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Implement Cosmos Image/Video to World (Video) diffusion models.

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Use CosmosImageToVideoLatent to set the input image/video.
This commit is contained in:
comfyanonymous 2025-01-14 05:14:10 -05:00
parent 1f1c7b7b56
commit 3aaabb12d4
6 changed files with 84 additions and 12 deletions
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29
comfy/model_base.py
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@ -189,9 +189,10 @@ class BaseModel(torch.nn.Module):
if denoise_mask is not None:
if len(denoise_mask.shape) == len(noise.shape):
denoise_mask = denoise_mask[:,:1]
denoise_mask = denoise_mask[:, :1]
denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
num_dim = noise.ndim - 2
denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:]))
if denoise_mask.shape[-2:] != noise.shape[-2:]:
denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
@ -201,12 +202,16 @@ class BaseModel(torch.nn.Module):
if ck == "mask":
cond_concat.append(denoise_mask.to(device))
elif ck == "masked_image":
cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
cond_concat.append(concat_latent_image.to(device)) # NOTE: the latent_image should be masked by the mask in pixel space
elif ck == "mask_inverted":
cond_concat.append(1.0 - denoise_mask.to(device))
else:
if ck == "mask":
cond_concat.append(torch.ones_like(noise)[:,:1])
cond_concat.append(torch.ones_like(noise)[:, :1])
elif ck == "masked_image":
cond_concat.append(self.blank_inpaint_image_like(noise))
elif ck == "mask_inverted":
cond_concat.append(torch.zeros_like(noise)[:, :1])
data = torch.cat(cond_concat, dim=1)
return data
return None
@ -294,6 +299,9 @@ class BaseModel(torch.nn.Module):
return blank_image
self.blank_inpaint_image_like = blank_inpaint_image_like
def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
def memory_required(self, input_shape):
if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
dtype = self.get_dtype()
@ -859,8 +867,11 @@ class HunyuanVideo(BaseModel):
return out
class CosmosVideo(BaseModel):
def __init__(self, model_config, model_type=ModelType.EDM, device=None):
def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None):
super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT)
self.image_to_video = image_to_video
if self.image_to_video:
self.concat_keys = ("mask_inverted",)
def extra_conds(self, **kwargs):
out = super().extra_conds(**kwargs)
@ -873,3 +884,11 @@ class CosmosVideo(BaseModel):
out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None))
return out
def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1))
sigma_noise_augmentation = 0 #TODO
if sigma_noise_augmentation != 0:
latent_image = latent_image + noise
latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)
return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5)

5
comfy/model_detection.py
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@ -245,13 +245,14 @@ def detect_unet_config(state_dict, key_prefix):
dit_config["max_img_h"] = 240
dit_config["max_img_w"] = 240
dit_config["max_frames"] = 128
dit_config["in_channels"] = 16
concat_padding_mask = True
dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
dit_config["out_channels"] = 16
dit_config["patch_spatial"] = 2
dit_config["patch_temporal"] = 1
dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0]
dit_config["block_config"] = "FA-CA-MLP"
dit_config["concat_padding_mask"] = True
dit_config["concat_padding_mask"] = concat_padding_mask
dit_config["pos_emb_cls"] = "rope3d"
dit_config["pos_emb_learnable"] = False
dit_config["pos_emb_interpolation"] = "crop"

2
comfy/samplers.py
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@ -376,7 +376,7 @@ class KSamplerX0Inpaint:
if "denoise_mask_function" in model_options:
denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
latent_mask = 1. - denoise_mask
x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask
out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
if denoise_mask is not None:
out = out * denoise_mask + self.latent_image * latent_mask

2
comfy/sd.py
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@ -534,7 +534,7 @@ class VAE:
def encode(self, pixel_samples):
pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
pixel_samples = pixel_samples.movedim(-1, 1)
if self.latent_dim == 3:
if self.latent_dim == 3 and pixel_samples.ndim < 5:
pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
try:
memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)

14
comfy/supported_models.py
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@ -824,9 +824,10 @@ class HunyuanVideo(supported_models_base.BASE):
hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref))
return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect))
class Cosmos(supported_models_base.BASE):
class CosmosT2V(supported_models_base.BASE):
unet_config = {
"image_model": "cosmos",
"in_channels": 16,
}
sampling_settings = {
@ -854,7 +855,16 @@ class Cosmos(supported_models_base.BASE):
t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))
class CosmosI2V(CosmosT2V):
unet_config = {
"image_model": "cosmos",
"in_channels": 17,
}
models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, Cosmos]
def get_model(self, state_dict, prefix="", device=None):
out = model_base.CosmosVideo(self, image_to_video=True, device=device)
return out
models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, CosmosT2V, CosmosI2V]
models += [SVD_img2vid]

44
comfy_extras/nodes_cosmos.py
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@ -1,6 +1,8 @@
import nodes
import torch
import comfy.model_management
import comfy.utils
class EmptyCosmosLatentVideo:
@classmethod
@ -16,8 +18,48 @@ class EmptyCosmosLatentVideo:
def generate(self, width, height, length, batch_size=1):
latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
return ({"samples":latent}, )
return ({"samples": latent}, )
class CosmosImageToVideoLatent:
@classmethod
def INPUT_TYPES(s):
return {"required": {"vae": ("VAE", ),
"image": ("IMAGE", ),
"width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
"length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
}}
RETURN_TYPES = ("LATENT",)
FUNCTION = "encode"
CATEGORY = "conditioning/inpaint"
def encode(self, vae, image, width, height, length, batch_size):
pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
pixel_len = min(pixels.shape[0], length)
padded_length = min(length, (((pixel_len - 1) // 8) + 2) * 8 - 7)
padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5
padded_pixels[:pixel_len] = pixels[:pixel_len]
latent_temp = vae.encode(padded_pixels)
latent = torch.zeros([1, latent_temp.shape[1], ((length - 1) // 8) + 1, latent_temp.shape[-2], latent_temp.shape[-1]], device=comfy.model_management.intermediate_device())
latent_len = ((pixel_len - 1) // 8) + 1
latent[:, :, :latent_len] = latent_temp[:, :, :latent_len]
mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
mask[:, :, :latent_len] *= 0.0
out_latent = {}
out_latent["samples"] = latent
out_latent["noise_mask"] = mask
return (out_latent,)
NODE_CLASS_MAPPINGS = {
"EmptyCosmosLatentVideo": EmptyCosmosLatentVideo,
"CosmosImageToVideoLatent": CosmosImageToVideoLatent,
}