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Merge pull request #5 from nkchocoai/1-add-resource-data

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#1 add resource data
This commit is contained in:
nkchocoai
2024-02-18 23:08:17 +09:00
committed by GitHub
parent 9a488a5436 8f81487601
commit dbc7771be0
11 changed files with 1189 additions and 28 deletions
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README.jp.md
+15
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@@ -41,9 +41,24 @@ git clone https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData.git
- Size
- Model
- Model hash
- VAE
- KSamplerノードではなくSaveImageWithMetadataノードの入力から参照されます。
- VAE hash
- KSamplerノードではなくSaveImageWithMetadataノードの入力から参照されます。
- Loras
- Model name
- Model hash
- Strength model
- Strength clip
- Embeddings
- Name
- Hash
- batch size >= 2の場合 :
- Batch index
- Batch size
- Hashes
- Model, Loras, Embeddings
- [Civitai](https://civitai.com/)用
## 対応しているノード・拡張機能
- 対応しているノードは以下のファイルをご確認ください。
README.md
+15
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@@ -43,9 +43,24 @@ git clone https://github.com/nkchocoai/ComfyUI-SaveImageWithMetaData.git
- Size
- Model
- Model hash
- VAE
- It is referenced from the input of SaveImageWithMetadata node, not KSampler node.
- VAE hash
- It is referenced from the input of SaveImageWithMetadata node, not KSampler node.
- Loras
- Model name
- Model hash
- Strength model
- Strength clip
- Embeddings
- Name
- Hash
- If batch size >= 2 :
- Batch index
- Batch size
- Hashes
- Model, Loras, Embeddings
- For [Civitai](https://civitai.com/)
## Supported nodes and extensions
- Please check the following file for supported nodes.
examples/lora_embedding_vae.json
+876
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@@ -0,0 +1,876 @@
{
"last_node_id": 19,
"last_link_id": 22,
"nodes": [
{
"id": 1,
"type": "CheckpointLoaderSimple",
"pos": [
24,
54
],
"size": {
"0": 315,
"1": 98
},
"flags": {},
"order": 0,
"mode": 0,
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
1
],
"shape": 3,
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
2
],
"shape": 3
},
{
"name": "VAE",
"type": "VAE",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "CheckpointLoaderSimple"
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"widgets_values": [
"CounterfeitV30_v30.safetensors"
]
},
{
"id": 5,
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"pos": [
1143,
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"0": 210,
"1": 66
},
"flags": {},
"order": 12,
"mode": 0,
"inputs": [
{
"name": "MODEL",
"type": "*",
"link": 5,
"slot_index": 0,
"color_on": "#B39DDB"
},
{
"name": "CLIP",
"type": "*",
"link": 6,
"color_on": "#FFD500"
},
{
"name": "anything",
"type": "*",
"link": null
}
],
"properties": {
"group_restricted": false,
"color_restricted": false,
"Node name for S&R": "Anything Everywhere3"
}
},
{
"id": 7,
"type": "PromptUtilitiesConstStringMultiLine",
"pos": [
21,
378
],
"size": {
"0": 287.9851379394531,
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},
"flags": {},
"order": 1,
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{
"name": "STRING",
"type": "STRING",
"links": [
7
],
"shape": 3
}
],
"properties": {
"Node name for S&R": "PromptUtilitiesConstStringMultiLine"
},
"widgets_values": [
"(low quality, worst quality:1.4), embedding:EasyNegative"
]
},
{
"id": 9,
"type": "CLIPTextEncode",
