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omost_nodes.py
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omost_nodes.py
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from __future__ import annotations
from enum import Enum
import json
from typing import Literal, Tuple, TypedDict, NamedTuple
import sys
import os
import logging
from typing_extensions import NotRequired
import requests
from openai import OpenAI
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import comfy.model_management
from comfy.model_patcher import ModelPatcher
from comfy.sd import CLIP
from nodes import CLIPTextEncode, ConditioningSetMask
from .lib_omost.canvas import (
Canvas as OmostCanvas,
OmostCanvasCondition,
system_prompt,
)
from .lib_omost.utils import numpy2pytorch, scoped_numpy_random, scoped_torch_random
from .lib_omost.greedy_encode import (
encode_bag_of_subprompts_greedy,
CLIPTokens,
EncoderOutput,
SPECIAL_TOKENS,
)
def create_logger(level=logging.INFO):
logger = logging.getLogger(__name__)
logger.setLevel(level)
if not logger.handlers:
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(
logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
)
logger.addHandler(handler)
return logger
logger = create_logger(level=logging.INFO)
# Canvas size used in original Omost repo.
CANVAS_SIZE = 90
# Type definitions.
class OmostConversationItem(TypedDict):
role: Literal["system", "user", "assistant"]
content: str
OmostConversation = list[OmostConversationItem]
class OmostLLM(NamedTuple):
model: AutoModelForCausalLM
tokenizer: AutoTokenizer
class OmostLLMServer(NamedTuple):
client: OpenAI
model_id: str
ComfyUIConditioning = list # Dummy type definitions for ComfyUI
ComfyCLIPTokensWithWeight = list[Tuple[int, float]]
class ComfyCLIPTokens(TypedDict):
l: list[ComfyCLIPTokensWithWeight]
g: NotRequired[list[ComfyCLIPTokensWithWeight]]
# End of type definitions.
class OmostLLMLoaderNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"llm_name": (
[
"lllyasviel/omost-phi-3-mini-128k-8bits",
"lllyasviel/omost-llama-3-8b-4bits",
"lllyasviel/omost-dolphin-2.9-llama3-8b-4bits",
],
{
"default": "lllyasviel/omost-llama-3-8b-4bits",
},
),
}
}
RETURN_TYPES = ("OMOST_LLM",)
FUNCTION = "load_llm"
CATEGORY = "omost"
def load_llm(self, llm_name: str) -> Tuple[OmostLLM]:
"""Load LLM model"""
HF_TOKEN = None
dtype = (
torch.float16 if comfy.model_management.should_use_fp16() else torch.float32
)
llm_model = AutoModelForCausalLM.from_pretrained(
llm_name,
torch_dtype=dtype, # This is computation type, not load/memory type. The loading quant type is baked in config.
token=HF_TOKEN,
device_map="auto", # This will load model to gpu with an offload system
trust_remote_code=True,
)
llm_tokenizer = AutoTokenizer.from_pretrained(llm_name, token=HF_TOKEN)
return (OmostLLM(llm_model, llm_tokenizer),)
class OmostLLMHTTPServerNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"address": ("STRING", {"multiline": True}),
"api_type":(
[
"OpenAI",
"TGI"
],
{
"default": "OpenAI"
}
)
}
}
RETURN_TYPES = ("OMOST_LLM",)
FUNCTION = "init_client"
CATEGORY = "omost"
def init_client(self, address: str, api_type: str) -> Tuple[OmostLLMServer]:
"""Initialize LLM client with HTTP server address."""
if api_type == "OpenAI":
if address.endswith("v1"):
server_address = address
else:
server_address = os.path.join(address, "v1")
model_id = ""
elif api_type == "TGI":
if address.endswith("v1"):
server_address = address
server_info_url = address.replace("v1", "info")
else:
server_address = os.path.join(address, "v1")
server_info_url = os.path.join(address, "info")
#Get model_id from server info
server_info = requests.get(server_info_url, timeout=5).json()
model_id = server_info["model_id"]
client = OpenAI(base_url=server_address, api_key="_")
return (OmostLLMServer(client, model_id),)
class OmostLLMChatNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"llm": ("OMOST_LLM",),
