├── .gitignore ├── nodes ├── prepare_mochi_sigmas_node.py ├── sampler_unsample_node.py ├── sampler_resample_node.py ├── sampler_custom_node.py ├── unsampler_node.py └── resampler_node.py ├── utils ├── latent_utils.py ├── sampling_utils.py └── callback_utils.py ├── __init__.py ├── sampling ├── sampler.py └── sampling_functions.py ├── README.md ├── example_workflows └── wrapper_inversion_example.json └── LICENSE /.gitignore: -------------------------------------------------------------------------------- 1 | **/__pycache__ 2 | .DS_Store 3 | -------------------------------------------------------------------------------- /nodes/prepare_mochi_sigmas_node.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class MochiPrepareSigmasNode: 5 | @classmethod 6 | def INPUT_TYPES(s): 7 | return { 8 | "required": { 9 | "sigmas": ("SIGMAS", {"tooltip": "Override sigma schedule and steps"}), 10 | } 11 | } 12 | 13 | RETURN_TYPES = ("SIGMAS",) 14 | FUNCTION = "process" 15 | CATEGORY = "MochiEdit" 16 | 17 | def process(self, sigmas): 18 | sigmas = sigmas.tolist() 19 | if sigmas[-1] != 0.0: 20 | sigmas = [*sigmas, 0.0] 21 | 22 | return (torch.Tensor(sigmas),) 23 | -------------------------------------------------------------------------------- /utils/latent_utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def get_latent_dimensions(num_frames, width, height): 5 | spatial_downsample = 8 6 | temporal_downsample = 6 7 | in_channels = 12 8 | B = 1 9 | C = in_channels 10 | T = (num_frames - 1) // temporal_downsample + 1 11 | H = height // spatial_downsample 12 | W = width // spatial_downsample 13 | 14 | return (B, C, T, H, W) 15 | 16 | 17 | def add_latent_noise(model, latent_shape, sigma_schedule, samples, generator): 18 | z = torch.randn( 19 | latent_shape, 20 | device=model.device, 21 | generator=generator, 22 | dtype=torch.float32, 23 | ) 24 | if samples is not None: 25 | z = z * sigma_schedule[0] + (1 -sigma_schedule[0]) * samples.to(model.device) 26 | return z 27 | -------------------------------------------------------------------------------- /nodes/sampler_unsample_node.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from comfy.samplers import KSAMPLER 4 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 5 | log = logging.getLogger(__name__) 6 | 7 | from ..sampling.sampling_functions import get_rf_forward_sample_fn 8 | 9 | 10 | class MochiUnsamplerNode: 11 | @classmethod 12 | def INPUT_TYPES(s): 13 | return { 14 | "required": { 15 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), 16 | "gamma": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 30.0, "step": 0.01}), 17 | } 18 | } 19 | 20 | RETURN_TYPES = ("SAMPLER",) 21 | FUNCTION = "process" 22 | CATEGORY = "MochiEdit" 23 | 24 | def process(self, seed, gamma): 25 | sampler_fn = get_rf_forward_sample_fn(gamma, seed) 26 | sampler = KSAMPLER(sampler_fn) 27 | 28 | return (sampler,) 29 | 30 | 31 | 32 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | # from .nodes.unsampler_node import MochiWrapperUnsamplerNode 2 | # from .nodes.resampler_node import MochiWrapperResamplerNode 3 | from .nodes.sampler_custom_node import MochiWrapperSamplerCustomNode 4 | from .nodes.sampler_unsample_node import MochiUnsamplerNode 5 | from .nodes.sampler_resample_node import MochiResamplerNode 6 | from .nodes.prepare_mochi_sigmas_node import MochiPrepareSigmasNode 7 | 8 | 9 | NODE_CLASS_MAPPINGS = { 10 | # "MochiWrapperUnsampler": MochiWrapperUnsamplerNode, 11 | # "MochiWrapperResampler": MochiWrapperResamplerNode, 12 | "MochiWrapperSamplerCustom": MochiWrapperSamplerCustomNode, 13 | "MochiUnsampler": MochiUnsamplerNode, 14 | "MochiResampler": MochiResamplerNode, 15 | "MochiPrepareSigmas": MochiPrepareSigmasNode, 16 | } 17 | 18 | NODE_DISPLAY_NAME_MAPPINGS = { 19 | # "MochiWrapperUnsampler": "Mochi Wrapper Unsampler", 20 | # "MochiWrapperResampler": "Mochi Wrapper Resampler", 21 | "MochiWrapperSamplerCustom": "SamplerCustom (Mochi Wrapper)", 22 | "MochiUnsampler": "Mochi Unsampler", 23 | "MochiResampler": "Mochi Resampler", 24 | "MochiPrepareSigmas": "Mochi Prepare Sigmas", 25 | } -------------------------------------------------------------------------------- /nodes/sampler_resample_node.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from comfy.samplers import KSAMPLER 4 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 5 | log = logging.getLogger(__name__) 6 | 7 | from ..sampling.sampling_functions import get_rf_reverse_sample_fn 8 | 9 | 10 | class MochiResamplerNode: 11 | @classmethod 12 | def INPUT_TYPES(s): 13 | return { 14 | "required": { 15 | "eta": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 30.0, "step": 0.01}), 16 | "start_step": ("INT", {"default": 0, "min": 0}), 17 | "end_step": ("INT", {"default": 10, "min": 0}), 18 | "eta_trend": (['constant', 'linear_decrease', 'linear_increase'],), 19 | "latents": ("LATENT", ), 20 | } 21 | } 22 | 23 | RETURN_TYPES = ("SAMPLER",) 24 | FUNCTION = "process" 25 | CATEGORY = "MochiEdit" 26 | 27 | def process(self, eta, start_step, end_step, eta_trend, latents): 28 | latent_image = latents['samples'] 29 | 30 | sampler_fn = get_rf_reverse_sample_fn(latent_image, eta, start_step, end_step, eta_trend) 31 | sampler = KSAMPLER(sampler_fn) 32 | 33 | return (sampler,) 34 | 35 | 36 | 37 | -------------------------------------------------------------------------------- /sampling/sampler.py: -------------------------------------------------------------------------------- 1 | import logging 2 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 3 | log = logging.getLogger(__name__) 4 | 5 | import torch 6 | 7 | from ..utils.callback_utils import get_callback_fn 8 | from ..utils.latent_utils import add_latent_noise 9 | from ..utils.sampling_utils import get_model_fn, get_sample_args 10 | 11 | 12 | def run_sampler(model, latents, positive, negative, sigmas, cfg, sampler_fn, add_noise=False, seed=0): 13 | # seed 14 | torch.manual_seed(seed) 15 | torch.cuda.manual_seed(seed) 16 | generator = torch.Generator(device=model.device) 17 | generator.manual_seed(seed) 18 | 19 | # prepare latents 20 | latent_shape = latents.shape 21 | 22 | if add_noise: 23 | z = add_latent_noise(model, latent_shape, sigmas, latents, generator) 24 | else: 25 | z = latents.clone() 26 | 27 | # prepare model and args 28 | positive, negative = get_sample_args(model, positive, negative) 29 | model_fn = get_model_fn(model) 30 | 31 | # sampling 32 | callback_fn = get_callback_fn(model, len(sigmas)-1) 33 | extra_args = { 34 | "positive": positive, 35 | "negative": negative, 36 | "cfg": cfg 37 | } 38 | z = sampler_fn(model_fn, z, sigmas, callback=callback_fn, extra_args=extra_args) 39 | 40 | # cleanup 41 | model.dit.to(model.offload_device) 42 | 43 | return z 44 | -------------------------------------------------------------------------------- /nodes/sampler_custom_node.py: -------------------------------------------------------------------------------- 1 | import logging 