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| import gradio as gr | |
| from rf_models import RF_model | |
| import torch | |
| from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize | |
| import torch.nn.functional as F | |
| from diffusers import StableDiffusionXLImg2ImgPipeline | |
| import time | |
| import copy | |
| import numpy as np | |
| pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained( | |
| "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16, variant="fp16", use_safetensors=True | |
| ) | |
| pipe = pipe.to("cuda") | |
| global model | |
| global img | |
| def set_model(model_id): | |
| global model | |
| if model_id == "InstaFlow-0.9B": | |
| model = RF_model("./instaflow_09b.pt") | |
| elif model_id == "InstaFlow-1.7B": | |
| model = RF_model("./instaflow_17b.pt") | |
| else: | |
| raise NotImplementedError | |
| print('Finished Loading Model!') | |
| def set_new_latent_and_generate_new_image(seed, prompt, negative_prompt="", num_inference_steps=1, guidance_scale=0.0): | |
| print('Generate with input seed') | |
| global model | |
| global img | |
| seed = int(seed) | |
| num_inference_steps = int(num_inference_steps) | |
| guidance_scale = float(guidance_scale) | |
| print(seed, num_inference_steps, guidance_scale) | |
| t_s = time.time() | |
| new_image = model.set_new_latent_and_generate_new_image(int(seed), prompt, negative_prompt, int(num_inference_steps), guidance_scale) | |
| print('time consumption:', time.time() - t_s) | |
| img = copy.copy(new_image[0]) | |
| return new_image[0] | |
| def set_new_latent_and_generate_new_image_and_random_seed(seed, prompt, negative_prompt="", num_inference_steps=1, guidance_scale=0.0): | |
| print('Generate with a random seed') | |
| global model | |
| global img | |
| seed = np.random.randint(0, 2**32) | |
| num_inference_steps = int(num_inference_steps) | |
| guidance_scale = float(guidance_scale) | |
| print(seed, num_inference_steps, guidance_scale) | |
| t_s = time.time() | |
| new_image = model.set_new_latent_and_generate_new_image(int(seed), prompt, negative_prompt, int(num_inference_steps), guidance_scale) | |
| print('time consumption:', time.time() - t_s) | |
| img = copy.copy(new_image[0]) | |
| return new_image[0], seed | |
| def refine_image_512(prompt): | |
| print('Refine with SDXL-Refiner (512)') | |
| global img | |
| t_s = time.time() | |
| img = torch.tensor(img).unsqueeze(0).permute(0, 3, 1, 2) | |
| img = img.permute(0, 2, 3, 1).squeeze(0).cpu().numpy() | |
| new_image = pipe(prompt, image=img).images[0] | |
| print('time consumption:', time.time() - t_s) | |
| new_image = np.array(new_image) * 1.0 / 255. | |
| img = new_image | |
| return new_image | |
| def refine_image_1024(prompt): | |
| print('Refine with SDXL-Refiner (1024)') | |
| global img | |
| t_s = time.time() | |
| img = torch.tensor(img).unsqueeze(0).permute(0, 3, 1, 2) | |
| img = torch.nn.functional.interpolate(img, size=1024, mode='bilinear') | |
| img = img.permute(0, 2, 3, 1).squeeze(0).cpu().numpy() | |
| new_image = pipe(prompt, image=img).images[0] | |
| print('time consumption:', time.time() - t_s) | |
| new_image = np.array(new_image) * 1.0 / 255. | |
| img = new_image | |
| return new_image | |
| set_model('InstaFlow-0.9B') | |
| with gr.Blocks() as gradio_gui: | |
| with gr.Row(): | |
| with gr.Column(scale=0.5): | |
| im = gr.Image() | |
| with gr.Column(): | |
| #model_id = gr.Dropdown(["InstaFlow-0.9B", "InstaFlow-1.7B"], label="Model ID", info="Choose Your Model") | |
| #set_model_button = gr.Button(value="Set New Model") | |
| #set_model_button.click(set_model, inputs=[model_id]) | |
| model_id = gr.Textbox(value='InstaFlow-0.9B', label="Model ID") | |
| seed_input = gr.Textbox(value='101098274', label="Random Seed") | |
| prompt_input = gr.Textbox(value='A high-resolution photograph of a waterfall in autumn; muted tone', label="Prompt") | |
| new_image_button = gr.Button(value="Generate Image with the Input Seed") | |
| new_image_button.click(set_new_latent_and_generate_new_image, inputs=[seed_input, prompt_input], outputs=[im]) | |
| next_image_button = gr.Button(value="Generate Image with a Random Seed") | |
| next_image_button.click(set_new_latent_and_generate_new_image_and_random_seed, inputs=[seed_input, prompt_input], outputs=[im, seed_input]) | |
| refine_button_512 = gr.Button(value="Refine with Refiner (Resolution: 512)") | |
| refine_button_512.click(refine_image_512, inputs=[prompt_input], outputs=[im]) | |
| refine_button_1024 = gr.Button(value="Refine with Refiner (Resolution: 1024)") | |
| refine_button_1024.click(refine_image_1024, inputs=[prompt_input], outputs=[im]) | |
| gradio_gui.launch() | |