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Runtime error
| import torch | |
| from torch import autocast | |
| from diffusers import StableDiffusionPipeline, DDIMScheduler | |
| from IPython.display import display | |
| model_path = "../800/" # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive | |
| pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float16).to("cuda") | |
| pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) | |
| pipe.enable_xformers_memory_efficient_attention() | |
| g_cuda = None | |
| import gradio as gr | |
| def inference(prompt, negative_prompt, num_samples, height=512, width=512, num_inference_steps=50, guidance_scale=7.5): | |
| with torch.autocast("cuda"), torch.inference_mode(): | |
| return pipe( | |
| prompt, height=int(height), width=int(width), | |
| negative_prompt=negative_prompt, | |
| num_images_per_prompt=int(num_samples), | |
| num_inference_steps=int(num_inference_steps), guidance_scale=guidance_scale, | |
| generator=g_cuda | |
| ).images | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(): | |
| prompt = gr.Textbox(label="Prompt", value="photo of aaabbbccc man") | |
| negative_prompt = gr.Textbox(label="Negative Prompt", value="") | |
| run = gr.Button(value="Generate") | |
| with gr.Row(): | |
| num_samples = gr.Number(label="Number of Samples", value=4) | |
| guidance_scale = gr.Number(label="Guidance Scale", value=7.5) | |
| with gr.Row(): | |
| height = gr.Number(label="Height", value=512) | |
| width = gr.Number(label="Width", value=512) | |
| num_inference_steps = gr.Slider(label="Steps", value=24) | |
| with gr.Column(): | |
| gallery = gr.Gallery() | |
| run.click(inference, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale], outputs=gallery) | |
| demo.launch(debug=True) |