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from typing import Union, List, Optional, Dict, Any, Callable
import numpy as np
import torch
from diffusers import FluxPipeline
from diffusers.pipelines.flux.pipeline_flux import calculate_shift, retrieve_timesteps
from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
from diffusers.utils import is_torch_xla_available
if is_torch_xla_available():
import torch_xla.core.xla_model as xm
XLA_AVAILABLE = True
else:
XLA_AVAILABLE = False
class ChromaPipeline(FluxPipeline):
def __call__(
self,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
negative_prompt: Optional[Union[str, List[str]]] = None,
negative_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 = 7.0,
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,
prompt_attn_mask: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_attn_mask: 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,
):
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
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
text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=torch.bfloat16)
if guidance_scale > 1.00001:
negative_text_ids = torch.zeros(batch_size, negative_prompt_embeds.shape[1], 3).to(device=device, dtype=torch.bfloat16)
# 4. Prepare latent variables
num_channels_latents = 64 // 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,
)
# extend img ids to match batch size
latent_image_ids = latent_image_ids.unsqueeze(0)
latent_image_ids = torch.cat([latent_image_ids] * batch_size, 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)
guidance = torch.full([1], 0, device=device, dtype=torch.float32)
guidance = guidance.expand(latents.shape[0])
# 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
noise_pred_text = self.transformer(
img=latents,
img_ids=latent_image_ids,
txt=prompt_embeds,
txt_ids=text_ids,
txt_mask=prompt_attn_mask, # todo add this
timesteps=timestep / 1000,
guidance=guidance
)
if guidance_scale > 1.00001:
noise_pred_uncond = self.transformer(
img=latents,
img_ids=latent_image_ids,
txt=negative_prompt_embeds,
txt_ids=negative_text_ids,
txt_mask=negative_prompt_attn_mask, # todo add this
timesteps=timestep / 1000,
guidance=guidance
)
noise_pred = noise_pred_uncond + self.guidance_scale * \
(noise_pred_text - noise_pred_uncond)
else:
noise_pred = noise_pred_text
# 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 XLA_AVAILABLE:
xm.mark_step()
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,)
return FluxPipelineOutput(images=image)
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