"text": ("STRING", {"multiline": True}),
"max_new_tokens": (
"INT",
{"min": 128, "max": 4096, "step": 1, "default": 4096},
),
"top_p": (
"FLOAT",
{"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.9},
),
"temperature": (
"FLOAT",
{"min": 0.0, "max": 2.0, "step": 0.01, "default": 0.6},
),
# Note: ComfyUI's front-end code randomizes the seed to 64-bit int.
"seed": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFFFFFFFFFFFF}),
},
"optional": {
"conversation": ("OMOST_CONVERSATION",),
},
}
RETURN_TYPES = (
"OMOST_CONVERSATION",
"OMOST_CANVAS_CONDITIONING",
)
FUNCTION = "run_llm"
CATEGORY = "omost"
def prepare_conversation(
self, text: str, conversation: OmostConversation | None = None
) -> Tuple[OmostConversation, OmostConversation, OmostConversationItem]:
conversation = conversation or [] # Default to empty list
system_conversation_item: OmostConversationItem = {
"role": "system",
"content": system_prompt,
}
user_conversation_item: OmostConversationItem = {
"role": "user",
"content": text,
}
input_conversation: list[OmostConversationItem] = [
system_conversation_item,
*conversation,
user_conversation_item,
]
return conversation, input_conversation, user_conversation_item
def run_local_llm(
self,
llm: OmostLLM,
input_conversation: list[OmostConversationItem],
max_new_tokens: int,
top_p: float,
temperature: float,
seed: int,
) -> str:
with scoped_torch_random(seed), scoped_numpy_random(seed):
llm_tokenizer: AutoTokenizer = llm.tokenizer
llm_model: AutoModelForCausalLM = llm.model
input_ids: torch.Tensor = llm_tokenizer.apply_chat_template(
input_conversation, return_tensors="pt", add_generation_prompt=True
).to(llm_model.device)
input_length = input_ids.shape[1]
output_ids: torch.Tensor = llm_model.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=temperature != 0,
)
generated_ids = output_ids[:, input_length:]
generated_text: str = llm_tokenizer.decode(
generated_ids[0],
skip_special_tokens=True,
skip_prompt=True,
timeout=10,
)
return generated_text
def run_llm(
self,
llm: OmostLLM | OmostLLMServer,
text: str,
max_new_tokens: int,
top_p: float,
temperature: float,
seed: int,
conversation: OmostConversation | None = None,
) -> Tuple[OmostConversation, OmostCanvas]:
"""Run LLM on text"""
if seed > 0xFFFFFFFF:
seed = seed & 0xFFFFFFFF
logger.warning("Seed is too large. Truncating to 32-bit: %d", seed)
conversation, input_conversation, user_conversation_item = (
self.prepare_conversation(text, conversation)
)
if isinstance(llm, OmostLLM):
generated_text = self.run_local_llm(
llm, input_conversation, max_new_tokens, top_p, temperature, seed
)
else:
generated_text = (
llm.client.chat.completions.create(
model=llm.model_id,
messages=input_conversation,
top_p=top_p,
temperature=temperature,
max_tokens=max_new_tokens,
seed=seed,
)
.choices[0]
.message.content
)
output_conversation = [
*conversation,
user_conversation_item,
{"role": "assistant", "content": generated_text},
]
return (
output_conversation,
OmostCanvas.from_bot_response(generated_text).process(),
)
class OmostRenderCanvasConditioningNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"canvas_conds": ("OMOST_CANVAS_CONDITIONING",),
}
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "render_canvas"
CATEGORY = "omost"
def render_canvas(
self, canvas_conds: list[OmostCanvasCondition]
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Render canvas conditioning to image"""
return (
numpy2pytorch(imgs=[OmostCanvas.render_initial_latent(canvas_conds)]),
torch.cat(
[OmostCanvas.render_mask(cond).unsqueeze(0) for cond in canvas_conds],
dim=0,
),
)
class PromptEncoding:
"""Namespace for different prompt encoding methods"""
ENCODE_NODE = CLIPTextEncode()
@staticmethod
def encode_bag_of_subprompts(
clip: CLIP, prefixes: list[str], suffixes: list[str]
) -> ComfyUIConditioning:
"""@Deprecated
Simplified way to encode bag of subprompts without omost's greedy approach.
