[Feature]: support changing_resolution

configs/changing_resolution/wan_t2v.json

+{
+    "infer_steps": 50,
+    "target_video_length": 81,
+    "text_len": 512,
+    "target_height": 480,
+    "target_width": 832,
+    "self_attn_1_type": "flash_attn3",
+    "cross_attn_1_type": "flash_attn3",
+    "cross_attn_2_type": "flash_attn3",
+    "seed": 42,
+    "sample_guide_scale": 6,
+    "sample_shift": 8,
+    "enable_cfg": true,
+    "cpu_offload": false,
+    "changing_resolution": true
+}

lightx2v/models/runners/wan/wan_runner.py

@@ -7,6 +7,7 @@ from PIL import Image
 from lightx2v.utils.registry_factory import RUNNER_REGISTER
 from lightx2v.models.runners.default_runner import DefaultRunner
 from lightx2v.models.schedulers.wan.scheduler import WanScheduler
+from lightx2v.models.schedulers.wan.changing_resolution.scheduler import WanScheduler4ChangingResolution
 from lightx2v.models.schedulers.wan.feature_caching.scheduler import (
     WanSchedulerTeaCaching,
     WanSchedulerTaylorCaching,
@@ -119,18 +120,21 @@ class WanRunner(DefaultRunner):
         return vae_encoder, vae_decoder
 
     def init_scheduler(self):
-        if self.config.feature_caching == "NoCaching":
-            scheduler = WanScheduler(self.config)
-        elif self.config.feature_caching == "Tea":
-            scheduler = WanSchedulerTeaCaching(self.config)
-        elif self.config.feature_caching == "TaylorSeer":
-            scheduler = WanSchedulerTaylorCaching(self.config)
-        elif self.config.feature_caching == "Ada":
-            scheduler = WanSchedulerAdaCaching(self.config)
-        elif self.config.feature_caching == "Custom":
-            scheduler = WanSchedulerCustomCaching(self.config)
+        if self.config.get("changing_resolution", False):
+            scheduler = WanScheduler4ChangingResolution(self.config)
         else:
-            raise NotImplementedError(f"Unsupported feature_caching type: {self.config.feature_caching}")
+            if self.config.feature_caching == "NoCaching":
+                scheduler = WanScheduler(self.config)
+            elif self.config.feature_caching == "Tea":
+                scheduler = WanSchedulerTeaCaching(self.config)
+            elif self.config.feature_caching == "TaylorSeer":
+                scheduler = WanSchedulerTaylorCaching(self.config)
+            elif self.config.feature_caching == "Ada":
+                scheduler = WanSchedulerAdaCaching(self.config)
+            elif self.config.feature_caching == "Custom":
+                scheduler = WanSchedulerCustomCaching(self.config)
+            else:
+                raise NotImplementedError(f"Unsupported feature_caching type: {self.config.feature_caching}")
         self.model.set_scheduler(scheduler)
 
     def run_text_encoder(self, text, img):

lightx2v/models/schedulers/wan/changing_resolution/scheduler.py

+import torch
+from lightx2v.models.schedulers.wan.scheduler import WanScheduler
+
+
+class WanScheduler4ChangingResolution(WanScheduler):
+    def __init__(self, config):
+        super().__init__(config)
+        self.resolution_rate = 0.75
+        self.changing_resolution_steps = 25
+
+    def prepare_latents(self, target_shape, dtype=torch.float32):
+        self.latents = torch.randn(
+            target_shape[0],
+            target_shape[1],
+            int(target_shape[2] * self.resolution_rate) // 2 * 2,
+            int(target_shape[3] * self.resolution_rate) // 2 * 2,
+            dtype=dtype,
+            device=self.device,
+            generator=self.generator,
+        )
+        
+        self.noise_original_resolution = torch.randn(
+            target_shape[0],
+            target_shape[1],
+            target_shape[2],
+            target_shape[3],
+            dtype=dtype,
+            device=self.device,
+            generator=self.generator,
+        )
+
+    def step_post(self):
+        if self.step_index == self.changing_resolution_steps:
+            self.step_post_upsample()
+        else:
+            super().step_post()
+
+    def step_post_upsample(self):
+        # 1. denoised sample to clean noise
+        model_output = self.noise_pred.to(torch.float32)
+        sample = self.latents.to(torch.float32)
+        sigma_t = self.sigmas[self.step_index]
+        x0_pred = sample - sigma_t * model_output
+        denoised_sample = x0_pred.to(sample.dtype)
+
+        # 2. upsample clean noise to target shape
+        denoised_sample_5d = denoised_sample.unsqueeze(0)  # (C,T,H,W) -> (1,C,T,H,W)
+        clean_noise = torch.nn.functional.interpolate(
+            denoised_sample_5d, 
+            size=(self.config.target_shape[1], self.config.target_shape[2], self.config.target_shape[3]), 
+            mode='trilinear'
+        )
+        clean_noise = clean_noise.squeeze(0)  # (1,C,T,H,W) -> (C,T,H,W)        
+        
+        # 3. add noise to clean noise
+        noisy_sample = self.add_noise(clean_noise, self.noise_original_resolution, self.timesteps[self.step_index + 1])
+        
+        # 4. update latents
+        self.latents = noisy_sample
+        
+        # self.disable_corrector = [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37] # maybe not needed
+        
+        # 5. update timesteps using shift + 2 更激进的去噪
+        self.set_timesteps(self.infer_steps, device=self.device, shift=self.sample_shift + 2)
+
+    def add_noise(self, original_samples, noise, timesteps):
+        sigma = self.sigmas[self.step_index]
+        alpha_t, sigma_t = self._sigma_to_alpha_sigma_t(sigma)
+        noisy_samples = alpha_t * original_samples + sigma_t * noise
+        return noisy_samples

scripts/wan/run_wan_t2v_changing_resolution.sh

+#!/bin/bash
+
+# set path and first
+lightx2v_path=
+model_path=
+
+# check section
+if [ -z "${CUDA_VISIBLE_DEVICES}" ]; then
+    cuda_devices=0
+    echo "Warn: CUDA_VISIBLE_DEVICES is not set, using default value: ${cuda_devices}, change at shell script or set env variable."
+    export CUDA_VISIBLE_DEVICES=${cuda_devices}
+fi
+
+if [ -z "${lightx2v_path}" ]; then
+    echo "Error: lightx2v_path is not set. Please set this variable first."
+    exit 1
+fi
+
+if [ -z "${model_path}" ]; then
+    echo "Error: model_path is not set. Please set this variable first."
+    exit 1
+fi
+
+export TOKENIZERS_PARALLELISM=false
+
+export PYTHONPATH=${lightx2v_path}:$PYTHONPATH
+export DTYPE=BF16
+export ENABLE_PROFILING_DEBUG=true
+export ENABLE_GRAPH_MODE=false
+
+python -m lightx2v.infer \
+--model_cls wan2.1 \
+--task t2v \
+--model_path $model_path \
+--config_json ${lightx2v_path}/configs/changing_resolution/wan_t2v.json \
+--prompt "Two anthropomorphic cats in comfy boxing gear and bright gloves fight intensely on a spotlighted stage." \
+--negative_prompt "镜头晃动，色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" \
+--save_video_path ${lightx2v_path}/save_results/output_lightx2v_wan_t2v_changing_resolution.mp4