Delete app.py

app.py

@@ -1,180 +0,0 @@
-import gradio as gr
-
-from rf_models import RF_model
-from sd_models import SD_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 base_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_base_model(model_id):
-    global base_model 
-    if model_id == "runwayml/stable-diffusion-v1-5":
-        base_model = SD_model("runwayml/stable-diffusion-v1-5")
-    else:
-        raise NotImplementedError
-    print('Finished Loading Base 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) 
-    inf_time = time.time() - t_s 
-
-    img = copy.copy(new_image[0])
-
-    return new_image[0], inf_time
-
-def set_new_latent_and_generate_new_image_with_base_model(seed, prompt, num_inference_steps=1, guidance_scale=0.0):
-    print('Generate with input seed')
-    global base_model
-    negative_prompt=""
-    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 = base_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) 
-    inf_time = time.time() - t_s
-
-    return new_image[0], inf_time
-
-
-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) 
-    inf_time = time.time() - t_s
-
-    img = copy.copy(new_image[0])
-
-    return new_image[0], seed, inf_time
-
-
-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')
-set_base_model("runwayml/stable-diffusion-v1-5")
-
-with gr.Blocks() as gradio_gui:
-    gr.Markdown(
-    """
-    # InstaFlow! One-Step Stable Diffusion with Rectified Flow
-    ## This Huggingface Space provides a demo of one-step InstaFlow-0.9B and measures the inference time. 
-    ## For fair comparison, Stable Difusion 1.5 is shown in parallel.
-    ## 
-    ##
-    """)
-    gr.Markdown("Set Input Seed and Text Prompts Here")
-    with gr.Row():
-        with gr.Column(scale=0.4):
-            seed_input = gr.Textbox(value='101098274', label="Random Seed") 
-        with gr.Column(scale=0.4):
-            prompt_input = gr.Textbox(value='A high-resolution photograph of a waterfall in autumn; muted tone', label="Prompt")
-
-    with gr.Row():
-        with gr.Column(scale=0.4):
-            with gr.Group():
-                gr.Markdown("Generation from InstaFlow-0.9B")
-                im = gr.Image()
-            
-            gr.Markdown("Model ID: One-Step InstaFlow-0.9B")
-            inference_time_output = gr.Textbox(value='0.0', label='Inference Time with One-Step Model (Second)')
-            new_image_button = gr.Button(value="One-Step Generation with the Input Seed")
-            new_image_button.click(set_new_latent_and_generate_new_image, inputs=[seed_input, prompt_input], outputs=[im, inference_time_output])
-
-            next_image_button = gr.Button(value="One-Step Generation with a New 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, inference_time_output])
-
-            refine_button_512 = gr.Button(value="Refine One-Step Generation with SDXL Refiner (Resolution: 512)")
-            refine_button_512.click(refine_image_512, inputs=[prompt_input], outputs=[im])
-
-            refine_button_1024 = gr.Button(value="Refine One-Step Generation with SDXL Refiner (Resolution: 1024)")
-            refine_button_1024.click(refine_image_1024, inputs=[prompt_input], outputs=[im])
-
-        with gr.Column(scale=0.4):
-            with gr.Group():
-                gr.Markdown("Generation from Stable Diffusion 1.5") 
-                im_base = gr.Image()
-
-            gr.Markdown("Model ID: Multi-Step Stable Diffusion 1.5")
-            base_model_inference_time_output = gr.Textbox(value='0.0', label='Inference Time with Multi-Step Stable Diffusion (Second)')
-
-            num_inference_steps = gr.Textbox(value='25', label="Number of Inference Steps for Stable Diffusion")
-            guidance_scale = gr.Textbox(value='5.0', label="Guidance Scale for Stable Diffusion")
-            
-            base_new_image_button = gr.Button(value="Multi-Step Generation with Stable Diffusion and the Input Seed") 
-            base_new_image_button.click(set_new_latent_and_generate_new_image_with_base_model, inputs=[seed_input, prompt_input,  num_inference_steps, guidance_scale], outputs=[im_base, base_model_inference_time_output])
-
-gradio_gui.launch()