File size: 5,249 Bytes
cb92d2b 1123781 33596ca cb92d2b 87914c1 8d5e68b cb92d2b 1123781 46bd9ac cb92d2b 43148fd fd757d2 43148fd d6fedfa 46bd9ac 43148fd 33596ca 1123781 ff9325e 1123781 ff9325e 2951b6b 1123781 2951b6b 1123781 ff9325e cb92d2b 5c81b63 592470d cb92d2b 2951b6b cb92d2b 8d5e68b a659304 cb92d2b cf3ff1a cb92d2b a659304 cb92d2b a659304 cb92d2b a659304 cb92d2b 5c81b63 cb92d2b |
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 |
from diffusers import DiffusionPipeline, AutoencoderTiny
from compel import Compel
import torch
try:
import intel_extension_for_pytorch as ipex # type: ignore
except:
pass
from config import Args
from pydantic import BaseModel, Field
from util import ParamsModel
from PIL import Image
from typing import List
from pruna import SmashConfig, smash
base_model = "SimianLuo/LCM_Dreamshaper_v7"
taesd_model = "madebyollin/taesd"
default_prompt = "Portrait of The Terminator with , glare pose, detailed, intricate, full of colour, cinematic lighting, trending on artstation, 8k, hyperrealistic, focused, extreme details, unreal engine 5 cinematic, masterpiece"
page_content = """<h1 class="text-3xl font-bold">Real-Time Latent Consistency Model</h1>
<h3 class="text-xl font-bold">Text-to-Image</h3>
<p class="text-sm">
This demo showcases
<a
href="https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7"
target="_blank"
class="text-blue-500 underline hover:no-underline">LCM</a>
Image to Image pipeline using
<a
href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/lcm#performing-inference-with-lcm"
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 = "txt2img"
title: str = "Text-to-Image LCM"
description: str = "Generates an image from a text prompt"
input_mode: str = "text"
page_content: str = page_content
class InputParams(ParamsModel):
prompt: str = Field(
default_prompt,
title="Prompt",
field="textarea",
id="prompt",
)
seed: int = Field(
2159232, min=0, title="Seed", field="seed", hide=True, id="seed"
)
steps: int = Field(
4, min=2, max=15, title="Steps", field="range", hide=True, id="steps"
)
width: int = Field(
768, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
)
height: int = Field(
768, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
)
guidance_scale: float = Field(
8.0,
min=1,
max=30,
step=0.001,
title="Guidance Scale",
field="range",
hide=True,
id="guidance_scale",
)
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
self.pipe = DiffusionPipeline.from_pretrained(base_model, safety_checker=None)
if args.taesd:
self.pipe.vae = AutoencoderTiny.from_pretrained(
taesd_model, torch_dtype=torch_dtype, use_safetensors=True
).to(device)
if args.pruna:
# Create and smash your model
smash_config = SmashConfig()
# smash_config["cacher"] = "deepcache"
smash_config["compiler"] = "stable_fast"
self.pipe = smash(model=self.pipe, smash_config=smash_config)
if args.sfast:
from sfast.compilers.stable_diffusion_pipeline_compiler import (
compile,
CompilationConfig,
)
config = CompilationConfig.Default()
config.enable_xformers = True
config.enable_triton = True
config.enable_cuda_graph = True
self.pipe = compile(self.pipe, config=config)
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)
if args.torch_compile:
self.pipe.unet = torch.compile(
self.pipe.unet, mode="reduce-overhead", fullgraph=True
)
self.pipe.vae = torch.compile(
self.pipe.vae, mode="reduce-overhead", fullgraph=True
)
self.pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)
if args.compel:
self.compel_proc = Compel(
tokenizer=self.pipe.tokenizer,
text_encoder=self.pipe.text_encoder,
truncate_long_prompts=False,
)
def predict(self, params: "Pipeline.InputParams") -> Image.Image:
generator = torch.manual_seed(params.seed)
prompt_embeds = None
prompt = params.prompt
if hasattr(self, "compel_proc"):
prompt_embeds = self.compel_proc(params.prompt)
prompt = None
results = self.pipe(
prompt_embeds=prompt_embeds,
prompt=prompt,
generator=generator,
num_inference_steps=params.steps,
guidance_scale=params.guidance_scale,
width=params.width,
height=params.height,
output_type="pil",
)
return results.images[0]
|