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kind of working

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This commit is contained in:
Enrico
2025-11-28 15:10:29 +01:00
parent de8350d232
commit a6c1abd08a
4 changed files with 698 additions and 166 deletions
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inspect_zimage.py
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import inspect
import torch
import sys
try:
# Try to import the class directly if possible
# Based on PR link, it might be in diffusers.models.transformers.z_image_transformer_2d
# But let's try to find it in diffusers.models
from diffusers import DiffusionPipeline
# We can try to load the pipeline (might be slow) or just inspect the module structure
# Let's try to find the class in diffusers.models
import diffusers.models
found = False
for name, obj in inspect.getmembers(diffusers.models):
if "ZImage" in name and "Transformer" in name:
print(f"Found class: {name}")
sig = inspect.signature(obj.forward)
print(f"Signature of {name}.forward:")
print(sig)
found = True
if not found:
print("Could not find ZImageTransformer class in diffusers.models directly.")
print("Trying to load pipeline to get the object...")
# Load a small dummy or just the class if we can
# Let's try to import specifically from the likely module path
try:
from diffusers.models.transformers.transformer_z_image import ZImageTransformer2DModel
print("Found ZImageTransformer2DModel in diffusers.models.transformers.transformer_z_image")
sig = inspect.signature(ZImageTransformer2DModel.forward)
print(f"Signature of ZImageTransformer2DModel.forward:")
print(sig)
except ImportError:
print("Could not import from diffusers.models.transformers.transformer_z_image")
except Exception as e:
print(f"Error: {e}")
pipeline_z_image.py
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# Copyright 2025 Alibaba Z-Image Team and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import AutoTokenizer, PreTrainedModel
from ...image_processor import VaeImageProcessor
from ...loaders import FromSingleFileMixin
from ...models.autoencoders import AutoencoderKL
from ...models.transformers import ZImageTransformer2DModel
from ...pipelines.pipeline_utils import DiffusionPipeline
from ...schedulers import FlowMatchEulerDiscreteScheduler
from ...utils import logging, replace_example_docstring
from ...utils.torch_utils import randn_tensor
from .pipeline_output import ZImagePipelineOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
EXAMPLE_DOC_STRING = """
Examples:
```py
>>> import torch
>>> from diffusers import ZImagePipeline
>>> pipe = ZImagePipeline.from_pretrained("Z-a-o/Z-Image-Turbo", torch_dtype=torch.bfloat16)
>>> pipe.to("cuda")
>>> # Optionally, set the attention backend to flash-attn 2 or 3, default is SDPA in PyTorch.
>>> # (1) Use flash attention 2
>>> # pipe.transformer.set_attention_backend("flash")
>>> # (2) Use flash attention 3
>>> # pipe.transformer.set_attention_backend("_flash_3")
>>> prompt = "一幅为名为“造相「Z-IMAGE-TURBO」”的项目设计的创意海报。画面巧妙地将文字概念视觉化：一辆复古蒸汽小火车化身为巨大的拉链头，正拉开厚厚的冬日积雪，展露出一个生机盎然的春天。"
>>> image = pipe(
... prompt,
... height=1024,
... width=1024,
... num_inference_steps=9,
... guidance_scale=0.0,
... generator=torch.Generator("cuda").manual_seed(42),
... ).images[0]
>>> image.save("zimage.png")
```
"""
# Copied from diffusers.pipelines.flux.pipeline_flux.calculate_shift
def calculate_shift(
image_seq_len,
base_seq_len: int = 256,
max_seq_len: int = 4096,
base_shift: float = 0.5,
max_shift: float = 1.15,
):
m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
b = base_shift - m * base_seq_len
mu = image_seq_len * m + b
return mu
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
def retrieve_timesteps(
scheduler,
num_inference_steps: Optional[int] = None,
device: Optional[Union[str, torch.device]] = None,
timesteps: Optional[List[int]] = None,
sigmas: Optional[List[float]] = None,
**kwargs,
):
r"""
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.
