from diffusers import ( AutoPipelineForImage2Image, LCMScheduler, 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 = "segmind/Segmind-Vega" lora_model = "segmind/Segmind-VegaRT" 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 = """

Real-Time SegmindVegaRT

Image-to-Image

This demo showcases SegmindVegaRT Image to Image pipeline using Diffusers with a MJPEG stream server.

Change the prompt to generate different images, accepts Compel syntax.

""" class Pipeline: class Info(BaseModel): name: str = "img2img" title: str = "Image-to-Image Playground 256" 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( 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=1, 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, variant="fp16", ) else: self.pipe = AutoPipelineForImage2Image.from_pretrained( base_model, safety_checker=None, variant="fp16", ) if args.use_taesd: self.pipe.vae = AutoencoderTiny.from_pretrained( taesd_model, torch_dtype=torch_dtype, use_safetensors=True ).to(device) self.pipe.load_lora_weights(lora_model) self.pipe.fuse_lora() self.pipe.scheduler = LCMScheduler.from_pretrained( base_model, subfolder="scheduler" ) 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=False ) self.pipe.vae = torch.compile( self.pipe.vae, mode="reduce-overhead", fullgraph=False ) self.pipe( prompt="warmup", image=[Image.new("RGB", (768, 768))], ) 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], ) def predict(self, params: "Pipeline.InputParams") -> Image.Image: generator = torch.manual_seed(params.seed) 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 strength = params.strength if int(steps * strength) < 1: steps = math.ceil(1 / max(0.10, strength)) results = self.pipe( image=params.image, 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, 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