micro_conditioning_aesthetic_scoremicro_conditioning_crop_coord temperaturec  | | | | td| | | | td|| | | tdt|tr|g}| t|}n| jd}||z}|-|j j j|jz}|-|j j j|jz}| ||j|ddd|jj jj|j}|j|dd }|j} |j d } | j#|d } | j#|d d } |d kDr| |dgt|z}t|tr|g}|j|ddd|jj jj|j}|j|dd }|j} |j d } | j#|d } | j#|d d } t%j&| | g} t%j&| | g} t%j(|||d|d |g|j| j*}|j-d}|j/|d kDrd|zn|d}|||jz||jzf}| Ot%j0||j2j j4t$j6|j} |j2j9|||jt|j2j:||j2j<zz }|j?|5}tA|j2j:D]0\}}|d kDrt%jB| gdz}n| }|j ||| | |} |d kDr| jEd\}!}"|!||"|!z zz} |j2jG| || |jH} |t|j2j:d z k(s'|d z|kDr]|d z|j2j<zdk(r>|jK|,||zdk(r$|tM|j2dd z}#||#|| tNstQjR3 ddd|dk(r| }$n|jTj*t$jVk(xr |jTj jX}%|%r|jTj[|jTj]| d|||jz||jz|jTj j^fj`jcdd }$|jdjg|$|}$|%r|jTji|jk|s|$fStm|$S#1swYDxYw)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`. 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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