"""
if timesteps is not None and sigmas is not None:
raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values")
if timesteps is not None:
accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accepts_timesteps:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" timestep schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys())
if not accept_sigmas:
raise ValueError(
f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
f" sigmas schedules. Please check whether you are using the correct scheduler."
)
scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
else:
scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
timesteps = scheduler.timesteps
return timesteps, num_inference_steps
class ZImagePipeline(DiffusionPipeline, FromSingleFileMixin):
model_cpu_offload_seq = "text_encoder->transformer->vae"
_optional_components = []
_callback_tensor_inputs = ["latents", "prompt_embeds"]
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKL,
text_encoder: PreTrainedModel,
tokenizer: AutoTokenizer,
transformer: ZImageTransformer2DModel,
):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
scheduler=scheduler,
transformer=transformer,
)
self.vae_scale_factor = (
2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
def encode_prompt(
self,
prompt: Union[str, List[str]],
device: Optional[torch.device] = None,
do_classifier_free_guidance: bool = True,
negative_prompt: Optional[Union[str, List[str]]] = None,
prompt_embeds: Optional[List[torch.FloatTensor]] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
max_sequence_length: int = 512,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_embeds = self._encode_prompt(
prompt=prompt,
device=device,
prompt_embeds=prompt_embeds,
max_sequence_length=max_sequence_length,
)
if do_classifier_free_guidance:
if negative_prompt is None:
negative_prompt = ["" for _ in prompt]
else:
negative_prompt = [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
assert len(prompt) == len(negative_prompt)
negative_prompt_embeds = self._encode_prompt(
prompt=negative_prompt,
device=device,
prompt_embeds=negative_prompt_embeds,
max_sequence_length=max_sequence_length,
)
else:
negative_prompt_embeds = []
return prompt_embeds, negative_prompt_embeds
def _encode_prompt(
self,
prompt: Union[str, List[str]],
device: Optional[torch.device] = None,
prompt_embeds: Optional[List[torch.FloatTensor]] = None,
max_sequence_length: int = 512,
) -> List[torch.FloatTensor]:
device = device or self._execution_device
if prompt_embeds is not None:
return prompt_embeds
if isinstance(prompt, str):
prompt = [prompt]
for i, prompt_item in enumerate(prompt):
messages = [
{"role": "user", "content": prompt_item},
]
prompt_item = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True,
)
prompt[i] = prompt_item
text_inputs = self.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 = self.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 prepare_latents(
self,
batch_size,
num_channels_latents,
height,
width,
dtype,
device,
generator,
latents=None,
):
height = 2 * (int(height) // (self.vae_scale_factor * 2))
width = 2 * (int(width) // (self.vae_scale_factor * 2))
shape = (batch_size, num_channels_latents, height, width)
if latents is None:
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
latents = latents.to(device)
return latents
@property
def guidance_scale(self):
return self._guidance_scale
@property
def do_classifier_free_guidance(self):
return self._guidance_scale > 1
@property
def joint_attention_kwargs(self):
return self._joint_attention_kwargs
@property
def num_timesteps(self):
return self._num_timesteps
@property
def interrupt(self):
return self._interrupt
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: Union[str, List[str]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
sigmas: Optional[List[float]] = None,
guidance_scale: float = 5.0,
cfg_normalization: bool = False,
cfg_truncation: float = 1.0,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[List[torch.FloatTensor]] = None,
negative_prompt_embeds: Optional[List[torch.FloatTensor]] = None,
output_type: Optional[str] = "pil",
return_dict: bool = True,
joint_attention_kwargs: Optional[Dict[str, Any]] = None,
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
max_sequence_length: int = 512,
):
r"""
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.
height (`int`, *optional*, defaults to 1024):
The height in pixels of the generated image.
width (`int`, *optional*, defaults to 1024):
The width in pixels of the generated image.
