Delete stable_cascade.py

stable_cascade.py

@@ -1,177 +0,0 @@
-import torch, os
-from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
-import gradio as gr
-from io import BytesIO
-import base64
-import re
-
-SECRET_TOKEN = os.getenv('SECRET_TOKEN', 'default_secret')
-
-# Regex pattern to match data URI scheme
-data_uri_pattern = re.compile(r'data:image/(png|jpeg|jpg|webp);base64,')
-
-def readb64(b64):
-    # Remove any data URI scheme prefix with regex
-    b64 = data_uri_pattern.sub("", b64)
-    # Decode and open the image with PIL
-    img = Image.open(BytesIO(base64.b64decode(b64)))
-    return img
-    
-# convert from PIL to base64
-def writeb64(image):
-    buffered = BytesIO()
-    image.save(buffered, format="PNG")
-    b64image = base64.b64encode(buffered.getvalue())
-    b64image_str = b64image.decode("utf-8")
-    return b64image_str
-
-
-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(
-    secret_token="",
-    prompt="",
-    negative_prompt="bad,ugly,deformed",
-    height=1024,
-    width=1024, 
-    guidance_scale=4.0, 
-    seed=42,
-    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.
-    """
-    if secret_token != SECRET_TOKEN:
-        raise gr.Error(
-            f'Invalid secret token. Please fork the original space if you want to use it for yourself.')
-
-    generator = torch.Generator(device="cuda").manual_seed(int(seed))
-
-    # Generate image embeddings using the prior model
-    prior_output = prior(
-        prompt=prompt,
-        generator=generator,
-        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,
-        generator=generator,
-        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
-
-    image = decoder_output[0]
-        
-    image_base64 = writeb64(image)
-        
-    return image_base64
-
-
-def web_demo():
-    with gr.Blocks():
-        with gr.Row():
-            with gr.Column():
-                secret_token = gr.Textbox(
-                    placeholder="Secret token",
-                    show_label=False,
-                )
-                text2image_prompt = gr.Textbox(
-                    lines=1,
-                    placeholder="Prompt",
-                    show_label=False,
-                )
-
-                text2image_negative_prompt = gr.Textbox(
-                    lines=1,
-                    placeholder="Negative Prompt",
-                    show_label=False,
-                )
-                
-                text2image_seed = gr.Number(
-                    value=42,
-                    label="Seed",
-                )
-                
-                with gr.Row():
-                    with gr.Column():
-                        text2image_height = gr.Slider(
-                            minimum=128,
-                            maximum=1024,
-                            step=32,
-                            value=1024,
-                            label="Image Height",
-                        )
-
-                        text2image_width = gr.Slider(
-                            minimum=128,
-                            maximum=1024,
-                            step=32,
-                            value=1024,
-                            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")
-                
-            output_image_base64 = gr.Text()
-                
-            text2image_predict.click(
-                fn=generate_images,
-                inputs=[
-                    secret_token,
-                    text2image_prompt,
-                    text2image_negative_prompt,
-                    text2image_height,
-                    text2image_width,
-                    text2image_guidance_scale,
-                    text2image_seed,
-                    text2image_prior_inference_step,
-                    text2image_decoder_inference_step
-                ],
-                outputs=output_image_base64,
-                api_name='run',
-            )