Spaces:
Paused
Paused
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 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.use_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_embeds, pooled_prompt_embeds = self.pipe.compel_proc( | |
[params.prompt, params.negative_prompt] | |
) | |
results = self.pipe( | |
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, | |
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 | |