update: app + dependencies

app.py

@@ -1,17 +1,14 @@
+import os
+
 import gradio as gr
 
 import torch
-import numpy as np
-import modin.pandas as pd
-from PIL import Image
 from diffusers import DiffusionPipeline
 
-import wandb
 from wandb_addons.diffusers import get_wandb_callback
 
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
-pipeline = None
 
 if torch.cuda.is_available():
     torch.cuda.empty_cache()
@@ -28,7 +25,6 @@ else:
 
 def generate_image(
     wandb_project,
-    wandb_entity,
     wandb_api_key,
     prompt,
     negative_prompt,
@@ -39,10 +35,11 @@ def generate_image(
     seed,
     guidance_rescale,
 ):
-    wandb.login(key=wandb_api_key)
+    os.environ["WANDB_API_KEY"] = wandb_api_key
     generator = torch.Generator(device="cuda").manual_seed(seed)
     configs = {
-        "guidance_rescale": 0.0,
+        "guidance_scale": guidance_scale,
+        "guidance_rescale": guidance_rescale,
         "seed": seed,
     }
     callback = get_wandb_callback(
@@ -50,7 +47,7 @@ def generate_image(
         prompt=prompt,
         negative_prompt=negative_prompt,
         wandb_project=wandb_project,
-        wandb_entity=wandb_entity,
+        num_inference_steps=num_inference_steps,
         configs=configs,
     )
     image = pipeline(
@@ -59,8 +56,8 @@ def generate_image(
         generator=generator,
         height=height,
         width=width,
+        num_inference_steps=num_inference_steps,
         callback=callback,
-        eta=configs["eta"],
         guidance_rescale=configs["guidance_rescale"],
     ).images[0]
     if torch.cuda.is_available():
@@ -71,9 +68,8 @@ def generate_image(
 gr.Interface(
     fn=generate_image,
     inputs=[
-        gr.Textbox(label="Wandb Project Name", value="Stable-Diffusion-XL"),
-        gr.Textbox(label="Wandb Entity Name (Username/Team-name)"),
-        gr.Textbox(label="Wandb API Key", type="password"),
+        gr.Textbox(label="WandB Project Name", value="Stable-Diffusion-XL"),
+        gr.Textbox(label="WandB API Key", type="password"),
         gr.Textbox(
             label="Prompt",
             lines=3,
@@ -90,7 +86,7 @@ gr.Interface(
             minimum=10,
             maximum=100,
             value=50,
-            step=128,
+            step=10,
             label="Number of Inference Steps",
         ),
         gr.Slider(
@@ -98,7 +94,7 @@ gr.Interface(
             maximum=15.0,
             value=5.0,
             step=0.25,
-            label="Guidance Scale: How Closely the model follows the Prompt",
+            label="Guidance Scale (How Closely the model follows the Prompt)",
         ),
         gr.Slider(
             minimum=0, step=1, maximum=999999999999999999, randomize=True, label="Seed"
@@ -106,7 +102,7 @@ gr.Interface(
         gr.Slider(
             minimum=0.0,
             maximum=1.0,
-            value=0.7,
+            value=0.0,
             step=0.1,
             label="Guidance Rescale Factor",
         ),
@@ -114,4 +110,4 @@ gr.Interface(
     outputs="image",
     title="Reproducible Stable Diffusion XL",
     description="Generate images using Stable Diffusion.",
-)
+).launch(debug=True, max_threads=80)

requirements.txt

@@ -1,9 +1,9 @@
+gradio
 transformers
 diffusers
 accelerate
 torch
 ftfy
-modin[all]
 xformers
 invisible_watermark
 weave