"pos": [
394,
382
],
"size": {
"0": 210,
"1": 54
},
"flags": {},
"order": 6,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": null,
"slot_index": 0
},
{
"name": "text",
"type": "STRING",
"link": 7,
"widget": {
"name": "text"
},
"slot_index": 1
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
10
],
"shape": 3
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
""
]
},
{
"id": 8,
"type": "CLIPTextEncode",
"pos": [
390,
257
],
"size": {
"0": 210,
"1": 54
},
"flags": {},
"order": 8,
"mode": 0,
"inputs": [
{
"name": "clip",
"type": "CLIP",
"link": null
},
{
"name": "text",
"type": "STRING",
"link": 8,
"widget": {
"name": "text"
}
}
],
"outputs": [
{
"name": "CONDITIONING",
"type": "CONDITIONING",
"links": [
9
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "CLIPTextEncode"
},
"widgets_values": [
""
]
},
{
"id": 10,
"type": "Prompts Everywhere",
"pos": [
674,
302
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 11,
"mode": 0,
"inputs": [
{
"name": "CONDITIONING",
"type": "*",
"link": 9,
"color_on": "#FFA931"
},
{
"name": "CONDITIONING",
"type": "*",
"link": 10,
"slot_index": 1,
"color_on": "#FFA931"
}
],
"properties": {
"group_restricted": false,
"color_restricted": false,
"Node name for S&R": "Prompts Everywhere"
}
},
{
"id": 12,
"type": "EmptyLatentImageFromPresetsSD15",
"pos": [
23,
550
],
"size": {
"0": 327.6000061035156,
"1": 122
},
"flags": {},
"order": 2,
"mode": 0,
"outputs": [
{
"name": "latent",
"type": "LATENT",
"links": [
11
],
"shape": 3,
"slot_index": 0
},
{
"name": "w",
"type": "INT",
"links": null,
"shape": 3
},
{
"name": "h",
"type": "INT",
"links": null,
"shape": 3
}
],
"properties": {
"Node name for S&R": "EmptyLatentImageFromPresetsSD15"
},
"widgets_values": [
"512 x 768",
1
]
},
{
"id": 14,
"type": "SaveImage",
"pos": [
1840,
260
],
"size": {
"0": 315,
"1": 270
},
"flags": {},
"order": 15,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 14,
"slot_index": 0
}
],
"properties": {},
"widgets_values": [
"without-meta"
]
},
{
"id": 17,
"type": "LatentUpscaleBy",
"pos": [
772,
532
],
"size": {
"0": 262.8999938964844,
"1": 82
},
"flags": {},
"order": 10,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 18,
"slot_index": 0
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
19
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "LatentUpscaleBy"
},
"widgets_values": [
"nearest-exact",
2
]
},
{
"id": 15,
"type": "SaveImageWithMetaData",
"pos": [
1840,
580
],
"size": {
"0": 315,
"1": 318
},
"flags": {},
"order": 16,
"mode": 0,
"inputs": [
{
"name": "images",
"type": "IMAGE",
"link": 15,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "SaveImageWithMetaData"
},
"widgets_values": [
"with-meta",
"Farthest",
0
]
},
{
"id": 11,
"type": "KSampler",
"pos": [
402,
532
],
"size": {
"0": 315,
"1": 262
},
"flags": {},
"order": 7,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": null
},
{
"name": "positive",
"type": "CONDITIONING",
"link": null
},
{
"name": "negative",
"type": "CONDITIONING",
"link": null
},
{
"name": "latent_image",
"type": "LATENT",
"link": 11
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
18
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KSampler"
},
"widgets_values": [
123,
"fixed",
20,
8,
"dpmpp_2m",
"karras",
1
]
},
{
"id": 13,
"type": "VAEDecode",
"pos": [
1501,
530
],
"size": {
"0": 210,
"1": 46
},
"flags": {},
"order": 14,
"mode": 0,
"inputs": [
