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Delete TeaCache4Lumina2/teacache_lumina2_v2.py
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import torch
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import torch.nn as nn
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import numpy as np
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from typing import Any, Dict, Optional, Tuple, Union, List
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from diffusers import Lumina2Transformer2DModel, Lumina2Pipeline
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from diffusers.models.modeling_outputs import Transformer2DModelOutput
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from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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def teacache_forward_working(
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self,
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hidden_states: torch.Tensor,
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timestep: torch.Tensor,
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encoder_hidden_states: torch.Tensor,
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encoder_attention_mask: torch.Tensor,
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attention_kwargs: Optional[Dict[str, Any]] = None,
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return_dict: bool = True,
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) -> Union[torch.Tensor, Transformer2DModelOutput]:
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if attention_kwargs is not None:
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attention_kwargs = attention_kwargs.copy()
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lora_scale = attention_kwargs.pop("scale", 1.0)
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else:
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lora_scale = 1.0
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if USE_PEFT_BACKEND:
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scale_lora_layers(self, lora_scale)
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batch_size, _, height, width = hidden_states.shape
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temb, encoder_hidden_states_processed = self.time_caption_embed(hidden_states, timestep, encoder_hidden_states)
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(image_patch_embeddings, context_rotary_emb, noise_rotary_emb, joint_rotary_emb,
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encoder_seq_lengths, seq_lengths) = self.rope_embedder(hidden_states, encoder_attention_mask)
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image_patch_embeddings = self.x_embedder(image_patch_embeddings)
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for layer in self.context_refiner:
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encoder_hidden_states_processed = layer(encoder_hidden_states_processed, encoder_attention_mask, context_rotary_emb)
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for layer in self.noise_refiner:
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image_patch_embeddings = layer(image_patch_embeddings, None, noise_rotary_emb, temb)
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max_seq_len = max(seq_lengths)
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input_to_main_loop = image_patch_embeddings.new_zeros(batch_size, max_seq_len, self.config.hidden_size)
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for i, (enc_len, seq_len_val) in enumerate(zip(encoder_seq_lengths, seq_lengths)):
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input_to_main_loop[i, :enc_len] = encoder_hidden_states_processed[i, :enc_len]
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input_to_main_loop[i, enc_len:seq_len_val] = image_patch_embeddings[i]
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use_mask = len(set(seq_lengths)) > 1
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attention_mask_for_main_loop_arg = None
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if use_mask:
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mask = input_to_main_loop.new_zeros(batch_size, max_seq_len, dtype=torch.bool)
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for i, (enc_len, seq_len_val) in enumerate(zip(encoder_seq_lengths, seq_lengths)):
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mask[i, :seq_len_val] = True
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attention_mask_for_main_loop_arg = mask
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should_calc = True
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if self.enable_teacache:
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cache_key = max_seq_len
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if cache_key not in self.cache:
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self.cache[cache_key] = {
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"accumulated_rel_l1_distance": 0.0,
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"previous_modulated_input": None,
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"previous_residual": None,
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}
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current_cache = self.cache[cache_key]
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modulated_inp, _, _, _ = self.layers[0].norm1(input_to_main_loop, temb)
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if self.cnt == 0 or self.cnt == self.num_steps - 1:
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should_calc = True
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current_cache["accumulated_rel_l1_distance"] = 0.0
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else:
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if current_cache["previous_modulated_input"] is not None:
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coefficients = [225.7042019806413, -608.8453716535591, 304.1869942338369, 124.21267720116742, -1.4089066892956552]
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rescale_func = np.poly1d(coefficients)
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prev_mod_input = current_cache["previous_modulated_input"]
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prev_mean = prev_mod_input.abs().mean()
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if prev_mean.item() > 1e-9:
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rel_l1_change = ((modulated_inp - prev_mod_input).abs().mean() / prev_mean).cpu().item()
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else:
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rel_l1_change = 0.0 if modulated_inp.abs().mean().item() < 1e-9 else float('inf')
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current_cache["accumulated_rel_l1_distance"] += rescale_func(rel_l1_change)
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if current_cache["accumulated_rel_l1_distance"] < self.rel_l1_thresh:
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should_calc = False
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else:
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should_calc = True
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current_cache["accumulated_rel_l1_distance"] = 0.0
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else:
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should_calc = True
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current_cache["accumulated_rel_l1_distance"] = 0.0
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current_cache["previous_modulated_input"] = modulated_inp.clone()
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if self.uncond_seq_len is None:
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self.uncond_seq_len = cache_key
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if cache_key != self.uncond_seq_len:
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self.cnt += 1
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if self.cnt >= self.num_steps:
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self.cnt = 0
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if self.enable_teacache and not should_calc:
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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:
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processed_hidden_states = input_to_main_loop + self.cache[max_seq_len]["previous_residual"]
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else:
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should_calc = True
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current_processing_states = input_to_main_loop
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for layer in self.layers:
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current_processing_states = layer(current_processing_states, attention_mask_for_main_loop_arg, joint_rotary_emb, temb)
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processed_hidden_states = current_processing_states
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if not (self.enable_teacache and not should_calc) :
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current_processing_states = input_to_main_loop
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for layer in self.layers:
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current_processing_states = layer(current_processing_states, attention_mask_for_main_loop_arg, joint_rotary_emb, temb)
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if self.enable_teacache:
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if max_seq_len in self.cache:
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self.cache[max_seq_len]["previous_residual"] = current_processing_states - input_to_main_loop
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else:
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logger.warning(f"TeaCache: Cache key {max_seq_len} not found when trying to save residual.")
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processed_hidden_states = current_processing_states
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output_after_norm = self.norm_out(processed_hidden_states, temb)
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p = self.config.patch_size
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final_output_list = []
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for i, (enc_len, seq_len_val) in enumerate(zip(encoder_seq_lengths, seq_lengths)):
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image_part = output_after_norm[i][enc_len:seq_len_val]
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h_p, w_p = height // p, width // p
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reconstructed_image = image_part.view(h_p, w_p, p, p, self.out_channels) \
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.permute(4, 0, 2, 1, 3) \
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.flatten(3, 4) \
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.flatten(1, 2)
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final_output_list.append(reconstructed_image)
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final_output_tensor = torch.stack(final_output_list, dim=0)
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if USE_PEFT_BACKEND:
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unscale_lora_layers(self, lora_scale)
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if not return_dict:
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return (final_output_tensor,)
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return Transformer2DModelOutput(sample=final_output_tensor)
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Lumina2Transformer2DModel.forward = teacache_forward_working
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ckpt_path = "NietaAniLumina_Alpha_full_round5_ep5_s182000.pth"
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transformer = Lumina2Transformer2DModel.from_single_file(
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ckpt_path, torch_dtype=torch.bfloat16
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)
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pipeline = Lumina2Pipeline.from_pretrained(
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"Alpha-VLLM/Lumina-Image-2.0",
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transformer=transformer,
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torch_dtype=torch.bfloat16
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).to("cuda")
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num_inference_steps = 30
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seed = 1024
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prompt = "a cat holding a sign that says hello"
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output_filename = f"teacache_lumina2_output.png"
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# TeaCache
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pipeline.transformer.__class__.enable_teacache = True
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pipeline.transformer.__class__.cnt = 0
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pipeline.transformer.__class__.num_steps = num_inference_steps
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pipeline.transformer.__class__.rel_l1_thresh = 0.3
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pipeline.transformer.__class__.cache = {}
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pipeline.transformer.__class__.uncond_seq_len = None
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pipeline.enable_model_cpu_offload()
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image = pipeline(
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prompt=prompt,
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num_inference_steps=num_inference_steps,
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generator=torch.Generator("cuda").manual_seed(seed)
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).images[0]
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image.save(output_filename)
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print(f"Image saved to {output_filename}")
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