"""
conds: ComfyUIConditioning = []
logger.debug("Start encoding bag of subprompts")
for target in suffixes:
complete_prompt = "".join(prefixes + [target])
logger.debug(f"Encoding prompt: {complete_prompt}")
cond: ComfyUIConditioning = PromptEncoding.ENCODE_NODE.encode(
clip, complete_prompt
)[0]
assert len(cond) == 1
conds.extend(cond)
logger.debug("End encoding bag of subprompts. Total conditions: %d", len(conds))
# Concat all conditions
return [
[
# cond
torch.cat([cond for cond, _ in conds], dim=1),
# extra_dict
{"pooled_output": conds[0][1]["pooled_output"]},
]
]
@staticmethod
def encode_subprompts(
clip: CLIP, prefixes: list[str], suffixes: list[str]
) -> ComfyUIConditioning:
"""@Deprecated
Simplified way to encode subprompts by joining them together. This is
more direct without re-organizing the prompts into optimal batches like
with the greedy approach.
Note: This function has the issue of semantic truncation.
"""
complete_prompt = ",".join(
["".join(prefixes + [target]) for target in suffixes]
)
logger.debug("Encoding prompt: %s", complete_prompt)
return PromptEncoding.ENCODE_NODE.encode(clip, complete_prompt)[0]
@staticmethod
def encode_bag_of_subprompts_greedy(
clip: CLIP, prefixes: list[str], suffixes: list[str]
) -> ComfyUIConditioning:
"""Encode bag of subprompts with greedy approach. This approach is used
by the original Omost repo."""
def convert_comfy_tokens(
comfy_tokens: list[ComfyCLIPTokensWithWeight],
) -> list[int]:
assert len(comfy_tokens) >= 1
tokens: list[int] = [token for token, _ in comfy_tokens[0]]
# Strip the first token which is the CLIP prefix.
# Strip padding tokens.
return tokens[1 : tokens.index(SPECIAL_TOKENS["end"])]
def convert_to_comfy_tokens(tokens: CLIPTokens) -> ComfyCLIPTokens:
return {
"l": [[(token, 1.0) for token in tokens.clip_l_tokens]],
"g": (
[[(token, 1.0) for token in tokens.clip_g_tokens]]
if tokens.clip_g_tokens is not None
else None
),
}
def tokenize(text: str) -> CLIPTokens:
tokens: ComfyCLIPTokens = clip.tokenize(text)
return CLIPTokens(
clip_l_tokens=convert_comfy_tokens(tokens["l"]),
clip_g_tokens=(
convert_comfy_tokens(tokens.get("g")) if "g" in tokens else None
),
)
def encode(tokens: CLIPTokens) -> EncoderOutput:
cond, pooled = clip.encode_from_tokens(
convert_to_comfy_tokens(tokens), return_pooled=True
)
return EncoderOutput(cond=cond, pooler=pooled)
encoder_output = encode_bag_of_subprompts_greedy(
prefixes,
suffixes,
tokenize_func=tokenize,
encode_func=encode,
logger=logger,
)
return [
[
encoder_output.cond,
{"pooled_output": encoder_output.pooler},
]
]
class OmostDenseDiffusionLayoutNode:
"""Apply Omost layout with Omost's area condition system. This is the regional
prompt system implemented in the original Omost repo.
You need to install https://github.com/huchenlei/ComfyUI_densediffusion to use this node.
"""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"canvas_conds": ("OMOST_CANVAS_CONDITIONING",),
"clip": ("CLIP",),
},
}
RETURN_TYPES = ("MODEL", "CONDITIONING")
FUNCTION = "layout_cond"
CATEGORY = "omost"
def __init__(self):
try:
from custom_nodes.ComfyUI_densediffusion.densediffusion_node import (
DenseDiffusionApplyNode,
DenseDiffusionAddCondNode,
)
except Exception as e:
logger.error(
"Failed to import ComfyUI_densediffusion. Make sure it's installed."
