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	fixed pytorch compilation
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				@ -454,7 +454,8 @@ class WanAny2V:
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            timesteps.append(0.)
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					            timesteps.append(0.)
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            timesteps = [torch.tensor([t], device=self.device) for t in timesteps]
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					            timesteps = [torch.tensor([t], device=self.device) for t in timesteps]
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            if self.use_timestep_transform:
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					            if self.use_timestep_transform:
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                timesteps = [timestep_transform(t, shift=shift, num_timesteps=self.num_timesteps) for t in timesteps][:-1]    
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					                timesteps = [timestep_transform(t, shift=shift, num_timesteps=self.num_timesteps) for t in timesteps][:-1]
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					            timesteps = torch.tensor(timesteps)
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            sample_scheduler = None                  
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					            sample_scheduler = None                  
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        elif sample_solver == 'causvid':
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					        elif sample_solver == 'causvid':
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            sample_scheduler = FlowMatchScheduler(num_inference_steps=sampling_steps, shift=shift, sigma_min=0, extra_one_step=True)
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					            sample_scheduler = FlowMatchScheduler(num_inference_steps=sampling_steps, shift=shift, sigma_min=0, extra_one_step=True)
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@ -1016,8 +1017,8 @@ class WanAny2V:
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            if sample_solver == "euler":
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					            if sample_solver == "euler":
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                dt = timesteps[i] if i == len(timesteps)-1 else (timesteps[i] - timesteps[i + 1])
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					                dt = timesteps[i] if i == len(timesteps)-1 else (timesteps[i] - timesteps[i + 1])
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                dt = dt / self.num_timesteps
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					                dt = dt.item() / self.num_timesteps
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                latents = latents - noise_pred * dt[:, None, None, None, None]
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					                latents = latents - noise_pred * dt
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            else:
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					            else:
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                latents = sample_scheduler.step(
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					                latents = sample_scheduler.step(
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                    noise_pred[:, :, :target_shape[1]],
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					                    noise_pred[:, :, :target_shape[1]],
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@ -44,7 +44,7 @@ pydantic==2.10.6
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# Math & modeling
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					# Math & modeling
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torchdiffeq>=0.2.5
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					torchdiffeq>=0.2.5
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tensordict>=0.6.1
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					tensordict>=0.6.1
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mmgp==3.5.12
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					mmgp==3.6.0
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peft==0.15.0
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					peft==0.15.0
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matplotlib
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					matplotlib
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										6
									
								
								wgp.py
									
									
									
									
									
								
							
							
						
						
									
										6
									
								
								wgp.py
									
									
									
									
									
								
							@ -59,8 +59,8 @@ global_queue_ref = []
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AUTOSAVE_FILENAME = "queue.zip"
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					AUTOSAVE_FILENAME = "queue.zip"
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PROMPT_VARS_MAX = 10
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					PROMPT_VARS_MAX = 10
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target_mmgp_version = "3.5.12"
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					target_mmgp_version = "3.6.0"
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WanGP_version = "8.33"
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					WanGP_version = "8.34"
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settings_version = 2.29
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					settings_version = 2.29
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max_source_video_frames = 3000
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					max_source_video_frames = 3000
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prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
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					prompt_enhancer_image_caption_model, prompt_enhancer_image_caption_processor, prompt_enhancer_llm_model, prompt_enhancer_llm_tokenizer = None, None, None, None
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@ -8509,7 +8509,7 @@ def generate_configuration_tab(state, blocks, header, model_family, model_choice
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                        ("Off", "" ),
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					                        ("Off", "" ),
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                    ],
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					                    ],
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                    value= compile,
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					                    value= compile,
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                    label="Compile Transformer (up to 50% faster and 30% more frames but requires Linux / WSL and Flash or Sage attention)",
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					                    label="Compile Transformer : up to 10-20% faster, useful only if multiple gens at same frames no / resolution",
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                    interactive= not lock_ui_compile
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					                    interactive= not lock_ui_compile
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                )              
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					                )              
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