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from diffusers import ( | |
AutoPipelineForImage2Image, | |
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 | |
import math | |
base_model = "stabilityai/sdxl-turbo" | |
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 SDXL Turbo</h1> | |
<h3 class="text-xl font-bold">Image-to-Image</h3> | |
<p class="text-sm"> | |
This demo showcases | |
<a | |
href="https://huggingface.co/stabilityai/sdxl-turbo" | |
target="_blank" | |
class="text-blue-500 underline hover:no-underline">SDXL Turbo</a> | |
Image to Image pipeline using | |
<a | |
href="https://huggingface.co/docs/diffusers/main/en/using-diffusers/sdxl_turbo" | |
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 = "img2img" | |
title: str = "Image-to-Image SDXL" | |
description: str = "Generates an image from a text prompt" | |
input_mode: str = "image" | |
page_content: str = page_content | |
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( | |
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=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", | |
) | |
def __init__(self, args: Args, device: torch.device, torch_dtype: torch.dtype): | |
if args.safety_checker: | |
self.pipe = AutoPipelineForImage2Image.from_pretrained(base_model) | |
else: | |
self.pipe = AutoPipelineForImage2Image.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 | |
).to(device) | |
self.pipe.set_progress_bar_config(disable=True) | |
self.pipe.to(device=device, dtype=torch_dtype) | |
if device.type != "mps": | |
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: | |
print("Running 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))], | |
) | |
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], | |
) | |
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] | |
) | |
steps = params.steps | |
strength = params.strength | |
if int(steps * strength) < 1: | |
steps = math.ceil(1 / max(0.10, strength)) | |
results = self.pipe( | |
image=params.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=strength, | |
num_inference_steps=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 | |