Merge branch 'main' into lstein/global-configuration

docs/installation/010_INSTALL_AUTOMATED.md

@@ -89,7 +89,7 @@ experimental versions later.
          sudo apt update
          sudo apt install -y software-properties-common
          sudo add-apt-repository -y ppa:deadsnakes/ppa
-         sudo apt install python3.10 python3-pip python3.10-venv
+         sudo apt install -y python3.10 python3-pip python3.10-venv
          sudo update-alternatives --install /usr/local/bin/python python /usr/bin/python3.10 3
          ```
 

invokeai/app/invocations/compel.py

@@ -0,0 +1,245 @@
+from typing import Literal, Optional, Union
+from pydantic import BaseModel, Field
+
+from invokeai.app.invocations.util.choose_model import choose_model
+from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
+
+from ...backend.util.devices import choose_torch_device, torch_dtype
+from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
+from ...backend.stable_diffusion.textual_inversion_manager import TextualInversionManager
+
+from compel import Compel
+from compel.prompt_parser import (
+    Blend,
+    CrossAttentionControlSubstitute,
+    FlattenedPrompt,
+    Fragment,
+)
+
+from invokeai.backend.globals import Globals
+
+
+class ConditioningField(BaseModel):
+    conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
+    class Config:
+        schema_extra = {"required": ["conditioning_name"]}
+
+
+class CompelOutput(BaseInvocationOutput):
+    """Compel parser output"""
+
+    #fmt: off
+    type: Literal["compel_output"] = "compel_output"
+
+    conditioning: ConditioningField = Field(default=None, description="Conditioning")
+    #fmt: on
+
+
+class CompelInvocation(BaseInvocation):
+    """Parse prompt using compel package to conditioning."""
+
+    type: Literal["compel"] = "compel"
+
+    prompt: str = Field(default="", description="Prompt")
+    model: str = Field(default="", description="Model to use")
+
+    # Schema customisation
+    class Config(InvocationConfig):
+        schema_extra = {
+            "ui": {
+                "title": "Prompt (Compel)",
+                "tags": ["prompt", "compel"],
+                "type_hints": {
+                  "model": "model"
+                }
+            },
+        }
+
+    def invoke(self, context: InvocationContext) -> CompelOutput:
+
+        # TODO: load without model
+        model = choose_model(context.services.model_manager, self.model)
+        pipeline = model["model"]
+        tokenizer = pipeline.tokenizer
+        text_encoder = pipeline.text_encoder
+
+        # TODO: global? input?
+        #use_full_precision = precision == "float32" or precision == "autocast"
+        #use_full_precision = False
+
+        # TODO: redo TI when separate model loding implemented
+        #textual_inversion_manager = TextualInversionManager(
+        #    tokenizer=tokenizer,
+        #    text_encoder=text_encoder,
+        #    full_precision=use_full_precision,
+        #)
+
+        def load_huggingface_concepts(concepts: list[str]):
+            pipeline.textual_inversion_manager.load_huggingface_concepts(concepts)
+
+        # apply the concepts library to the prompt
+        prompt_str = pipeline.textual_inversion_manager.hf_concepts_library.replace_concepts_with_triggers(
+            self.prompt,
+            lambda concepts: load_huggingface_concepts(concepts),
+            pipeline.textual_inversion_manager.get_all_trigger_strings(),
+        )
+
+        # lazy-load any deferred textual inversions.
+        # this might take a couple of seconds the first time a textual inversion is used.
+        pipeline.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(
+            prompt_str
+        )
+
+        compel = Compel(
+            tokenizer=tokenizer,
+            text_encoder=text_encoder,
+            textual_inversion_manager=pipeline.textual_inversion_manager,
+            dtype_for_device_getter=torch_dtype,
+            truncate_long_prompts=True, # TODO:
+        )
+
+        # TODO: support legacy blend?
+
+        prompt: Union[FlattenedPrompt, Blend] = Compel.parse_prompt_string(prompt_str)
+
+        if getattr(Globals, "log_tokenization", False):
+            log_tokenization_for_prompt_object(prompt, tokenizer)
+
+        c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
+
+        # TODO: long prompt support
+        #if not self.truncate_long_prompts:
+        #    [c, uc] = compel.pad_conditioning_tensors_to_same_length([c, uc])
+
+        ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
+            tokens_count_including_eos_bos=get_max_token_count(tokenizer, prompt),