{
"name": "samples",
"type": "LATENT",
"link": 17,
"slot_index": 0
},
{
"name": "vae",
"type": "VAE",
"link": 13
}
],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
14,
15
],
"shape": 3
}
],
"properties": {
"Node name for S&R": "VAEDecode"
}
},
{
"id": 16,
"type": "KSampler",
"pos": [
1090,
470
],
"size": {
"0": 315,
"1": 262
},
"flags": {},
"order": 13,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": null
},
{
"name": "positive",
"type": "CONDITIONING",
"link": null
},
{
"name": "negative",
"type": "CONDITIONING",
"link": null
},
{
"name": "latent_image",
"type": "LATENT",
"link": 19
}
],
"outputs": [
{
"name": "LATENT",
"type": "LATENT",
"links": [
17
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "KSampler"
},
"widgets_values": [
123,
"fixed",
20,
8,
"dpmpp_2m",
"karras",
0.6
]
},
{
"id": 4,
"type": "LoraLoader",
"pos": [
761,
52
],
"size": {
"0": 340.8315734863281,
"1": 126
},
"flags": {},
"order": 9,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 3,
"slot_index": 0
},
{
"name": "clip",
"type": "CLIP",
"link": 4
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
5
],
"shape": 3,
"slot_index": 0
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
6
],
"shape": 3,
"slot_index": 1
}
],
"properties": {
"Node name for S&R": "LoraLoader"
},
"widgets_values": [
"leco\\leco_cyborg_last.safetensors",
-3,
-3
]
},
{
"id": 2,
"type": "VAELoader",
"pos": [
1090,
810
],
"size": {
"0": 323.17578125,
"1": 58
},
"flags": {},
"order": 3,
"mode": 0,
"outputs": [
{
"name": "VAE",
"type": "VAE",
"links": [
13
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "VAELoader"
},
"widgets_values": [
"clearvae_nanslesstest.safetensors"
]
},
{
"id": 6,
"type": "PromptUtilitiesConstStringMultiLine",
"pos": [
18,
225
],
"size": {
"0": 287.9851379394531,
"1": 99.69133758544922
},
"flags": {},
"order": 4,
"mode": 0,
"outputs": [
{
"name": "STRING",
"type": "STRING",
"links": [
8
],
"shape": 3,
"slot_index": 0
}
],
"properties": {
"Node name for S&R": "PromptUtilitiesConstStringMultiLine"
},
"widgets_values": [
"masterpiece, best quality, hatsune miku, upper body"
]
},
{
"id": 3,
"type": "LoraLoader",
"pos": [
397,
50
],
"size": {
"0": 315,
"1": 126
},
"flags": {},
"order": 5,
"mode": 0,
"inputs": [
{
"name": "model",
"type": "MODEL",
"link": 1
},
{
"name": "clip",
"type": "CLIP",
"link": 2,
"slot_index": 1
}
],
"outputs": [
{
"name": "MODEL",
"type": "MODEL",
"links": [
3
],
"shape": 3
},
{
"name": "CLIP",
"type": "CLIP",
"links": [
4
],
"shape": 3,
"slot_index": 1
}
],
"properties": {
"Node name for S&R": "LoraLoader"
},
"widgets_values": [
"util\\flat2.safetensors",
-1,
-1
]
}
],
"links": [
[
1,
1,
0,
3,
0,
"MODEL"
],
[
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1,
1,
3,
1,
"CLIP"
],
[
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"MODEL"
],
[
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],
[
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[
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],
[
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"STRING"
],
[
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],
[
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],
[
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"*"
],
[
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"LATENT"
],
[
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"VAE"
],
[
