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import ipdb
from accelerate import Accelerator
from diffusers.configuration_utils import register_to_config
from diffusers.pipelines import FluxPipeline
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from .condition import Condition
from diffusers.pipelines.flux.pipeline_flux import (
FluxPipelineOutput,
calculate_shift,
retrieve_timesteps,
np,
)
from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
from diffusers.models import AutoencoderKL,FluxTransformer2DModel
class SubjectGeniusPipeline(FluxPipeline):
@register_to_config
def __init__(
self,
scheduler: FlowMatchEulerDiscreteScheduler,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
text_encoder_2: T5EncoderModel,
tokenizer_2: T5TokenizerFast,
transformer: FluxTransformer2DModel,
image_encoder = None,
feature_extractor = None,
):
super().__init__(
scheduler=scheduler,
vae=vae,
text_encoder=text_encoder,
tokenizer=tokenizer,
text_encoder_2=text_encoder_2,
tokenizer_2=tokenizer_2,
transformer=transformer,
image_encoder = image_encoder,
feature_extractor = feature_extractor,
)
@property
def all_adapters(self):
list_adapters = self.get_list_adapters() # eg {"unet": ["adapter1", "adapter2"], "text_encoder": ["adapter2"]}
# eg ["adapter1", "adapter2"]
all_adapters = list({adapter for adapters in list_adapters.values() for adapter in adapters})
return all_adapters
@torch.no_grad()
def __call__(self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
# additional begin
conditions: List[Condition] = None,
model_config: Optional[Dict[str, Any]] = {},
condition_scale: float = 1.0,
# additional over
height: Optional[int] = 512,
width: Optional[int] = 512,
num_inference_steps: int = 28,
timesteps: List[int] = None,
guidance_scale: float = 3.5,
num_images_per_prompt: Optional[int] = 1,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[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,
accelerator: Accelerator = None,
):
# self.block_mask_routers = nn.ModuleList(
# [nn.Sequential(nn.Linear(self.transformer.config.attention_head_dim * self.transformer.config.num_attention_heads, 1, bias=False), nn.Tanh()) for _ in
# range(self.transformer.config.num_layers)]
# ).to(accelerator.device,dtype=torch.bfloat16)
# self.single_block_mask_routers = nn.ModuleList(
# [nn.Sequential(nn.Linear(self.transformer.config.attention_head_dim * self.transformer.config.num_attention_heads, 1, bias=False), nn.Tanh()) for _ in
# range(self.transformer.config.num_single_layers)]
# ).to(accelerator.device,dtype=torch.bfloat16)
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
height,
width,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
max_sequence_length=max_sequence_length,
)
self._guidance_scale = guidance_scale
self._joint_attention_kwargs = joint_attention_kwargs
self._interrupt = False
# 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 = prompt_embeds.shape[0]
device = self._execution_device
lora_scale = (
self.joint_attention_kwargs.get("scale", None)
if self.joint_attention_kwargs is not None
else None
)
(
prompt_embeds,
pooled_prompt_embeds,
text_ids,
) = self.encode_prompt(
prompt=prompt,
prompt_2=prompt_2,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
device=device,
num_images_per_prompt=num_images_per_prompt,
max_sequence_length=max_sequence_length,
lora_scale=lora_scale,
)
# 3. Prepare latent variables
num_channels_latents = self.transformer.config.in_channels // 4
latents, latent_image_ids = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 4. Prepare conditions
condition_latents, condition_ids, condition_type_ids, condition_types = ([] for _ in range(4))
use_condition = conditions is not None
if use_condition:
for condition in conditions:
tokens,ids,type_id = condition.encode(self)
condition_latents.append(tokens)
condition_ids.append(ids)
condition_type_ids.append(type_id)
condition_types.append(condition.condition_type)
# 5. Prepare timesteps
sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
image_seq_len = latents.shape[1]
mu = calculate_shift(
image_seq_len,
self.scheduler.config.base_image_seq_len,
self.scheduler.config.max_image_seq_len,
self.scheduler.config.base_shift,
self.scheduler.config.max_shift,
)
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler,
num_inference_steps,
device,
timesteps,
sigmas,
mu=mu,
)
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
self._num_timesteps = len(timesteps)
# handle guidance: Decide whether to enable guidance according to the configuration in base model's config file.
# example: Flux-dev: True ; Flux-schnell: False.
if self.transformer.config.guidance_embeds:
guidance = torch.full([1], guidance_scale, device=device, dtype=latents.dtype)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
# 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]).to(latents.dtype)
noise_pred, conditional_output = self.transformer(
model_config=model_config,
# Inputs of the condition (new feature)
condition_latents=condition_latents if use_condition else None,
condition_ids=condition_ids if use_condition else None,
condition_type_ids=condition_type_ids if use_condition else None, # the condition_type_ids is not used so far.
condition_types = condition_types if use_condition else None,
return_condition_latents = model_config.get("return_condition_latents",False),
# Inputs to the original transformer
hidden_states=latents,
timestep=timestep / 1000,
guidance=guidance,
pooled_projections=pooled_prompt_embeds,
encoder_hidden_states=prompt_embeds,
txt_ids=text_ids,
img_ids=latent_image_ids,
joint_attention_kwargs=self.joint_attention_kwargs,
return_dict=False,
)
# compute the previous noisy sample x_t -> x_t-1
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
# prepare for callback
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)
# 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()
# 7 finish denoising process
if output_type == "latent":
image = latents
else:
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
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,conditional_output) if model_config.get("return_condition_latents",False) else (image,)
return FluxPipelineOutput(images=image)
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