+            cross_attention_control_args=options.get("cross_attention_control", None),
+        )
+
+        conditioning_name = f"{context.graph_execution_state_id}_{self.id}_conditioning"
+
+        # TODO: hacky but works ;D maybe rename latents somehow?
+        context.services.latents.set(conditioning_name, (c, ec))
+
+        return CompelOutput(
+            conditioning=ConditioningField(
+                conditioning_name=conditioning_name,
+            ),
+        )
+
+
+def get_max_token_count(
+    tokenizer, prompt: Union[FlattenedPrompt, Blend], truncate_if_too_long=False
+) -> int:
+    if type(prompt) is Blend:
+        blend: Blend = prompt
+        return max(
+            [
+                get_max_token_count(tokenizer, c, truncate_if_too_long)
+                for c in blend.prompts
+            ]
+        )
+    else:
+        return len(
+            get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long)
+        )
+
+
+def get_tokens_for_prompt_object(
+    tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
+) -> [str]:
+    if type(parsed_prompt) is Blend:
+        raise ValueError(
+            "Blend is not supported here - you need to get tokens for each of its .children"
+        )
+
+    text_fragments = [
+        x.text
+        if type(x) is Fragment
+        else (
+            " ".join([f.text for f in x.original])
+            if type(x) is CrossAttentionControlSubstitute
+            else str(x)
+        )
+        for x in parsed_prompt.children
+    ]
+    text = " ".join(text_fragments)
+    tokens = tokenizer.tokenize(text)
+    if truncate_if_too_long:
+        max_tokens_length = tokenizer.model_max_length - 2  # typically 75
+        tokens = tokens[0:max_tokens_length]
+    return tokens
+
+
+def log_tokenization_for_prompt_object(
+    p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
+):
+    display_label_prefix = display_label_prefix or ""
+    if type(p) is Blend:
+        blend: Blend = p
+        for i, c in enumerate(blend.prompts):
+            log_tokenization_for_prompt_object(
+                c,
+                tokenizer,
+                display_label_prefix=f"{display_label_prefix}(blend part {i + 1}, weight={blend.weights[i]})",
+            )
+    elif type(p) is FlattenedPrompt:
+        flattened_prompt: FlattenedPrompt = p
+        if flattened_prompt.wants_cross_attention_control:
+            original_fragments = []
+            edited_fragments = []
+            for f in flattened_prompt.children:
+                if type(f) is CrossAttentionControlSubstitute:
+                    original_fragments += f.original
+                    edited_fragments += f.edited
+                else:
+                    original_fragments.append(f)
+                    edited_fragments.append(f)
+
+            original_text = " ".join([x.text for x in original_fragments])
+            log_tokenization_for_text(
+                original_text,
+                tokenizer,
+                display_label=f"{display_label_prefix}(.swap originals)",
+            )
+            edited_text = " ".join([x.text for x in edited_fragments])
+            log_tokenization_for_text(
+                edited_text,
+                tokenizer,
+                display_label=f"{display_label_prefix}(.swap replacements)",
+            )
+        else:
+            text = " ".join([x.text for x in flattened_prompt.children])
+            log_tokenization_for_text(
+                text, tokenizer, display_label=display_label_prefix
+            )
+
+
+def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
+    """shows how the prompt is tokenized
+    # usually tokens have '</w>' to indicate end-of-word,
+    # but for readability it has been replaced with ' '
+    """
+    tokens = tokenizer.tokenize(text)
+    tokenized = ""
+    discarded = ""
+    usedTokens = 0
+    totalTokens = len(tokens)
+
+    for i in range(0, totalTokens):
+        token = tokens[i].replace("</w>", " ")
+        # alternate color
+        s = (usedTokens % 6) + 1
+        if truncate_if_too_long and i >= tokenizer.model_max_length:
+            discarded = discarded + f"\x1b[0;3{s};40m{token}"
+        else:
+            tokenized = tokenized + f"\x1b[0;3{s};40m{token}"
+            usedTokens += 1
+
+    if usedTokens > 0:
+        print(f'\n>> [TOKENLOG] Tokens {display_label or ""} ({usedTokens}):')
+        print(f"{tokenized}\x1b[0m")
+
+    if discarded != "":
+        print(f"\n>> [TOKENLOG] Tokens Discarded ({totalTokens - usedTokens}):")
+        print(f"{discarded}\x1b[0m")