2 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 3 | log = logging.getLogger(__name__) 4 | 5 | import comfy.model_management as mm 6 | 7 | from ..sampling.sampler import run_sampler 8 | from ..utils.sampling_utils import prepare_conds 9 | 10 | 11 | class MochiWrapperSamplerCustomNode: 12 | @classmethod 13 | def INPUT_TYPES(s): 14 | return { 15 | "required": { 16 | "model": ("MOCHIMODEL",), 17 | "positive": ("CONDITIONING", ), 18 | "negative": ("CONDITIONING", ), 19 | "cfg": ("FLOAT", {"default": 4.5, "min": 0.0, "max": 30.0, "step": 0.01}), 20 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), 21 | "sigmas": ("SIGMAS", {"tooltip": "Override sigma schedule and steps"}), 22 | "latents": ("LATENT", ), 23 | "sampler": ("SAMPLER", ), 24 | "add_noise": ("BOOLEAN", ), 25 | } 26 | } 27 | 28 | RETURN_TYPES = ("LATENT",) 29 | RETURN_NAMES = ("samples",) 30 | FUNCTION = "process" 31 | CATEGORY = "MochiEdit/Wrapper" 32 | 33 | def process(self, model, positive, negative, cfg, seed, sigmas, latents, sampler, add_noise): 34 | mm.soft_empty_cache() 35 | 36 | sigmas = sigmas.tolist() 37 | latents = latents['samples'] 38 | positive, negative = prepare_conds(positive, negative) 39 | 40 | latents = run_sampler(model, latents, positive, negative, sigmas, cfg, sampler.sampler_function, add_noise, seed) 41 | 42 | mm.soft_empty_cache() 43 | 44 | return ({"samples": latents},) 45 | 46 | 47 | 48 | -------------------------------------------------------------------------------- /nodes/unsampler_node.py: -------------------------------------------------------------------------------- 1 | import logging 2 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 3 | log = logging.getLogger(__name__) 4 | 5 | import comfy.model_management as mm 6 | 7 | from ..sampling.sampler import run_sampler 8 | from ..sampling.sampling_functions import get_rf_forward_sample_fn 9 | from ..utils.sampling_utils import prepare_conds 10 | 11 | 12 | class MochiWrapperUnsamplerNode: 13 | @classmethod 14 | def INPUT_TYPES(s): 15 | return { 16 | "required": { 17 | "model": ("MOCHIMODEL",), 18 | "positive": ("CONDITIONING", ), 19 | "negative": ("CONDITIONING", ), 20 | "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), 21 | "gamma": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 30.0, "step": 0.01}), 22 | "sigmas": ("SIGMAS", {"tooltip": "Override sigma schedule and steps"}), 23 | "latents": ("LATENT", ), 24 | } 25 | } 26 | 27 | RETURN_TYPES = ("LATENT",) 28 | RETURN_NAMES = ("samples",) 29 | FUNCTION = "process" 30 | CATEGORY = "MochiEdit" 31 | 32 | def process(self, model, positive, negative, seed, gamma, sigmas, latents): 33 | mm.soft_empty_cache() 34 | 35 | sigmas = sigmas.tolist() 36 | if sigmas[0] != 0.0: 37 | sigmas = [0.0, *sigmas] 38 | latents = latents['samples'] 39 | positive, negative = prepare_conds(positive, negative) 40 | 41 | sampler_fn = get_rf_forward_sample_fn(gamma, seed) 42 | latents = run_sampler(model, latents, positive, negative, sigmas, 1.0, sampler_fn, False, 0) 43 | 44 | mm.soft_empty_cache() 45 | 46 | return ({"samples": latents},) 47 | 48 | 49 | 50 | -------------------------------------------------------------------------------- /nodes/resampler_node.py: -------------------------------------------------------------------------------- 1 | import logging 2 | logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') 3 | log = logging.getLogger(__name__) 4 | 5 | import comfy.model_management as mm 6 | 7 | from ..sampling.sampler import run_sampler 8 | from ..sampling.sampling_functions import get_rf_reverse_sample_fn 9 | from ..utils.sampling_utils import prepare_conds 10 | 11 | 12 | class MochiWrapperResamplerNode: 13 | @classmethod 14 | def INPUT_TYPES(s): 15 | return { 16 | "required": { 17 | "model": ("MOCHIMODEL",), 18 | "positive": ("CONDITIONING", ), 19 | "negative": ("CONDITIONING", ), 20 | "cfg": ("FLOAT", {"default": 4.5, "min": 0.0, "max": 30.0, "step": 0.01}), 21 | "eta": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 30.0, "step": 0.01}), 22 | "start_step": ("INT", {"default": 0, "min": 0}), 23 | "end_step": ("INT", {"default": 10, "min": 0}), 24 | "eta_trend": (['constant', 'linear_decrease', 'linear_increase'],), 25 | "sigmas": ("SIGMAS", {"tooltip": "Override sigma schedule and steps"}), 26 | "latents": ("LATENT", ), 27 | "original_latents": ("LATENT", ), 28 | } 29 | } 30 | 31 | RETURN_TYPES = ("LATENT",) 32 | RETURN_NAMES = ("samples",) 33 | FUNCTION = "process" 34 | CATEGORY = "MochiEdit/Wrapper" 35 | 36 | def process(self, model, positive, negative, cfg, eta, start_step, end_step, eta_trend, sigmas, latents, original_latents): 37 | mm.soft_empty_cache() 38 | 39 | sigmas = sigmas.tolist() 40 | if sigmas[-1] != 0.0: 41 | sigmas = [*sigmas, 0.0] 42 | latents = latents['samples'] 43 | original_latents = original_latents['samples'] 44 | positive, negative = prepare_conds(positive, negative) 45 | 46 | sampler_fn = get_rf_reverse_sample_fn(original_latents, eta, start_step, end_step, eta_trend) 47 | latents = run_sampler(model, latents, positive, negative, sigmas, cfg, sampler_fn, False, 0) 48 | 49 | mm.soft_empty_cache() 50 | 51 | return ({"samples": latents},) 52 | 53 | 54 | 55 | -------------------------------------------------------------------------------- /utils/sampling_utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | import comfy.model_management as mm 4 | 5 | 6 | def get_model_fn(model): 7 | # sample, sample_null, cfg_scale 8 | def model_fn(z, sigma, positive, negative, cfg): 9 | model.dit.to(model.device) 10 | if hasattr(model.dit, "cublas_half_matmul") and model.dit.cublas_half_matmul: 11 | autocast_dtype = torch.float16 12 | else: 13 | autocast_dtype = torch.bfloat16 14 | 15 | with torch.autocast(mm.get_autocast_device(model.device), dtype=autocast_dtype): 16 | if cfg > 1.0: 17 | out_cond = model.dit(z, sigma, **positive) 18 | out_uncond = model.dit(z, sigma, **negative) 19 | else: 20 | out_cond = model.dit(z, sigma, **positive) 21 | return out_cond 22 | 23 | return out_uncond + cfg * (out_cond - out_uncond) 24 | 25 | return model_fn 26 | 27 | 28 | def get_sample_args(model, cond_embeds, uncond_embeds): 29 | cond_args = { 30 | "y_mask": [cond_embeds["attention_mask"].to(model.device)], 31 | "y_feat": [cond_embeds["embeds"].to(model.device)] 32 | } 33 | 34 | uncond_args = { 35 | "y_mask": [uncond_embeds["attention_mask"].to(model.device)], 36 | "y_feat": [uncond_embeds["embeds"].to(model.device)] 37 | } 38 | return cond_args, uncond_args 39 | 40 | 41 | def prepare_conds(positive, negative): 42 | #For compatibility with Comfy CLIPTextEncode 43 | if not isinstance(positive, dict): 44 | positive = { 45 | "embeds": positive[0][0], 46 | "attention_mask": positive[0][1]["attention_mask"].bool(), 47 | } 48 | if not isinstance(negative, dict): 49 | negative = { 50 | "embeds": negative[0][0], 51 | "attention_mask": negative[0][1]["attention_mask"].bool(), 52 | } 53 | return positive, negative 54 | 