Upload app.py

app.py

@@ -15,256 +15,30 @@ from diffusers import (
     EulerDiscreteScheduler  # <-- Added import
 )
 
+print(f"Is CUDA available: {torch.cuda.is_available()}")
+print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
+
 import time
 from style import css
 
 BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
 
-# Initialize both pipelines
-vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
-#init_pipe = DiffusionPipeline.from_pretrained("SG161222/Realistic_Vision_V5.1_noVAE", torch_dtype=torch.float16)
-controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)#, torch_dtype=torch.float16)
-main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
-    BASE_MODEL,
-    controlnet=controlnet,
-    vae=vae,
-    safety_checker=None,
-    torch_dtype=torch.float16,
-).to("cuda")
-
-#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
-#main_pipe.unet.to(memory_format=torch.channels_last)
-#main_pipe.unet = torch.compile(main_pipe.unet, mode="reduce-overhead", fullgraph=True)
-#model_id = "stabilityai/sd-x2-latent-upscaler"
-image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
-
-#image_pipe.unet = torch.compile(image_pipe.unet, mode="reduce-overhead", fullgraph=True)
-#upscaler = StableDiffusionLatentUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-#upscaler.to("cuda")
-
-
-# Sampler map
-SAMPLER_MAP = {
-    "DPM++ Karras SDE": lambda config: DPMSolverMultistepScheduler.from_config(config, use_karras=True, algorithm_type="sde-dpmsolver++"),
-    "Euler": lambda config: EulerDiscreteScheduler.from_config(config),
-}
-
-def center_crop_resize(img, output_size=(512, 512)):
-    width, height = img.size
-
-    # Calculate dimensions to crop to the center
-    new_dimension = min(width, height)
-    left = (width - new_dimension)/2
-    top = (height - new_dimension)/2
-    right = (width + new_dimension)/2
-    bottom = (height + new_dimension)/2
-
-    # Crop and resize
-    img = img.crop((left, top, right, bottom))
-    img = img.resize(output_size)
-
-    return img
-
-def common_upscale(samples, width, height, upscale_method, crop=False):
-        if crop == "center":
-            old_width = samples.shape[3]
-            old_height = samples.shape[2]
-            old_aspect = old_width / old_height
-            new_aspect = width / height
-            x = 0
-            y = 0
-            if old_aspect > new_aspect:
-                x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
-            elif old_aspect < new_aspect:
-                y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
-            s = samples[:,:,y:old_height-y,x:old_width-x]
-        else:
-            s = samples
-
-        return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
-
-def upscale(samples, upscale_method, scale_by):
-        #s = samples.copy()
-        width = round(samples["images"].shape[3] * scale_by)
-        height = round(samples["images"].shape[2] * scale_by)
-        s = common_upscale(samples["images"], width, height, upscale_method, "disabled")
-        return (s)
-
-def check_inputs(prompt: str, control_image: Image.Image):
-    if control_image is None:
-        raise gr.Error("Please select or upload a photo of a person.")
-    if prompt is None or prompt == "":
-        raise gr.Error("Prompt is required")
-
-def convert_to_pil(base64_image):
-    pil_image = processing_utils.decode_base64_to_image(base64_image)
-    return pil_image
-
-def convert_to_base64(pil_image):
-    base64_image = processing_utils.encode_pil_to_base64(pil_image)
-    return base64_image
-
-# Inference function
-def inference(
-    control_image: Image.Image,
-    prompt: str,
-    negative_prompt: str,
-    guidance_scale: float = 8.0,
-    controlnet_conditioning_scale: float = 1,
-    control_guidance_start: float = 1,    
-    control_guidance_end: float = 1,
-    upscaler_strength: float = 0.5,
-    seed: int = -1,
-    sampler = "DPM++ Karras SDE",
-    progress = gr.Progress(track_tqdm=True),
-    profile: gr.OAuthProfile | None = None,
-):
-    start_time = time.time()
-    start_time_struct = time.localtime(start_time)
-    start_time_formatted = time.strftime("%H:%M:%S", start_time_struct)
-    print(f"Inference started at {start_time_formatted}")
-    
-    # Generate the initial image
-    #init_image = init_pipe(prompt).images[0]
-
-    # Rest of your existing code
-    control_image_small = center_crop_resize(control_image)
-    control_image_large = center_crop_resize(control_image, (1024, 1024))
-
-    main_pipe.scheduler = SAMPLER_MAP[sampler](main_pipe.scheduler.config)
-    my_seed = random.randint(0, 2**32 - 1) if seed == -1 else seed
-    generator = torch.Generator(device="cuda").manual_seed(my_seed)
-    
-    out = main_pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=control_image_small,
-        guidance_scale=float(guidance_scale),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-        generator=generator,
-        control_guidance_start=float(control_guidance_start),
-        control_guidance_end=float(control_guidance_end),
-        num_inference_steps=15,
-        output_type="latent"
-    )
-    upscaled_latents = upscale(out, "nearest-exact", 2)
-    out_image = image_pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        control_image=control_image_large,        
-        image=upscaled_latents,
-        guidance_scale=float(guidance_scale),
-        generator=generator,
-        num_inference_steps=20,
