import random import gradio as gr import torch from diffusers import StableDiffusionXLPipeline from scheduling_tcd import TCDScheduler device = "cuda" base_model_id = "stabilityai/stable-diffusion-xl-base-1.0" tcd_lora_id = "h1t/TCD-SDXL-LoRA" pipe = StableDiffusionXLPipeline.from_pretrained( base_model_id, torch_dtype=torch.float16, variant="fp16" ).to(device) pipe.scheduler = TCDScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(tcd_lora_id) pipe.fuse_lora() def inference(prompt, num_inference_steps=4, seed=-1, eta=0.3): if seed is None or seed == '' or seed == -1: seed = int(random.randrange(4294967294)) generator = torch.Generator(device=device).manual_seed(int(seed)) image = pipe( prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=0, eta=eta, generator=generator, ).images[0] return image # Define style title = "

Trajectory Consistency Distillation

" description = "Official 🤗 Gradio demo for Trajectory Consistency Distillation" article = "

Trajectory Consistency Distillation | Github Repo

" default_prompt = "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna." examples = [ [ "Beautiful woman, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", 4 ], [ "Beautiful man, bubblegum pink, lemon yellow, minty blue, futuristic, high-detail, epic composition, watercolor.", 8 ], [ "Painting of the orange cat Otto von Garfield, Count of Bismarck-Schönhausen, Duke of Lauenburg, Minister-President of Prussia. Depicted wearing a Prussian Pickelhaube and eating his favorite meal - lasagna.", 16 ], [ "closeup portrait of 1 Persian princess, royal clothing, makeup, jewelry, wind-blown long hair, symmetric, desert, sands, dusty and foggy, sand storm, winds bokeh, depth of field, centered.", 16 ], ] outputs = gr.Label(label='Generated Images') with gr.Blocks() as demo: gr.Markdown(f'# {title}\n### {description}') with gr.Row(): with gr.Column(): prompt = gr.Textbox(label='Prompt', value=default_prompt) num_inference_steps = gr.Slider( label='Inference steps', minimum=4, maximum=16, value=4, step=1, ) with gr.Accordion("Advanced Options", visible=False): with gr.Row(): with gr.Column(): seed = gr.Number(label="Random Seed", value=-1) with gr.Column(): eta = gr.Slider( label='Gamma', minimum=0., maximum=1., value=0.3, step=0.1, ) with gr.Row(): clear = gr.ClearButton( components=[prompt, num_inference_steps, seed, eta]) submit = gr.Button(value='Submit') examples = gr.Examples( label="Quick Examples", examples=examples, inputs=[prompt, num_inference_steps, 0, 0.3], outputs="outputs", # 适当调整此处 cache_examples=False ) with gr.Column(): outputs = gr.Image(label='Generated Images') gr.Markdown(f'{article}') submit.click( fn=inference, inputs=[prompt, num_inference_steps, seed, eta], outputs=outputs, ) demo.launch()