Update code

app.py

@@ -0,0 +1,26 @@
+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

@@ -0,0 +1,2 @@
+git+https://github.com/kashif/diffusers.git@wuerstchen-v3
+peft

stable_cascade.py

@@ -0,0 +1,136 @@
+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,
+ )