Delete app.py

app.py

@@ -1,140 +0,0 @@
-import gradio as gr
-from PIL import Image
-import torch
-from diffusers import DiffusionPipeline, AutoencoderTiny
-import os
-
-SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
-TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)
-
-if SAFETY_CHECKER:
-    pipe = DiffusionPipeline.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-        custom_pipeline="lcm_txt2img",
-        scheduler=None,
-    )
-else:
-    pipe = DiffusionPipeline.from_pretrained(
-        "SimianLuo/LCM_Dreamshaper_v7",
-        custom_pipeline="lcm_txt2img",
-        scheduler=None,
-        safety_checker=None,
-    )
-pipe.to(device="cuda", dtype=torch.float16)
-pipe.vae = AutoencoderTiny.from_pretrained(
-    "madebyollin/taesd", device="cuda", torch_dtype=torch.float16
-)
-pipe.vae = pipe.vae.cuda()
-pipe.unet.to(memory_format=torch.channels_last)
-pipe.set_progress_bar_config(disable=True)
-
-if TORCH_COMPILE:
-    pipe.text_encoder = torch.compile(pipe.text_encoder, mode="max-autotune")
-    pipe.tokenizer = torch.compile(pipe.tokenizer, mode="max-autotune")
-    pipe.unet = torch.compile(pipe.unet, mode="max-autotune")
-    pipe.vae = torch.compile(pipe.vae, mode="max-autotune")
-
-
-def predict(prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed=1231231):
-    torch.manual_seed(seed)
-    results = pipe(
-        prompt1=prompt1,
-        prompt2=prompt2,
-        sv=merge_ratio,
-        sharpness=sharpness,
-        width=512,
-        height=512,
-        num_inference_steps=steps,
-        guidance_scale=guidance,
-        lcm_origin_steps=50,
-        output_type="pil",
-        # return_dict=False,
-    )
-    nsfw_content_detected = (
-        results.nsfw_content_detected[0]
-        if "nsfw_content_detected" in results
-        else False
-    )
-    if nsfw_content_detected:
-        raise gr.Error("NSFW content detected. Please try another prompt.")
-    return results.images[0]
-
-
-css = """
-#container{
-    margin: 0 auto;
-    max-width: 80rem;
-}
-#intro{
-    max-width: 32rem;
-    text-align: center;
-    margin: 0 auto;
-}
-"""
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="container"):
-        gr.Markdown(
-            """# SDZoom
-
-    Welcome to sdzoom, a testbed application designed for optimizing and experimenting with various 
-    configurations to achieve the fastest Stable Diffusion (SD) pipelines.
-    RTSD leverages the expertise provided by Latent Consistency Models (LCM). For more information about LCM,
-    visit their website at [Latent Consistency Models](https://latent-consistency-models.github.io/).
-
-    """,
-            elem_id="intro",
-        )
-        with gr.Row():
-            with gr.Column():
-                image = gr.Image(type="pil")
-            with gr.Column():
-                merge_ratio = gr.Slider(
-                    value=50, minimum=1, maximum=100, step=1, label="Merge Ratio"
-                )
-                guidance = gr.Slider(
-                    label="Guidance", minimum=1, maximum=50, value=10.0, step=0.01
-                )
-                steps = gr.Slider(label="Steps", value=4, minimum=2, maximum=20, step=1)
-                sharpness = gr.Slider(
-                    value=1.0, minimum=0, maximum=1, step=0.001, label="Sharpness"
-                )
-                seed = gr.Slider(
-                    randomize=True, minimum=0, maximum=12013012031030, label="Seed"
-                )
-                prompt1 = gr.Textbox(label="Prompt 1")
-                prompt2 = gr.Textbox(label="Prompt 2")
-                generate_bt = gr.Button("Generate")
-
-        inputs = [prompt1, prompt2, merge_ratio, guidance, steps, sharpness, seed]
-        gr.Examples(
-            examples=[
-                ["Elon Musk", "Mark Zuckerberg", 50, 10.0, 4, 1.0, 1231231],
-                ["Elon Musk", "Bill Gates", 50, 10.0, 4, 1.0, 53453],
-                [
-                    "Asian women, intricate jewlery in her hair, 8k",
-                    "Tom Cruise, intricate jewlery in her hair, 8k",
-                    50,
-                    10.0,
-                    4,
-                    1.0,
-                    542343,
-                ],
-            ],
-            fn=predict,
-            inputs=inputs,
-            outputs=image,
-        )
-        generate_bt.click(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-        seed.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-        merge_ratio.change(
-            fn=predict, inputs=inputs, outputs=image, show_progress=False
-        )
-        guidance.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-        steps.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-        sharpness.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-        prompt1.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-        prompt2.change(fn=predict, inputs=inputs, outputs=image, show_progress=False)
-
-demo.queue()
-if __name__ == "__main__":
-    demo.launch()