55 | 56 | def generate_eta_values(steps, start_time, end_time, eta, eta_trend): 57 | end_time = min(end_time, steps) 58 | eta_values = [0] * steps 59 | 60 | if eta_trend == 'constant': 61 | for i in range(start_time, end_time): 62 | eta_values[i] = eta 63 | elif eta_trend == 'linear_increase': 64 | for i in range(start_time, end_time): 65 | progress = (i - start_time) / (end_time - start_time - 1) 66 | eta_values[i] = eta * progress 67 | elif eta_trend == 'linear_decrease': 68 | for i in range(start_time, end_time): 69 | progress = 1 - (i - start_time) / (end_time - start_time - 1) 70 | eta_values[i] = eta * progress 71 | 72 | return eta_values 73 | -------------------------------------------------------------------------------- /sampling/sampling_functions.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from tqdm import tqdm, trange 3 | 4 | from ..utils.sampling_utils import generate_eta_values 5 | 6 | 7 | @torch.no_grad() 8 | def mochi_sample(model, z, sigmas, callback=None): 9 | total_steps = len(sigmas)-1 10 | latent_shape = z.shape 11 | for i in tqdm(range(0, total_steps), desc="Processing Samples", total=total_steps): 12 | pred = model(z=z, sigma=torch.full([latent_shape[0]], sigmas[i], device=z.device)) 13 | z = z + pred * (sigmas[i] - sigmas[i + 1]) 14 | 15 | if callback is not None: 16 | callback(i, z) 17 | 18 | return z 19 | 20 | 21 | def get_rf_forward_sample_fn(gamma, seed, correction=True): 22 | # Controlled Forward ODE (Algorithm 1) 23 | generator = torch.Generator() 24 | generator.manual_seed(seed) 25 | 26 | @torch.no_grad() 27 | def sample_forward(model, y0, sigmas, extra_args={}, callback=None, disable=None): 28 | Y = y0.clone() 29 | y1 = torch.randn(Y.shape, generator=generator).to(y0.device) 30 | N = len(sigmas)-1 31 | s_in = y0.new_ones([y0.shape[0]]) 32 | for i in trange(N, disable=disable): 33 | # t_i = i/N 34 | t_i = sigmas[i] 35 | 36 | # 6. Unconditional Vector field uti(Yti) = u(Yti, ti, Φ(“”); φ) 37 | unconditional_vector_field = -model(Y, sigmas[i]*s_in, **extra_args) 38 | 39 | if correction: 40 | # 7.Conditional Vector field uti(Yti|y1) = (y1−Yti)/1−ti 41 | conditional_vector_field = (y1-Y)/(1-t_i) 42 | 43 | # 8. Controlled Vector field ti(Yti) = uti(Yti) + γ (uti(Yti|y1) − uti(Yti)) 44 | controlled_vector_field = unconditional_vector_field + gamma * (conditional_vector_field - unconditional_vector_field) 45 | else: 46 | controlled_vector_field = unconditional_vector_field 47 | 48 | # 9. Next state Yti+1 = Yti + ˆuti(Yti) (σ(ti+1) − σ(ti)) 49 | Y = Y + controlled_vector_field * (sigmas[i+1] - sigmas[i]) 50 | 51 | if callback is not None: 52 | callback({'x': Y, 'denoised': Y, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i]}) 53 | 54 | return Y 55 | 56 | return sample_forward 57 | 58 | 59 | def get_rf_reverse_sample_fn(latent_image, eta, start_time, end_time, eta_trend): 60 | # Controlled Reverse ODE (Algorithm 2) 61 | @torch.no_grad() 62 | def sample_reverse(model, y1, sigmas, extra_args={}, callback=None, disable=None): 63 | latent_shape = y1.shape 64 | X = y1.clone() 65 | N = len(sigmas)-1 66 | y0 = latent_image.clone().to(y1.device) 67 | eta_values = generate_eta_values(N, start_time, end_time, eta, eta_trend) 68 | s_in = y0.new_ones([y0.shape[0]]) 69 | for i in trange(N, disable=disable): 70 | # t_i = i/N 71 | t_i = 1 - sigmas[i] 72 | 73 | # 5. Unconditional Vector field uti(Xti) = -u(Xti, 1-ti, Φ(“prompt”); φ) 74 | # torch.full([latent_shape[0]], sigmas[i], device=X.device) 