invokeai/app/invocations/generate.py

@@ -250,8 +250,8 @@ class InpaintInvocation(ImageToImageInvocation):
 
         outputs = Inpaint(model).generate(
             prompt=self.prompt,
-            init_img=image,
+            init_image=image,
-            init_mask=mask,
+            mask_image=mask,
             step_callback=partial(self.dispatch_progress, context, source_node_id),
             **self.dict(
                 exclude={"prompt", "image", "mask"}

invokeai/app/invocations/latent.py

@@ -13,13 +13,13 @@ from ...backend.model_management.model_manager import ModelManager
 from ...backend.util.devices import choose_torch_device, torch_dtype
 from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
 from ...backend.image_util.seamless import configure_model_padding
-from ...backend.prompting.conditioning import get_uc_and_c_and_ec
 from ...backend.stable_diffusion.diffusers_pipeline import ConditioningData, StableDiffusionGeneratorPipeline
 from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext, InvocationConfig
 import numpy as np
 from ..services.image_storage import ImageType
 from .baseinvocation import BaseInvocation, InvocationContext
 from .image import ImageField, ImageOutput, build_image_output
+from .compel import ConditioningField
 from ...backend.stable_diffusion import PipelineIntermediateState
 from diffusers.schedulers import SchedulerMixin as Scheduler
 import diffusers
@@ -138,14 +138,14 @@ class NoiseInvocation(BaseInvocation):
 
 # Text to image
 class TextToLatentsInvocation(BaseInvocation):
-    """Generates latents from a prompt."""
+    """Generates latents from conditionings."""
 
     type: Literal["t2l"] = "t2l"
 
     # Inputs
-    # TODO: consider making prompt optional to enable providing prompt through a link
     # fmt: off
-    prompt: Optional[str] = Field(description="The prompt to generate an image from")
+    positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
+    negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
     noise: Optional[LatentsField] = Field(description="The noise to use")
     steps:       int = Field(default=10, gt=0, description="The number of steps to use to generate the image")
     cfg_scale: float = Field(default=7.5, gt=0, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
@@ -203,8 +203,10 @@ class TextToLatentsInvocation(BaseInvocation):
         return model
 
 
-    def get_conditioning_data(self, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
+    def get_conditioning_data(self, context: InvocationContext, model: StableDiffusionGeneratorPipeline) -> ConditioningData:
-        uc, c, extra_conditioning_info = get_uc_and_c_and_ec(self.prompt, model=model)
+        c, extra_conditioning_info = context.services.latents.get(self.positive_conditioning.conditioning_name)
+        uc, _ = context.services.latents.get(self.negative_conditioning.conditioning_name)
+
         conditioning_data = ConditioningData(
             uc,
             c,
@@ -231,7 +233,7 @@ class TextToLatentsInvocation(BaseInvocation):
             self.dispatch_progress(context, source_node_id, state)
 
         model = self.get_model(context.services.model_manager)
-        conditioning_data = self.get_conditioning_data(model)
+        conditioning_data = self.get_conditioning_data(context, model)
 