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],
[
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[
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],
"groups": [],
"config": {},
"extra": {},
"version": 0.4
}
examples/lora_embedding_vae.png
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py/capture.py
+117 -15
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@@ -1,3 +1,6 @@
import json
import os
from . import hook
from .defs.captures import CAPTURE_FIELD_LIST
from .defs.meta import MetaField
@@ -32,6 +35,12 @@ class Capture:
if meta not in inputs:
inputs[meta] = []
value = field_data.get("value")
if value is not None:
inputs[meta].append((node_id, value))
continue
field_name = field_data["field_name"]
value = input_data.get(field_name)
if value is not None:
@@ -41,45 +50,69 @@ class Capture:
v = value[0]
if format is not None:
v = format(v, input_data)
inputs[meta].append((node_id, v))
if isinstance(v, list):
for x in v:
inputs[meta].append((node_id, x))
else:
inputs[meta].append((node_id, v))
return inputs
@classmethod
def gen_pnginfo_dict(cls, inputs):
def gen_pnginfo_dict(cls, inputs_before_sampler_node, inputs_before_this_node):
pnginfo_dict = {}
def update_pnginfo_dict(metafield, key):
def update_pnginfo_dict(inputs, metafield, key):
x = inputs.get(metafield, [])
if len(x) > 0:
pnginfo_dict[key] = x[0][1]
update_pnginfo_dict(MetaField.POSITIVE_PROMPT, "Positive prompt")
update_pnginfo_dict(MetaField.NEGATIVE_PROMPT, "Negative prompt")
update_pnginfo_dict(
inputs_before_sampler_node, MetaField.POSITIVE_PROMPT, "Positive prompt"
)
update_pnginfo_dict(
inputs_before_sampler_node, MetaField.NEGATIVE_PROMPT, "Negative prompt"
)
update_pnginfo_dict(MetaField.STEPS, "Steps")
update_pnginfo_dict(inputs_before_sampler_node, MetaField.STEPS, "Steps")
sampler_names = inputs.get(MetaField.SAMPLER_NAME, [])
sampler_names = inputs_before_sampler_node.get(MetaField.SAMPLER_NAME, [])
if len(sampler_names) > 0:
pnginfo_dict["Sampler"] = sampler_names[0][1]
schedulers = inputs.get(MetaField.SCHEDULER, [])
schedulers = inputs_before_sampler_node.get(MetaField.SCHEDULER, [])
if len(schedulers) > 0:
scheduler = schedulers[0][1]
if scheduler != "normal":
pnginfo_dict["Sampler"] += "_" + scheduler
update_pnginfo_dict(MetaField.CFG, "CFG Scale")
update_pnginfo_dict(MetaField.SEED, "Seed")
update_pnginfo_dict(inputs_before_sampler_node, MetaField.CFG, "CFG Scale")
update_pnginfo_dict(inputs_before_sampler_node, MetaField.SEED, "Seed")
update_pnginfo_dict(MetaField.CLIP_SKIP, "Clip skip")
update_pnginfo_dict(
inputs_before_sampler_node, MetaField.CLIP_SKIP, "Clip skip"
)
image_widths = inputs.get(MetaField.IMAGE_WIDTH, [])
image_heights = inputs.get(MetaField.IMAGE_HEIGHT, [])
image_widths = inputs_before_sampler_node.get(MetaField.IMAGE_WIDTH, [])
image_heights = inputs_before_sampler_node.get(MetaField.IMAGE_HEIGHT, [])
if len(image_widths) > 0 and len(image_heights) > 0:
pnginfo_dict["Size"] = f"{image_widths[0][1]}x{image_heights[0][1]}"
update_pnginfo_dict(MetaField.MODEL_NAME, "Model")
update_pnginfo_dict(MetaField.MODEL_HASH, "Model hash")
update_pnginfo_dict(inputs_before_sampler_node, MetaField.MODEL_NAME, "Model")
update_pnginfo_dict(
inputs_before_sampler_node, MetaField.MODEL_HASH, "Model hash"
)
update_pnginfo_dict(inputs_before_this_node, MetaField.VAE_NAME, "VAE")
update_pnginfo_dict(inputs_before_this_node, MetaField.VAE_HASH, "VAE hash")
