Update app.py

app.py

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+from base64 import b64encode
+from utils import *
+from device import torch_device,vae,text_encoder,unet,tokenizer,scheduler,token_emb_layer,pos_emb_layer,position_embeddings
+import numpy
+import torch
+from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel
+from huggingface_hub import notebook_login
+import gradio as gr
+import random
+import torch
+import pathlib
+
+import gradio as gr
+import random
+import torch
+import pathlib
+# For video display:
+from IPython.display import HTML
+from matplotlib import pyplot as plt
+from pathlib import Path
+from PIL import Image
+from torch import autocast
+from torchvision import transforms as tfms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer, logging
+import os
+import shutil
+
+from stablediffusion import *
+path="/Project/concept_styles"
+concept_styles={
+    "cubex":"cubex.bin",
+    "hours-style":"hours-style.bin",
+    "orange-jacket":"orange-jacket.bin",
+    "simple_styles(2)":"simple_styles(2).bin",
+    "xyz":"xyz.bin"
+
+}
+
+
+def generate(prompt, styles,num_inference_steps, loss_scale,noised_image):
+    lossless_images, lossy_images = [], []
+    for style in styles:
+        concept_lib_path = f"{path}/{concept_styles[style]}"
+        concept_lib = pathlib.Path(concept_lib_path)
+        concept_embed = torch.load(concept_lib)
+
+        manual_seed = random.randint(0, 100)
+
+        generated_image_lossless = generate_image(prompt,concept_embed,num_inference_steps=num_inference_steps,color_postprocessing=False,noised_image=noised_image,loss_scale=loss_scale,seed=manual_seed
+        )
+        generated_image_lossy = generate_image(prompt,concept_embed,num_inference_steps=num_inference_steps,color_postprocessing=True,noised_image=noised_image,loss_scale=loss_scale,seed=manual_seed
+        )
+        lossless_images.append((generated_image_lossless, style))
+        lossy_images.append((generated_image_lossy, style))
+    return {lossless_gallery: lossless_images,lossy_gallery: lossy_images}
+
+with gr.Blocks() as app:
+    gr.Markdown("## ERA V1 Session20 - Stable Diffusion Model: Generative Art with Guidance")
+    with gr.Row():
+        with gr.Column():
+            prompt_box = gr.Textbox(label="Prompt", interactive=True)
+            style_selector = gr.Dropdown(
+                choices=list(concept_styles.keys()),
+                value=list(concept_styles.keys())[0],
+                multiselect=True,
+                label="Select a Concept Style",
+                interactive=True,
+            )
+            num_inference_steps = gr.Slider(
+                minimum=10,
+                maximum=50,
+                value=30,
+                step=10,
+                label="Select Number of Steps",
+                interactive=True,
+            )
+
+            loss_scale = gr.Slider(
+                minimum=0,
+                maximum=10,
+                value=8,
+                step=1,
+                label="Select Guidance Scale",
+                interactive=True,
+            )
+            noised_image = gr.Checkbox(
+                label="Include Noised Image",
+                default=False,
+                interactive=True,
+            )
+
+
+            submit_btn = gr.Button(value="Generate")
+
+        with gr.Column():
+            lossless_gallery = gr.Gallery(
+                label="Generated Images without Guidance", show_label=True
+            )
+            lossy_gallery = gr.Gallery(
+                label="Generated Images with Guidance", show_label=True
+            )
+
+        submit_btn.click(
+            generate,
+            inputs=[prompt_box, style_selector, num_inference_steps, loss_scale,noised_image],
+            outputs=[lossless_gallery,lossy_gallery],
+        )
+
+app.launch()
+