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Runtime error
Runtime error
| import torch | |
| from diffusers import DDIMPipeline, DDPMPipeline, PNDMPipeline | |
| from diffusers import DDIMScheduler, DDPMScheduler, PNDMScheduler | |
| from diffusers import UNet2DModel | |
| import gradio as gr | |
| import PIL.Image | |
| import numpy as np | |
| import random | |
| model_id = "google/ddpm-celebahq-256" | |
| model = UNet2DModel.from_pretrained(model_id) | |
| # load model and scheduler | |
| ddpm_scheduler = DDPMScheduler.from_config(model_id) | |
| ddpm_pipeline = DDPMPipeline(unet=model, scheduler=ddpm_scheduler) | |
| ddim_scheduler = DDIMScheduler.from_config(model_id) | |
| ddim_pipeline = DDIMPipeline(unet=model, scheduler=ddim_scheduler) | |
| pndm_scheduler = PNDMScheduler.from_config(model_id) | |
| pndm_pipeline = PNDMPipeline(unet=model, scheduler=pndm_scheduler) | |
| # run pipeline in inference (sample random noise and denoise) | |
| def predict(steps=100, seed=42,scheduler="ddim"): | |
| torch.cuda.empty_cache() | |
| generator = torch.manual_seed(seed) | |
| if(scheduler == "ddim"): | |
| images = ddim_pipeline(generator=generator, num_inference_steps=steps)["sample"] | |
| elif(scheduler == "ddpm"): | |
| images = ddpm_pipeline(generator=generator)["sample"] | |
| elif(scheduler == "pndm"): | |
| if(steps > 100): | |
| steps = 100 | |
| images = pndm_pipeline(generator=generator, num_inference_steps=steps)["sample"] | |
| return(images[0]) | |
| random_seed = random.randint(0, 2147483647) | |
| gr.Interface( | |
| predict, | |
| inputs=[ | |
| gr.inputs.Slider(1, 1000, label='Inference Steps (ignored for the ddpm scheduler, that diffuses for 1000 steps - limited to 100 steps max for pndm)', default=20, step=1), | |
| gr.inputs.Slider(0, 2147483647, label='Seed', default=random_seed, step=1), | |
| gr.inputs.Radio(["ddpm", "ddim", "pndm"], default="ddpm",label="Diffusion scheduler") | |
| ], | |
| outputs=gr.Image(shape=[256,256], type="pil", elem_id="output_image"), | |
| css="#output_image{width: 256px}", | |
| title="ddpm-celebahq-256 diffusion - 🧨 diffusers library", | |
| description="This Spaces contains an unconditional diffusion process for the <a href=\"https://huggingface.co/google/ddpm-celebahq-256\">ddpm-celebahq-256</a> face generator model by <a href=\"https://huggingface.co/google\">Google</a> using the <a href=\"https://github.com/huggingface/diffusers\">diffusers library</a>. You can try the diffusion process not only with the default <code>ddpm</code> scheduler but also with <code>ddim</code> and <code>pndm</code>, showcasing the modularity of the library. <a href=\"https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers\">Learn more about schedulers here.</a>", | |
| ).launch() |