how the mellon config is saved
from diffusers.modular_pipelines.mellon_node_utils import MellonParam, MellonPipelineConfig
ZIMAGE_NODE_SPECS = {
"controlnet": None,
"denoise": {
"inputs": [
MellonParam.embeddings(display="input"),
MellonParam.width(),
MellonParam.height(),
MellonParam.seed(),
MellonParam.num_inference_steps(default=9),
MellonParam.image_latents_with_strength(),
MellonParam.strength(),
],
"model_inputs": [
MellonParam.unet(),
MellonParam.scheduler(),
],
"outputs": [
MellonParam.latents(display="output"),
MellonParam.doc(),
],
"required_inputs": ["embeddings"],
"required_model_inputs": ["unet", "scheduler"],
"block_name": "denoise",
},
"vae_encoder": {
"inputs": [
MellonParam.image(),
],
"model_inputs": [
MellonParam.vae(),
],
"outputs": [
MellonParam.image_latents(display="output"),
MellonParam.doc(),
],
"required_inputs": ["image"],
"required_model_inputs": ["vae"],
"block_name": "vae_encoder",
},
"text_encoder": {
"inputs": [
MellonParam.prompt(),
],
"model_inputs": [
MellonParam.text_encoders(),
],
"outputs": [
MellonParam.embeddings(display="output"),
MellonParam.doc(),
],
"required_inputs": ["prompt"],
"required_model_inputs": ["text_encoders"],
"block_name": "text_encoder",
},
"decoder": {
"inputs": [
MellonParam.latents(display="input"),
],
"model_inputs": [
MellonParam.vae(),
],
"outputs": [
MellonParam.images(),
MellonParam.doc(),
],
"required_inputs": ["latents"],
"required_model_inputs": ["vae"],
"block_name": "decode",
},
}
ZIMAGE_PIPELINE_CONFIG = MellonPipelineConfig(
node_specs=ZIMAGE_NODE_SPECS,
label="ZImage",
default_repo="Tongyi-MAI/Z-Image-Turbo",
default_dtype="bfloat16",
)
ZIMAGE_PIPELINE_CONFIG.save("YiYiXu/image_z_modular", push_to_hub=True)
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