"https://github.com/huchenlei/ComfyUI_densediffusion"
)
raise e
self.dense_diffusion_apply_node = DenseDiffusionApplyNode()
self.dense_diffusion_add_cond_node = DenseDiffusionAddCondNode()
def layout_cond(
self,
model: ModelPatcher,
canvas_conds: list[OmostCanvasCondition],
clip: CLIP,
) -> tuple[ModelPatcher, ComfyUIConditioning]:
"""Layout conditioning"""
work_model: ModelPatcher = model.clone()
for canvas_cond in canvas_conds:
cond: ComfyUIConditioning = PromptEncoding.encode_bag_of_subprompts_greedy(
clip, canvas_cond["prefixes"], canvas_cond["suffixes"]
)
# Set area cond
work_model = self.dense_diffusion_add_cond_node.append(
work_model,
conditioning=cond,
mask=OmostCanvas.render_mask(canvas_cond),
strength=1.0,
)[0]
return self.dense_diffusion_apply_node.apply(work_model)
class OmostGreedyBagsTextEmbeddingNode:
"""Just encode the omost canvas conditions with greedy bags approach.
Ignoring region conditions."""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"canvas_conds": ("OMOST_CANVAS_CONDITIONING",),
"clip": ("CLIP",),
},
}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "layout_cond"
CATEGORY = "omost"
def layout_cond(
self,
canvas_conds: list[OmostCanvasCondition],
clip: CLIP,
) -> tuple[ComfyUIConditioning]:
conds: ComfyUIConditioning = [
PromptEncoding.encode_bag_of_subprompts_greedy(
clip, canvas_cond["prefixes"], canvas_cond["suffixes"]
)[0]
for canvas_cond in canvas_conds
]
assert len(conds) > 0
return ([
[
# cond
torch.cat([cond[0] for cond in conds], dim=1),
# pooled_output
{"pooled_output": conds[0][1]["pooled_output"]},
]
],)
class OmostComfyLayoutNode:
"""Apply Omost layout with ComfyUI's area condition system."""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"canvas_conds": ("OMOST_CANVAS_CONDITIONING",),
"clip": ("CLIP",),
"global_strength": (
"FLOAT",
{"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.2},
),
"region_strength": (
"FLOAT",
{"min": 0.0, "max": 1.0, "step": 0.01, "default": 0.8},
),
"overlap_method": (
[e.value for e in OmostComfyLayoutNode.AreaOverlapMethod],
{"default": OmostComfyLayoutNode.AreaOverlapMethod.AVERAGE.value},
),
},
"optional": {
"positive": ("CONDITIONING",),
},
}
RETURN_TYPES = ("CONDITIONING", "MASK")
FUNCTION = "layout_cond"
CATEGORY = "omost"
class AreaOverlapMethod(Enum):
"""Methods to handle overlapping areas."""
# The top layer overwrites the bottom layer.
OVERLAY = "overlay"
# Take the average of the two layers.
AVERAGE = "average"
def __init__(self):
self.cond_set_mask_node = ConditioningSetMask()
@staticmethod
def calc_cond_mask(
canvas_conds: list[OmostCanvasCondition],
method: AreaOverlapMethod = AreaOverlapMethod.OVERLAY,
) -> list[OmostCanvasCondition]:
"""Calculate canvas cond mask."""
assert len(canvas_conds) > 0
canvas_conds = canvas_conds.copy()
global_cond = canvas_conds[0]
global_cond["mask"] = torch.ones(
[CANVAS_SIZE, CANVAS_SIZE], dtype=torch.float32
)
region_conds = canvas_conds[1:]
canvas_state = torch.zeros([CANVAS_SIZE, CANVAS_SIZE], dtype=torch.float32)
if method == OmostComfyLayoutNode.AreaOverlapMethod.OVERLAY:
for canvas_cond in region_conds[::-1]:
a, b, c, d = canvas_cond["rect"]
mask = torch.zeros([CANVAS_SIZE, CANVAS_SIZE], dtype=torch.float32)
mask[a:b, c:d] = 1.0
mask = mask * (1 - canvas_state)
canvas_state += mask
canvas_cond["mask"] = mask
elif method == OmostComfyLayoutNode.AreaOverlapMethod.AVERAGE:
canvas_state += 1e-6 # Avoid division by zero
for canvas_cond in region_conds:
a, b, c, d = canvas_cond["rect"]
canvas_state[a:b, c:d] += 1.0
for canvas_cond in region_conds:
a, b, c, d = canvas_cond["rect"]
mask = torch.zeros([CANVAS_SIZE, CANVAS_SIZE], dtype=torch.float32)
mask[a:b, c:d] = 1.0
mask = mask / canvas_state
canvas_cond["mask"] = mask
return canvas_conds
def layout_cond(
self,
canvas_conds: list[OmostCanvasCondition],
clip: CLIP,
global_strength: float,
region_strength: float,
overlap_method: str,
positive: ComfyUIConditioning | None = None,
):
"""Layout conditioning"""
overlap_method = OmostComfyLayoutNode.AreaOverlapMethod(overlap_method)
positive: ComfyUIConditioning = positive or []
positive = positive.copy()
masks: list[torch.Tensor] = []
canvas_conds = OmostComfyLayoutNode.calc_cond_mask(
canvas_conds, method=overlap_method
)
for i, canvas_cond in enumerate(canvas_conds):
is_global = i == 0
prefixes = canvas_cond["prefixes"]
