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import torch
import numpy as np
from diffusers import FluxPipeline, AutoencoderTiny, FlowMatchEulerDiscreteScheduler
from typing import Any, Dict, List, Optional, Union
# Helper functions
def calculate_shift(
image_seq_len,
base_seq_len: int = 256,
max_seq_len: int = 4096,
base_shift: float = 0.5,
max_shift: float = 1.16,
):
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
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,
):
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:
scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
timesteps = scheduler.timesteps
num_inference_steps = len(timesteps)
elif sigmas is not None:
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
# FLUX pipeline function
@torch.inference_mode()
def flux_pipe_call_that_returns_an_iterable_of_images(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
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,
max_sequence_length: int = 512,
good_vae: Optional[Any] = None,
):
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
self.check_inputs(
prompt,
prompt_2,
height,
width,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
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
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
# 3. Encode prompt
lora_scale = joint_attention_kwargs.get("scale", None) if 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,
)
# 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,
)
self._num_timesteps = len(timesteps)
# Handle guidance
guidance = torch.full([1], guidance_scale, device=device, dtype=torch.float32).expand(latents.shape[0]) if self.transformer.config.guidance_embeds else None
# 6. Denoising loop
for i, t in enumerate(timesteps):
if self.interrupt:
continue
timestep = t.expand(latents.shape[0]).to(latents.dtype)
noise_pred = self.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,
)[0]
# Yield intermediate result
latents_for_image = self._unpack_latents(latents, height, width, self.vae_scale_factor)
latents_for_image = (latents_for_image / self.vae.config.scaling_factor) + self.vae.config.shift_factor
image = self.vae.decode(latents_for_image, return_dict=False)[0]
yield self.image_processor.postprocess(image, output_type=output_type)[0]
latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
torch.cuda.empty_cache()
# Final image using good_vae
latents = self._unpack_latents(latents, height, width, self.vae_scale_factor)
latents = (latents / good_vae.config.scaling_factor) + good_vae.config.shift_factor
image = good_vae.decode(latents, return_dict=False)[0]
self.maybe_free_model_hooks()
torch.cuda.empty_cache()
yield self.image_processor.postprocess(image, output_type=output_type)[0] |