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sf-modular
...
modular-10
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317e83646d | ||
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47baa47bfd |
@@ -217,6 +217,11 @@ MELLON_OUTPUT_PARAMS = {
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"display": "output",
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"type": "controlnet",
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},
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"doc": {
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"label": "Doc",
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"display": "output",
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"type": "string",
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},
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}
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@@ -697,67 +702,3 @@ class MellonNodeConfig(PushToHubMixin):
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blocks_names=blocks_names,
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node_type=node_type,
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)
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# Minimal modular registry for Mellon node configs
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class ModularMellonNodeRegistry:
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"""Registry mapping (pipeline class, blocks_name) -> list of MellonNodeConfig."""
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def __init__(self):
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self._registry = {}
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self._initialized = False
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def register(self, pipeline_cls: type, node_params: Dict[str, MellonNodeConfig]):
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if not self._initialized:
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_initialize_registry(self)
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self._registry[pipeline_cls] = node_params
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def get(self, pipeline_cls: type) -> MellonNodeConfig:
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if not self._initialized:
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_initialize_registry(self)
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return self._registry.get(pipeline_cls, None)
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def get_all(self) -> Dict[type, Dict[str, MellonNodeConfig]]:
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if not self._initialized:
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_initialize_registry(self)
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return self._registry
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def _register_preset_node_types(
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pipeline_cls, params_map: Dict[str, Dict[str, Any]], registry: ModularMellonNodeRegistry
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):
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"""Register all node-type presets for a given pipeline class from a params map."""
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node_configs = {}
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for node_type, spec in params_map.items():
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node_config = MellonNodeConfig(
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inputs=spec.get("inputs", []),
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model_inputs=spec.get("model_inputs", []),
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outputs=spec.get("outputs", []),
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blocks_names=spec.get("block_names", []),
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node_type=node_type,
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)
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node_configs[node_type] = node_config
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registry.register(pipeline_cls, node_configs)
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def _initialize_registry(registry: ModularMellonNodeRegistry):
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"""Initialize the registry and register all available pipeline configs."""
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print("Initializing registry")
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registry._initialized = True
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try:
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from .qwenimage.modular_pipeline import QwenImageModularPipeline
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from .qwenimage.node_utils import QwenImage_NODE_TYPES_PARAMS_MAP
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_register_preset_node_types(QwenImageModularPipeline, QwenImage_NODE_TYPES_PARAMS_MAP, registry)
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except Exception:
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raise Exception("Failed to register QwenImageModularPipeline")
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try:
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from .stable_diffusion_xl.modular_pipeline import StableDiffusionXLModularPipeline
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from .stable_diffusion_xl.node_utils import SDXL_NODE_TYPES_PARAMS_MAP
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_register_preset_node_types(StableDiffusionXLModularPipeline, SDXL_NODE_TYPES_PARAMS_MAP, registry)
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except Exception:
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raise Exception("Failed to register StableDiffusionXLModularPipeline")
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@@ -1,95 +0,0 @@
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# Copyright 2025 Qwen-Image Team and The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# mellon nodes
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QwenImage_NODE_TYPES_PARAMS_MAP = {
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"controlnet": {
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"inputs": [
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"control_image",
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"controlnet_conditioning_scale",
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"control_guidance_start",
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"control_guidance_end",
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"height",
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"width",
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],
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"model_inputs": [
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"controlnet",
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"vae",
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],
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"outputs": [
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"controlnet_out",
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],
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"block_names": ["controlnet_vae_encoder"],
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},
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"denoise": {
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"inputs": [
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"embeddings",
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"width",
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"height",
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"seed",
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"num_inference_steps",
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"guidance_scale",
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"image_latents",
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"strength",
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"controlnet",
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],
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"model_inputs": [
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"unet",
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"guider",
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"scheduler",
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],
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"outputs": [
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"latents",
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"latents_preview",
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],
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"block_names": ["denoise"],
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},
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"vae_encoder": {
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"inputs": [
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"image",
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"width",
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"height",
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],
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"model_inputs": [
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"vae",
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],
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"outputs": [
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"image_latents",
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],
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},
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"text_encoder": {
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"inputs": [
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"prompt",
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"negative_prompt",
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],
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"model_inputs": [
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"text_encoders",
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],
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"outputs": [
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"embeddings",
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],
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},
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"decoder": {
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"inputs": [
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"latents",
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],
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"model_inputs": [
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"vae",
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],
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"outputs": [
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"images",
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],
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},
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}
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@@ -1,99 +0,0 @@
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# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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SDXL_NODE_TYPES_PARAMS_MAP = {
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"controlnet": {
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"inputs": [
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"control_image",
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"controlnet_conditioning_scale",
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"control_guidance_start",
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"control_guidance_end",
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"height",
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"width",
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],
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"model_inputs": [
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"controlnet",
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],
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"outputs": [
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"controlnet_out",
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],
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"block_names": [None],
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},
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"denoise": {
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"inputs": [
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"embeddings",
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"width",
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"height",
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"seed",
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"num_inference_steps",
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"guidance_scale",
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"image_latents",
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"strength",
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# custom adapters coming in as inputs
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"controlnet",
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# ip_adapter is optional and custom; include if available
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"ip_adapter",
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],
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"model_inputs": [
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"unet",
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"guider",
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"scheduler",
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],
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"outputs": [
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"latents",
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"latents_preview",
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],
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"block_names": ["denoise"],
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},
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"vae_encoder": {
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"inputs": [
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"image",
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"width",
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"height",
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],
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"model_inputs": [
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"vae",
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],
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"outputs": [
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"image_latents",
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],
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"block_names": ["vae_encoder"],
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},
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"text_encoder": {
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"inputs": [
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"prompt",
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"negative_prompt",
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],
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"model_inputs": [
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"text_encoders",
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],
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"outputs": [
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"embeddings",
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],
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"block_names": ["text_encoder"],
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},
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"decoder": {
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"inputs": [
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"latents",
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],
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"model_inputs": [
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"vae",
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],
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"outputs": [
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"images",
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],
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"block_names": ["decode"],
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},
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}
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