         # TODO: Verify the noise is the right size
 

invokeai/app/services/default_graphs.py

@@ -1,4 +1,5 @@
 from ..invocations.latent import LatentsToImageInvocation, NoiseInvocation, TextToLatentsInvocation
+from ..invocations.compel import CompelInvocation
 from ..invocations.params import ParamIntInvocation
 from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Graph, LibraryGraph
 from .item_storage import ItemStorageABC
@@ -16,24 +17,32 @@ def create_text_to_image() -> LibraryGraph:
             nodes={
                 'width': ParamIntInvocation(id='width', a=512),
                 'height': ParamIntInvocation(id='height', a=512),
+                'seed': ParamIntInvocation(id='seed', a=-1),
                 '3': NoiseInvocation(id='3'),
-                '4': TextToLatentsInvocation(id='4'),
+                '4': CompelInvocation(id='4'),
-                '5': LatentsToImageInvocation(id='5')
+                '5': CompelInvocation(id='5'),
+                '6': TextToLatentsInvocation(id='6'),
+                '7': LatentsToImageInvocation(id='7'),
             },
             edges=[
                 Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
                 Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
-                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='4', field='noise')),
+                Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
-                Edge(source=EdgeConnection(node_id='4', field='latents'), destination=EdgeConnection(node_id='5', field='latents')),
+                Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
+                Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
+                Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
+                Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
             ]
         ),
         exposed_inputs=[
-            ExposedNodeInput(node_path='4', field='prompt', alias='prompt'),
+            ExposedNodeInput(node_path='4', field='prompt', alias='positive_prompt'),
+            ExposedNodeInput(node_path='5', field='prompt', alias='negative_prompt'),
             ExposedNodeInput(node_path='width', field='a', alias='width'),
-            ExposedNodeInput(node_path='height', field='a', alias='height')
+            ExposedNodeInput(node_path='height', field='a', alias='height'),
+            ExposedNodeInput(node_path='seed', field='a', alias='seed'),
         ],
         exposed_outputs=[
-            ExposedNodeOutput(node_path='5', field='image', alias='image')
+            ExposedNodeOutput(node_path='7', field='image', alias='image')
         ])
 
 

invokeai/backend/web/invoke_ai_web_server.py

@@ -78,7 +78,6 @@ class InvokeAIWebServer:
         mimetypes.add_type("application/javascript", ".js")
         mimetypes.add_type("text/css", ".css")
         # Socket IO
-        logger = True if args.web_verbose else False
         engineio_logger = True if args.web_verbose else False
         max_http_buffer_size = 10000000
 

invokeai/frontend/web/src/common/util/parseMetadata.ts

@@ -1,5 +1,5 @@
 import { forEach, size } from 'lodash-es';
-import { ImageField, LatentsField } from 'services/api';
+import { ImageField, LatentsField, ConditioningField } from 'services/api';
 
 const OBJECT_TYPESTRING = '[object Object]';
 const STRING_TYPESTRING = '[object String]';
@@ -74,8 +74,38 @@ const parseLatentsField = (latentsField: unknown): LatentsField | undefined => {
   };
 };
 
+const parseConditioningField = (
+  conditioningField: unknown
+): ConditioningField | undefined => {
+  // Must be an object
+  if (!isObject(conditioningField)) {
+    return;
+  }
+
+  // A ConditioningField must have a `conditioning_name`
+  if (!('conditioning_name' in conditioningField)) {
+    return;
+  }
+
+  // A ConditioningField's `conditioning_name` must be a string
+  if (typeof conditioningField.conditioning_name !== 'string') {
+    return;
+  }
+
+  // Build a valid ConditioningField
+  return {
+    conditioning_name: conditioningField.conditioning_name,
+  };
+};
+
 type NodeMetadata = {
-  [key: string]: string | number | boolean | ImageField | LatentsField;
+  [key: string]:
+    | string
+    | number
+    | boolean
+    | ImageField
+    | LatentsField
+    | ConditioningField;
 };
 
 type InvokeAIMetadata = {
@@ -101,7 +131,7 @@ export const parseNodeMetadata = (
       return;
     }
 
