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| import subprocess | |
| def install_spacy_model(model_name): | |
| try: | |
| subprocess.check_call(["python", "-m", "spacy", "download", model_name]) | |
| except subprocess.CalledProcessError as e: | |
| print(f"Error occurred while installing the model: {model_name}") | |
| print(f"Error details: {str(e)}") | |
| install_spacy_model("en_core_web_trf") | |
| import gradio as gr | |
| import torch | |
| from syngen_diffusion_pipeline import SynGenDiffusionPipeline | |
| model_path = 'CompVis/stable-diffusion-v1-4' | |
| device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu') | |
| pipe = SynGenDiffusionPipeline.from_pretrained(model_path).to(device) | |
| def generate_fn(prompt, seed): | |
| generator = torch.Generator(device.type).manual_seed(int(seed)) | |
| result = pipe(prompt=prompt, generator=generator, num_inference_steps=50) | |
| return result['images'][0] | |
| title = "SynGen" | |
| description = """ | |
| This is the demo for [SynGen](https://github.com/RoyiRa/Syntax-Guided-Generation), an image synthesis approach which first syntactically analyses the prompt to identify entities and their modifiers, and then uses a novel loss function that encourages the cross-attention maps to agree with the linguistic binding reflected by the syntax. Preprint: \"Linguistic Binding in Diffusion Models: Enhancing Attribute Correspondence through Attention Map Alignment\"(https://arxiv.org/abs/2306.08877). | |
| """ | |
| examples = [ | |
| ["the apple is blue and the carrot is purple", "20"], | |
| ["a yellow flamingo and a pink sunflower", "16"], | |
| ["a checkered bowl in a cluttered room", "77"], | |
| ["a horned lion and a spotted monkey", "1269"] | |
| ] | |
| prompt_textbox = gr.Textbox(label="Prompt", placeholder="a pink sunflower and a yellow flamingo", lines=1) | |
| seed_textbox = gr.Textbox(label="Seed", placeholder="42", lines=1) | |
| output = gr.Image(label="generation") | |
| demo = gr.Interface(fn=generate_fn, inputs=[prompt_textbox, seed_textbox], outputs=output, examples=examples, | |
| title=title, description=description, allow_flagging=False) | |
| demo.launch() | |