moved

latentblending/gradio_ui.py

@@ -1,153 +0,0 @@
-import os
-import torch
-torch.backends.cudnn.benchmark = False
-torch.set_grad_enabled(False)
-import numpy as np
-import warnings
-warnings.filterwarnings('ignore')
-from tqdm.auto import tqdm
-from PIL import Image
-import gradio as gr
-import shutil
-import uuid
-from diffusers import AutoPipelineForText2Image
-from latentblending.blending_engine import BlendingEngine
-import datetime
-
-warnings.filterwarnings('ignore')
-torch.set_grad_enabled(False)
-torch.backends.cudnn.benchmark = False
-import json
-
-
-
-class BlendingFrontend():
-    def __init__(
-            self,
-            be,
-            share=False):
-        r"""
-        Gradio Helper Class to collect UI data and start latent blending.
-        Args:
-            be:
-                Blendingengine
-            share: bool
-                Set true to get a shareable gradio link (e.g. for running a remote server)
-        """
-        self.be = be
-        self.share = share
-
-        # UI Defaults
-        self.seed1 = 420
-        self.seed2 = 420
-        self.prompt1 = ""
-        self.prompt2 = ""
-        self.negative_prompt = ""
-
-        # Vars
-        self.prompt = None
-        self.negative_prompt = None
-        self.list_seeds = []
-        self.idx_movie = 0
-        self.data = []
-
-    def take_image0(self):
-        return self.take_image(0)
-    
-    def take_image1(self):
-        return self.take_image(1)
-    
-    def take_image2(self):
-        return self.take_image(2)
-    
-    def take_image3(self):
-        return self.take_image(3)
-    
-
-    def take_image(self, id_img):
-        if self.prompt is None:
-            print("Cannot take because no prompt was set!")
-            return [None, None, None, None, ""]
-        if self.idx_movie == 0:
-            current_time = datetime.datetime.now()
-            self.fp_out = "movie_" + current_time.strftime("%y%m%d_%H%M") + ".json"
-            self.data.append({"settings": "sdxl", "width": bf.be.dh.width_img, "height": self.be.dh.height_img, "num_inference_steps": self.be.dh.num_inference_steps})
-
-        seed = self.list_seeds[id_img]
-        
-        self.data.append({"iteration": self.idx_movie, "seed": seed, "prompt": self.prompt, "negative_prompt": self.negative_prompt})
-
-        # Write the data list to a JSON file
-        with open(self.fp_out, 'w') as f:
-            json.dump(self.data, f, indent=4)
-
-        self.idx_movie += 1
-        self.prompt = None
-        return [None, None, None, None, ""]
-
-
-    def compute_imgs(self, prompt, negative_prompt):
-        self.prompt = prompt
-        self.negative_prompt = negative_prompt
-        self.be.set_prompt1(prompt)
-        self.be.set_prompt2(prompt)
-        self.be.set_negative_prompt(negative_prompt)
-        self.list_seeds = []
-        self.list_images = []
-        for i in range(4):
-            seed = np.random.randint(0, 1000000000)
-            self.be.seed1 = seed
-            self.list_seeds.append(seed)
-            img = self.be.compute_latents1(return_image=True)
-            self.list_images.append(img)
-        return self.list_images
-        
-        
-
-
-if __name__ == "__main__":
-
-    width = 786
-    height = 1024
-    num_inference_steps = 4
-    
-    pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16")
-    # pipe = AutoPipelineForText2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, variant="fp16")
-    pipe.to("cuda")
-
-    be = BlendingEngine(pipe)
-    be.set_dimensions((width, height))
-    be.set_num_inference_steps(num_inference_steps)
-
-    bf = BlendingFrontend(be)
-
-    with gr.Blocks() as demo:
-
-        with gr.Row():
-            prompt = gr.Textbox(label="prompt")
-            negative_prompt = gr.Textbox(label="negative prompt")
-
-        with gr.Row():
-            b_compute = gr.Button('compute new images', variant='primary')
-
-        with gr.Row():
-            with gr.Column():
-                img0 = gr.Image(label="seed1")
-                b_take0 = gr.Button('take', variant='primary')
-            with gr.Column():
-                img1 = gr.Image(label="seed2")
-                b_take1 = gr.Button('take', variant='primary')
-            with gr.Column():
-                img2 = gr.Image(label="seed3")
-                b_take2 = gr.Button('take', variant='primary')
-            with gr.Column():
-                img3 = gr.Image(label="seed4")
-                b_take3 = gr.Button('take', variant='primary')
-
-        b_compute.click(bf.compute_imgs, inputs=[prompt, negative_prompt], outputs=[img0, img1, img2, img3])
-        b_take0.click(bf.take_image0, outputs=[img0, img1, img2, img3, prompt])
-        b_take1.click(bf.take_image1, outputs=[img0, img1, img2, img3, prompt])
-        b_take2.click(bf.take_image2, outputs=[img0, img1, img2, img3, prompt])
-        b_take3.click(bf.take_image3, outputs=[img0, img1, img2, img3, prompt])
-
-    demo.launch(share=bf.share, inbrowser=True, inline=False, server_name="10.40.49.100")