-        strength=upscaler_strength,
-        control_guidance_start=float(control_guidance_start),
-        control_guidance_end=float(control_guidance_end),
-        controlnet_conditioning_scale=float(controlnet_conditioning_scale)
-    )
-    end_time = time.time()
-    end_time_struct = time.localtime(end_time)
-    end_time_formatted = time.strftime("%H:%M:%S", end_time_struct)
-    print(f"Inference ended at {end_time_formatted}, taking {end_time-start_time}s")
-
-    # Save image + metadata
-    # user_history.save_image(
-    #     label=prompt,
-    #     image=out_image["images"][0],
-    #     profile=profile,
-    #     metadata={
-    #         "prompt": prompt,
-    #         "negative_prompt": negative_prompt,
-    #         "guidance_scale": guidance_scale,
-    #         "controlnet_conditioning_scale": controlnet_conditioning_scale,
-    #         "control_guidance_start": control_guidance_start,
-    #         "control_guidance_end": control_guidance_end,
-    #         "upscaler_strength": upscaler_strength,
-    #         "seed": seed,
-    #         "sampler": sampler,
-    #     },
-    # )
-
-    return out_image["images"][0], gr.update(visible=True), gr.update(visible=True), my_seed
-
-with gr.Blocks() as app:
-    gr.Markdown(
-        '''
-        <center><h1>Core Ultra Heroes</h1></span>  
-        <span font-size:16px;">Turn yourself into an AI-powered superhero!</span>  
-        </center>
- 
-        '''
-    )
-    state_img_input = gr.State()
-    state_img_output = gr.State()
-    with gr.Row():
-        with gr.Column():
-            control_image = gr.Image(label="Provide a photo of yourself", type="pil", elem_id="control_image")
-            # controlnet_conditioning_scale = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.8, label="Illusion strength", elem_id="illusion_strength", info="ControlNet conditioning scale")
-            prompt = gr.Textbox(label="Prompt", elem_id="prompt", info="Type what you want to generate", placeholder="Medieval village scene with busy streets and castle in the distance")
-            negative_prompt = gr.Textbox(label="Negative Prompt", info="Type what you don't want to see", value="low quality", elem_id="negative_prompt")
-            with gr.Accordion(label="Advanced Options", open=False):
-                guidance_scale = gr.Slider(minimum=0.0, maximum=50.0, step=0.25, value=7.5, label="Guidance Scale")
-                sampler = gr.Dropdown(choices=list(SAMPLER_MAP.keys()), value="Euler")
-                control_start = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0, label="Start of ControlNet")
-                control_end = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="End of ControlNet")
-                strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=1, label="Strength of the upscaler")
-                seed = gr.Slider(minimum=-1, maximum=9999999999, step=1, value=-1, label="Seed", info="-1 means random seed")
-                used_seed = gr.Number(label="Last seed used",interactive=False)
-            run_btn = gr.Button("Run")
-        with gr.Column():
-            result_image = gr.Image(label="You're a hero!", interactive=False, elem_id="output")
-
-    controlnet_conditioning_scale = 0.5
-
-    prompt.submit(
-        check_inputs,
-        inputs=[prompt, control_image],
-        queue=False
-    ).success(
-        convert_to_pil,
-        inputs=[control_image],
-        outputs=[state_img_input],
-        queue=False,
-        preprocess=False,
-    ).success(
-        inference,
-        inputs=[state_img_input, prompt, negative_prompt, guidance_scale, control_start, control_end, strength, seed, sampler],
-        outputs=[state_img_output, result_image, used_seed]
-    ).success(
-        convert_to_base64,
-        inputs=[state_img_output],
-        outputs=[result_image],
-        queue=False,
-        postprocess=False
-    )
-    run_btn.click(
-        check_inputs,
-        inputs=[prompt, control_image],
-        queue=False
-    ).success(
-        convert_to_pil,
-        inputs=[control_image],
-        outputs=[state_img_input],
-        queue=False,
-        preprocess=False,
-    ).success(
-        inference,
-        inputs=[state_img_input, prompt, negative_prompt, guidance_scale, controlnet_conditioning_scale, control_start, control_end, strength, seed, sampler],
-        outputs=[state_img_output, result_image, used_seed]
-    ).success(
-        convert_to_base64,
-        inputs=[state_img_output],
-        outputs=[result_image],
-        queue=False,
-        postprocess=False
-    )
-
-with gr.Blocks(css=css) as app_with_history:
-    with gr.Tab("Demo"):
-        app.render()
-
-app_with_history.queue(max_size=20,api_open=False )
-
-if __name__ == "__main__":
-    app_with_history.launch(max_threads=400)
+title = "Flan T5 and Vanilla T5"
+description = "This demo compares [T5-large](https://huggingface.co/t5-large) and [Flan-T5-XX-large](https://huggingface.co/google/flan-t5-xxl). Note that T5 expects a very specific format of the prompts, so the examples below are not necessarily the best prompts to compare."
+
+def inference(text):
+  output_flan = ""
+  output_vanilla = ""
+  return [output_flan, output_vanilla]
+
+io = gr.Interface(
+  inference,
+  gr.Textbox(lines=3),
+  outputs=[
+    gr.Textbox(lines=3, label="Flan T5"),
+    gr.Textbox(lines=3, label="T5")
+  ],
+  title=title,
+  description=description,
+)
+io.launch()