Spaces:
Running on Zero
Running on Zero
File size: 9,147 Bytes
ccfee12 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 | import torch, math
from PIL import Image
from typing import Union
from tqdm import tqdm
from einops import rearrange
import numpy as np
from typing import Union, List, Optional, Tuple
from ..diffusion import FlowMatchScheduler
from ..core import ModelConfig, gradient_checkpoint_forward
from ..diffusion.base_pipeline import BasePipeline, PipelineUnit, ControlNetInput
from transformers import AutoTokenizer
from ..models.z_image_text_encoder import ZImageTextEncoder
from ..models.z_image_dit import ZImageDiT
from ..models.flux_vae import FluxVAEEncoder, FluxVAEDecoder
class ZImagePipeline(BasePipeline):
def __init__(self, device="cuda", torch_dtype=torch.bfloat16):
super().__init__(
device=device, torch_dtype=torch_dtype,
height_division_factor=16, width_division_factor=16,
)
self.scheduler = FlowMatchScheduler("Z-Image")
self.text_encoder: ZImageTextEncoder = None
self.dit: ZImageDiT = None
self.vae_encoder: FluxVAEEncoder = None
self.vae_decoder: FluxVAEDecoder = None
self.tokenizer: AutoTokenizer = None
self.in_iteration_models = ("dit",)
self.units = [
ZImageUnit_ShapeChecker(),
ZImageUnit_PromptEmbedder(),
ZImageUnit_NoiseInitializer(),
ZImageUnit_InputImageEmbedder(),
]
self.model_fn = model_fn_z_image
@staticmethod
def from_pretrained(
torch_dtype: torch.dtype = torch.bfloat16,
device: Union[str, torch.device] = "cuda",
model_configs: list[ModelConfig] = [],
tokenizer_config: ModelConfig = ModelConfig(model_id="Tongyi-MAI/Z-Image-Turbo", origin_file_pattern="tokenizer/"),
vram_limit: float = None,
):
# Initialize pipeline
pipe = ZImagePipeline(device=device, torch_dtype=torch_dtype)
model_pool = pipe.download_and_load_models(model_configs, vram_limit)
# Fetch models
pipe.text_encoder = model_pool.fetch_model("z_image_text_encoder")
pipe.dit = model_pool.fetch_model("z_image_dit")
pipe.vae_encoder = model_pool.fetch_model("flux_vae_encoder")
pipe.vae_decoder = model_pool.fetch_model("flux_vae_decoder")
if tokenizer_config is not None:
tokenizer_config.download_if_necessary()
pipe.tokenizer = AutoTokenizer.from_pretrained(tokenizer_config.path)
# VRAM Management
pipe.vram_management_enabled = pipe.check_vram_management_state()
return pipe
@torch.no_grad()
def __call__(
self,
# Prompt
prompt: str,
negative_prompt: str = "",
cfg_scale: float = 1.0,
# Image
input_image: Image.Image = None,
denoising_strength: float = 1.0,
# Shape
height: int = 1024,
width: int = 1024,
# Randomness
seed: int = None,
rand_device: str = "cpu",
# Steps
num_inference_steps: int = 8,
# Progress bar
progress_bar_cmd = tqdm,
):
# Scheduler
self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength)
# Parameters
inputs_posi = {
"prompt": prompt,
}
inputs_nega = {
"negative_prompt": negative_prompt,
}
inputs_shared = {
"cfg_scale": cfg_scale,
"input_image": input_image, "denoising_strength": denoising_strength,
"height": height, "width": width,
"seed": seed, "rand_device": rand_device,
"num_inference_steps": num_inference_steps,
}
for unit in self.units:
inputs_shared, inputs_posi, inputs_nega = self.unit_runner(unit, self, inputs_shared, inputs_posi, inputs_nega)
# Denoise
self.load_models_to_device(self.in_iteration_models)
models = {name: getattr(self, name) for name in self.in_iteration_models}
for progress_id, timestep in enumerate(progress_bar_cmd(self.scheduler.timesteps)):
timestep = timestep.unsqueeze(0).to(dtype=self.torch_dtype, device=self.device)
noise_pred = self.cfg_guided_model_fn(
self.model_fn, cfg_scale,
inputs_shared, inputs_posi, inputs_nega,
