Instructions to use bbbboiwow/cocccck with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use bbbboiwow/cocccck with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("bbbboiwow/cocccck", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
| import os | |
| import torch | |
| import numpy as np | |
| from ..utils import log | |
| from accelerate import init_empty_weights | |
| from accelerate.utils import set_module_tensor_to_device | |
| import comfy.model_management as mm | |
| from comfy.utils import load_torch_file, ProgressBar | |
| import folder_paths | |
| script_directory = os.path.dirname(os.path.abspath(__file__)) | |
| device = mm.get_torch_device() | |
| offload_device = mm.unet_offload_device() | |
| class WanVideoAddSteadyDancerEmbeds: | |
| def INPUT_TYPES(s): | |
| return {"required": { | |
| "embeds": ("WANVIDIMAGE_EMBEDS",), | |
| "pose_latents_positive": ("LATENT",), | |
| "pose_strength_spatial": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "Strength of the pose embedding"}), | |
| "pose_strength_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "tooltip": "Strength of the pose embedding"}), | |
| "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Start percentage of the embedding application"}), | |
| "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "End percentage of the embedding application"}), | |
| }, | |
| "optional": { | |
| "pose_latents_negative": ("LATENT",), | |
| "clip_vision_embeds": ("WANVIDIMAGE_CLIPEMBEDS",), | |
| } | |
| } | |
| RETURN_TYPES = ("WANVIDIMAGE_EMBEDS",) | |
| RETURN_NAMES = ("image_embeds",) | |
| FUNCTION = "add" | |
| CATEGORY = "WanVideoWrapper" | |
| def add(self, embeds, pose_latents_positive, pose_strength_spatial, pose_strength_temporal, start_percent=0.0, end_percent=1.0, pose_latents_negative=None, clip_vision_embeds=None): | |
| sdancer_embeds = { | |
| "cond_pos": pose_latents_positive["samples"][0], | |
| "cond_neg": pose_latents_negative["samples"][0] if pose_latents_negative else None, | |
| "pose_strength_spatial": pose_strength_spatial, | |
| "pose_strength_temporal": pose_strength_temporal, | |
| "start_percent": start_percent, | |
| "end_percent": end_percent, | |
| "clip_fea": clip_vision_embeds, | |
| } | |
| updated = dict(embeds) | |
| updated["sdancer_embeds"] = sdancer_embeds | |
| return (updated,) | |
| NODE_CLASS_MAPPINGS = { | |
| "WanVideoAddSteadyDancerEmbeds": WanVideoAddSteadyDancerEmbeds, | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "WanVideoAddSteadyDancerEmbeds": "WanVideo Add SteadyDancer Embeds", | |
| } | |