# Skip the global prefix for region prompts.
if not is_global:
prefixes = prefixes[1:]
cond: ComfyUIConditioning = PromptEncoding.encode_bag_of_subprompts_greedy(
clip, prefixes, canvas_cond["suffixes"]
)
# Set area cond
cond: ComfyUIConditioning = self.cond_set_mask_node.append(
cond,
mask=canvas_cond["mask"],
set_cond_area="default",
strength=global_strength if is_global else region_strength,
)[0]
assert len(cond) == 1
positive.extend(cond)
masks.append(canvas_cond["mask"].unsqueeze(0))
return (
positive,
# Output masks in case it's needed for debugging or the user might
# want to apply extra condition such as ControlNet/IPAdapter to
# specified region.
torch.cat(masks, dim=0),
)
class OmostLoadCanvasConditioningNode:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"json_str": ("STRING", {"multiline": True}),
}
}
RETURN_TYPES = ("OMOST_CANVAS_CONDITIONING",)
FUNCTION = "load_canvas"
CATEGORY = "omost"
def load_canvas(self, json_str: str) -> Tuple[list[OmostCanvasCondition]]:
"""Load canvas from file"""
return (json.loads(json_str),)
class OmostLoadCanvasPythonCodeNode:
"""Load python code generated by Omost demo app."""
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"python_str": ("STRING", {"multiline": True}),
}
}
RETURN_TYPES = ("OMOST_CANVAS_CONDITIONING",)
FUNCTION = "load_canvas"
CATEGORY = "omost"
def load_canvas(self, python_str: str) -> Tuple[list[OmostCanvasCondition]]:
"""Load canvas from file"""
canvas = OmostCanvas.from_python_code(python_str)
return (canvas.process(),)
NODE_CLASS_MAPPINGS = {
"OmostLLMLoaderNode": OmostLLMLoaderNode,
"OmostLLMHTTPServerNode": OmostLLMHTTPServerNode,
"OmostLLMChatNode": OmostLLMChatNode,
"OmostGreedyBagsTextEmbeddingNode": OmostGreedyBagsTextEmbeddingNode,
"OmostLayoutCondNode": OmostComfyLayoutNode,
"OmostDenseDiffusionLayoutNode": OmostDenseDiffusionLayoutNode,
"OmostLoadCanvasConditioningNode": OmostLoadCanvasConditioningNode,
"OmostLoadCanvasPythonCodeNode": OmostLoadCanvasPythonCodeNode,
"OmostRenderCanvasConditioningNode": OmostRenderCanvasConditioningNode,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"OmostLLMLoaderNode": "Omost LLM Loader",
"OmostLLMHTTPServerNode": "Omost LLM HTTP Server",
"OmostLLMChatNode": "Omost LLM Chat",
"OmostGreedyBagsTextEmbeddingNode": "Omost Greedy Bags Text Embedding",
"OmostLayoutCondNode": "Omost Layout Cond (ComfyUI-Area)",
"OmostDenseDiffusionLayoutNode": "Omost Layout Cond (OmostDenseDiffusion)",
"OmostLoadCanvasConditioningNode": "Omost Load Canvas Conditioning",
"OmostLoadCanvasPythonCodeNode": "Omost Load Canvas Python Code",
"OmostRenderCanvasConditioningNode": "Omost Render Canvas Conditioning",
}