Create teacache_lumina2_v1.py

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}")