-    // the only valid object types are ImageField and LatentsField
+    // the only valid object types are ImageField, LatentsField and ConditioningField
     if (isObject(nodeItem)) {
       if ('image_name' in nodeItem || 'image_type' in nodeItem) {
         const imageField = parseImageField(nodeItem);
@@ -118,6 +148,14 @@ export const parseNodeMetadata = (
         }
         return;
       }
+
+      if ('conditioning_name' in nodeItem) {
+        const conditioningField = parseConditioningField(nodeItem);
+        if (conditioningField) {
+          parsed[nodeKey] = conditioningField;
+        }
+        return;
+      }
     }
 
     // otherwise we accept any string, number or boolean

invokeai/frontend/web/src/features/nodes/components/InputFieldComponent.tsx

@@ -6,6 +6,7 @@ import BooleanInputFieldComponent from './fields/BooleanInputFieldComponent';
 import EnumInputFieldComponent from './fields/EnumInputFieldComponent';
 import ImageInputFieldComponent from './fields/ImageInputFieldComponent';
 import LatentsInputFieldComponent from './fields/LatentsInputFieldComponent';
+import ConditioningInputFieldComponent from './fields/ConditioningInputFieldComponent';
 import ModelInputFieldComponent from './fields/ModelInputFieldComponent';
 import NumberInputFieldComponent from './fields/NumberInputFieldComponent';
 import StringInputFieldComponent from './fields/StringInputFieldComponent';
@@ -84,6 +85,16 @@ const InputFieldComponent = (props: InputFieldComponentProps) => {
     );
   }
 
+  if (type === 'conditioning' && template.type === 'conditioning') {
+    return (
+      <ConditioningInputFieldComponent
+        nodeId={nodeId}
+        field={field}
+        template={template}
+      />
+    );
+  }
+
   if (type === 'model' && template.type === 'model') {
     return (
       <ModelInputFieldComponent

invokeai/frontend/web/src/features/nodes/components/fields/ConditioningInputFieldComponent.tsx

@@ -0,0 +1,19 @@
+import {
+  ConditioningInputFieldTemplate,
+  ConditioningInputFieldValue,
+} from 'features/nodes/types/types';
+import { memo } from 'react';
+import { FieldComponentProps } from './types';
+
+const ConditioningInputFieldComponent = (
+  props: FieldComponentProps<
+    ConditioningInputFieldValue,
+    ConditioningInputFieldTemplate
+  >
+) => {
+  const { nodeId, field } = props;
+
+  return null;
+};
+
+export default memo(ConditioningInputFieldComponent);

invokeai/frontend/web/src/features/nodes/types/constants.ts

@@ -11,6 +11,7 @@ export const FIELD_TYPE_MAP: Record<string, FieldType> = {
   enum: 'enum',
   ImageField: 'image',
   LatentsField: 'latents',
+  ConditioningField: 'conditioning',
   model: 'model',
   array: 'array',
 };
@@ -63,6 +64,12 @@ export const FIELDS: Record<FieldType, FieldUIConfig> = {
     title: 'Latents',
     description: 'Latents may be passed between nodes.',
   },
+  conditioning: {
+    color: 'cyan',
+    colorCssVar: getColorTokenCssVariable('cyan'),
+    title: 'Conditioning',
+    description: 'Conditioning may be passed between nodes.',
+  },
   model: {
     color: 'teal',
     colorCssVar: getColorTokenCssVariable('teal'),

invokeai/frontend/web/src/features/nodes/types/types.ts

@@ -56,6 +56,7 @@ export type FieldType =
   | 'enum'
   | 'image'
   | 'latents'
+  | 'conditioning'
   | 'model'
   | 'array';
 
@@ -74,6 +75,7 @@ export type InputFieldValue =
   | BooleanInputFieldValue
   | ImageInputFieldValue
   | LatentsInputFieldValue
+  | ConditioningInputFieldValue
   | EnumInputFieldValue
   | ModelInputFieldValue
   | ArrayInputFieldValue;
@@ -91,6 +93,7 @@ export type InputFieldTemplate =
   | BooleanInputFieldTemplate
   | ImageInputFieldTemplate
   | LatentsInputFieldTemplate
+  | ConditioningInputFieldTemplate
   | EnumInputFieldTemplate
   | ModelInputFieldTemplate
   | ArrayInputFieldTemplate;
@@ -162,6 +165,11 @@ export type LatentsInputFieldValue = FieldValueBase & {
   value?: undefined;
 };
 
