495 lines
20 KiB
Python
495 lines
20 KiB
Python
#!../../.venv python3
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import os
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import sys
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import base64
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import numpy as np
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from PIL import Image
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import io
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from io import BytesIO
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import torch
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import torchvision
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import asyncio
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import logging
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import grpc
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import flatbuffers
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import google.protobuf as pb
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from . import imageService_pb2
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from . import imageService_pb2_grpc
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from . import Control
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from . import LoRA
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from . import GenerationConfiguration
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import hashlib
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import json
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sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
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import comfy.utils
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from comfy.cli_args import args
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MAX_RESOLUTION=16384
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MAX_PREVIEW_RESOLUTION = args.preview_size
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def pil2tensor(image):
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return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
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def prepare_callback(step, total_steps, preview_image):
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pbar = comfy.utils.ProgressBar(step)
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def callback(step, total_steps):
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preview_bytes = None
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if preview_image is not None:
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preview_bytes = ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION)
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pbar.update_absolute(step, total_steps, preview_bytes)
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return callback(step, total_steps)
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def image_to_base64(image_tensor):
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if image_tensor is not None:
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image_tensor = image_tensor.permute(3, 1, 2, 0)
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image_tensor = image_tensor.squeeze(3)
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transform = torchvision.transforms.ToPILImage()
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img = transform(image_tensor)
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# Save the image to a BytesIO object (in memory) rather than to a file
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buffered = BytesIO()
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img.save(buffered, format="PNG")
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# Encode the image as base64
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encoded_string = base64.b64encode(buffered.getvalue())
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return encoded_string
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return None
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def get_files(server, port):
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with grpc.insecure_channel(f"{server}:{port}") as channel:
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stub = imageService_pb2_grpc.ImageGenerationServiceStub(channel)
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response = stub.Echo(imageService_pb2.EchoRequest(name="ComfyUI"))
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DrawThingsSampler.files_list = json.loads(pb.json_format.MessageToJson(response))["files"]
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print(response.message) # HELLO ComfyUI
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return json.loads(pb.json_format.MessageToJson(response))["files"]
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async def dt_sampler(
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server,
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port,
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model,
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seed,
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steps,
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cfg,
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strength,
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sampler_name,
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positive,
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negative,
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width,
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height,
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batch_count,
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scale_factor=1,
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image=None,
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mask=None,
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control_net=None,
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lora=None
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) -> None:
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builder = flatbuffers.Builder(0)
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loras = None
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if lora is not None:
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fin_loras = []
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for lora_cfg in lora["loras"]:
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lora_name = builder.CreateString(lora_cfg["lora_name"])
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LoRA.Start(builder)
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LoRA.AddFile(builder, lora_name)
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LoRA.AddWeight(builder, lora_cfg["lora_weight"])
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fin_lora = LoRA.End(builder)
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fin_loras.append(fin_lora)
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GenerationConfiguration.StartLorasVector(builder, len(lora["loras"]))
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for i, lora_cfg in enumerate(lora["loras"]):
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builder.PrependUOffsetTRelative(fin_loras[i])
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loras = builder.EndVector()
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controls = None
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if control_net is not None:
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fin_controls = []
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for control_cfg in control_net["control_nets"]:
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control_name = builder.CreateString(control_cfg["control_net_name"])
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Control.Start(builder)
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Control.AddFile(builder, control_name)
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Control.AddInputOverride(builder, DrawThingsLists.control_input_type.index(control_cfg["control_input_type"]))
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Control.AddControlMode(builder, DrawThingsLists.control_mode.index(control_cfg["control_mode"]))
