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from typing import List, Optional, Union
import torch
from diffusers import PixArtAlphaPipeline
from diffusers.pipelines.pixart_alpha.pipeline_pixart_alpha import retrieve_timesteps
def freeze_params(params):
for param in params:
param.requires_grad = False
class RewardPixartPipeline(PixArtAlphaPipeline):
def __init__(
self, tokenizer, text_encoder, transformer, scheduler, vae, memsave=False
):
super().__init__(
tokenizer,
text_encoder,
vae,
transformer,
scheduler,
)
# optionally enable memsave_torch
if memsave:
import memsave_torch.nn
self.vae = memsave_torch.nn.convert_to_memory_saving(self.vae)
self.text_encoder = memsave_torch.nn.convert_to_memory_saving(
self.text_encoder
)
self.text_encoder.gradient_checkpointing_enable()
self.vae.enable_gradient_checkpointing()
self.text_encoder.eval()
self.vae.eval()
freeze_params(self.vae.parameters())
freeze_params(self.text_encoder.parameters())
def apply(
self,
latents: torch.Tensor = None,
prompt: Union[str, List[str]] = None,
negative_prompt: str = "",
num_inference_steps: int = 20,
timesteps: List[int] = [400],
sigmas: List[float] = None,
guidance_scale: float = 1.0,
num_images_per_prompt: Optional[int] = 1,
height: Optional[int] = 512,
width: Optional[int] = 512,
eta: float = 0.0,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None,
prompt_attention_mask: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_attention_mask: Optional[torch.FloatTensor] = None,
callback_steps: int = 1,
clean_caption: bool = False,
use_resolution_binning: bool = True,
max_sequence_length: int = 120,
**kwargs,
):
# 1. Check inputs. Raise error if not correct
height = height or self.transformer.config.sample_size * self.vae_scale_factor
width = width or self.transformer.config.sample_size * self.vae_scale_factor
if use_resolution_binning:
if self.transformer.config.sample_size == 128:
aspect_ratio_bin = ASPECT_RATIO_1024_BIN
elif self.transformer.config.sample_size == 64:
aspect_ratio_bin = ASPECT_RATIO_512_BIN
elif self.transformer.config.sample_size == 32:
aspect_ratio_bin = ASPECT_RATIO_256_BIN
else:
raise ValueError("Invalid sample size")
orig_height, orig_width = height, width
height, width = self.image_processor.classify_height_width_bin(
height, width, ratios=aspect_ratio_bin
)
self.check_inputs(
prompt,
height,
width,
negative_prompt,
callback_steps,
prompt_embeds,
negative_prompt_embeds,
prompt_attention_mask,
negative_prompt_attention_mask,
)
# 2. Default height and width to transformer
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
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0
# 3. Encode input prompt
(
prompt_embeds,
prompt_attention_mask,
negative_prompt_embeds,
negative_prompt_attention_mask,
) = self.encode_prompt(
prompt,
do_classifier_free_guidance,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
device=device,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
prompt_attention_mask=prompt_attention_mask,
negative_prompt_attention_mask=negative_prompt_attention_mask,
clean_caption=clean_caption,
max_sequence_length=max_sequence_length,
)
if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
prompt_attention_mask = torch.cat(
[negative_prompt_attention_mask, prompt_attention_mask], dim=0
)
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(
self.scheduler, num_inference_steps, device, timesteps, sigmas
)
# 5. Prepare latents.
latent_channels = self.transformer.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
latent_channels,
height,
width,
prompt_embeds.dtype,
device,
generator,
latents,
)
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 6.1 Prepare micro-conditions.
