o Gi0>@sddlmZmZddlZddlmZmZddlmZddl m Z m Z ddl m Z ddlmZmZd d lmZmZmZerIddlmmZd Znd Zd ZGdddeeZdS))AnyCallableN)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.seZdZUdZeed<eed<eed<eed<e ed<e ed<dZ dededede de f fd d Z e ee       d*deeeBd Bded Bded BdededeeeBd Bded Bde jd Bde jd Bde jd Bde jd Bde jd Bd e jd Bd!ed"eeee jgd fd Bd#ed$eeefd Bd%ed&eeefd'eeeefBeeBf(d(d)ZZS)+AmusedPipelinez0.33.1image_processorvqvae tokenizer text_encoder transformer schedulerz text_encoder->transformer->vqvaecsZt|j|||||dt|ddr dt|jjjdnd|_t |jdd|_ dS)N)rrrrrrr F)vae_scale_factor do_normalize) super__init__register_modulesgetattrlenrconfigblock_out_channelsrrr)selfrrrrr __class__^/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/amused/pipeline_amused.pyr=s $zAmusedPipeline.__init__N $@rpilTrrr rpromptheightwidthnum_inference_stepsguidance_scalenegative_promptnum_images_per_prompt generatorlatents prompt_embedsencoder_hidden_statesnegative_prompt_embedsnegative_encoder_hidden_states return_dictcallbackcallback_stepscross_attention_kwargs"micro_conditioning_aesthetic_scoremicro_conditioning_crop_coord temperaturec& Cs| dur| dus| dur| durtd| dur| dus$| 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 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 (`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 truncationrC)r;output_hidden_statesrg?)devicedtyper )rKrJ)total) micro_condspooled_text_embr8r>) model_outputtimestepsampler5orderlatent)force_not_quantizeshape)7 ValueError isinstancestrr rVrr! sample_sizerrmodel_max_length input_idsto_execution_devicer text_embeds hidden_statesrepeattorchconcattensorrK unsqueezeexpandfullr mask_token_idlong set_timesteps timestepsrS progress_bar enumeratecatchunkstep prev_sampleupdater XLA_AVAILABLExm mark_steprfloat16 force_upcastfloatdecodelatent_channelsrRclipr postprocesshalfmaybe_free_model_hooksr)&r#r.r/r0r1r2r3r4r5r6r7r8r9r: output_typer;r<r=r>r?r@rA batch_sizer\outputsrNrVnum_warmup_stepsrlirQ model_inputrP uncond_logits cond_logitsstep_idxoutputneeds_upcastingr&r&r'__call__Ss _               $  %    zAmusedPipeline.__call__)NNNr(r)NrNNNNNNr*TNrNr+r,r-)__name__ __module__ __qualname___last_supported_versionr__annotations__r rrrr model_cpu_offload_seqrrbno_gradr EXAMPLE_DOC_STRINGlistrYintrx Generator IntTensorTensorboolrdictrtupler __classcell__r&r&r$r'r2s       r)typingrrrb transformersrrrrmodelsrr schedulersr utilsr r pipeline_utilsrrrtorch_xla.core.xla_modelcore xla_modelrtrsrrr&r&r&r's