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Control.AddWeight(builder, control_cfg["control_net_weight"])
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Control.AddGuidanceStart(builder, 0.0)
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Control.AddGuidanceEnd(builder, 1.0)
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fin_control = Control.End(builder)
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fin_controls.append(fin_control)
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GenerationConfiguration.StartControlsVector(builder, len(control_net["control_nets"]))
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for i, control_cfg in enumerate(control_net["control_nets"]):
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builder.PrependUOffsetTRelative(fin_controls[i])
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controls = builder.EndVector()
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start_width = width // 64 // scale_factor
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start_height = height // 64 // scale_factor
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model_name = builder.CreateString(model)
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GenerationConfiguration.Start(builder)
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GenerationConfiguration.AddModel(builder, model_name)
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GenerationConfiguration.AddBatchCount(builder, batch_count)
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GenerationConfiguration.AddBatchSize(builder, 1)
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GenerationConfiguration.AddSampler(builder, DrawThingsLists.sampler_list.index(sampler_name))
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GenerationConfiguration.AddSeedMode(builder, DrawThingsLists.seed_mode.index("ScaleAlike"))
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GenerationConfiguration.AddSteps(builder, steps)
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GenerationConfiguration.AddSeed(builder, seed)
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GenerationConfiguration.AddStartWidth(builder, start_width)
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GenerationConfiguration.AddStartHeight(builder, start_height)
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GenerationConfiguration.AddGuidanceScale(builder, cfg)
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GenerationConfiguration.AddStrength(builder, strength)
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GenerationConfiguration.AddTargetImageWidth(builder, width)
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GenerationConfiguration.AddTargetImageHeight(builder, height)
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GenerationConfiguration.AddSharpness(builder, 0.6)
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if controls is not None:
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GenerationConfiguration.AddControls(builder, controls)
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if loras is not None:
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GenerationConfiguration.AddLoras(builder, loras)
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builder.Finish(GenerationConfiguration.End(builder))
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configuration = builder.Output()
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# generated = GenerationConfiguration.GenerationConfiguration.GetRootAs(configuration, 0)
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contents = []
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img2img = None
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maskimg = None
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if image is not None:
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img2img = bytes(hashlib.sha256(image_to_base64(image)).digest())
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contents.append(base64.b64decode(image_to_base64(image)))
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if mask is not None:
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maskimg = bytes(hashlib.sha256(image_to_base64(mask)).digest())
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contents.append(base64.b64decode(image_to_base64(mask)))
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models_override = [{
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"default_scale": 8,
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"file": "sd_v1.5_f16.ckpt",
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"name": "Generic (Stable Diffusion v1.5)",
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"prefix": "",
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"upcast_attention": False,
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"version": "v1"}]
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override = imageService_pb2.MetadataOverride(
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models = bytes(f"{models_override}", encoding='utf-8'),
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loras = b"[]",
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controlNets = b"[]",
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textualInversions = b"[]",
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upscalers = b"[]"
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)
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tensor_and_weight = []
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if control_net is not None:
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for control_cfg in control_net["control_nets"]:
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image = control_cfg["image"]
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if image is not None:
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tensor_and_weight.append(
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imageService_pb2.TensorAndWeight(
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tensor = bytes(hashlib.sha256(image_to_base64(image)).digest()),
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weight = control_cfg["control_net_weight"]
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)
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)
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contents.append(base64.b64decode(image_to_base64(image)))
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hints = [
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imageService_pb2.HintProto(
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hintType = "depth",
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tensors = tensor_and_weight
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)
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]
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async with grpc.aio.insecure_channel(f"{server}:{port}") as channel:
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stub = imageService_pb2_grpc.ImageGenerationServiceStub(channel)
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generate_stream = stub.GenerateImage(imageService_pb2.ImageGenerationRequest(
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image = img2img, # Image data as sha256 content.
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scaleFactor = scale_factor,
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mask = maskimg, # Optional Mask data as sha256 content.
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hints = hints, # List of hints
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prompt = positive, # Optional prompt string
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negativePrompt = negative, # Optional negative prompt string
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configuration = bytes(configuration), # Configuration data as bytes (FlatBuffer)
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override = override, # Override the existing metadata on various Zoo objects.
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user = "ComfyUI", # The name of the client.
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device = "LAPTOP", # The type of the device uses.
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contents = contents # The image data as array of bytes. It is addressed by its sha256 content. This is modeled as content-addressable storage.
