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from diffusers import (
StableDiffusionXLPipeline,
AutoencoderKL,
TCDScheduler,
)
from compel import Compel, ReturnedEmbeddingsType
import torch
from transformers import CLIPVisionModelWithProjection
from huggingface_hub import hf_hub_download
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
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
taesd_model = "madebyollin/taesdxl"
ip_adapter_model = "ostris/ip-composition-adapter"
file_name = "ip_plus_composition_sdxl.safetensors"
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"
default_negative_prompt = "blurry, low quality, render, 3D, oversaturated"
page_content = """
<h1 class="text-3xl font-bold">Hyper-SDXL Unified + IP Adpater Composition</h1>
<h3 class="text-xl font-bold">Image-to-Image ControlNet</h3>
"""
class Pipeline:
class Info(BaseModel):
name: str = "controlnet+SDXL+Turbo"
title: str = "SDXL Turbo + Controlnet"
description: str = "Generates an image from a text prompt"
input_mode: str = "image"
page_content: str = page_content
class InputParams(ParamsModel):
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(
2, 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=10,
step=0.001,
title="Guidance Scale",
field="range",
hide=True,
id="guidance_scale",
)
ip_adapter_scale: float = Field(
0.8,
min=0.0,
max=1.0,
step=0.001,
title="IP Adapter Scale",
field="range",
hide=True,
id="ip_adapter_scale",
)
eta: float = Field(
1.0,
min=0,
max=1.0,
step=0.001,
title="Eta",
field="range",
hide=True,
id="eta",
)
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
)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16,
).to(device)
self.pipe = StableDiffusionXLPipeline.from_pretrained(
model_id,
safety_checker=None,
torch_dtype=torch_dtype,
vae=vae,
image_encoder=image_encoder,
variant="fp16",
)
self.pipe.load_ip_adapter(
ip_adapter_model,
subfolder="",
weight_name=[file_name],
image_encoder_folder=None,
)
self.pipe.load_lora_weights(
hf_hub_download("ByteDance/Hyper-SD", "Hyper-SDXL-1step-lora.safetensors")
)
self.pipe.fuse_lora()
self.pipe.scheduler = TCDScheduler.from_config(self.pipe.scheduler.config)
self.pipe.set_ip_adapter_scale([0.8])
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)
if device.type != "mps":
self.pipe.unet.to(memory_format=torch.channels_last)
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],
)
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",
image=[Image.new("RGB", (768, 768))],
)
def predict(self, params: "Pipeline.InputParams") -> Image.Image:
generator = torch.manual_seed(params.seed)
self.pipe.set_ip_adapter_scale([params.ip_adapter_scale])
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
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=steps,
guidance_scale=params.guidance_scale,
width=params.width,
eta=params.eta,
height=params.height,
ip_adapter_image=[params.image],
output_type="pil",
)
return results.images[0]
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