import torch
import yaml, os
from diffusers.pipelines import FluxPipeline
from typing import List, Union, Optional, Dict, Any, Callable
from .transformer import tranformer_forward
from .condition import Condition
from diffusers.pipelines.flux.pipeline_flux import (
FluxPipelineOutput,
calculate_shift,
retrieve_timesteps,
np,
)
def prepare_params(
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
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,
**kwargs: dict,
):
return (
prompt,
prompt_2,
height,
width,
num_inference_steps,
timesteps,
guidance_scale,
num_images_per_prompt,
generator,
latents,
prompt_embeds,
pooled_prompt_embeds,
output_type,
return_dict,
joint_attention_kwargs,
callback_on_step_end,
callback_on_step_end_tensor_inputs,
max_sequence_length,
)
def seed_everything(seed: int = 42):
torch.backends.cudnn.deterministic = True
torch.manual_seed(seed)
np.random.seed(seed)
@torch.no_grad()
def generate(
pipeline: FluxPipeline,
conditions: List[Condition] = None,
model_config: Optional[Dict[str, Any]] = {},
condition_scale: float = 1.0,
**params: dict,
):
# model_config = model_config or get_config(config_path).get("model", {})
if condition_scale != 1:
for name, module in pipeline.transformer.named_modules():
if not name.endswith(".attn"):
continue
module.c_factor = torch.ones(1, 1) * condition_scale
self = pipeline
(
prompt,
prompt_2,
height,
width,
num_inference_steps,
timesteps,
guidance_scale,
num_images_per_prompt,
generator,
latents,
prompt_embeds,
pooled_prompt_embeds,
output_type,
return_dict,
joint_attention_kwargs,
callback_on_step_end,
callback_on_step_end_tensor_inputs,
max_sequence_length,
) = prepare_params(**params)
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,
)
# 4. 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,
)
# 4.1. Prepare conditions
condition_latents, condition_ids, condition_type_ids = ([] for _ in range(3))
use_condition = conditions is not None or []
if use_condition:
assert len(conditions) <= 1, "Only one condition is supported for now."
pipeline.set_adapters(
{
512: "subject_512",
1024: "subject_1024",
}[height]
)
for condition in conditions:
tokens, ids, type_id = condition.encode(self)
condition_latents.append(tokens) # [batch_size, token_n, token_dim]
condition_ids.append(ids) # [token_n, id_dim(3)]
condition_type_ids.append(type_id) # [token_n, 1]
condition_latents = torch.cat(condition_latents, dim=1)
condition_ids = torch.cat(condition_ids, dim=0)
if condition.condition_type == "subject":
delta = 32 if height == 512 else -32
# print(f"Condition delta: {delta}")
condition_ids[:, 2] += delta
condition_type_ids = torch.cat(condition_type_ids, dim=0)
# 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)
# 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)
# handle guidance
if self.transformer.config.guidance_embeds:
guidance = torch.tensor([guidance_scale], device=device)
guidance = guidance.expand(latents.shape[0])
else:
guidance = None
noise_pred = tranformer_forward(
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,
# Inputs to the original transformer
hidden_states=latents,
# YiYi notes: divide it by 1000 for now because we scale it by 1000 in the transforme rmodel (we should not keep it but I want to keep the inputs same for the model for testing)
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,
)[0]
# compute the previous noisy sample x_t -> x_t-1
latents_dtype = latents.dtype
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
if latents.dtype != latents_dtype:
if torch.backends.mps.is_available():
# some platforms (eg. apple mps) misbehave due to a pytorch bug: https://github.com/pytorch/pytorch/pull/99272
latents = latents.to(latents_dtype)
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()
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 condition_scale != 1:
for name, module in pipeline.transformer.named_modules():
if not name.endswith(".attn"):
continue
del module.c_factor
if not return_dict:
return (image,)
return FluxPipelineOutput(images=image)