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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"
class Pipeline:
class Info(BaseModel):
name: str = "txt2imglora"
title: str = "txt2imglora"
description: str = "Generates an image from a text prompt"
input_mode: str = "text"
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)
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]
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