**models, timestep=timestep, progress_id=progress_id
)
inputs_shared["latents"] = self.step(self.scheduler, progress_id=progress_id, noise_pred=noise_pred, **inputs_shared)
# Decode
self.load_models_to_device(['vae_decoder'])
image = self.vae_decoder(inputs_shared["latents"])
image = self.vae_output_to_image(image)
self.load_models_to_device([])
return image
class ZImageUnit_ShapeChecker(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("height", "width"),
output_params=("height", "width"),
)
def process(self, pipe: ZImagePipeline, height, width):
height, width = pipe.check_resize_height_width(height, width)
return {"height": height, "width": width}
class ZImageUnit_PromptEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
seperate_cfg=True,
input_params_posi={"prompt": "prompt"},
input_params_nega={"prompt": "negative_prompt"},
output_params=("prompt_embeds",),
onload_model_names=("text_encoder",)
)
def encode_prompt(
self,
pipe,
prompt: Union[str, List[str]],
device: Optional[torch.device] = None,
max_sequence_length: int = 512,
) -> List[torch.FloatTensor]:
if isinstance(prompt, str):
prompt = [prompt]
for i, prompt_item in enumerate(prompt):
messages = [
{"role": "user", "content": prompt_item},
]
prompt_item = pipe.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True,
)
prompt[i] = prompt_item
text_inputs = pipe.tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids.to(device)
prompt_masks = text_inputs.attention_mask.to(device).bool()
prompt_embeds = pipe.text_encoder(
input_ids=text_input_ids,
attention_mask=prompt_masks,
output_hidden_states=True,
).hidden_states[-2]
embeddings_list = []
for i in range(len(prompt_embeds)):
embeddings_list.append(prompt_embeds[i][prompt_masks[i]])
return embeddings_list
def process(self, pipe: ZImagePipeline, prompt):
pipe.load_models_to_device(self.onload_model_names)
prompt_embeds = self.encode_prompt(pipe, prompt, pipe.device)
return {"prompt_embeds": prompt_embeds}
class ZImageUnit_NoiseInitializer(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("height", "width", "seed", "rand_device"),
output_params=("noise",),
)
def process(self, pipe: ZImagePipeline, height, width, seed, rand_device):
noise = pipe.generate_noise((1, 16, height//8, width//8), seed=seed, rand_device=rand_device, rand_torch_dtype=pipe.torch_dtype)
return {"noise": noise}
class ZImageUnit_InputImageEmbedder(PipelineUnit):
def __init__(self):
super().__init__(
input_params=("input_image", "noise"),
output_params=("latents", "input_latents"),
onload_model_names=("vae_encoder",)
)
def process(self, pipe: ZImagePipeline, input_image, noise):
if input_image is None:
return {"latents": noise, "input_latents": None}
pipe.load_models_to_device(['vae'])
image = pipe.preprocess_image(input_image)
input_latents = pipe.vae_encoder(image)
if pipe.scheduler.training:
return {"latents": noise, "input_latents": input_latents}
else:
latents = pipe.scheduler.add_noise(input_latents, noise, timestep=pipe.scheduler.timesteps[0])
return {"latents": latents, "input_latents": input_latents}
def model_fn_z_image(
dit: ZImageDiT,
latents=None,
timestep=None,
prompt_embeds=None,
use_gradient_checkpointing=False,
use_gradient_checkpointing_offload=False,
**kwargs,
):
latents = [rearrange(latents, "B C H W -> C B H W")]
timestep = (1000 - timestep) / 1000
model_output = dit(
latents,
timestep,
prompt_embeds,
use_gradient_checkpointing=use_gradient_checkpointing,
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
)[0][0]
model_output = -model_output
model_output = rearrange(model_output, "C B H W -> B C H W")
return model_output
|