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from diffusers import (
StableDiffusionXLControlNetImg2ImgPipeline,
ControlNetModel,
LCMScheduler,
AutoencoderKL,
)
from compel import Compel, ReturnedEmbeddingsType
import torch
from pipelines.utils.canny_gpu import SobelOperator
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
controlnet_model = "diffusers/controlnet-canny-sdxl-1.0"
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
# # base model with activation token, it will prepend the prompt with the activation token
base_models = {
"plasmo/woolitize": "woolitize",
"nitrosocke/Ghibli-Diffusion": "ghibli style",
"nitrosocke/mo-di-diffusion": "modern disney style",
}
# lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
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"
class Pipeline:
class Info(BaseModel):
name: str = "controlnet+loras+sdxl"
title: str = "SDXL + LCM + LoRA + Controlnet "
description: str = "Generates an image from a text prompt"
input_mode: str = "image"
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=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(
1.0,
min=0,
max=20,
step=0.001,
title="Guidance Scale",
field="range",
hide=True,
id="guidance_scale",
)
strength: float = Field(
0.5,
min=0.25,
max=1.0,
step=0.001,
title="Strength",
field="range",
hide=True,
id="strength",
)
controlnet_scale: float = Field(
0.5,
min=0,
max=1.0,
step=0.001,
title="Controlnet Scale",
field="range",
hide=True,
id="controlnet_scale",
)
controlnet_start: float = Field(
0.0,
min=0,
max=1.0,
step=0.001,
title="Controlnet Start",
field="range",
hide=True,
id="controlnet_start",
)
controlnet_end: float = Field(
1.0,
min=0,
max=1.0,
step=0.001,
title="Controlnet End",
field="range",
hide=True,
id="controlnet_end",
)
canny_low_threshold: float = Field(
0.31,
min=0,
max=1.0,
step=0.001,
title="Canny Low Threshold",
field="range",
hide=True,
id="canny_low_threshold",
)
canny_high_threshold: float = Field(
0.125,
min=0,
max=1.0,
step=0.001,
title="Canny High Threshold",
field="range",
hide=True,
id="canny_high_threshold",
)
debug_canny: bool = Field(
False,
title="Debug Canny",
field="checkbox",
hide=True,
id="debug_canny",
)
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype):
controlnet_canny = ControlNetModel.from_pretrained(
controlnet_model, torch_dtype=torch_dtype
).to(device)
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
)
if args.safety_checker:
self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
model_id,
controlnet=controlnet_canny,
vae=vae,
)
else:
self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
model_id,
safety_checker=None,
controlnet=controlnet_canny,
vae=vae,
)
self.canny_torch = SobelOperator(device=device)
# Load LCM LoRA
self.pipe.load_lora_weights(lcm_lora_id, adapter_name="lcm")
self.pipe.load_lora_weights(
"CiroN2022/toy-face",
weight_name="toy_face_sdxl.safetensors",
adapter_name="toy",
)
self.pipe.set_adapters(["lcm", "toy"], adapter_weights=[1.0, 0.8])
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 device.type != "mps":
self.pipe.unet.to(memory_format=torch.channels_last)
if psutil.virtual_memory().total < 64 * 1024**3:
self.pipe.enable_attention_slicing()
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))],
control_image=[Image.new("RGB", (768, 768))],
)
def predict(self, params: "Pipeline.InputParams") -> Image.Image:
generator = torch.manual_seed(params.seed)
prompt_embeds, pooled_prompt_embeds = self.pipe.compel_proc(
[params.prompt, params.negative_prompt]
)
control_image = self.canny_torch(
params.image, params.canny_low_threshold, params.canny_high_threshold
)
results = self.pipe(
image=params.image,
control_image=control_image,
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],
generator=generator,
strength=params.strength,
num_inference_steps=params.steps,
guidance_scale=params.guidance_scale,
width=params.width,
height=params.height,
output_type="pil",
controlnet_conditioning_scale=params.controlnet_scale,
control_guidance_start=params.controlnet_start,
control_guidance_end=params.controlnet_end,
)
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]
if params.debug_canny:
# paste control_image on top of result_image
w0, h0 = (200, 200)
control_image = control_image.resize((w0, h0))
w1, h1 = result_image.size
result_image.paste(control_image, (w1 - w0, h1 - h0))
return result_image
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