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 5.0):
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
`guidance_scale` is defined as `w` of equation 2. of [Imagen
Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
usually at the expense of lower image quality.
cfg_normalization (`bool`, *optional*, defaults to False):
Whether to apply configuration normalization.
cfg_truncation (`float`, *optional*, defaults to 1.0):
The truncation value for configuration.
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
less than `1`).
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.FloatTensor`, *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 be generated by sampling using the supplied random `generator`.
prompt_embeds (`List[torch.FloatTensor]`, *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.
negative_prompt_embeds (`List[torch.FloatTensor]`, *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.
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.stable_diffusion.ZImagePipelineOutput`] 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`, *optional*, defaults to 512):
Maximum sequence length to use with the `prompt`.
Examples:
Returns:
[`~pipelines.z_image.ZImagePipelineOutput`] or `tuple`: [`~pipelines.z_image.ZImagePipelineOutput`] if
`return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the
generated images.
"""
height = height or 1024
width = width or 1024
vae_scale = self.vae_scale_factor * 2
if height % vae_scale != 0:
raise ValueError(
f"Height must be divisible by {vae_scale} (got {height}). "
f"Please adjust the height to a multiple of {vae_scale}."
)
if width % vae_scale != 0:
raise ValueError(
f"Width must be divisible by {vae_scale} (got {width}). "
f"Please adjust the width to a multiple of {vae_scale}."
)
device = self._execution_device
self._guidance_scale = guidance_scale
self._joint_attention_kwargs = joint_attention_kwargs
self._interrupt = False
self._cfg_normalization = cfg_normalization
self._cfg_truncation = cfg_truncation
# 2. Define call parameters
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = len(prompt_embeds)
# If prompt_embeds is provided and prompt is None, skip encoding
if prompt_embeds is not None and prompt is None:
if self.do_classifier_free_guidance and negative_prompt_embeds is None:
raise ValueError(
"When `prompt_embeds` is provided without `prompt`, "
"`negative_prompt_embeds` must also be provided for classifier-free guidance."
)
else:
(
prompt_embeds,
negative_prompt_embeds,
) = self.encode_prompt(
prompt=prompt,
negative_prompt=negative_prompt,
do_classifier_free_guidance=self.do_classifier_free_guidance,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
device=device,
max_sequence_length=max_sequence_length,
)
# 4. Prepare latent variables
num_channels_latents = self.transformer.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
torch.float32,
device,
generator,
latents,
)
# Repeat prompt_embeds for num_images_per_prompt
if num_images_per_prompt > 1:
prompt_embeds = [pe for pe in prompt_embeds for _ in range(num_images_per_prompt)]
if self.do_classifier_free_guidance and negative_prompt_embeds:
negative_prompt_embeds = [npe for npe in negative_prompt_embeds for _ in range(num_images_per_prompt)]
actual_batch_size = batch_size * num_images_per_prompt
image_seq_len = (latents.shape[2] // 2) * (latents.shape[3] // 2)
# 5. Prepare timesteps
mu = calculate_shift(
image_seq_len,
self.scheduler.config.get("base_image_seq_len", 256),
self.scheduler.config.get("max_image_seq_len", 4096),
self.scheduler.config.get("base_shift", 0.5),
self.scheduler.config.get("max_shift", 1.15),
)
self.scheduler.sigma_min = 0.0
scheduler_kwargs = {"mu": mu}
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler,
num_inference_steps,
device,
sigmas=sigmas,
**scheduler_kwargs,
)
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
# 6. Denoising loop
with self.progress_bar(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
if self.interrupt:
continue
