bi ddlZddlmZmZmZmZmZmZddlZ ddl Z ddl m Z m Z mZmZddlmZmZddlmZmZmZmZddlmZmZddlmZdd lmZmZm Z m!Z!m"Z"m#Z#dd l$m%Z%d d l&m'Z'd dl(m)Z)er ddl*m+cm,Z-dZ.ndZ.e j^e0Z1dZ2 dde3de3de4de4fdZ5 ddee3deee6e jnfdeee3deee4fdZ8Gdde'eeeeZ9y) N)AnyCallableDictListOptionalUnion)CLIPImageProcessorCLIPVisionModelWithProjectionT5EncoderModelT5TokenizerFast)PipelineImageInputVaeImageProcessor)FluxIPAdapterMixinFluxLoraLoaderMixinFromSingleFileMixinTextualInversionLoaderMixin) AutoencoderKLChromaTransformer2DModel)FlowMatchEulerDiscreteScheduler)USE_PEFT_BACKENDis_torch_xla_availableloggingreplace_example_docstringscale_lora_layersunscale_lora_layers) randn_tensor)DiffusionPipeline)ChromaPipelineOutputTFa Examples: ```py >>> import torch >>> from diffusers import ChromaPipeline >>> model_id = "lodestones/Chroma" >>> ckpt_path = "https://huggingface.co/lodestones/Chroma/blob/main/chroma-unlocked-v37.safetensors" >>> transformer = ChromaTransformer2DModel.from_single_file(ckpt_path, torch_dtype=torch.bfloat16) >>> pipe = ChromaPipeline.from_pretrained( ... model_id, ... transformer=transformer, ... torch_dtype=torch.bfloat16, ... ) >>> pipe.enable_model_cpu_offload() >>> prompt = [ ... "A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done." ... ] >>> negative_prompt = [ ... "low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors" ... ] >>> image = pipe(prompt, negative_prompt=negative_prompt).images[0] >>> image.save("chroma.png") ``` base_seq_len max_seq_len base_shift max_shiftc<||z ||z z }|||zz }||z|z}|SN) image_seq_lenr"r#r$r%mbmus e/home/cdr/jupyterlab/.venv/lib/python3.12/site-packages/diffusers/pipelines/chroma/pipeline_chroma.pycalculate_shiftr.Ms; Z K,$>?AQ%%A  Q B Inum_inference_stepsdevice timestepssigmasc | | td|dttj|jj j v}|std|jd|jd ||d||j}t|}||fS|dttj|jj j v}|std|jd|jd ||d||j}t|}||fS|j|fd |i||j}||fS) a Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`List[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. zYOnly one of `timesteps` or `sigmas` can be passed. Please choose one to set custom valuesr2zThe current scheduler class zx's `set_timesteps` does not support custom timestep schedules. Please check whether you are using the correct scheduler.)r2r1r3zv's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.)r3r1r1r() ValueErrorsetinspect signature set_timesteps parameterskeys __class__r2len) schedulerr0r1r2r3kwargsaccepts_timesteps accept_sigmass r-retrieve_timestepsrB[s>!3tuu'3w/@/@AXAX/Y/d/d/i/i/k+ll .y/B/B.CDab  M)FMfM'' !)n ) ))   C(9(9):Q:Q(R(](](b(b(d$ee .y/B/B.CD_`  GvfGG'' !)n ) ))  3MFMfM'' ) ))r/c3eZdZdZdZddgZddgZ dCded ed e d e d e de de ffd Z dDdeeeefdededeej*deej,f dZ dEdeeeefdeeeefdeej*dedeej0deej0deej0deej0dededeefdZdZdZ dFd Zed!Z ed"Z!ed#Z"d$Z#d%Z$d&Z%d'Z& dGd(Z' dGd)Z(e)d*Z*e)d+Z+e)d,Z,e)d-Z-e)d.Z.e)d/Z/ej`e1e2ddddd0dd1dddddddddddd2ddddgdfdeeeefdeeeefd3eed4eed5ed6eeed7edeed8eeejfeejffdeej0deej0d9ee4d:eeej0d;ee4ded?ee5ee6fd@ee7eee5gdfdAeedef0dBZ8xZ9S)HChromaPipelinea The Chroma pipeline for text-to-image generation. Reference: https://huggingface.co/lodestones/Chroma/ Args: transformer ([`ChromaTransformer2DModel`]): Conditional Transformer (MMDiT) architecture to denoise the encoded image latents. scheduler ([`FlowMatchEulerDiscreteScheduler`]): A scheduler to be used in combination with `transformer` to denoise the encoded image latents. vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representation text_encoder ([`T5EncoderModel`]): [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically the [google/t5-v1_1-xxl](https://huggingface.co/google/t5-v1_1-xxl) variant. tokenizer (`T5TokenizerFast`): Second Tokenizer of class [T5TokenizerFast](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5TokenizerFast). z-text_encoder->image_encoder->transformer->vae image_encoderfeature_extractorlatents prompt_embedsNr>vae text_encoder tokenizer transformerc "t||j|||||||t|ddr/dt |j j jdz znd|_t|jdz|_ d|_ y)N)rIrJrKrLr>rErFrIrr )vae_scale_factor) super__init__register_modulesgetattrr=rIconfigblock_out_channelsrOrimage_processordefault_sample_size) selfr>rIrJrKrLrErFr<s r-rRzChromaPipeline.__init__s  %#'/  W^^bdikoVpc$((//*L*L&MPQ&Q Rvw 1$BWBWZ[B[\#& r/r promptnum_images_per_promptmax_sequence_lengthr1dtypec |xs |j}|xs|jj}t|tr|gn|}t |}t|t r|j||j}|j|d|dddd}|j}|jj} | jd} tj| jdj!dj#|d } | | j!dkj%} |j|j'|d| j'| d} |jj}| j'|| } | j'|| } | j(\} }} | j+d|d} | j-||z|d } | j+d|} | j-||z|} | | fS) N max_lengthTFpt)paddingr` truncation return_lengthreturn_overflowing_tokensreturn_tensorsr dimr)output_hidden_statesattention_mask)r^r1)_execution_devicerJr^ isinstancestrr=rmaybe_convert_promptrK input_idsrkclonesumtorcharangesize unsqueezeexpandlongtoshaperepeatview)rYr[r\r]r1r^ batch_size text_inputstext_input_idsrk seq_lengths mask_indicesrH_seq_lens r-_get_t5_prompt_embedsz$ChromaPipeline._get_t5_prompt_embedss14110**00'4&&[ d7 8..vt~~FFnn  *&+% %..$3399;%((Q(/ ||N$7$7$:;EEaHOOPZ\^_ &+*?*?*BBHHJ))   f %ER`RcRcdjRk*  !!''%((uV(D '**v*F%++ 7A&,,Q0EqI %**:8M+MwXZ[ '..q2GH',,Z:O-OQXYn,,r/Tnegative_promptnegative_prompt_embedsprompt_attention_masknegative_prompt_attention_maskdo_classifier_free_guidance lora_scalec J|xs |j}| ?t|tr/| |_|jt rt |j| t|tr|gn|}| t|} n|jd} ||j||| |\}}|j|jjn|jj} tj|jddj|| }d}| r||xsd}t|tr| |gzn|}|:t!|t!|ur$t#dt!|d t!|d | t|k7r!t%d |d t|d |d | d |j||| |\}}tj|jddj|| }|j,t|trt rt'|j| ||||||fS)a Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded negative_prompt (`str` or `List[str]`, *optional*): The prompt not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. lora_scale (`float`, *optional*): A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. Nr)r[r\r]r1r r r1r^z?`negative_prompt` should be the same type to `prompt`, but got z != .z`negative_prompt`: z has batch size z, but `prompt`: zT. Please make sure that passed `negative_prompt` matches the batch size of `prompt`.)rlrmr _lora_scalerJrrrnr=rzrr^rLrszerosrytype TypeErrorr5r)rYr[rr1r\rHrrrrr]rr}r^text_idsnegative_text_idss r- encode_promptzChromaPipeline.encode_prompts~@1411  !j7J&K)D   ,1A!$"3"3Z@'4&&  VJ&,,Q/J  373M3M&;$7 4N4 0M0,0+<+<+H!!''dN^N^NdNd;;}2215q9<S*S#YZ^_nZoYpq L>, 3#77$-o->>NsSbOcNde"8#3J<@77 JNIcIc**?