added_cond_kwargs = {"resolution": None, "aspect_ratio": None}
if self.transformer.config.sample_size == 128:
resolution = torch.tensor([height, width]).repeat(
batch_size * num_images_per_prompt, 1
)
aspect_ratio = torch.tensor([float(height / width)]).repeat(
batch_size * num_images_per_prompt, 1
)
resolution = resolution.to(dtype=prompt_embeds.dtype, device=device)
aspect_ratio = aspect_ratio.to(dtype=prompt_embeds.dtype, device=device)
if do_classifier_free_guidance:
resolution = torch.cat([resolution, resolution], dim=0)
aspect_ratio = torch.cat([aspect_ratio, aspect_ratio], dim=0)
added_cond_kwargs = {"resolution": resolution, "aspect_ratio": aspect_ratio}
# 7. Denoising loop
num_warmup_steps = max(
len(timesteps) - num_inference_steps * self.scheduler.order, 0
)
for i, t in enumerate(timesteps):
latent_model_input = (
torch.cat([latents] * 2) if do_classifier_free_guidance else latents
)
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
current_timestep = t
if not torch.is_tensor(current_timestep):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = latent_model_input.device.type == "mps"
if isinstance(current_timestep, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
current_timestep = torch.tensor(
[current_timestep], dtype=dtype, device=latent_model_input.device
)
elif len(current_timestep.shape) == 0:
current_timestep = current_timestep[None].to(latent_model_input.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
current_timestep = current_timestep.expand(latent_model_input.shape[0])
# predict noise model_output
noise_pred = self.transformer(
latent_model_input,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
timestep=current_timestep,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
# perform guidance
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (
noise_pred_text - noise_pred_uncond
)
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
noise_pred = noise_pred.chunk(2, dim=1)[0]
else:
noise_pred = noise_pred
# compute previous image: x_t -> x_t-1
if num_inference_steps == 1:
# For DMD one step sampling: https://arxiv.org/abs/2311.18828
latents = self.scheduler.step(
noise_pred, t, latents, **extra_step_kwargs
).pred_original_sample
image = self.vae.decode(
latents / self.vae.config.scaling_factor, return_dict=False
)[0]
if use_resolution_binning:
image = self.image_processor.resize_and_crop_tensor(
image, orig_width, orig_height
)
image = (image / 2 + 0.5).clamp(0, 1)
# Offload all models
self.maybe_free_model_hooks()
return image
ASPECT_RATIO_2048_BIN = {
"0.25": [1024.0, 4096.0],
"0.26": [1024.0, 3968.0],
"0.27": [1024.0, 3840.0],
"0.28": [1024.0, 3712.0],
"0.32": [1152.0, 3584.0],
"0.33": [1152.0, 3456.0],
"0.35": [1152.0, 3328.0],
"0.4": [1280.0, 3200.0],
"0.42": [1280.0, 3072.0],
"0.48": [1408.0, 2944.0],
"0.5": [1408.0, 2816.0],
"0.52": [1408.0, 2688.0],
"0.57": [1536.0, 2688.0],
"0.6": [1536.0, 2560.0],
"0.68": [1664.0, 2432.0],
"0.72": [1664.0, 2304.0],
"0.78": [1792.0, 2304.0],
"0.82": [1792.0, 2176.0],