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timestep = t.expand(latents.shape[0])
timestep = (1000 - timestep) / 1000
# Normalized time for time-aware config (0 at start, 1 at end)
t_norm = timestep[0].item()
# Handle cfg truncation
current_guidance_scale = self.guidance_scale
if (
self.do_classifier_free_guidance
and self._cfg_truncation is not None
and float(self._cfg_truncation) <= 1
):
if t_norm > self._cfg_truncation:
current_guidance_scale = 0.0
# Run CFG only if configured AND scale is non-zero
apply_cfg = self.do_classifier_free_guidance and current_guidance_scale > 0
if apply_cfg:
latents_typed = latents.to(self.transformer.dtype)
latent_model_input = latents_typed.repeat(2, 1, 1, 1)
prompt_embeds_model_input = prompt_embeds + negative_prompt_embeds
timestep_model_input = timestep.repeat(2)
else:
latent_model_input = latents.to(self.transformer.dtype)
prompt_embeds_model_input = prompt_embeds
timestep_model_input = timestep
latent_model_input = latent_model_input.unsqueeze(2)
latent_model_input_list = list(latent_model_input.unbind(dim=0))
model_out_list = self.transformer(
latent_model_input_list,
timestep_model_input,
prompt_embeds_model_input,
)[0]
if apply_cfg:
# Perform CFG
pos_out = model_out_list[:actual_batch_size]
neg_out = model_out_list[actual_batch_size:]
noise_pred = []
for j in range(actual_batch_size):
pos = pos_out[j].float()
neg = neg_out[j].float()
pred = pos + current_guidance_scale * (pos - neg)
# Renormalization
if self._cfg_normalization and float(self._cfg_normalization) > 0.0:
ori_pos_norm = torch.linalg.vector_norm(pos)
new_pos_norm = torch.linalg.vector_norm(pred)
max_new_norm = ori_pos_norm * float(self._cfg_normalization)
if new_pos_norm > max_new_norm:
pred = pred * (max_new_norm / new_pos_norm)
noise_pred.append(pred)
noise_pred = torch.stack(noise_pred, dim=0)
else:
noise_pred = torch.stack([t.float() for t in model_out_list], dim=0)
noise_pred = noise_pred.squeeze(2)
noise_pred = -noise_pred
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred.to(torch.float32), t, latents, return_dict=False)[0]
assert latents.dtype == torch.float32
if callback_on_step_end is not None:
callback_kwargs = {}
for k in callback_on_step_end_tensor_inputs:
callback_kwargs[k] = locals()[k]
callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
latents = callback_outputs.pop("latents", latents)
prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
progress_bar.update()
if output_type == "latent":
image = latents
else:
latents = latents.to(self.vae.dtype)
latents = (latents / self.vae.config.scaling_factor) + self.vae.config.shift_factor
image = self.vae.decode(latents, return_dict=False)[0]
image = self.image_processor.postprocess(image, output_type=output_type)
# Offload all models
self.maybe_free_model_hooks()
if not return_dict:
return (image,)
return ZImagePipelineOutput(images=image)
sdnq_nodes.py
+32 -166
View File
@@ -36,143 +36,6 @@ except ImportError:
except ImportError:
raise ImportError("SDNQ installation failed. Please install manually.")
# Custom Pipeline for Img2Img support
class ZImageImg2ImgPipeline(DiffusionPipeline):
# Define offload sequence for CPU offloading
model_cpu_offload_seq = "text_encoder->transformer->vae"
def __init__(self, vae, text_encoder, tokenizer, transformer, scheduler):
super().__init__()
self.register_modules(
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self.vae, "config") else 8
def __call__(
self,
prompt=None,
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=5.0,
negative_prompt=None,
num_images_per_prompt=1,
generator=None,
latents=None,
prompt_embeds=None,
negative_prompt_embeds=None,
output_type="pil",
return_dict=True,
max_sequence_length=512,
# Img2Img specific parameters
image=None,
strength=1.0,
noise_scale=1.0,
):
# 0. Default height and width to 1024
height = height or 1024
width = width or 1024
# 1. Check inputs
if prompt is not None and isinstance(prompt, str):
batch_size = 1
elif prompt is not None and isinstance(prompt, list):
batch_size = len(prompt)
else:
batch_size = prompt_embeds.shape[0]
