Update app.py

app.py

@@ -1,99 +1,99 @@
-import torch
-from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
-import gradio as gr
-import random
-import tqdm
-import spaces
-
-# Enable TQDM progress tracking
-tqdm.monitor_interval = 0
-
-# Load the diffusion pipeline
-pipe = StableDiffusionXLPipeline.from_pretrained(
-    "kayfahaarukku/UrangDiffusion-1.0", 
-    torch_dtype=torch.float16, 
-    custom_pipeline="lpw_stable_diffusion_xl",
-    use_safetensors=True, 
-)
-pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
-
-# Function to generate an image
-@spaces.GPU(duration=120)  # Adjust the duration as needed
-def generate_image(prompt, negative_prompt, use_defaults, width, height, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
-    pipe.to('cuda')  # Move the model to GPU when the function is called
-    
-    if randomize_seed:
-        seed = random.randint(0, 99999999)
-    if use_defaults:
-        prompt = f"{prompt}, masterpiece, best quality"
-        negative_prompt = f"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, {negative_prompt}"
-    generator = torch.manual_seed(seed)
-    
-    def callback(step, timestep, latents):
-        progress(step / num_inference_steps)
-        return
-    
-    image = pipe(
-        prompt, 
-        negative_prompt=negative_prompt,
-        width=width,
-        height=height, 
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        generator=generator,
-        callback=callback,
-        callback_steps=1
-    ).images[0]
-
-    torch.cuda.empty_cache()
-    pipe.to('cpu')  # Move the model back to CPU after generation
-
-    return image, seed
-
-# Define Gradio interface
-def interface_fn(prompt, negative_prompt, use_defaults, width, height, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
-    image, seed = generate_image(prompt, negative_prompt, use_defaults, width, height, guidance_scale, num_inference_steps, seed, randomize_seed, progress)
-    return image, seed, gr.update(value=seed)
-
-def reset_inputs():
-    return gr.update(value=''), gr.update(value=''), gr.update(value=True), gr.update(value=832), gr.update(value=1216), gr.update(value=7), gr.update(value=28), gr.update(value=0), gr.update(value=False)
-
-with gr.Blocks(title="UrangDiffusion 1.0 Demo", theme="NoCrypt/[email protected]") as demo:
-    gr.HTML(
-        "<h1>UrangDiffusion 1.0 Demo</h1>"
-        )
-    with gr.Row():
-        with gr.Column():
-            prompt_input = gr.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt")
-            negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt")
-            use_defaults_input = gr.Checkbox(label="Use Default Quality Tags and Negative Prompt", value=True)
-            width_input = gr.Slider(minimum=256, maximum=2048, step=32, label="Width", value=832)
-            height_input = gr.Slider(minimum=256, maximum=2048, step=32, label="Height", value=1216)
-            guidance_scale_input = gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7)
-            num_inference_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=28)
-            seed_input = gr.Slider(minimum=0, maximum=99999999, step=1, label="Seed", value=0, interactive=True)
-            randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=False)
-            generate_button = gr.Button("Generate")
-            reset_button = gr.Button("Reset")
-
-        with gr.Column():
-            output_image = gr.Image(type="pil", label="Generated Image")
-            output_seed = gr.Number(label="Seed", interactive=False)
-
-    generate_button.click(
-        interface_fn,
-        inputs=[
-            prompt_input, negative_prompt_input, use_defaults_input, width_input, height_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
-        ],
-        outputs=[output_image, output_seed, seed_input]
-    )
-    
-    reset_button.click(
-        reset_inputs,
-        inputs=[],
-        outputs=[
-            prompt_input, negative_prompt_input, use_defaults_input, width_input, height_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
-        ]
-    )
-
+import spaces
+import torch
