File size: 7,126 Bytes
ee78277 394d08e ee78277 394d08e ee78277 394d08e ee78277 394d08e ee78277 394d08e ee78277 592470d ee78277 394d08e ee78277 394d08e ee78277 394d08e ee78277 394d08e ee78277 394d08e ee78277 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
from diffusers import (
AutoPipelineForImage2Image,
LCMScheduler,
AutoencoderTiny,
)
from compel import Compel, ReturnedEmbeddingsType
import torch
try:
import intel_extension_for_pytorch as ipex # type: ignore
except:
pass
import psutil
from config import Args
from pydantic import BaseModel, Field
from PIL import Image
import math
base_model = "segmind/Segmind-Vega"
lora_model = "segmind/Segmind-VegaRT"
taesd_model = "madebyollin/taesdxl"
default_prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux"
default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"
page_content = """
<h1 class="text-3xl font-bold">Real-Time SegmindVegaRT</h1>
<h3 class="text-xl font-bold">Image-to-Image</h3>
<p class="text-sm">
This demo showcases
<a
href="https://huggingface.co/segmind/Segmind-VegaRT"
target="_blank"
class="text-blue-500 underline hover:no-underline">SegmindVegaRT</a>
Image to Image pipeline using
<a
href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/sdxl_turbo"
target="_blank"
class="text-blue-500 underline hover:no-underline">Diffusers</a
> with a MJPEG stream server.
</p>
<p class="text-sm text-gray-500">
Change the prompt to generate different images, accepts <a
href="https://github.com/damian0815/compel/blob/main/doc/syntax.md"
target="_blank"
class="text-blue-500 underline hover:no-underline">Compel</a
> syntax.
</p>
"""
class Pipeline:
class Info(BaseModel):
name: str = "img2img"
title: str = "Image-to-Image Playground 256"
description: str = "Generates an image from a text prompt"
input_mode: str = "image"
page_content: str = page_content
class InputParams(BaseModel):
prompt: str = Field(
default_prompt,
title="Prompt",
field="textarea",
id="prompt",
)
negative_prompt: str = Field(
default_negative_prompt,
title="Negative Prompt",
field="textarea",
id="negative_prompt",
hide=True,
)
seed: int = Field(
2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
)
steps: int = Field(
4, min=1, max=15, title="Steps", field="range", hide=True, id="steps"
)
width: int = Field(
1024, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
)
height: int = Field(
1024, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
)
guidance_scale: float = Field(
0.0,
min=0,
max=1,
step=0.001,
title="Guidance Scale",
field="range",
hide=True,
id="guidance_scale",
)
strength: float = Field(
0.5,
min=0.25,
max=1.0,
step=0.001,
title="Strength",
field="range",
hide=True,
id="strength",
)
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
if args.safety_checker:
self.pipe = AutoPipelineForImage2Image.from_pretrained(
base_model,
variant="fp16",
)
else:
self.pipe = AutoPipelineForImage2Image.from_pretrained(
base_model,
safety_checker=None,
variant="fp16",
)
if args.taesd:
self.pipe.vae = AutoencoderTiny.from_pretrained(
taesd_model, torch_dtype=torch_dtype, use_safetensors=True
).to(device)
self.pipe.load_lora_weights(lora_model)
self.pipe.fuse_lora()
self.pipe.scheduler = LCMScheduler.from_pretrained(
base_model, subfolder="scheduler"
)
self.pipe.set_progress_bar_config(disable=True)
self.pipe.to(device=device, dtype=torch_dtype)
if device.type != "mps":
self.pipe.unet.to(memory_format=torch.channels_last)
# check if computer has less than 64GB of RAM using sys or os
if psutil.virtual_memory().total < 64 * 1024**3:
self.pipe.enable_attention_slicing()
if args.torch_compile:
print("Running torch compile")
self.pipe.unet = torch.compile(
self.pipe.unet, mode="reduce-overhead", fullgraph=False
)
self.pipe.vae = torch.compile(
self.pipe.vae, mode="reduce-overhead", fullgraph=False
)
self.pipe(
prompt="warmup",
image=[Image.new("RGB", (768, 768))],
)
if args.compel:
self.pipe.compel_proc = Compel(
tokenizer=[self.pipe.tokenizer, self.pipe.tokenizer_2],
text_encoder=[self.pipe.text_encoder, self.pipe.text_encoder_2],
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
requires_pooled=[False, True],
)
def predict(self, params: "Pipeline.InputParams") -> Image.Image:
generator = torch.manual_seed(params.seed)
prompt = params.prompt
negative_prompt = params.negative_prompt
prompt_embeds = None
pooled_prompt_embeds = None
negative_prompt_embeds = None
negative_pooled_prompt_embeds = None
if hasattr(self.pipe, "compel_proc"):
_prompt_embeds, pooled_prompt_embeds = self.pipe.compel_proc(
[params.prompt, params.negative_prompt]
)
prompt = None
negative_prompt = None
prompt_embeds = _prompt_embeds[0:1]
pooled_prompt_embeds = pooled_prompt_embeds[0:1]
negative_prompt_embeds = _prompt_embeds[1:2]
negative_pooled_prompt_embeds = pooled_prompt_embeds[1:2]
steps = params.steps
strength = params.strength
if int(steps * strength) < 1:
steps = math.ceil(1 / max(0.10, strength))
results = self.pipe(
image=params.image,
prompt=prompt,
negative_prompt=negative_prompt,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
generator=generator,
strength=strength,
num_inference_steps=steps,
guidance_scale=params.guidance_scale,
width=params.width,
height=params.height,
output_type="pil",
)
nsfw_content_detected = (
results.nsfw_content_detected[0]
if "nsfw_content_detected" in results
else False
)
if nsfw_content_detected:
return None
result_image = results.images[0]
return result_image
|