75 | unconditional_vector_field = model(X, sigmas[i]*s_in, **extra_args) 76 | 77 | # 6.Conditional Vector field uti(Xti|y0) = (y0−Xti)/(1−ti) 78 | conditional_vector_field = (y0-X)/(1-t_i) 79 | 80 | # 7. Controlled Vector field ti(Yti) = uti(Yti) + γ (uti(Yti|y1) − uti(Yti)) 81 | controlled_vector_field = unconditional_vector_field + eta_values[i] * (conditional_vector_field - unconditional_vector_field) 82 | 83 | # 8. Next state Yti+1 = Yti + ˆuti(Yti) (σ(ti+1) − σ(ti)) 84 | X = X + controlled_vector_field * (sigmas[i] - sigmas[i+1]) 85 | # X = X + -unconditional_vector_field * (sigmas[i] - sigmas[i+1]) 86 | 87 | if callback is not None: 88 | callback({'x': X, 'denoised': X, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i]}) 89 | 90 | return X 91 | 92 | return sample_reverse 93 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI-MochiEdit 2 | 3 | ComfyUI nodes to edit videos using Genmo Mochi 4 | 5 | https://github.com/user-attachments/assets/41830ff3-6ac6-4b5a-be35-4429c571aa97 6 | 7 | ## Installation 8 | 9 | These nodes are built to work with the [ComfyUI-MochiWrapper](https://github.com/kijai/ComfyUI-MochiWrapper) nodes and soon will work with native ComfyUI Mochi too. 10 | For now please follow the installation for the wrapper. 11 | 12 | Then git clone this repo into your `ComfyUI/custom_nodes/` directory or use the ComfyUI Manager to install (when this repo is added there). 13 | 14 | There are no additional requirements. 15 | 16 | 17 | 18 | https://github.com/user-attachments/assets/88a9c4d4-a6d2-4d68-9c07-7fcba32ce84a 19 | 20 | 21 | 22 | ## How to Use 23 | 24 | There is an example workflow in the `example_workflows` directory. 25 | 26 | First, the input video is inverted into noise and then this noise is used to resample the video with the target prompt. 27 | A similar strategy as [RF-Inversion](https://rf-inversion.github.io/) is used. 28 | 29 | ### Unsampling Nodes 30 | 31 | unsampling_nodes 32 | 33 | #### Mochi Unsampler 34 | 35 | This node creates a sampler that can convert the video into noise. 36 | 37 | - `gamma`: the amount to do noise correction. Leave this to 0 as it does not work well with Mochi. 38 | - `seed`: if performing noise correction the seed to use for the random noise 39 | 40 | #### Mochi Prepare Sigmas 41 | 42 | This node makes a small change to the sigmas that the Mochi Sigma Schedule node from the wrapper produces. 43 | 44 | #### SamplerCustom (MochiWrapper) 45 | 46 | This is the classic KSampler or SamplerCustom from ComfyUI but for the MochiWrapper. 47 | 48 | - `positive` and `negative` should be blank prompts 49 | - `cfg`: should always be 1.0 for unsampling 50 | - `add_noise`: should always be False for unsampling 51 | - `seed`: there is no reason to change the seed 52 | - `sigmas`: must be prepared then flipped first 53 | 54 | ### Sampling Nodes 55 | 56 | sampling_nodes 57 | 58 | #### Mochi Resampler 59 | 60 | This node creates a sampler that can convert the noise into a video. 61 | 62 | - `latents`: the latents of the original video 63 | - `eta`: the strength that the generation should align with the original video 64 | - higher values lead the generation closer to the original 65 | - `start_step`: the starting step to where the original video should guide the generation 66 | - a lower value (e.g. 0) will have much closer following but not allow for additional objects like a hat to be placed 67 | - a higher value (e.g. 6) will allow for new objects like a hat to be placed, but may not follow the original video. Higher values can also lead to bad results (blurs) 68 | - `end_step` the step to stop guiding the generation closer to the original video 69 | - a lower value will lead to more differences in the video output 70 | - `eta_trend`: whether the eta (strength of guidance) should stay constant, increase, or decrease as steps progress. `linear_decrease` is the recommended setting for most changes. 