device = self._execution_device
# 2. Encode prompt
# Note: We assume the original pipeline has an encode_prompt method or similar logic
# Since we are inheriting/wrapping, we might need to access the original method if available
# But ZImagePipeline structure is specific. Let's try to reuse the components directly.
# Simplified prompt encoding for Z-Image (based on observation of original pipeline)
if prompt_embeds is None:
text_inputs = self.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_embeds = self.text_encoder(text_input_ids)[0]
# 3. Prepare latents
# If image is provided, encode it
if image is not None:
# Image is already a tensor [B, C, H, W] in range [-1, 1] on correct device/dtype
if latents is None:
latents = self.vae.encode(image).latent_dist.sample(generator)
latents = latents * self.vae.config.scaling_factor
# If no latents (text-to-image), generate random noise
if latents is None:
shape = (batch_size, self.transformer.config.in_channels, height // self.vae_scale_factor, width // self.vae_scale_factor)
latents = torch.randn(shape, generator=generator, device=device, dtype=prompt_embeds.dtype)
# 4. Prepare noise and mix for Img2Img
if image is not None and strength < 1.0:
# Generate noise
noise = torch.randn(latents.shape, generator=generator, device=device, dtype=latents.dtype)
# Apply noise scale if requested
if noise_scale != 1.0:
noise = noise * noise_scale
# Rectified Flow Interpolation: latents = (1 - t) * image_latents + t * noise
# t = strength (0.0 = image, 1.0 = noise)
t = strength
latents = (1 - t) * latents + t * noise
# We don't need to skip steps in Rectified Flow the same way as DDIM
# The ODE solver integrates from t=0 (noise) to t=1 (data) or vice versa
# But standard pipelines usually go from Noise -> Data
# Here we are starting from an intermediate state
# However, standard schedulers might expect to start from pure noise
# For simplicity in this custom implementation, we'll just pass the mixed latents
# as the starting point.
# 5. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
# 6. Denoising loop
for i, t in enumerate(timesteps):
# Expand latents if needed
latent_model_input = latents
# Predict noise/velocity
noise_pred = self.transformer(
latent_model_input,
timestep=t,
encoder_hidden_states=prompt_embeds,
return_dict=False,
)[0]
# Compute previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
# 7. Decode latents
if not output_type == "latent":
image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
else:
image = latents
# Convert to PIL if requested
if output_type == "pil":
image = self.image_processor.postprocess(image, output_type=output_type)
from diffusers.pipelines.pipeline_utils import ImagePipelineOutput
return ImagePipelineOutput(images=image)
class LoadZImageSDNQ:
@classmethod
def INPUT_TYPES(cls):
@@ -224,24 +87,13 @@ class LoadZImageSDNQ:
else:
dev = device
# Load original pipeline first
original_pipeline = DiffusionPipeline.from_pretrained(
# Load the original ZImagePipeline directly
pipeline = DiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=low_cpu_mem_usage
)
# Wrap in our custom pipeline
# Note: We need to extract components. Z-Image pipeline structure:
# vae, text_encoder, tokenizer, transformer, scheduler
pipeline = ZImageImg2ImgPipeline(
vae=original_pipeline.vae,
text_encoder=original_pipeline.text_encoder,
tokenizer=original_pipeline.tokenizer,
transformer=original_pipeline.transformer,
scheduler=original_pipeline.scheduler
)
# Apply attention backend
if attention_backend != "default":
print(f"Setting attention backend to: {attention_backend}")
@@ -278,7 +130,7 @@ class LoadZImageSDNQ:
except Exception as e:
print(f"Warning: Compilation failed: {e}")
print("SDNQ Pipeline (Custom Img2Img) loaded successfully.")
print("SDNQ Pipeline loaded successfully.")
return (pipeline,)
# Resolution presets from official Gradio app
@@ -450,20 +302,18 @@ class ZImageSDNQGenerate:
generator = torch.manual_seed(seed)
# Prepare input image if provided
image_tensor = None
# Prepare latents for img2img if input_image is provided
latents = None
if input_image is not None:
print(f"Preparing input image for img2img (strength={strength})...")