(;! JdJF&(F!& ,B,H,H,KQ O R RZ`hm R n     ($ 349I#D$5$5zB   ! "  *   r/cPt|jjj}t |t j s|j|dj}|j||}|j|j}|j|d}|S)Nra)rfrrrg) nextrEr:r^rmrsTensorrF pixel_valuesry image_embedsrepeat_interleave)rYimager1r\r^rs r- encode_imagezChromaPipeline.encode_imagensT''2245;;%.**5*FSSEe4))%0== #556KQR5S r/cg}|t|ts|g}t||jjj k7r9t dt|d|jjj d|D]-}|j||d}|j|dddf/nt|ts|g}t||jjj k7r9t dt|d|jjj d|D]}|j|g}|D]@}tj|g|zd}|j| }|j|B|S) NzK`ip_adapter_image` must have same length as the number of IP Adapters. Got z images and z IP Adapters.r zR`ip_adapter_image_embeds` must have same length as the number of IP Adapters. Got z image embeds and rrgr1) rmlistr=rLencoder_hid_projnum_ip_adaptersr5rappendrscatry)rYip_adapter_imageip_adapter_image_embedsr1r\rsingle_ip_adapter_imagesingle_image_embedss r-prepare_ip_adapter_image_embedsz.ChromaPipeline.prepare_ip_adapter_image_embedszs " *.5$4#5 #$(8(8(I(I(Y(YY abefvbwaxyEFJFVFVFgFgFwFwExxEF,< B'&*&7&78OQWYZ&[###$7a$@A B5t<+B*C'*+t/?/?/P/P/`/`` hilnEjFiGGYZ^ZjZjZ{Z{ZKZKYLLYZ(? 9###$78 9#%#/ @ "'))-@,ADY,Y_`"a "5"8"8"8"G  # * *+> ? @ '&r/c |jdzzdk7s|jdzzdk7r,tjdjdzd|d|d| Ltfd| Ds8t dj d | D cgc]} | j vs| c} ||t d |d |d | | t d |7t |ts't |tst dt|||t d|d|d | | t d| | t d| | dkDrt d| yycc} w)Nrrz-`height` and `width` have to be divisible by z but are z and z(. Dimensions will be resized accordinglyc3:K|]}|jvywr')_callback_tensor_inputs).0krYs r- z.ChromaPipeline.check_inputs..s#F 23A-- -F sz2`callback_on_step_end_tensor_inputs` has to be in z , but found zCannot forward both `prompt`: z and `prompt_embeds`: z2. Please make sure to only forward one of the two.zeProvide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined.z2`prompt` has to be of type `str` or `list` but is z'Cannot forward both `negative_prompt`: z and `negative_prompt_embeds`: zLCannot provide `prompt_embeds` without also providing `prompt_attention_maskz^Cannot provide `negative_prompt_embeds` without also providing `negative_prompt_attention_maskrZz8`max_sequence_length` cannot be greater than 512 but is ) rOloggerwarningallr5rrmrnrr) rYr[heightwidthrrHrrr"callback_on_step_end_tensor_inputsr]rs ` r- check_inputszChromaPipeline.check_inputss' T**Q. /1 4AVAVYZAZ8[_`8` NN?@U@UXY@Y?ZZcdjckkpqvpwx`a  . 9#F 7YF C DTEaEaDbbn|^pHvwbckokGkGbGpqpHoIJ   -";08N}o^00 ^ 5w  FC)@TZ\`IaQRVW]R^Q_`a a  &+A+M9/9JK*++]_   $)>)Fkl l ! -2P2Xp   */BS/HWXkWlmn n0I *;pHs EEc4tj||d}|dtj|dddfz|d<|dtj|dddfz|d<|j\}}}|j ||z|}|j ||S)Nr ).r ).rr)rsrrtrzreshapery) r}rrr1r^latent_image_idslatent_image_id_heightlatent_image_id_widthlatent_image_id_channelss r-_prepare_latent_image_idsz(ChromaPipeline._prepare_latent_image_idss ;;vua8#3F#;ell6>RSTVZSZ>[#[ #3F#;ell5>QRVXYRY>Z#Z RbRhRhO 57O+33 "%: :>r/c|j|||dzd|dzd}|jdddddd}|j||dz|dzz|dz}|S)Nrrr r )r|permuter)rGr}num_channels_latentsrrs r- _pack_latentszChromaPipeline._pack_latentssj,,z+?1aQVZ[Q[]^_//!Q1a3//*v{uz.JL`cdLder/c|j\}}}dt||dzzz}dt||dzzz}|j||dz|dz|dzdd}|jdddddd}|j ||dz||}|S)Nrrrr r r)rzintr|rr)rGrrrOr} num_patcheschannelss