+export type ConditioningInputFieldValue = FieldValueBase & {
+  type: 'conditioning';
+  value?: undefined;
+};
+
 export type ImageInputFieldValue = FieldValueBase & {
   type: 'image';
   value?: Pick<ImageField, 'image_name' | 'image_type'>;
@@ -229,6 +237,11 @@ export type LatentsInputFieldTemplate = InputFieldTemplateBase & {
   type: 'latents';
 };
 
+export type ConditioningInputFieldTemplate = InputFieldTemplateBase & {
+  default: undefined;
+  type: 'conditioning';
+};
+
 export type EnumInputFieldTemplate = InputFieldTemplateBase & {
   default: string | number;
   type: 'enum';

invokeai/frontend/web/src/features/nodes/util/fieldTemplateBuilders.ts

@@ -9,6 +9,7 @@ import {
   ImageInputFieldTemplate,
   IntegerInputFieldTemplate,
   LatentsInputFieldTemplate,
+  ConditioningInputFieldTemplate,
   StringInputFieldTemplate,
   ModelInputFieldTemplate,
   InputFieldTemplateBase,
@@ -196,6 +197,21 @@ const buildLatentsInputFieldTemplate = ({
   return template;
 };
 
+const buildConditioningInputFieldTemplate = ({
+  schemaObject,
+  baseField,
+}: BuildInputFieldArg): ConditioningInputFieldTemplate => {
+  const template: ConditioningInputFieldTemplate = {
+    ...baseField,
+    type: 'conditioning',
+    inputRequirement: 'always',
+    inputKind: 'connection',
+    default: schemaObject.default ?? undefined,
+  };
+
+  return template;
+};
+
 const buildEnumInputFieldTemplate = ({
   schemaObject,
   baseField,
@@ -266,6 +282,9 @@ export const buildInputFieldTemplate = (
   if (['latents'].includes(fieldType)) {
     return buildLatentsInputFieldTemplate({ schemaObject, baseField });
   }
+  if (['conditioning'].includes(fieldType)) {
+    return buildConditioningInputFieldTemplate({ schemaObject, baseField });
+  }
   if (['model'].includes(fieldType)) {
     return buildModelInputFieldTemplate({ schemaObject, baseField });
   }

invokeai/frontend/web/src/features/nodes/util/fieldValueBuilders.ts

@@ -48,6 +48,10 @@ export const buildInputFieldValue = (
       fieldValue.value = undefined;
     }
 
+    if (template.type === 'conditioning') {
+      fieldValue.value = undefined;
+    }
+
     if (template.type === 'model') {
       fieldValue.value = undefined;
     }

tests/nodes/test_node_graph.py

@@ -463,16 +463,16 @@ def test_graph_subgraph_t2i():
     
     n4 = ShowImageInvocation(id = "4")
     g.add_node(n4)
-    g.add_edge(create_edge("1.5","image","4","image"))
+    g.add_edge(create_edge("1.7","image","4","image"))
 
     # Validate
     dg = g.nx_graph_flat()
-    assert set(dg.nodes) == set(['1.width', '1.height', '1.3', '1.4', '1.5', '2', '3', '4'])
+    assert set(dg.nodes) == set(['1.width', '1.height', '1.seed', '1.3', '1.4', '1.5', '1.6', '1.7', '2', '3', '4'])
     expected_edges = [(f'1.{e.source.node_id}',f'1.{e.destination.node_id}') for e in lg.graph.edges]
     expected_edges.extend([
         ('2','1.width'),
         ('3','1.height'),
-        ('1.5','4')
+        ('1.7','4')
     ])
     print(expected_edges)
     print(list(dg.edges))