71 | 72 | #### SamplerCustom (MochiWrapper) 73 | 74 | This is the classic KSampler or SamplerCustom from ComfyUI but for the MochiWrapper. 75 | 76 | - `positive` and `negative` can be anything you like. `positive` shoud be the target prompt. 77 | - `cfg`: can have any cfg that would work with normal Mochi (e.g. 4.50) 78 | - `latents`: should be the latent from unsampling 79 | - `sigmas`: must be prepared but NOT flipped 80 | - `seed`: the seed has no effect 81 | 82 | ## Acknowledgements 83 | 84 | [RF-Inversion](https://rf-inversion.github.io/) 85 | 86 | ``` 87 | @article{rout2024rfinversion, 88 | title={Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations}, 89 | author={Litu Rout and Yujia Chen and Nataniel Ruiz and Constantine Caramanis and Sanjay Shakkottai and Wen-Sheng Chu}, 90 | journal={arXiv preprint arXiv:2410.10792}, 91 | year={2024} 92 | } 93 | ``` 94 | 95 | https://github.com/user-attachments/assets/d1d8e73a-680d-4671-b5f0-b2efd7ac05f2 96 | -------------------------------------------------------------------------------- /utils/callback_utils.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from PIL import Image 3 | from comfy.cli_args import args, LatentPreviewMethod 4 | from comfy.taesd.taesd import TAESD 5 | import comfy.model_management 6 | import comfy.utils 7 | from tqdm import tqdm 8 | 9 | MAX_PREVIEW_RESOLUTION = args.preview_size 10 | 11 | 12 | def preview_to_image(latent_image): 13 | latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1) # change scale from -1..1 to 0..1 14 | .mul(0xFF) # to 0..255 15 | ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device)) 16 | 17 | return Image.fromarray(latents_ubyte.numpy()) 18 | 19 | 20 | class LatentPreviewer: 21 | def decode_latent_to_preview(self, x0): 22 | pass 23 | 24 | def decode_latent_to_preview_image(self, preview_format, x0): 25 | preview_image = self.decode_latent_to_preview(x0) 26 | return ("GIF", preview_image, MAX_PREVIEW_RESOLUTION) 27 | 28 | 29 | class Latent2RGBPreviewer(LatentPreviewer): 30 | def __init__(self): 31 | #latent_rgb_factors = [[0.05389399697934166, 0.025018778505575393, -0.009193515248318657], [0.02318250640590553, -0.026987363837713156, 0.040172639061236956], [0.046035451343323666, -0.02039565868920197, 0.01275569344290342], [-0.015559161155025095, 0.051403973219861246, 0.03179031307996347], [-0.02766167769640129, 0.03749545161530447, 0.003335141009473408], [0.05824598730479011, 0.021744367381243884, -0.01578925627951616], [0.05260929401500947, 0.0560165014956886, -0.027477296572565126], [0.018513891242931686, 0.041961785217662514, 0.004490763489747966], [0.024063060899760215, 0.065082853069653, 0.044343437673514896], [0.05250992323006226, 0.04361117432588933, 0.01030076055524387], [0.0038921710021782366, -0.025299228133723792, 0.019370764014574535], [-0.00011950534333568519, 0.06549370069727675, -0.03436712163379723], [-0.026020578032683626, -0.013341758571090847, -0.009119046570271953], [0.024412451175602937, 0.030135064560817174, -0.008355486384198006], [0.04002209845752687, -0.017341304390739463, 