stuff and more stuff
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DeepBeepMeep
@@ -494,7 +494,7 @@ class MMSingleStreamBlock(nn.Module):
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class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
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def preprocess_loras(self, model_type, sd):
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if model_type != "i2v" :
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if model_type != "hunyuan_i2v" :
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return sd
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new_sd = {}
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for k,v in sd.items():
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@@ -797,6 +797,59 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
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for block in self.single_blocks:
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block.disable_deterministic()
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def compute_magcache_threshold(self, start_step, num_inference_steps = 0, speed_factor =0):
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def nearest_interp(src_array, target_length):
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src_length = len(src_array)
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if target_length == 1:
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return np.array([src_array[-1]])
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scale = (src_length - 1) / (target_length - 1)
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mapped_indices = np.round(np.arange(target_length) * scale).astype(int)
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return src_array[mapped_indices]
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if len(self.def_mag_ratios) != num_inference_steps:
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self.mag_ratios = nearest_interp(self.def_mag_ratios, num_inference_steps)
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else:
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self.mag_ratios = self.def_mag_ratios
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best_deltas = None
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best_threshold = 0.01
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best_diff = 1000
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best_signed_diff = 1000
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target_nb_steps= int(num_inference_steps / speed_factor)
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threshold = 0.01
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while threshold <= 0.6:
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nb_steps = 0
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diff = 1000
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accumulated_err, accumulated_steps, accumulated_ratio = 0, 0, 1.0
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for i in range(num_inference_steps):
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if i<=start_step:
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skip = False
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else:
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cur_mag_ratio = self.mag_ratios[i] # conditional and unconditional in one list
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accumulated_ratio *= cur_mag_ratio # magnitude ratio between current step and the cached step
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accumulated_steps += 1 # skip steps plus 1
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cur_skip_err = np.abs(1-accumulated_ratio) # skip error of current steps
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accumulated_err += cur_skip_err # accumulated error of multiple steps
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if accumulated_err<threshold and accumulated_steps<=self.magcache_K:
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skip = True
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else:
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skip = False
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accumulated_err, accumulated_steps, accumulated_ratio = 0, 0, 1.0
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if not skip:
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nb_steps += 1
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signed_diff = target_nb_steps - nb_steps
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diff = abs(signed_diff)
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if diff < best_diff:
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best_threshold = threshold
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best_diff = diff
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best_signed_diff = signed_diff
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elif diff > best_diff:
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break
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threshold += 0.01
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self.magcache_thresh = best_threshold
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print(f"Mag Cache, best threshold found:{best_threshold:0.2f} with gain x{num_inference_steps/(target_nb_steps - best_signed_diff):0.2f} for a target of x{speed_factor}")
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return best_threshold
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def forward(
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self,
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x: torch.Tensor,
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@@ -925,25 +978,38 @@ class HYVideoDiffusionTransformer(ModelMixin, ConfigMixin):
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if self.enable_cache:
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if x_id == 0:
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self.should_calc = True
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inp = img[0:1]
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vec_ = vec[0:1]
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( img_mod1_shift, img_mod1_scale, _ , _ , _ , _ , ) = self.double_blocks[0].img_mod(vec_).chunk(6, dim=-1)
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normed_inp = self.double_blocks[0].img_norm1(inp)
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normed_inp = normed_inp.to(torch.bfloat16)
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modulated_inp = modulate( normed_inp, shift=img_mod1_shift, scale=img_mod1_scale )
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del normed_inp, img_mod1_shift, img_mod1_scale
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if step_no <= self.cache_start_step or step_no == self.num_steps-1:
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self.accumulated_rel_l1_distance = 0
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else:
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coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02]
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rescale_func = np.poly1d(coefficients)
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self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
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if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
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self.should_calc = False
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self.teacache_skipped_steps += 1
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else:
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if self.enable_cache == "mag":
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if step_no > self.cache_start_step:
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cur_mag_ratio = self.mag_ratios[step_no]
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self.accumulated_ratio = self.accumulated_ratio*cur_mag_ratio
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cur_skip_err = np.abs(1-self.accumulated_ratio)
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self.accumulated_err += cur_skip_err
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self.accumulated_steps += 1
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if self.accumulated_err<=self.magcache_thresh and self.accumulated_steps<=self.magcache_K:
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self.should_calc = False
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self.cache_skipped_steps += 1
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else:
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self.accumulated_ratio, self.accumulated_steps, self.accumulated_err = 1.0, 0, 0
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else:
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inp = img[0:1]
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vec_ = vec[0:1]
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( img_mod1_shift, img_mod1_scale, _ , _ , _ , _ , ) = self.double_blocks[0].img_mod(vec_).chunk(6, dim=-1)
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normed_inp = self.double_blocks[0].img_norm1(inp)
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normed_inp = normed_inp.to(torch.bfloat16)
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modulated_inp = modulate( normed_inp, shift=img_mod1_shift, scale=img_mod1_scale )
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del normed_inp, img_mod1_shift, img_mod1_scale
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if step_no <= self.cache_start_step or step_no == self.num_steps-1:
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self.accumulated_rel_l1_distance = 0
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self.previous_modulated_input = modulated_inp
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else:
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coefficients = [7.33226126e+02, -4.01131952e+02, 6.75869174e+01, -3.14987800e+00, 9.61237896e-02]
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rescale_func = np.poly1d(coefficients)
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self.accumulated_rel_l1_distance += rescale_func(((modulated_inp-self.previous_modulated_input).abs().mean() / self.previous_modulated_input.abs().mean()).cpu().item())
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if self.accumulated_rel_l1_distance < self.rel_l1_thresh:
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self.should_calc = False
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self.cache_skipped_steps += 1
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else:
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self.accumulated_rel_l1_distance = 0
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self.previous_modulated_input = modulated_inp
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else:
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self.should_calc = True
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