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))
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while True:
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response = await generate_stream.read()
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if response == grpc.aio.EOF:
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break
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current_step = response.currentSignpost.sampling.step
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preview_image = response.previewImage
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generated_images = response.generatedImages
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print(f"current_step: {current_step}")
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if current_step:
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img = None
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if preview_image:
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# Convert the image data to a Pillow Image object
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img = Image.frombytes('RGB', (64, 64), response.previewImage, 'raw')
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prepare_callback(current_step, steps, img)
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if generated_images:
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images = []
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for img_data in response.generatedImages:
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# Convert the image data to a Pillow Image object
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img = Image.frombytes('RGB', (width, height), img_data, 'raw')
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image_np = np.array(img)
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# Convert to float32 tensor and normalize
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tensor_image = torch.from_numpy(image_np.astype(np.float32) / 255.0)
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images.append(tensor_image)
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return (torch.stack(images),)
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class DrawThingsLists:
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dtserver = "localhost"
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dtport = "7859"
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files_list = ["Press R to (re)load this list"]
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sampler_list = [
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"DPMPP 2M Karras",
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"Euler A",
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"DDIM",
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"PLMS",
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"DPMPP SDE Karras",
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"UniPC",
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"LCM",
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"Euler A Substep",
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"DPMPP SDE Substep",
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"TCD",
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"Euler A Trailing",
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"DPMPP SDE Trailing",
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"DPMPP 2MA YS",
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"Euler A AYS",
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"DPMPP SDE AYS",
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"DPMPP 2M Trailing",
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"DDIM Trailing",
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]
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seed_mode = [
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"Legacy",
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"TorchCpuCompatible",
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"ScaleAlike",
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"NvidiaGpuCompatible",
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]
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control_mode = [
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"Balanced",
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"Prompt",
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"Control",
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]
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control_input_type = [
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"Unspecified",
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"Custom",
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"Depth",
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"Canny",
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"Scribble",
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"Pose",
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"Normalbae",
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"Color",
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"Lineart",
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"Softedge",
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"Seg",
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"Inpaint",
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"Ip2p",
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"Shuffle",
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"Mlsd",
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"Tile",
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"Blur",
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"Lowquality",
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"Gray",
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]
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class DrawThingsSampler:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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DrawThingsLists.files_list = get_files(DrawThingsLists.dtserver, DrawThingsLists.dtport)
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return {
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"required": {
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"server": ("STRING", {"multiline": False, "default": DrawThingsLists.dtserver, "tooltip": "The IP address of the Draw Things gRPC Server."}),
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"port": ("STRING", {"multiline": False, "default": DrawThingsLists.dtport, "tooltip": "The port that the Draw Things gRPC Server is listening on."}),
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"model": (DrawThingsLists.files_list, {"default": "Press R to (re)load this list", "tooltip": "The model used for denoising the input latent. Please note that this lists all files, so be sure to pick the right one. Press R to (re)load this list."}),
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"strength": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 1.00, "step": 0.01, "tooltip": "When generating from an image, a high value allows more artistic freedom from the original. 1.0 means no influence from the existing image (a.k.a. text to image)."}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
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"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
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"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
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"cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
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"sampler_name": (DrawThingsLists.sampler_list, {"default": "DPMPP 2M Trailing", "tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}),
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"batch_count": ("INT", {"default": 1, "min": 1, "max": 4096}),
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},
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"hidden": {
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"scale_factor": ("INT", {"default": 1, "min": 1, "max": 4, "step": 1}),
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},
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"optional": {
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"positive": ("STRING", {
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"multiline": True, "default": "a lovely cat", "tooltip": "The conditioning describing the attributes you want to include in the image."}),
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"negative": ("STRING", {
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"multiline": True, "default": "text, watermark", "tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
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"image": ("IMAGE", ),
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"mask": ("MASK", ),
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"lora": ("dict", ),
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"control_net": ("dict", ),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("IMAGE",)
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DESCRIPTION = ""
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FUNCTION = "sample"
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CATEGORY = "DrawThings"
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def sample(self,
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server,
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port,
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model,
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seed,
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steps,
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cfg,
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strength,