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+import gradio as gr
+import random
+import tqdm
+
+# Enable TQDM progress tracking
+tqdm.monitor_interval = 0
+
+# Load the diffusion pipeline
+pipe = StableDiffusionXLPipeline.from_pretrained(
+    "kayfahaarukku/UrangDiffusion-1.0", 
+    torch_dtype=torch.float16, 
+    custom_pipeline="lpw_stable_diffusion_xl",
+    use_safetensors=True, 
+)
+pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+
+# Function to generate an image
+@spaces.GPU(duration=120)  # Adjust the duration as needed
+def generate_image(prompt, negative_prompt, use_defaults, width, height, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
+    pipe.to('cuda')  # Move the model to GPU when the function is called
+    
+    if randomize_seed:
+        seed = random.randint(0, 99999999)
+    if use_defaults:
+        prompt = f"{prompt}, masterpiece, best quality"
+        negative_prompt = f"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, {negative_prompt}"
+    generator = torch.manual_seed(seed)
+    
+    def callback(step, timestep, latents):
+        progress(step / num_inference_steps)
+        return
+    
+    image = pipe(
+        prompt, 
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height, 
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        callback=callback,
+        callback_steps=1
+    ).images[0]
+
+    torch.cuda.empty_cache()
+    pipe.to('cpu')  # Move the model back to CPU after generation
+
+    return image, seed
+
+# Define Gradio interface
+def interface_fn(prompt, negative_prompt, use_defaults, width, height, guidance_scale, num_inference_steps, seed, randomize_seed, progress=gr.Progress()):
+    image, seed = generate_image(prompt, negative_prompt, use_defaults, width, height, guidance_scale, num_inference_steps, seed, randomize_seed, progress)
+    return image, seed, gr.update(value=seed)
+
+def reset_inputs():
+    return gr.update(value=''), gr.update(value=''), gr.update(value=True), gr.update(value=832), gr.update(value=1216), gr.update(value=7), gr.update(value=28), gr.update(value=0), gr.update(value=False)
+
+with gr.Blocks(title="UrangDiffusion 1.0 Demo", theme="NoCrypt/[email protected]") as demo:
+    gr.HTML(
+        "<h1>UrangDiffusion 1.0 Demo</h1>"
+        )
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(lines=2, placeholder="Enter prompt here", label="Prompt")
+            negative_prompt_input = gr.Textbox(lines=2, placeholder="Enter negative prompt here", label="Negative Prompt")
+            use_defaults_input = gr.Checkbox(label="Use Default Quality Tags and Negative Prompt", value=True)
+            width_input = gr.Slider(minimum=256, maximum=2048, step=32, label="Width", value=832)
+            height_input = gr.Slider(minimum=256, maximum=2048, step=32, label="Height", value=1216)
+            guidance_scale_input = gr.Slider(minimum=1, maximum=20, step=0.5, label="Guidance Scale", value=7)
+            num_inference_steps_input = gr.Slider(minimum=1, maximum=100, step=1, label="Number of Inference Steps", value=28)
+            seed_input = gr.Slider(minimum=0, maximum=99999999, step=1, label="Seed", value=0, interactive=True)
+            randomize_seed_input = gr.Checkbox(label="Randomize Seed", value=False)
+            generate_button = gr.Button("Generate")
+            reset_button = gr.Button("Reset")
+
+        with gr.Column():
+            output_image = gr.Image(type="pil", label="Generated Image")
+            output_seed = gr.Number(label="Seed", interactive=False)
+
+    generate_button.click(
+        interface_fn,
+        inputs=[
+            prompt_input, negative_prompt_input, use_defaults_input, width_input, height_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
+        ],
+        outputs=[output_image, output_seed, seed_input]
+    )
+    
+    reset_button.click(
+        reset_inputs,
+        inputs=[],
+        outputs=[
+            prompt_input, negative_prompt_input, use_defaults_input, width_input, height_input, guidance_scale_input, num_inference_steps_input, seed_input, randomize_seed_input
+        ]
+    )
+
 demo.queue(max_size=20).launch(share=True)