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from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderKL, 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
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
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 Latent Consistency Model</h1>
<h3 class="text-xl font-bold">Text-to-Image SDXL + 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 LoRA</a
>
Text 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 = "LCM+Lora+SDXL"
title: str = "Text-to-Image SDXL + LCM + LoRA"
description: str = "Generates an image from a text prompt"
page_content: str = page_content
input_mode: str = "text"
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(
1.0,
min=0,
max=20,
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):
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
)
if args.safety_checker:
self.pipe = DiffusionPipeline.from_pretrained(
model_id,
vae=vae,
)
else:
self.pipe = DiffusionPipeline.from_pretrained(
model_id,
safety_checker=None,
vae=vae,
)
# Load LCM LoRA
self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
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).to(device)
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 device.type != "mps":
self.pipe.unet.to(memory_format=torch.channels_last)
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],
)
if args.taesd:
self.pipe.vae = AutoencoderTiny.from_pretrained(
taesd_model, torch_dtype=torch_dtype, use_safetensors=True
).to(device)
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",
)
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]
results = self.pipe(
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,
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
result_image = results.images[0]
return result_image
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