xinyun/ComfyUI-CogVideoXWrapper
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allow scheduling cfg

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kijai 2024-11-10 19:27:40 +02:00
parent 87ed4a56cf
commit ed167ecfff
2 changed files with 16 additions and 8 deletions
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12
nodes.py
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@ -349,6 +349,7 @@ class CogVideoImageEncode:
"chunk_size": ("INT", {"default": 16, "min": 4}), "chunk_size": ("INT", {"default": 16, "min": 4}),
"enable_tiling": ("BOOLEAN", {"default": False, "tooltip": "Enable tiling for the VAE to reduce memory usage"}), "enable_tiling": ("BOOLEAN", {"default": False, "tooltip": "Enable tiling for the VAE to reduce memory usage"}),
"mask": ("MASK", ), "mask": ("MASK", ),
"noise_aug_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001, "tooltip": "Augment image with noise"}),
}, },
} }
@ -357,7 +358,7 @@ class CogVideoImageEncode:
FUNCTION = "encode" FUNCTION = "encode"
CATEGORY = "CogVideoWrapper" CATEGORY = "CogVideoWrapper"
def encode(self, pipeline, image, chunk_size=8, enable_tiling=False, mask=None): def encode(self, pipeline, image, chunk_size=8, enable_tiling=False, mask=None, noise_aug_strength=0.0):
device = mm.get_torch_device() device = mm.get_torch_device()
offload_device = mm.unet_offload_device() offload_device = mm.unet_offload_device()
generator = torch.Generator(device=device).manual_seed(0) generator = torch.Generator(device=device).manual_seed(0)
@ -395,6 +396,8 @@ class CogVideoImageEncode:
input_image = input_image.to(vae.dtype).to(device) input_image = input_image.to(vae.dtype).to(device)
input_image = input_image.unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W input_image = input_image.unsqueeze(0).permute(0, 4, 1, 2, 3) # B, C, T, H, W
B, C, T, H, W = input_image.shape B, C, T, H, W = input_image.shape
if noise_aug_strength > 0:
input_image = add_noise_to_reference_video(input_image, ratio=noise_aug_strength)
latents_list = [] latents_list = []
# Loop through the temporal dimension in chunks of 16 # Loop through the temporal dimension in chunks of 16
@ -786,7 +789,7 @@ class CogVideoSampler:
"negative": ("CONDITIONING", ), "negative": ("CONDITIONING", ),
"height": ("INT", {"default": 480, "min": 128, "max": 2048, "step": 16}), "height": ("INT", {"default": 480, "min": 128, "max": 2048, "step": 16}),
"width": ("INT", {"default": 720, "min": 128, "max": 2048, "step": 16}), "width": ("INT", {"default": 720, "min": 128, "max": 2048, "step": 16}),
"num_frames": ("INT", {"default": 48, "min": 16, "max": 1024, "step": 1}), "num_frames": ("INT", {"default": 49, "min": 17, "max": 1024, "step": 4}),
"steps": ("INT", {"default": 50, "min": 1}), "steps": ("INT", {"default": 50, "min": 1}),
"cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}), "cfg": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 30.0, "step": 0.01}),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
@ -868,6 +871,11 @@ class CogVideoSampler:
pipe.transformer.use_fastercache = False pipe.transformer.use_fastercache = False
pipe.transformer.fastercache_counter = 0 pipe.transformer.fastercache_counter = 0
if not isinstance(cfg, list):
cfg = [cfg for _ in range(steps)]
else:
assert len(cfg) == steps, "Length of cfg list must match number of steps"
autocastcondition = not pipeline["onediff"] or not dtype == torch.float32 autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
autocast_context = torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocastcondition else nullcontext() autocast_context = torch.autocast(mm.get_autocast_device(device), dtype=dtype) if autocastcondition else nullcontext()
with autocast_context: with autocast_context:

12
pipeline_cogvideox.py
View File

@ -451,7 +451,7 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
# corresponds to doing no classifier free guidance. # corresponds to doing no classifier free guidance.
do_classifier_free_guidance = guidance_scale > 1.0 do_classifier_free_guidance = guidance_scale[0] > 1.0
if do_classifier_free_guidance: if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
@ -660,7 +660,7 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
if self.do_classifier_free_guidance: if self.do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = noise_pred_uncond + self._guidance_scale[i] * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1 # compute the previous noisy sample x_t -> x_t-1
latents_tile = self.scheduler.step(noise_pred, t, latents_tile.to(self.vae.dtype), **extra_step_kwargs, return_dict=False)[0] latents_tile = self.scheduler.step(noise_pred, t, latents_tile.to(self.vae.dtype), **extra_step_kwargs, return_dict=False)[0]
@ -801,7 +801,7 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
noise_pred /= counter noise_pred /= counter
if do_classifier_free_guidance: if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = noise_pred_uncond + self._guidance_scale[i] * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1 # compute the previous noisy sample x_t -> x_t-1
if not isinstance(self.scheduler, CogVideoXDPMScheduler): if not isinstance(self.scheduler, CogVideoXDPMScheduler):
@ -865,15 +865,15 @@ class CogVideoXPipeline(VideoSysPipeline, CogVideoXLoraLoaderMixin):
video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None, video_flow_features=video_flow_features if (tora is not None and tora["start_percent"] <= current_step_percentage <= tora["end_percent"]) else None,
)[0] )[0]
noise_pred = noise_pred.float() noise_pred = noise_pred.float()
print(self._guidance_scale[i])
if isinstance(self.scheduler, CogVideoXDPMScheduler): if isinstance(self.scheduler, CogVideoXDPMScheduler):
self._guidance_scale = 1 + guidance_scale * ( self._guidance_scale[i] = 1 + guidance_scale[i] * (
(1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2 (1 - math.cos(math.pi * ((num_inference_steps - t.item()) / num_inference_steps) ** 5.0)) / 2
) )
if do_classifier_free_guidance: if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self._guidance_scale * (noise_pred_text - noise_pred_uncond) noise_pred = noise_pred_uncond + self._guidance_scale[i] * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1 # compute the previous noisy sample x_t -> x_t-1
if not isinstance(self.scheduler, CogVideoXDPMScheduler): if not isinstance(self.scheduler, CogVideoXDPMScheduler):