pnginfo_dict.update(cls.gen_loras(inputs_before_sampler_node))
pnginfo_dict.update(cls.gen_embeddings(inputs_before_sampler_node))
hashes_for_civitai = cls.get_hashes_for_civitai(
inputs_before_sampler_node, inputs_before_this_node
)
if len(hashes_for_civitai) > 0:
pnginfo_dict["Hashes"] = json.dumps(hashes_for_civitai)
return pnginfo_dict
@@ -99,3 +132,72 @@ class Capture:
s_list.append(f"{k}: {s}")
return result + ", ".join(s_list)
@classmethod
def get_hashes_for_civitai(
cls, inputs_before_sampler_node, inputs_before_this_node
):
resource_hashes = {}
model_hashes = inputs_before_sampler_node.get(MetaField.MODEL_HASH, [])
if len(model_hashes) > 0:
resource_hashes["model"] = model_hashes[0][1]
vae_hashes = inputs_before_this_node.get(MetaField.VAE_HASH, [])
if len(vae_hashes) > 0:
resource_hashes["vae"] = vae_hashes[0][1]
lora_model_names = inputs_before_sampler_node.get(MetaField.LORA_MODEL_NAME, [])
lora_model_hashes = inputs_before_sampler_node.get(
MetaField.LORA_MODEL_HASH, []
)
for lora_model_name, lora_model_hash in zip(
lora_model_names, lora_model_hashes
):
lora_model_name = os.path.splitext(os.path.basename(lora_model_name[1]))[0]
resource_hashes[f"lora:{lora_model_name}"] = lora_model_hash[1]
embedding_names = inputs_before_sampler_node.get(MetaField.EMBEDDING_NAME, [])
embedding_hashes = inputs_before_sampler_node.get(MetaField.EMBEDDING_HASH, [])
for embedding_name, embedding_hash in zip(embedding_names, embedding_hashes):
embedding_name = os.path.splitext(os.path.basename(embedding_name[1]))[0]
resource_hashes[f"embed:{embedding_name}"] = embedding_hash[1]
return resource_hashes
@classmethod
def gen_loras(cls, inputs):
pnginfo_dict = {}
model_names = inputs.get(MetaField.LORA_MODEL_NAME, [])
model_hashes = inputs.get(MetaField.LORA_MODEL_HASH, [])
strength_models = inputs.get(MetaField.LORA_STRENGTH_MODEL, [])
strength_clips = inputs.get(MetaField.LORA_STRENGTH_CLIP, [])
index = 0
for model_name, model_hashe, strength_model, strength_clip in zip(
model_names, model_hashes, strength_models, strength_clips
):
field_prefix = f"Lora_{index}"
pnginfo_dict[f"{field_prefix} Model name"] = os.path.basename(model_name[1])
pnginfo_dict[f"{field_prefix} Model hash"] = model_hashe[1]
pnginfo_dict[f"{field_prefix} Strength model"] = strength_model[1]
pnginfo_dict[f"{field_prefix} Strength clip"] = strength_clip[1]
index += 1
return pnginfo_dict
@classmethod
def gen_embeddings(cls, inputs):
pnginfo_dict = {}
embedding_names = inputs.get(MetaField.EMBEDDING_NAME, [])
embedding_hashes = inputs.get(MetaField.EMBEDDING_HASH, [])
index = 0
for embedding_name, embedding_hashe in zip(embedding_names, embedding_hashes):
field_prefix = f"Embedding_{index}"
pnginfo_dict[f"{field_prefix} name"] = os.path.basename(embedding_name[1])
pnginfo_dict[f"{field_prefix} hash"] = embedding_hashe[1]
index += 1
return pnginfo_dict
py/defs/captures.py
+34
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@@ -2,9 +2,13 @@ from .meta import MetaField
from .validators import is_positive_prompt, is_negative_prompt
from .formatters import (
calc_model_hash,
calc_vae_hash,
calc_lora_hash,
convert_skip_clip,
get_scaled_width,
get_scaled_height,
extract_embedding_names,
extract_embedding_hashes,
)
@@ -19,6 +23,10 @@ CAPTURE_FIELD_LIST = {
"format": convert_skip_clip,
},
},
"VAELoader": {
MetaField.VAE_NAME: {"field_name": "vae_name"},