# Convert ComfyUI image format [B, H, W, C] to torch [B, C, H, W]
# Match VAE dtype and device
vae_dtype = pipeline.vae.dtype
# Use _execution_device if available (handles CPU offloading correctly)
print(f"Preparing img2img latents (strength={strength}, noise_scale={noise_scale})...")
# Get device
if hasattr(pipeline, "_execution_device"):
device = pipeline._execution_device
else:
device = pipeline.device
# Convert ComfyUI image format [B, H, W, C] to torch [B, C, H, W]
vae_dtype = pipeline.vae.dtype
image_tensor = input_image.permute(0, 3, 1, 2).to(device=device, dtype=vae_dtype)
# Resize if needed
@@ -473,6 +323,24 @@ class ZImageSDNQGenerate:
# Normalize to [-1, 1] (ComfyUI images are [0, 1])
image_tensor = 2.0 * image_tensor - 1.0
# Encode to latents using VAE
with torch.no_grad():
latents = pipeline.vae.encode(image_tensor).latent_dist.sample()
latents = latents * pipeline.vae.config.scaling_factor
# Apply noise mixing for img2img effect
# Generate noise on CPU (compatible with CPU generator), then move to device
noise = torch.randn(latents.shape, generator=generator, device="cpu", dtype=latents.dtype).to(device)
# Apply noise scale
if noise_scale != 1.0:
noise = noise * noise_scale
# Rectified Flow interpolation: latents = (1 - strength) * image_latents + strength * noise
# strength=0.0 → pure image, strength=1.0 → pure noise
latents = (1 - strength) * latents + strength * noise
print(f"Mixed latents: {100*(1-strength):.1f}% image + {100*strength:.1f}% noise")
# Configure shift on the scheduler before generation
original_shift = None
@@ -493,16 +361,14 @@ class ZImageSDNQGenerate:
"guidance_scale": guidance_scale,
"max_sequence_length": max_sequence_length,
"generator": generator,
"noise_scale": noise_scale,
}
# Add img2img parameters if image is provided
if image_tensor is not None:
gen_kwargs["image"] = image_tensor
gen_kwargs["strength"] = strength
# Add pre-processed latents if we have them (img2img)
if latents is not None:
gen_kwargs["latents"] = latents
# Call the custom pipeline
# The custom pipeline handles encoding, noise mixing, and denoising loop
# Call the original ZImagePipeline
# It handles all the transformer calls and denoising internally
image = pipeline(**gen_kwargs).images[0]
# Restore original shift value if we changed it
test_device.py
+31
View File
@@ -0,0 +1,31 @@
import torch
from diffusers import DiffusionPipeline
# Load a small test to see how pipeline handles latents
print("Testing latents device handling...")
# Simulate what we're doing
class MockPipeline:
def __init__(self):
self.device = torch.device("cuda")
self._execution_device = torch.device("cuda")
# Our current approach
pipeline_mock = MockPipeline()
# Get device (our current code)
if hasattr(pipeline_mock, "_execution_device"):
device = pipeline_mock._execution_device
else:
device = pipeline_mock.device
print(f"Device selected: {device}")
# Create latents (simulating our VAE encode result)
latents = torch.randn(1, 4, 128, 128, device=device, dtype=torch.bfloat16)
print(f"Latents device: {latents.device}")
print(f"Latents dtype: {latents.dtype}")
# When we pass to pipeline, it should already be on the correct device
print(f"\n✓ Latents are already on the execution device: {device}")
print("✓ Pipeline will accept them directly without device transfers")