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sampler_name,
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positive,
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negative,
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width,
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height,
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batch_count,
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scale_factor=1,
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image=None,
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mask=None,
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control_net=None,
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lora=None
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):
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DrawThingsLists.dtserver = server
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DrawThingsLists.dtport = port
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DrawThingsLists.files_list = get_files(DrawThingsLists.dtserver, DrawThingsLists.dtport)
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return asyncio.run(dt_sampler(
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server,
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port,
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model,
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seed,
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steps,
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cfg,
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strength,
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sampler_name,
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positive,
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negative,
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width,
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height,
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batch_count=batch_count,
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scale_factor=scale_factor,
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image=image,
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mask=mask,
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control_net=control_net,
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lora=lora
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))
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# @classmethod
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# def IS_CHANGED(s, control_net_name, **kwargs):
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# DrawThingsSampler.files_list = get_files(DrawThingsSampler.dtserver, DrawThingsSampler.dtport)
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# control_net_name = DrawThingsSampler.files_list
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# print("IS_CHANGED")
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# return float("NaN")
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# setattr(self.__class__, 'IS_CHANGED', IS_CHANGED)
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@classmethod
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def VALIDATE_INPUTS(s, **kwargs):
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return True
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class DrawThingsControlNet:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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DrawThingsLists.files_list = get_files(DrawThingsLists.dtserver, DrawThingsLists.dtport)
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return {
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"required": {
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"control_net_name": (DrawThingsLists.files_list, {"default": "Press R to (re)load this list", "tooltip": "The model used. Please note that this lists all files, so be sure to pick the right one. Press R to (re)load this list."}),
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"control_input_type": (DrawThingsLists.control_input_type, {"default": "Unspecified"}),
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"control_mode": (DrawThingsLists.control_mode, {"default": "Balanced", "tooltip": ""}),
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"control_net_weight": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.50, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
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},
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"optional": {
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"control_net": ("dict", ),
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"image": ("IMAGE", ),
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}
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}
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RETURN_TYPES = ("dict",)
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RETURN_NAMES = ("CONTROL_NET",)
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CATEGORY = "DrawThings"
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FUNCTION = "add_to_pipeline"
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def add_to_pipeline(self, control_net_name, control_input_type, control_mode, control_net_weight, control_net={}, image=None ):
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# Check if 'control_nets' exists in the pipeline
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if "control_nets" not in control_net:
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# Create 'control_nets' as an empty list
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control_net["control_nets"] = []
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# Append the new entry as a dictionary to the list
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control_net["control_nets"].append({
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"control_net_name": control_net_name,
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"control_input_type": control_input_type,
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"control_mode": control_mode,
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"control_net_weight": control_net_weight,
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"image": image
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})
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return (control_net,)
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@classmethod
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def VALIDATE_INPUTS(s, **kwargs):
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return True
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class DrawThingsLoRA:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(s):
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DrawThingsLists.files_list = get_files(DrawThingsLists.dtserver, DrawThingsLists.dtport)
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return {
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"required": {
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"lora_name": (DrawThingsLists.files_list, {"default": "Press R to (re)load this list", "tooltip": "The model used. Please note that this lists all files, so be sure to pick the right one. Press R to (re)load this list."}),
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"lora_weight": ("FLOAT", {"default": 1.00, "min": 0.00, "max": 2.50, "step": 0.01, "tooltip": "How strongly to modify the diffusion model. This value can be negative."}),
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},
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"optional": {
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"lora": ("dict", ),
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}
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}
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RETURN_TYPES = ("dict",)
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RETURN_NAMES = ("LORA",)
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CATEGORY = "DrawThings"
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DESCRIPTION = "LoRAs are used to modify diffusion and CLIP models, altering the way in which latents are denoised such as applying styles. Multiple LoRA nodes can be linked together."
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FUNCTION = "add_to_pipeline"
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def add_to_pipeline(self, lora_name, lora_weight, lora={}):
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# Check if 'loras' exists in the pipeline
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if "loras" not in lora:
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# Create 'loras' as an empty list
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lora["loras"] = []
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# Append the new entry as a dictionary to the list
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lora["loras"].append({"lora_name": lora_name, "lora_weight": lora_weight})
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# print(f"lora: {lora}")
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return (lora,)
|
|
|
|
@classmethod
|
|
def VALIDATE_INPUTS(s, **kwargs):
|
|
return True
|
|
|
|
# A dictionary that contains all nodes you want to export with their names
|
|
# NOTE: names should be globally unique
|
|
NODE_CLASS_MAPPINGS = {
|
|
"DrawThingsSampler": DrawThingsSampler,
|
|
"DrawThingsControlNet": DrawThingsControlNet,
|
|
"DrawThingsLoRA": DrawThingsLoRA,
|
|
}
|
|
|
|
# A dictionary that contains the friendly/humanly readable titles for the nodes
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"DrawThingsSampler": "Draw Things Sampler",
|
|
"DrawThingsControlNet": "Draw Things ControlNet",
|
|
"DrawThingsLoRA": "Draw Things LoRA",
|
|
}
|