r-_unpack_latentszChromaPipeline._unpack_latentss,3MM) Kc&k&6&:;<SZ$4q$89:,,z6Q; HPQMSTVWX//!Q1a3//*h5.A65Qr/c8|jjy)z Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. N)rIenable_slicingrYs r-enable_vae_slicingz!ChromaPipeline.enable_vae_slicing !r/c8|jjy)z Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step. N)rIdisable_slicingrs r-disable_vae_slicingz"ChromaPipeline.disable_vae_slicings   "r/c8|jjy)a Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images. N)rI enable_tilingrs r-enable_vae_tilingz ChromaPipeline.enable_vae_tilings  r/c8|jjy)z Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step. N)rIdisable_tilingrs r-disable_vae_tilingz!ChromaPipeline.disable_vae_tilingrr/c dt||jdzzz}dt||jdzzz}||||f} |0|j||dz|dz||} |j||| fSt |t r)t ||k7rtdt |d|dt| |||}|j|||||}|j||dz|dz||} || fS)Nrrz/You have passed a list of generators of length z+, but requested an effective batch size of z@. Make sure the batch size matches the length of the generators.) generatorr1r^) rrOrryrmrr=r5rr) rYr}rrrr^r1rrGrzrs r-prepare_latentszChromaPipeline.prepare_latentss8c&kd&;&;a&?@ASZD$9$9A$=>?165A  #==j&TU+W\`aWacikpq ::V5:9;KK K i &3y>Z+GA#i.AQR&<'gi  u &PUV$$Wj:NPVX]^99*fPQkSX\]S]_eglm(((r/c||Stj|tj|||jgd}|j |}|S)Nrr rg)rsronesr1ry)rYr}sequence_lengthr^rks r-_prepare_attention_maskz&ChromaPipeline._prepare_attention_mask;sV  !! ! UZZ ONLaLab c (**51r/c|jSr'_guidance_scalers r-guidance_scalezChromaPipeline.guidance_scaleNs###r/c|jSr')_joint_attention_kwargsrs r-joint_attention_kwargsz%ChromaPipeline.joint_attention_kwargsRs+++r/c |jdkDS)Nr rrs r-rz*ChromaPipeline.do_classifier_free_guidanceVs##a''r/c|jSr')_num_timestepsrs r- num_timestepszChromaPipeline.num_timestepsZs"""r/c|jSr')_current_timesteprs r-current_timestepzChromaPipeline.current_timestep^s%%%r/c|jSr') _interruptrs r- interruptzChromaPipeline.interruptbs r/#g@pilrrr0r3rrrrnegative_ip_adapter_image negative_ip_adapter_image_embeds output_type return_dictrcallback_on_step_endrc  |xs|j|jz}|xs|j|jz}|j||||| ||||| ||_||_d|_d|_|t|trd}n-|t|tr t|}n| jd}|j}|j|jjddnd}|j||| ||||j |||| \} }}}}}|j"j$j&dz}|j)||z|||| j*|| | \} }|t-j.d d|z |n|}| jd} t1| |j2j$jd d |j2j$jd d |j2j$jdd|j2j$jdd}!|j5| jd| | j*|}"|j5| jd| | j*|}#t7|j2||||!\}$}t9t|$||j2j:zz d}%t|$|_| | Q|O|Mt-j>||dft,j@}|g|j"jBjDz}nT| R| P||Lt-j>||dft,j@} | g|j"jBjDz} |ji|_d}&d}'| | |jG| | |||z}&|||jG|||||z}'|jI|5}(tK|$D]\})}*|jLr|*|_|&|&|jd<|*jO| jdjQ| j*}+|j#| |+dz | |||"|jdd},|j rE|'|'|jd<|j#| |+dz ||||#|jdd}-|-||,|-z zz},| j*}.|j2jS|,|*| dd} | j*|.k7r9tTjVjXj[r| jQ|.} |Hi}/|D]}0t]|0|/|0<|||)|*|/}1|1j_d| } |1j_d| } |)t|$dz k(s'|)dz|%kDr/|)dz|j2j:zdk(r|(jatbstejf dddd|_|dk(r| }2n|ji| |||j} | |jjj$jlz |jjj$jnz} |jjjq| dd}2|jrju|2|}2|jw|s|2fSty|2S#1swYxYw) a Function invoked when calling the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. instead. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is not greater than `1`). height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image. This is set to 1024 by default for the best results. width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image. This is set to 1024 by default for the best results. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. guidance_scale (`float`, *optional*, defaults to 3.5): Embedded guiddance scale is enabled by setting `guidance_scale` > 1. Higher `guidance_scale` encourages a model to generate images more aligned with `prompt` at the expense of lower image quality. Guidance-distilled models approximates true classifer-free guidance for `guidance_scale` > 1. Refer to the [paper](https://huggingface.co/papers/2210.03142) to learn more. num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.Tensor`, *optional*): Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor will ge generated by sampling using the supplied random `generator`. prompt_embeds (`torch.Tensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. negative_ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. negative_ip_adapter_image_embeds (`List[torch.Tensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. negative_prompt_embeds (`torch.Tensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. prompt_attention_mask (torch.Tensor, *optional*): Attention mask for the prompt embeddings. Used to mask out padding tokens in the prompt sequence. Chroma requires a single padding token remain unmasked. Please refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training negative_prompt_attention_mask (torch.Tensor, *optional*): Attention mask for the negative prompt embeddings. Used to mask out padding tokens in the negative prompt sequence. Chroma requires a single padding token remain unmasked. PLease refer to https://huggingface.co/lodestones/Chroma#tldr-masking-t5-padding-tokens-enhanced-fidelity-and-increased-stability-during-training output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generate image. Choose between [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.flux.ChromaPipelineOutput`] instead of a plain tuple. joint_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeline class. max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`. Examples: Returns: [`~pipelines.chroma.ChromaPipelineOutput`] or `tuple`: [`~pipelines.chroma.ChromaPipelineOutput`] if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated images. )rrHrrrrr]NFr rscale) r[rrHrrrrr1r\r]rrg?base_image_seq_lenmax_image_seq_lenr$?r%ffffff?)r}rr^rk)r3r,r )r^)totalri) hidden_statestimestepencoder_hidden_statestxt_idsimg_idsrkrr)rrGrHlatent)r)images)=rXrOrrrrrrmrnrr=rzrlrgetrrrLrU in_channelsrr^nplinspacer.r>rrBmaxorderrruint8rrr progress_bar enumeraterrwrysteprsbackendsmps is_availablelocalspopupdate XLA_AVAILABLExm mark_steprrIscaling_factor shift_factordecoderW postprocessmaybe_free_model_hooksr!)3rYr[rrrr0r3rr\rrGrHrrrrrrrrrrrrr]r}r1rrrrrr)r,rknegative_attention_maskr2num_warmup_stepsrnegative_image_embedsr itr noise_predneg_noise_pred latents_dtypecallback_kwargsrcallback_outputsrs3 r-__call__zChromaPipeline.__call__fsXpK433d6K6KKI11D4I4II    +'"7#9+I/Q 3   .'=$!%  *VS"9J  Jvt$<VJ&,,Q/J''?C>Y>Y>eD ' ' + +GT :ko    +'#9"7+I(,(H(H"7 3!     ! 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