-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathnodes.py
177 lines (163 loc) · 6.81 KB
/
nodes.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
import os
import json
import requests
import base64
import torch
import numpy as np
from io import BytesIO
from PIL import Image
import folder_paths
class fastsdcpu:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"prompt": ("STRING", {"multiline": True,}),
"negative_prompt": ("STRING", {"multiline": True,}),
"width": (["256","512","768","1024",],),
"height": (["256","512","768","1024",],),
"steps": ("INT", {"default": 1, "min": 1, "max": 50}),
"cfg": ("FLOAT", {"default": 1, "min": 1, "max": 20, "step": 0.5,}),
"seed": ("INT", {"default": 1337, "min": 1, "max": 16777215}),
"batch_size": ("INT", {"default": 1, "min": 1, "max": 16}),
"batch_count": ("INT", {"default": 1, "min": 1, "max": 16}),
"clip_skip": ("INT", {"default": 1, "min": 1, "max": 5}),
"token_merging": ("FLOAT", {"default": 0, "min": 0, "max": 1, "step": 0.1,}),
"use_taesd": ("BOOLEAN", {"default": True},),
"use_seed": ("BOOLEAN", {"default": False},),
"use_local_path": ("BOOLEAN", {"default": False}),
#"apply_LCM_lora": ("BOOLEAN", {"default": False}),
"endpoint": ("STRING", {"default": "http://localhost:8000",}),
},
"optional": {
"openvino_model": ("STRING",),
"lcm_model": ("STRING",),
"i2i_strength": ("FLOAT", {"default": 0.75, "min": 0, "max": 1, "step": 0.01,}),
"image": ("IMAGE",),
}
}
RETURN_TYPES = ("IMAGE","STRING",)
RETURN_NAMES = ("Image","Latency",)
FUNCTION = "generate"
CATEGORY = "fastsdcpu"
def generate(self, prompt, negative_prompt, width, height, steps, cfg, seed, batch_size, batch_count, clip_skip, token_merging, use_taesd, use_seed, use_local_path, endpoint, openvino_model="", lcm_model="", i2i_strength=None, image=None):
#main args
body = {
"use_offline_model": use_local_path,
#"use_lcm_lora": apply_LCM_lora,
"openvino_lcm_model_id": "filler", #an input is required even if it's not used (I think)
"use_tiny_auto_encoder": use_taesd,
"prompt": prompt,
"negative_prompt": negative_prompt,
"image_height": height,
"image_width": width,
"inference_steps": steps,
"guidance_scale": cfg,
"clip_skip": clip_skip,
"token_merging": token_merging,
"number_of_images": batch_size,
"use_seed": use_seed,
"diffusion_task": "text_to_image",
"rebuild_pipeline": False
}
#enable i2i
if image is not None:
image_np = 255. * image.cpu().numpy().squeeze()
image_np = np.clip(image_np, 0, 255).astype(np.uint8)
img_pil = Image.fromarray(image_np)
buffer = BytesIO()
img_pil.save(buffer, format='JPEG')
img_b64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
image_params = {
"init_image": img_b64,
"strength": i2i_strength,
"diffusion_task": "image_to_image",
}
body.update(image_params)
#select model type
if openvino_model != "":
models = {
"use_openvino": True,
"openvino_lcm_model_id": openvino_model,
}
else:
models = {
"use_openvino": False,
"lcm_model_id": lcm_model,
}
body.update(models)
'''if apply_LCM_lora:
lcm_lora = {
"lcm_lora": {
"base_model_id": openvino_model if openvino_model is not None and not "" else lcm_model,
"lcm_lora_id": "latent-consistency/lcm-lora-sdv1-5"
},
}
body.update(lcm_lora)'''
image_list = []
for batch in range(batch_count):
seeds = {
"seed": seed + batch - 1,
}
body.update(seeds)
print("Request: \n" + str(body))
response = requests.post(endpoint + "/api/generate", data=json.dumps(body),)
for output in range(batch_size):
image = Image.open(BytesIO(base64.b64decode(response.json()['images'][output - 1])))
image = np.array(image).astype(np.float32) / 255.0
image_list.append(torch.from_numpy(image))
images = torch.stack(image_list, dim=0)
return (images,str(response.json()['latency']) + " Seconds",)
class fastsdcpu_vino_models:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": (["Disty0/LCM_SoteMix","rupeshs/sd-turbo-openvino","rupeshs/sdxs-512-0.9-openvino","rupeshs/hyper-sd-sdxl-1-step-openvino-int8","rupeshs/SDXL-Lightning-2steps-openvino-int8","rupeshs/sdxl-turbo-openvino-int8","rupeshs/LCM-dreamshaper-v7-openvino","rupeshs/FLUX.1-schnell-openvino-int4","rupeshs/sd15-lcm-square-openvino-int8",],),
},
}
RETURN_TYPES = ("STRING",)
FUNCTION = "choose"
CATEGORY = "fastsdcpu"
def choose(self, model):
return (model,)
class fastsdcpu_lcm_models:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"model": (["stabilityai/sd-turbo","rupeshs/sdxs-512-0.9-orig-vae","rupeshs/hyper-sd-sdxl-1-step","rupeshs/SDXL-Lightning-2steps","stabilityai/sdxl-turbo","SimianLuo/LCM_Dreamshaper_v7","latent-consistency/lcm-sdxl","latent-consistency/lcm-ssd-1b",],),
},
}
RETURN_TYPES = ("STRING",)
FUNCTION = "choose"
CATEGORY = "fastsdcpu"
def choose(self, model):
return (model,)
class fastsdcpu_loadModel:
@classmethod
def INPUT_TYPES(cls):
model_path = os.path.join(folder_paths.models_dir, "diffusers")
models = [f for f in os.listdir(model_path) if os.path.isdir(os.path.join(model_path, f))]
return {
"required": {
"model": (models,),
},
}
RETURN_TYPES = ("STRING",)
FUNCTION = "choose"
CATEGORY = "fastsdcpu"
def choose(self, model):
return (os.path.join(folder_paths.models_dir, "diffusers", model),)
NODE_CLASS_MAPPINGS = {
"fastsdcpu": fastsdcpu,
"fastsdcpu_vino_models": fastsdcpu_vino_models,
"fastsdcpu_lcm_models": fastsdcpu_lcm_models,
"fastsdcpu_loadModel": fastsdcpu_loadModel,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"fastsdcpu": "fastsdcpu",
"fastsdcpu_vino_models": "fastsdcpu_vino_models",
"fastsdcpu_lcm_models": "fastsdcpu_lcm_models",
"fastsdcpu_loadModel": "fastsdcpu_loadModel",
}