MetaField.VAE_HASH: {"field_name": "vae_name", "format": calc_vae_hash},
},
"EmptyLatentImage": {
MetaField.IMAGE_WIDTH: {"field_name": "width"},
MetaField.IMAGE_HEIGHT: {"field_name": "height"},
@@ -32,6 +40,14 @@ CAPTURE_FIELD_LIST = {
"field_name": "text",
"validate": is_negative_prompt,
},
MetaField.EMBEDDING_NAME: {
"field_name": "text",
"format": extract_embedding_names,
},
MetaField.EMBEDDING_HASH: {
"field_name": "text",
"format": extract_embedding_hashes,
},
},
"KSampler": {
MetaField.SEED: {"field_name": "seed"},
@@ -58,4 +74,22 @@ CAPTURE_FIELD_LIST = {
"format": get_scaled_height,
},
},
"LoraLoader": {
MetaField.LORA_MODEL_NAME: {"field_name": "lora_name"},
MetaField.LORA_MODEL_HASH: {
"field_name": "lora_name",
"format": calc_lora_hash,
},
MetaField.LORA_STRENGTH_MODEL: {"field_name": "strength_model"},
MetaField.LORA_STRENGTH_CLIP: {"field_name": "strength_clip"},
},
"LoraLoaderModelOnly": {
MetaField.LORA_MODEL_NAME: {"field_name": "lora_name"},
MetaField.LORA_MODEL_HASH: {
"field_name": "lora_name",
"format": calc_lora_hash,
},
MetaField.LORA_STRENGTH_MODEL: {"field_name": "strength_model"},
MetaField.LORA_STRENGTH_CLIP: {"value": 0},
},
}
py/defs/formatters.py
+64 -11
View File
@@ -1,24 +1,31 @@
import hashlib
import os
import folder_paths
from ..utils.hash import calc_hash
from ..utils.embedding import get_embedding_file_path
from comfy.sd1_clip import escape_important, token_weights, unescape_important
from comfy.sd1_clip import SD1Tokenizer
from comfy.sd2_clip import SD2Tokenizer
from comfy.sdxl_clip import SDXLTokenizer
cache_model_hash = {}
def calc_model_hash(model_name, input_data):
if model_name in cache_model_hash:
return cache_model_hash[model_name]
filename = folder_paths.get_full_path("checkpoints", model_name)
sha256_hash = hashlib.sha256()
return calc_hash(filename)
with open(filename, "rb") as f:
for byte_block in iter(lambda: f.read(4096), b""):
sha256_hash.update(byte_block)
model_hash = sha256_hash.hexdigest()[:10]
cache_model_hash[model_name] = model_hash
return model_hash
def calc_vae_hash(model_name, input_data):
filename = folder_paths.get_full_path("vae", model_name)
return calc_hash(filename)
def calc_lora_hash(model_name, input_data):
filename = folder_paths.get_full_path("loras", model_name)
return calc_hash(filename)
def convert_skip_clip(stop_at_clip_layer, input_data):
@@ -33,3 +40,49 @@ def get_scaled_width(scaled_by, input_data):
def get_scaled_height(scaled_by, input_data):
samples = input_data["samples"][0]["samples"]
return round(samples.shape[2] * scaled_by * 8)
def extract_embedding_names(text, input_data):
embedding_names, _ = _extract_embedding_names(text, input_data)
return [os.path.basename(embedding_name) for embedding_name in embedding_names]
def extract_embedding_hashes(text, input_data):
embedding_names, tokenizer = _extract_embedding_names(text, input_data)
embedding_hashes = []
for embedding_name in embedding_names:
embedding_file_path = get_embedding_file_path(embedding_name, tokenizer)
embedding_hashes.append(calc_hash(embedding_file_path))
return embedding_hashes
def _extract_embedding_names(text, input_data):
clip = input_data["clip"][0]
tokenizer = clip.tokenizer
if isinstance(tokenizer, SD1Tokenizer):
tokenizer = tokenizer.clip_l
elif isinstance(tokenizer, SD2Tokenizer):
tokenizer = tokenizer.clip_h
elif isinstance(tokenizer, SDXLTokenizer):
