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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.taesd:
self.pipe.vae = AutoencoderTiny.from_pretrained(
taesd_model, torch_dtype=torch_dtype, use_safetensors=True
).to(device)
self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
self.pipe.set_progress_bar_config(disable=True)
self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
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.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)
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=prompt,
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]
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