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https://github.com/modelscope/DiffSynth-Studio.git
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DiffSynth-Studio 2.0 major update
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257
diffsynth/pipelines/z_image.py
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257
diffsynth/pipelines/z_image.py
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import torch, math
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from PIL import Image
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from typing import Union
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from tqdm import tqdm
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from einops import rearrange
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import numpy as np
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from typing import Union, List, Optional, Tuple
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from ..diffusion import FlowMatchScheduler
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from ..core import ModelConfig, gradient_checkpoint_forward
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from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
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from transformers import AutoTokenizer
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from ..models.z_image_text_encoder import ZImageTextEncoder
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from ..models.z_image_dit import ZImageDiT
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from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
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class ZImagePipeline(BasePipeline):
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def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
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super().__init__(
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device=device, torch_dtype=torch_dtype,
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height_division_factor=16, width_division_factor=16,
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)
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self.scheduler = FlowMatchScheduler("Z-Image")
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self.text_encoder: ZImageTextEncoder = None
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self.dit: ZImageDiT = None
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self.vae_encoder: FluxVAEEncoder = None
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self.vae_decoder: FluxVAEDecoder = None
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self.tokenizer: AutoTokenizer = None
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self.in_iteration_models = ("dit",)
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self.units = [
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ZImageUnit_ShapeChecker(),
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ZImageUnit_PromptEmbedder(),
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ZImageUnit_NoiseInitializer(),
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ZImageUnit_InputImageEmbedder(),
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]
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self.model_fn = model_fn_z_image
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@staticmethod
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def from_pretrained(
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torch_dtype: torch.dtype = torch.bfloat16,
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device: Union[str, torch.device] = "cuda",
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model_configs: list[ModelConfig] = [],
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tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
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vram_limit: float = None,
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):
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# Initialize pipeline
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pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
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model_pool = pipe.download_and_load_models(model_configs, vram_limit)
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# Fetch models
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pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
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pipe.dit = model_pool.fetch_model("z_image_dit")
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pipe.vae_encoder = model_pool.fetch_model("flux_vae_encoder")
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pipe.vae_decoder = model_pool.fetch_model("flux_vae_decoder")
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if tokenizer_config is not None:
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tokenizer_config.download_if_necessary()
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pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
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# VRAM Management
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pipe.vram_management_enabled = pipe.check_vram_management_state()
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return pipe
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@torch.no_grad()
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def __call__(
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self,
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# Prompt
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prompt: str,
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negative_prompt: str = "",
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cfg_scale: float = 1.0,
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# Image
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input_image: Image.Image = None,
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denoising_strength: float = 1.0,
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# Shape
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height: int = 1024,
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width: int = 1024,
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# Randomness
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seed: int = None,
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rand_device: str = "cpu",
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# Steps
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num_inference_steps: int = 8,
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# Progress bar
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progress_bar_cmd = tqdm,
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):
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# Scheduler
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self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
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# Parameters
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inputs_posi = {
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"prompt": prompt,
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}
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inputs_nega = {
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"negative_prompt": negative_prompt,
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}
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inputs_shared = {
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"cfg_scale": cfg_scale,
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"input_image": input_image, "denoising_strength": denoising_strength,
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"height": height, "width": width,
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"seed": seed, "rand_device": rand_device,
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"num_inference_steps": num_inference_steps,
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}
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for unit in self.units:
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inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
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# Denoise
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self.load_models_to_device(self.in_iteration_models)
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models = {name: getattr(self, name) for name in self.in_iteration_models}
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for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
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timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
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noise_pred = self.cfg_guided_model_fn(
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self.model_fn, cfg_scale,
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inputs_shared, inputs_posi, inputs_nega,
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**models, timestep=timestep, progress_id=progress_id
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)
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inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
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# Decode
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self.load_models_to_device(['vae_decoder'])
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image = self.vae_decoder(inputs_shared["latents"])
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image = self.vae_output_to_image(image)
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self.load_models_to_device([])
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return image
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class ZImageUnit_ShapeChecker(PipelineUnit):
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def __init__(self):
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super().__init__(
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input_params=("height", "width"),
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output_params=("height", "width"),
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)
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def process(self, pipe: ZImagePipeline, height, width):
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height, width = pipe.check_resize_height_width(height, width)
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return {"height": height, "width": width}
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class ZImageUnit_PromptEmbedder(PipelineUnit):
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def __init__(self):
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super().__init__(
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seperate_cfg=True,
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input_params_posi={"prompt": "prompt"},
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input_params_nega={"prompt": "negative_prompt"},
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output_params=("prompt_embeds",),
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onload_model_names=("text_encoder",)
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)
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def encode_prompt(
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self,
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pipe,
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prompt: Union[str, List[str]],
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device: Optional[torch.device] = None,
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max_sequence_length: int = 512,
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) -> List[torch.FloatTensor]:
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if isinstance(prompt, str):
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prompt = [prompt]
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for i, prompt_item in enumerate(prompt):
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messages = [
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{"role": "user", "content": prompt_item},
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]
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prompt_item = pipe.tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True,
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)
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prompt[i] = prompt_item
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text_inputs = pipe.tokenizer(
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prompt,
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padding="max_length",
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max_length=max_sequence_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids.to(device)
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prompt_masks = text_inputs.attention_mask.to(device).bool()
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prompt_embeds = pipe.text_encoder(
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input_ids=text_input_ids,
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attention_mask=prompt_masks,
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output_hidden_states=True,
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).hidden_states[-2]
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embeddings_list = []
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for i in range(len(prompt_embeds)):
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embeddings_list.append(prompt_embeds[i][prompt_masks[i]])
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return embeddings_list
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def process(self, pipe: ZImagePipeline, prompt):
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pipe.load_models_to_device(self.onload_model_names)
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prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
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return {"prompt_embeds": prompt_embeds}
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class ZImageUnit_NoiseInitializer(PipelineUnit):
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def __init__(self):
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super().__init__(
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input_params=("height", "width", "seed", "rand_device"),
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output_params=("noise",),
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)
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def process(self, pipe: ZImagePipeline, height, width, seed, rand_device):
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noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
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return {"noise": noise}
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class ZImageUnit_InputImageEmbedder(PipelineUnit):
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def __init__(self):
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super().__init__(
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input_params=("input_image", "noise"),
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output_params=("latents", "input_latents"),
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onload_model_names=("vae_encoder",)
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)
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def process(self, pipe: ZImagePipeline, input_image, noise):
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if input_image is None:
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return {"latents": noise, "input_latents": None}
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pipe.load_models_to_device(['vae'])
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image = pipe.preprocess_image(input_image)
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input_latents = pipe.vae_encoder(image)
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if pipe.scheduler.training:
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return {"latents": noise, "input_latents": input_latents}
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else:
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latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
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return {"latents": latents, "input_latents": input_latents}
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def model_fn_z_image(
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dit: ZImageDiT,
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latents=None,
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timestep=None,
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prompt_embeds=None,
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use_gradient_checkpointing=False,
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use_gradient_checkpointing_offload=False,
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**kwargs,
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):
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latents = [rearrange(latents, "B C H W -> C B H W")]
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timestep = (1000 - timestep) / 1000
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model_output = dit(
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latents,
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timestep,
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prompt_embeds,
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use_gradient_checkpointing=use_gradient_checkpointing,
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use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
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)[0][0]
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model_output = -model_output
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model_output = rearrange(model_output, "C B H W -> B C H W")
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return model_output
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