0.02818338690302971], [-0.032575108695213684, -0.009588338926775117, -0.03077312160940468]] 32 | latent_rgb_factors = [[0.1236769792512748, 0.11775175335219157, -0.17700629766423637], [-0.08504104329270078, 0.026605813147523694, -0.006843165704926019], [-0.17093308616366876, 0.027991854696200386, 0.14179146288816308], [-0.17179555328757623, 0.09844317368603078, 0.14470997015982784], [-0.16975067171668484, -0.10739852629856643, -0.1894254942909962], [-0.19315259266769888, -0.011029760569485209, -0.08519702054654255], [-0.08399895091432583, -0.0964246452052032, -0.033622359523655665], [0.08148916330842498, 0.027500645903400067, -0.06593099749891196], [0.0456603103902293, -0.17844808072462398, 0.04204775167149785], [0.001751626383204502, -0.030567890189647867, -0.022078082809772193], [0.05110631095056278, -0.0709677393548804, 0.08963683539504264], [0.010515800868829, -0.18382052841762514, -0.08554553339721907]] 33 | 34 | self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu").transpose(0, 1) 35 | self.latent_rgb_factors_bias = None 36 | # if latent_rgb_factors_bias is not None: 37 | # self.latent_rgb_factors_bias = torch.tensor(latent_rgb_factors_bias, device="cpu") 38 | 39 | def decode_latent_to_preview(self, x0): 40 | self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device) 41 | if self.latent_rgb_factors_bias is not None: 42 | self.latent_rgb_factors_bias = self.latent_rgb_factors_bias.to(dtype=x0.dtype, device=x0.device) 43 | 44 | latent_image = torch.nn.functional.linear(x0[0].permute(1, 2, 0), self.latent_rgb_factors, 45 | bias=self.latent_rgb_factors_bias) 46 | return preview_to_image(latent_image) 47 | 48 | 49 | def get_previewer(): 50 | previewer = None 51 | method = args.preview_method 52 | if method != LatentPreviewMethod.NoPreviews: 53 | # TODO previewer method 54 | 55 | if method == LatentPreviewMethod.Auto: 56 | method = LatentPreviewMethod.Latent2RGB 57 | 58 | if previewer is None: 59 | previewer = Latent2RGBPreviewer() 60 | return previewer 61 | 62 | 63 | def prepare_callback(model, steps, x0_output_dict=None): 64 | preview_format = "JPEG" 65 | if preview_format not in ["JPEG", "PNG"]: 66 | preview_format = "JPEG" 67 | 68 | previewer = get_previewer() 69 | 70 | pbar = comfy.utils.ProgressBar(steps) 71 | tqdm_pbar = tqdm(total=steps, desc="Sampling", unit="it") 72 | def callback(step, x0, x, total_steps): 73 | if x0_output_dict is not None: 74 | x0_output_dict["x0"] = x0 75 | preview_bytes = None 76 | if previewer: 77 | preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) 78 | pbar.update_absolute(step + 1, total_steps, preview_bytes) 79 | tqdm_pbar.update(1) 80 | return callback 81 | 82 | 83 | def get_callback_fn(model, total_steps): 84 | callback = prepare_callback(model.dit, total_steps) 85 | comfy_pbar = comfy.utils.ProgressBar(total_steps) 86 | if callback is None: 87 | tqdm_pbar = tqdm(total=total_steps, desc="Sampling", unit="it") 88 | 89 | def callback_fn(args): 90 | i = args['i'] 91 | z = args['x'] 92 | if callback is not None: 93 | callback(i, z.detach()[0].permute(1,0,2,3), None, total_steps) 94 | else: 95 | comfy_pbar.update(1) 96 | tqdm_pbar.update(1) 97 | 98 | return callback_fn 99 | 100 | -------------------------------------------------------------------------------- /example_workflows/wrapper_inversion_example.json: 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For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------