Update code

app.py

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+from stable_cascade import web_demo
+import gradio as gr
+
+# Create a Gradio interface
+
+gradio_app = gr.Blocks()
+with gradio_app:
+    gr.HTML(
+        """
+    <h1 style='text-align: center'>
+    WhisperPlus: Advancing Speech-to-Text Processing 🚀
+    </h1>
+    """)
+    gr.HTML(
+        """
+        <h3 style='text-align: center'>
+        Follow me for more!
+        <a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a>  | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
+        </h3>
+        """)
+    with gr.Row():
+        with gr.Column():
+            web_demo()
+
+gradio_app.queue()
+gradio_app.launch(debug=True)

requirements.txt

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+git+https://github.com/kashif/diffusers.git@wuerstchen-v3
+peft

stable_cascade.py

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+import torch
+from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
+import gradio as gr
+
+
+# Initialize the prior and decoder pipelines
+prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to("cuda")
+decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16).to("cuda")
+
+def generate_images(
+    prompt="a photo of a girl",
+    negative_prompt="bad,ugly,deformed",
+    height=1024,
+    width=1024, 
+    guidance_scale=4.0, 
+    prior_inference_steps=20, 
+    decoder_inference_steps=10
+    ):
+    """
+    Generates images based on a given prompt using Stable Diffusion models on CUDA device.
+
+    Parameters:
+    - prompt (str): The prompt to generate images for.
+    - negative_prompt (str): The negative prompt to guide image generation away from.
+    - height (int): The height of the generated images.
+    - width (int): The width of the generated images.
+    - guidance_scale (float): The scale of guidance for the image generation.
+    - prior_inference_steps (int): The number of inference steps for the prior model.
+    - decoder_inference_steps (int): The number of inference steps for the decoder model.
+
+    Returns:
+    - List[PIL.Image]: A list of generated PIL Image objects.
+    """
+
+    # Generate image embeddings using the prior model
+    prior_output = prior(
+        prompt=prompt,
+        height=height,
+        width=width,
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale,
+        num_images_per_prompt=1,
+        num_inference_steps=prior_inference_steps
+    )
+
+    # Generate images using the decoder model and the embeddings from the prior model
+    decoder_output = decoder(
+        image_embeddings=prior_output.image_embeddings.half(),
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        guidance_scale=0.0,  # Guidance scale typically set to 0 for decoder as guidance is applied in the prior
+        output_type="pil",
+        num_inference_steps=decoder_inference_steps
+    ).images
+
+    return decoder_output[0]
+
+
+def web_demo():
+    with gr.Blocks():
+        with gr.Row():
+            with gr.Column():
+                text2image_prompt = gr.Textbox(
+                    lines=1,
+                    placeholder="Prompt",
+                    show_label=False,
+                )
+
+                text2image_negative_prompt = gr.Textbox(
+                    lines=1,
+                    placeholder="Negative Prompt",
+                    show_label=False,
+                )
+                with gr.Row():
+                    with gr.Column():
+                        text2image_height = gr.Slider(
+                            minimum=128,
+                            maximum=1280,
+                            step=32,
+                            value=512,
+                            label="Image Height",
+                        )
+
+                        text2image_width = gr.Slider(
+                            minimum=128,
+                            maximum=1280,
+                            step=32,
+                            value=512,
+                            label="Image Width",
+                        )
+                        with gr.Row():
+                            with gr.Column():
+                                text2image_guidance_scale = gr.Slider(
+                                    minimum=0.1,
+                                    maximum=15,
+                                    step=0.1,
+                                    value=4.0,
+                                    label="Guidance Scale",
+                                )                
+                                text2image_prior_inference_step = gr.Slider(
+                                    minimum=1,
+                                    maximum=50,
+                                    step=1,
+                                    value=20,
+                                    label="Prior Inference Step",
+                                )                
+                                
+                                text2image_decoder_inference_step = gr.Slider(
+                                    minimum=1,
+                                    maximum=50,
+                                    step=1,
+                                    value=10,
+                                    label="Decoder Inference Step",
+                                )               
+                text2image_predict = gr.Button(value="Generate Image")
+                
+            with gr.Column():
+                output_image = gr.Gallery(
+                    label="Generated images",
+                    show_label=False,
+                    elem_id="gallery",
+                )
+                
+            text2image_predict.click(
+                fn=generate_images,
+                inputs=[
+                    text2image_prompt,
+                    text2image_negative_prompt,
+                    text2image_height,
+                    text2image_width,
+                    text2image_guidance_scale,
+                    text2image_prior_inference_step,
+                    text2image_decoder_inference_step
+                ],
+                outputs=output_image,
+            )