"0.88": [1920.0, 2176.0],
"0.94": [1920.0, 2048.0],
"1.0": [2048.0, 2048.0],
"1.07": [2048.0, 1920.0],
"1.13": [2176.0, 1920.0],
"1.21": [2176.0, 1792.0],
"1.29": [2304.0, 1792.0],
"1.38": [2304.0, 1664.0],
"1.46": [2432.0, 1664.0],
"1.67": [2560.0, 1536.0],
"1.75": [2688.0, 1536.0],
"2.0": [2816.0, 1408.0],
"2.09": [2944.0, 1408.0],
"2.4": [3072.0, 1280.0],
"2.5": [3200.0, 1280.0],
"2.89": [3328.0, 1152.0],
"3.0": [3456.0, 1152.0],
"3.11": [3584.0, 1152.0],
"3.62": [3712.0, 1024.0],
"3.75": [3840.0, 1024.0],
"3.88": [3968.0, 1024.0],
"4.0": [4096.0, 1024.0],
}
ASPECT_RATIO_256_BIN = {
"0.25": [128.0, 512.0],
"0.28": [128.0, 464.0],
"0.32": [144.0, 448.0],
"0.33": [144.0, 432.0],
"0.35": [144.0, 416.0],
"0.4": [160.0, 400.0],
"0.42": [160.0, 384.0],
"0.48": [176.0, 368.0],
"0.5": [176.0, 352.0],
"0.52": [176.0, 336.0],
"0.57": [192.0, 336.0],
"0.6": [192.0, 320.0],
"0.68": [208.0, 304.0],
"0.72": [208.0, 288.0],
"0.78": [224.0, 288.0],
"0.82": [224.0, 272.0],
"0.88": [240.0, 272.0],
"0.94": [240.0, 256.0],
"1.0": [256.0, 256.0],
"1.07": [256.0, 240.0],
"1.13": [272.0, 240.0],
"1.21": [272.0, 224.0],
"1.29": [288.0, 224.0],
"1.38": [288.0, 208.0],
"1.46": [304.0, 208.0],
"1.67": [320.0, 192.0],
"1.75": [336.0, 192.0],
"2.0": [352.0, 176.0],
"2.09": [368.0, 176.0],
"2.4": [384.0, 160.0],
"2.5": [400.0, 160.0],
"3.0": [432.0, 144.0],
"4.0": [512.0, 128.0],
}
ASPECT_RATIO_1024_BIN = {
"0.25": [512.0, 2048.0],
"0.28": [512.0, 1856.0],
"0.32": [576.0, 1792.0],
"0.33": [576.0, 1728.0],
"0.35": [576.0, 1664.0],
"0.4": [640.0, 1600.0],
"0.42": [640.0, 1536.0],
"0.48": [704.0, 1472.0],
"0.5": [704.0, 1408.0],
"0.52": [704.0, 1344.0],
"0.57": [768.0, 1344.0],
"0.6": [768.0, 1280.0],
"0.68": [832.0, 1216.0],
"0.72": [832.0, 1152.0],
"0.78": [896.0, 1152.0],
"0.82": [896.0, 1088.0],
"0.88": [960.0, 1088.0],
"0.94": [960.0, 1024.0],
"1.0": [1024.0, 1024.0],
"1.07": [1024.0, 960.0],
"1.13": [1088.0, 960.0],
"1.21": [1088.0, 896.0],
"1.29": [1152.0, 896.0],
"1.38": [1152.0, 832.0],
"1.46": [1216.0, 832.0],
"1.67": [1280.0, 768.0],
"1.75": [1344.0, 768.0],
"2.0": [1408.0, 704.0],
"2.09": [1472.0, 704.0],
"2.4": [1536.0, 640.0],
"2.5": [1600.0, 640.0],
"3.0": [1728.0, 576.0],
"4.0": [2048.0, 512.0],
}
ASPECT_RATIO_512_BIN = {
"0.25": [256.0, 1024.0],
"0.28": [256.0, 928.0],
"0.32": [288.0, 896.0],
"0.33": [288.0, 864.0],
"0.35": [288.0, 832.0],
"0.4": [320.0, 800.0],
"0.42": [320.0, 768.0],
"0.48": [352.0, 736.0],
"0.5": [352.0, 704.0],
"0.52": [352.0, 672.0],
"0.57": [384.0, 672.0],
"0.6": [384.0, 640.0],
"0.68": [416.0, 608.0],
"0.72": [416.0, 576.0],
"0.78": [448.0, 576.0],
"0.82": [448.0, 544.0],
"0.88": [480.0, 544.0],
"0.94": [480.0, 512.0],
"1.0": [512.0, 512.0],
"1.07": [512.0, 480.0],
"1.13": [544.0, 480.0],
"1.21": [544.0, 448.0],
"1.29": [576.0, 448.0],
"1.38": [576.0, 416.0],
"1.46": [608.0, 416.0],
"1.67": [640.0, 384.0],
"1.75": [672.0, 384.0],
"2.0": [704.0, 352.0],
"2.09": [736.0, 352.0],
"2.4": [768.0, 320.0],
"2.5": [800.0, 320.0],
"3.0": [864.0, 288.0],
"4.0": [1024.0, 256.0],
} |