tokenizer = tokenizer.clip_l
text = escape_important(text)
parsed_weights = token_weights(text, 1.0)
# tokenize words
embedding_names = []
for weighted_segment, weight in parsed_weights:
to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(" ")
to_tokenize = [x for x in to_tokenize if x != ""]
for word in to_tokenize:
# if we find an embedding, deal with the embedding
if (
word.startswith(tokenizer.embedding_identifier)
and tokenizer.embedding_directory is not None
):
embedding_name = word[len(tokenizer.embedding_identifier) :].strip("\n")
embedding_names.append(embedding_name)
return embedding_names, tokenizer
py/defs/meta.py
+8
View File
@@ -4,6 +4,8 @@ from enum import IntEnum
class MetaField(IntEnum):
MODEL_NAME = 0
MODEL_HASH = 1
VAE_NAME = 2
VAE_HASH = 3
POSITIVE_PROMPT = 10
NEGATIVE_PROMPT = 11
CLIP_SKIP = 12
@@ -14,3 +16,9 @@ class MetaField(IntEnum):
SCHEDULER = 24
IMAGE_WIDTH = 30
IMAGE_HEIGHT = 31
EMBEDDING_NAME = 40
EMBEDDING_HASH = 41
LORA_MODEL_NAME = 50
LORA_MODEL_HASH = 51
LORA_STRENGTH_MODEL = 52
LORA_STRENGTH_CLIP = 53
py/nodes/node.py
+7 -2
View File
@@ -110,6 +110,9 @@ class SaveImageWithMetaData(BaseNode):
trace_tree_from_this_node = Trace.trace(
hook.current_node_id, hook.current_prompt
)
inputs_before_this_node = Trace.filter_inputs_by_trace_tree(
inputs, trace_tree_from_this_node
)
sampler_node_id = Trace.find_sampler_node_id(
trace_tree_from_this_node,
sampler_selection_method,
@@ -118,10 +121,12 @@ class SaveImageWithMetaData(BaseNode):
# get inputs before sampler node
trace_tree_from_sampler_node = Trace.trace(sampler_node_id, hook.current_prompt)
filtered_inputs = Trace.filter_inputs_by_trace_tree(
inputs_before_sampler_node = Trace.filter_inputs_by_trace_tree(
inputs, trace_tree_from_sampler_node
)
# generate PNGInfo from inputs
pnginfo_dict = Capture.gen_pnginfo_dict(filtered_inputs)
pnginfo_dict = Capture.gen_pnginfo_dict(
inputs_before_sampler_node, inputs_before_this_node
)
return pnginfo_dict
py/utils/embedding.py
+35
View File
@@ -0,0 +1,35 @@
import os
from comfy.sd1_clip import expand_directory_list
# refer. https://github.com/comfyanonymous/ComfyUI/blob/dccca1daa5af1954d55918f365e83a3331019549/comfy/sd1_clip.py#L284-L309
def get_embedding_file_path(embedding_name, tokenizer):
embedding_directory = tokenizer.embedding_directory
if isinstance(embedding_directory, str):
embedding_directory = [embedding_directory]
embedding_directory = expand_directory_list(embedding_directory)
valid_file = None
for embed_dir in embedding_directory:
embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name))
embed_dir = os.path.abspath(embed_dir)
try:
if os.path.commonpath((embed_dir, embed_path)) != embed_dir:
continue
except Exception:
continue
if not os.path.isfile(embed_path):
extensions = [".safetensors", ".pt", ".bin"]
for x in extensions:
t = embed_path + x
if os.path.isfile(t):
valid_file = t
break
else:
valid_file = embed_path
if valid_file is not None:
break
return valid_file
py/utils/hash.py
+18
View File
@@ -0,0 +1,18 @@
import hashlib
cache_model_hash = {}
def calc_hash(filename):
if filename in cache_model_hash:
return cache_model_hash[filename]
sha256_hash = hashlib.sha256()
with open(filename, "rb") as f:
for byte_block in iter(lambda: f.read(4096), b""):
sha256_hash.update(byte_block)
model_hash = sha256_hash.hexdigest()[:10]
cache_model_hash[filename] = model_hash
return model_hash