Spaces:
Sleeping
Sleeping
File size: 5,105 Bytes
0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 46bd9ac cb92d2b 0b5ceff fd757d2 0b5ceff 46bd9ac 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cf3ff1a cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff cb92d2b 0b5ceff |
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 |
from diffusers import DiffusionPipeline, AutoencoderTiny, LCMScheduler
from compel import Compel
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
base_model = "wavymulder/Analog-Diffusion"
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
taesd_model = "madebyollin/taesd"
default_prompt = "Analog style photograph of young Harrison Ford as Han Solo, star wars behind the scenes"
page_content = """
<h1 class="text-3xl font-bold">Real-Time Latent Consistency Model SDv1.5</h1>
<h3 class="text-xl font-bold">Text-to-Image LCM + LoRa</h3>
<p class="text-sm">
This demo showcases
<a
href="https://huggingface.co/blog/lcm_lora"
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. Featuring <a
href="https://huggingface.co/wavymulder/Analog-Diffusion"
target="_blank"
class="text-blue-500 underline hover:no-underline">Analog-Diffusion</a>
</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 = "controlnet"
title: str = "Text-to-Image LCM + LoRa"
description: str = "Generates an image from a text prompt"
input_mode: str = "text"
page_content: str = page_content
class InputParams(BaseModel):
prompt: str = Field(
default_prompt,
title="Prompt",
field="textarea",
id="prompt",
)
seed: int = Field(
8638236174640251, 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(
512, min=2, max=15, title="Width", disabled=True, hide=True, id="width"
)
height: int = Field(
512, min=2, max=15, title="Height", disabled=True, hide=True, id="height"
)
guidance_scale: float = Field(
0.2,
min=0,
max=4,
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):
if args.safety_checker:
self.pipe = DiffusionPipeline.from_pretrained(base_model)
else:
self.pipe = DiffusionPipeline.from_pretrained(
base_model, safety_checker=None
)
if args.use_taesd:
self.pipe.vae = AutoencoderTiny.from_pretrained(
taesd_model, torch_dtype=torch_dtype, use_safetensors=True
)
self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.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)
# 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:
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)
self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
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 = self.compel_proc(params.prompt)
results = self.pipe(
prompt_embeds=prompt_embeds,
generator=generator,
num_inference_steps=params.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
return results.images[0]
|