From 6a470cfade8025c9b794f2a3bbf447b1e838f840 Mon Sep 17 00:00:00 2001 From: spawner Date: Sun, 8 Jun 2025 17:49:04 +0800 Subject: [PATCH] Create teacache_lumina2_v1.py --- TeaCache4Lumina2/teacache_lumina2_v1.py | 182 ++++++++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 TeaCache4Lumina2/teacache_lumina2_v1.py diff --git a/TeaCache4Lumina2/teacache_lumina2_v1.py b/TeaCache4Lumina2/teacache_lumina2_v1.py new file mode 100644 index 0000000..0145d24 --- /dev/null +++ b/TeaCache4Lumina2/teacache_lumina2_v1.py @@ -0,0 +1,182 @@ +import torch +import torch.nn as nn +import numpy as np +from typing import Any, Dict, Optional, Tuple, Union, List + +from diffusers import Lumina2Transformer2DModel, Lumina2Pipeline +from diffusers.models.modeling_outputs import Transformer2DModelOutput +from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +def teacache_forward_working( + self, + hidden_states: torch.Tensor, + timestep: torch.Tensor, + encoder_hidden_states: torch.Tensor, + encoder_attention_mask: torch.Tensor, + attention_kwargs: Optional[Dict[str, Any]] = None, + return_dict: bool = True, +) -> Union[torch.Tensor, Transformer2DModelOutput]: + if attention_kwargs is not None: + attention_kwargs = attention_kwargs.copy() + lora_scale = attention_kwargs.pop("scale", 1.0) + else: + lora_scale = 1.0 + if USE_PEFT_BACKEND: + scale_lora_layers(self, lora_scale) + + batch_size, _, height, width = hidden_states.shape + temb, encoder_hidden_states_processed = self.time_caption_embed(hidden_states, timestep, encoder_hidden_states) + (image_patch_embeddings, context_rotary_emb, noise_rotary_emb, joint_rotary_emb, + encoder_seq_lengths, seq_lengths) = self.rope_embedder(hidden_states, encoder_attention_mask) + image_patch_embeddings = self.x_embedder(image_patch_embeddings) + for layer in self.context_refiner: + encoder_hidden_states_processed = layer(encoder_hidden_states_processed, encoder_attention_mask, context_rotary_emb) + for layer in self.noise_refiner: + image_patch_embeddings = layer(image_patch_embeddings, None, noise_rotary_emb, temb) + + max_seq_len = max(seq_lengths) + input_to_main_loop = image_patch_embeddings.new_zeros(batch_size, max_seq_len, self.config.hidden_size) + for i, (enc_len, seq_len_val) in enumerate(zip(encoder_seq_lengths, seq_lengths)): + input_to_main_loop[i, :enc_len] = encoder_hidden_states_processed[i, :enc_len] + input_to_main_loop[i, enc_len:seq_len_val] = image_patch_embeddings[i] + + use_mask = len(set(seq_lengths)) > 1 + attention_mask_for_main_loop_arg = None + if use_mask: + mask = input_to_main_loop.new_zeros(batch_size, max_seq_len, dtype=torch.bool) + for i, (enc_len, seq_len_val) in enumerate(zip(encoder_seq_lengths, seq_lengths)): + mask[i, :seq_len_val] = True + attention_mask_for_main_loop_arg = mask + + should_calc = True + if self.enable_teacache: + cache_key = max_seq_len + if cache_key not in self.cache: + self.cache[cache_key] = { + "accumulated_rel_l1_distance": 0.0, + "previous_modulated_input": None, + "previous_residual": None, + } + + current_cache = self.cache[cache_key] + modulated_inp, _, _, _ = self.layers[0].norm1(input_to_main_loop, temb) + + if self.cnt == 0 or self.cnt == self.num_steps - 1: + should_calc = True + current_cache["accumulated_rel_l1_distance"] = 0.0 + else: + if current_cache["previous_modulated_input"] is not None: + coefficients = [393.76566581, -603.50993606, 209.10239044, -23.00726601, 0.86377344] # taken from teacache_lumina_next.py + rescale_func = np.poly1d(coefficients) + prev_mod_input = current_cache["previous_modulated_input"] + prev_mean = prev_mod_input.abs().mean() + + if prev_mean.item() > 1e-9: + rel_l1_change = ((modulated_inp - prev_mod_input).abs().mean() / prev_mean).cpu().item() + else: + rel_l1_change = 0.0 if modulated_inp.abs().mean().item() < 1e-9 else float('inf') + + current_cache["accumulated_rel_l1_distance"] += rescale_func(rel_l1_change) + + if current_cache["accumulated_rel_l1_distance"] < self.rel_l1_thresh: + should_calc = False + else: + should_calc = True + current_cache["accumulated_rel_l1_distance"] = 0.0 + else: + should_calc = True + current_cache["accumulated_rel_l1_distance"] = 0.0 + + current_cache["previous_modulated_input"] = modulated_inp.clone() + + if self.uncond_seq_len is None: + self.uncond_seq_len = cache_key + if cache_key != self.uncond_seq_len: + self.cnt += 1 + if self.cnt >= self.num_steps: + self.cnt = 0 + + if self.enable_teacache and not should_calc: + if max_seq_len in self.cache and "previous_residual" in self.cache[max_seq_len] and self.cache[max_seq_len]["previous_residual"] is not None: + processed_hidden_states = input_to_main_loop + self.cache[max_seq_len]["previous_residual"] + else: + should_calc = True + current_processing_states = input_to_main_loop + for layer in self.layers: + current_processing_states = layer(current_processing_states, attention_mask_for_main_loop_arg, joint_rotary_emb, temb) + processed_hidden_states = current_processing_states + + + if not (self.enable_teacache and not should_calc) : + current_processing_states = input_to_main_loop + for layer in self.layers: + current_processing_states = layer(current_processing_states, attention_mask_for_main_loop_arg, joint_rotary_emb, temb) + + if self.enable_teacache: + if max_seq_len in self.cache: + self.cache[max_seq_len]["previous_residual"] = current_processing_states - input_to_main_loop + else: + logger.warning(f"TeaCache: Cache key {max_seq_len} not found when trying to save residual.") + + processed_hidden_states = current_processing_states + + output_after_norm = self.norm_out(processed_hidden_states, temb) + p = self.config.patch_size + final_output_list = [] + for i, (enc_len, seq_len_val) in enumerate(zip(encoder_seq_lengths, seq_lengths)): + image_part = output_after_norm[i][enc_len:seq_len_val] + h_p, w_p = height // p, width // p + reconstructed_image = image_part.view(h_p, w_p, p, p, self.out_channels) \ + .permute(4, 0, 2, 1, 3) \ + .flatten(3, 4) \ + .flatten(1, 2) + final_output_list.append(reconstructed_image) + + final_output_tensor = torch.stack(final_output_list, dim=0) + + if USE_PEFT_BACKEND: + unscale_lora_layers(self, lora_scale) + + if not return_dict: + return (final_output_tensor,) + + return Transformer2DModelOutput(sample=final_output_tensor) + + +Lumina2Transformer2DModel.forward = teacache_forward_working + +ckpt_path = "NietaAniLumina_Alpha_full_round5_ep5_s182000.pth" +transformer = Lumina2Transformer2DModel.from_single_file( + ckpt_path, torch_dtype=torch.bfloat16 +) +pipeline = Lumina2Pipeline.from_pretrained( + "Alpha-VLLM/Lumina-Image-2.0", + transformer=transformer, + torch_dtype=torch.bfloat16 +).to("cuda") + +num_inference_steps = 30 +seed = 1024 +prompt = "a cat holding a sign that says hello" +output_filename = f"teacache_lumina2_output.png" + +# TeaCache +pipeline.transformer.__class__.enable_teacache = True +pipeline.transformer.__class__.cnt = 0 +pipeline.transformer.__class__.num_steps = num_inference_steps +pipeline.transformer.__class__.rel_l1_thresh = 0.3 +pipeline.transformer.__class__.cache = {} +pipeline.transformer.__class__.uncond_seq_len = None + + +pipeline.enable_model_cpu_offload() +image = pipeline( + prompt=prompt, + num_inference_steps=num_inference_steps, + generator=torch.Generator("cuda").manual_seed(seed) +).images[0] + +image.save(output_filename) +print(f"Image saved to {output_filename}")