EasyLoader (SV3D)¶
Documentation¶
- Class name:
easy sv3dLoader - Category:
EasyUse/Loaders - Output node:
False
The sv3DLoader node is designed for loading and managing 3D model files specifically formatted for Stable Diffusion, facilitating the integration of 3D assets into generative AI workflows. It emphasizes ease of use and compatibility with a specific subset of 3D model files, aiming to streamline the process of incorporating 3D models into creative projects.
Input types¶
Required¶
ckpt_name- Specifies the checkpoint name for loading the model, serving as a key identifier for selecting the appropriate 3D model files.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
vae_name- Defines the VAE model name to be used in conjunction with the 3D models, ensuring compatibility and optimal performance within the generative workflow.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
init_image- An initial image to start the generation process, providing a visual context or basis for further 3D model integration.
- Comfy dtype:
IMAGE - Python dtype:
str
empty_latent_width- Sets the width for the latent space representation, defining the dimensions for the generated or manipulated 3D model output.
- Comfy dtype:
INT - Python dtype:
int
empty_latent_height- Sets the height for the latent space representation, similar to 'empty_latent_width', it defines the vertical dimension for the 3D model output.
- Comfy dtype:
INT - Python dtype:
int
batch_size- Determines the number of instances to process in a single batch, affecting the efficiency and speed of the 3D model loading operation.
- Comfy dtype:
INT - Python dtype:
int
interp_easing- Specifies the interpolation easing type to apply during the 3D model loading process, affecting the transition smoothness between models.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
easing_mode- Defines the mode of easing to be applied, such as azimuth or elevation, providing control over the specific aspect of 3D model manipulation.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
Optional¶
scheduler- An optional scheduler for managing the loading and processing tasks, offering advanced control over the execution flow.
- Comfy dtype:
STRING - Python dtype:
str
Output types¶
pipe- Comfy dtype:
PIPE_LINE - The pipeline configuration resulting from the loaded 3D models, ready for further processing or generation tasks.
- Python dtype:
object
- Comfy dtype:
model- Comfy dtype:
MODEL - The specific 3D model loaded and prepared for integration into the generative workflow, encapsulated within the pipeline.
- Python dtype:
object
- Comfy dtype:
interp_log- Comfy dtype:
STRING - A log of the interpolation and easing operations applied during the model loading process, providing insights into the manipulation effects.
- Python dtype:
str
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class sv3DLoader(EasingBase):
def __init__(self):
super().__init__()
@classmethod
def INPUT_TYPES(cls):
def get_file_list(filenames):
return [file for file in filenames if file != "put_models_here.txt" and "sv3d" in file]
return {"required": {
"ckpt_name": (get_file_list(folder_paths.get_filename_list("checkpoints")),),
"vae_name": (["Baked VAE"] + folder_paths.get_filename_list("vae"),),
"init_image": ("IMAGE",),
"empty_latent_width": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"empty_latent_height": ("INT", {"default": 576, "min": 16, "max": MAX_RESOLUTION, "step": 8}),
"batch_size": ("INT", {"default": 21, "min": 1, "max": 4096}),
"interp_easing": (["linear", "ease_in", "ease_out", "ease_in_out"], {"default": "linear"}),
"easing_mode": (["azimuth", "elevation", "custom"], {"default": "azimuth"}),
},
"optional": {"scheduler": ("STRING", {"default": "", "multiline": True})},
"hidden": {"prompt": "PROMPT", "my_unique_id": "UNIQUE_ID"}
}
RETURN_TYPES = ("PIPE_LINE", "MODEL", "STRING")
RETURN_NAMES = ("pipe", "model", "interp_log")
FUNCTION = "adv_pipeloader"
CATEGORY = "EasyUse/Loaders"
def adv_pipeloader(self, ckpt_name, vae_name, init_image, empty_latent_width, empty_latent_height, batch_size, interp_easing, easing_mode, scheduler='',prompt=None, my_unique_id=None):
model: ModelPatcher | None = None
vae: VAE | None = None
clip: CLIP | None = None
# Clean models from loaded_objects
easyCache.update_loaded_objects(prompt)
model, clip, vae, clip_vision = easyCache.load_checkpoint(ckpt_name, "Default", True)
output = clip_vision.encode_image(init_image)
pooled = output.image_embeds.unsqueeze(0)
pixels = comfy.utils.common_upscale(init_image.movedim(-1, 1), empty_latent_width, empty_latent_height, "bilinear", "center").movedim(1,
-1)
encode_pixels = pixels[:, :, :, :3]
t = vae.encode(encode_pixels)
azimuth_points = []
elevation_points = []
if easing_mode == 'azimuth':
azimuth_points = [(0, 0), (batch_size-1, 360)]
elevation_points = [(0, 0)] * batch_size
elif easing_mode == 'elevation':
azimuth_points = [(0, 0)] * batch_size
elevation_points = [(0, -90), (batch_size-1, 90)]
else:
schedulers = scheduler.rstrip('\n')
for line in schedulers.split('\n'):
frame_str, point_str = line.split(':')
point_str = point_str.strip()[1:-1]
point = point_str.split(',')
azimuth_point = point[0]
elevation_point = point[1] if point[1] else 0.0
frame = int(frame_str.strip())
azimuth = float(azimuth_point)
azimuth_points.append((frame, azimuth))
elevation_val = float(elevation_point)
elevation_points.append((frame, elevation_val))
azimuth_points.sort(key=lambda x: x[0])
elevation_points.sort(key=lambda x: x[0])
#interpolation
next_point = 1
next_elevation_point = 1
elevations = []
azimuths = []
# For azimuth interpolation
for i in range(batch_size):
# Find the interpolated azimuth for the current frame
while next_point < len(azimuth_points) and i >= azimuth_points[next_point][0]:
next_point += 1
if next_point == len(azimuth_points):
next_point -= 1
prev_point = max(next_point - 1, 0)
if azimuth_points[next_point][0] != azimuth_points[prev_point][0]:
timing = (i - azimuth_points[prev_point][0]) / (
azimuth_points[next_point][0] - azimuth_points[prev_point][0])
interpolated_azimuth = self.ease(azimuth_points[prev_point][1], azimuth_points[next_point][1], self.easing(timing, interp_easing))
else:
interpolated_azimuth = azimuth_points[prev_point][1]
# Interpolate the elevation
next_elevation_point = 1
while next_elevation_point < len(elevation_points) and i >= elevation_points[next_elevation_point][0]:
next_elevation_point += 1
if next_elevation_point == len(elevation_points):
next_elevation_point -= 1
prev_elevation_point = max(next_elevation_point - 1, 0)
if elevation_points[next_elevation_point][0] != elevation_points[prev_elevation_point][0]:
timing = (i - elevation_points[prev_elevation_point][0]) / (
elevation_points[next_elevation_point][0] - elevation_points[prev_elevation_point][0])
interpolated_elevation = self.ease(elevation_points[prev_point][1], elevation_points[next_point][1], self.easing(timing, interp_easing))
else:
interpolated_elevation = elevation_points[prev_elevation_point][1]
azimuths.append(interpolated_azimuth)
elevations.append(interpolated_elevation)
log_node_info("easy sv3dLoader", "azimuths:" + str(azimuths))
log_node_info("easy sv3dLoader", "elevations:" + str(elevations))
log = 'azimuths:' + str(azimuths) + '\n\n' + "elevations:" + str(elevations)
# Structure the final output
positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
negative = [[torch.zeros_like(pooled),
{"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8])
samples = {"samples": latent}
image = easySampler.pil2tensor(Image.new('RGB', (1, 1), (0, 0, 0)))
pipe = {"model": model,
"positive": positive,
"negative": negative,
"vae": vae,
"clip": clip,
"samples": samples,
"images": image,
"seed": 0,
"loader_settings": {"ckpt_name": ckpt_name,
"vae_name": vae_name,
"positive": positive,
"negative": negative,
"empty_latent_width": empty_latent_width,
"empty_latent_height": empty_latent_height,
"batch_size": batch_size,
"seed": 0,
}
}
return (pipe, model, log)