add app

app.py

@@ -0,0 +1,274 @@
+import os
+import socket
+import requests
+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import FileResponse
+from PIL import Image
+import torch
+from diffusers import (
+    DiffusionPipeline,
+    AutoencoderKL,
+    StableDiffusionControlNetPipeline,
+    ControlNetModel,
+    StableDiffusionLatentUpscalePipeline,
+    StableDiffusionImg2ImgPipeline,
+    StableDiffusionControlNetImg2ImgPipeline,
+    DPMSolverMultistepScheduler,
+    EulerDiscreteScheduler
+)
+import random
+import time
+import tempfile
+
+app = FastAPI()
+
+BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
+
+# Initialize both pipelines
+vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
+controlnet = ControlNetModel.from_pretrained("monster-labs/control_v1p_sd15_qrcode_monster", torch_dtype=torch.float16)
+main_pipe = StableDiffusionControlNetPipeline.from_pretrained(
+    BASE_MODEL,
+    controlnet=controlnet,
+    vae=vae,
+    safety_checker=None,
+    torch_dtype=torch.float16,
+).to("cuda")
+image_pipe = StableDiffusionControlNetImg2ImgPipeline(**main_pipe.components)
+
+# 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 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",
+    #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], my_seed
+        
+import os
+
+def generate_image_from_parameters(prompt, guidance_scale, controlnet_scale, controlnet_end, upscaler_strength, seed, sampler_type, image):
+    try:
+        # Save the uploaded image to a temporary file
+        temp_image_path = f"/tmp/{int(time.time())}_{image.filename}"
+        with open(temp_image_path, "wb") as temp_image:
+            temp_image.write(image.file.read())
+
+        # Open the uploaded image using PIL
+        control_image = Image.open(temp_image_path)
+
+        # Call existing inference function with the provided parameters
+        generated_image, _, _, _ = inference(control_image, prompt, "", guidance_scale, controlnet_scale, 0, controlnet_end, upscaler_strength, seed, sampler_type)
+
+        # Specify the desired output directory for saving generated images
+        output_directory = "/home/user/app/generated_files"
+
+        # Create the output directory if it doesn't exist
+        os.makedirs(output_directory, exist_ok=True)
+
+        # Generate a unique filename for the saved image
+        filename = f"generated_image_{int(time.time())}.png"
+
+        # Save the generated image to the permanent location
+        output_path = os.path.join(output_directory, filename)
+        generated_image.save(output_path, format="PNG")
+
+        # Return the generated image path
+        return output_path
+
+    except Exception as e:
+        # Handle exceptions and return an error message if something goes wrong
+        return str(e)
+
+@app.post("/generate_image")
+async def generate_image(
+    prompt: str = Form(...),
+    guidance_scale: float = Form(...),
+    controlnet_scale: float = Form(...),
+    controlnet_end: float = Form(...),
+    upscaler_strength: float = Form(...),
+    seed: int = Form(...),
+    sampler_type: str = Form(...),
+    image: UploadFile = File(...)
+):
+    try:
+        # Save the uploaded image to a temporary file
+        temp_image_path = f"/tmp/{int(time.time())}_{image.filename}"
+        with open(temp_image_path, "wb") as temp_image:
+            temp_image.write(image.file.read())
+
+        # Open the uploaded image using PIL
+        control_image = Image.open(temp_image_path)
+
+        # Call existing inference function with the provided parameters
+        generated_image, _, _, _ = inference(control_image, prompt, "", guidance_scale, controlnet_scale, 0, controlnet_end, upscaler_strength, seed, sampler_type)
+
+        # Specify the desired output directory for saving generated images
+        output_directory = "/home/user/app/generated_files"
+
+        # Create the output directory if it doesn't exist
+        os.makedirs(output_directory, exist_ok=True)
+
+        # Generate a unique filename for the saved image
+        filename = f"generated_image_{int(time.time())}.png"
+
+        # Save the generated image to the permanent location
+        output_path = os.path.join(output_directory, filename)
+        generated_image.save(output_path, format="PNG")
+
+        # Return the generated image path
+        return output_path
+
+    except Exception as e:
+        # Handle exceptions and return an error message if something goes wrong
+        return str(e)
+
+if __name__ == "__main__":
+    import uvicorn
+
+    # Get internal IP address
+    internal_ip = socket.gethostbyname(socket.gethostname())
+
+    # Get public IP address using a public API (this may not work if you are behind a router/NAT)
+    try:
+        public_ip = requests.get("http://api.ipify.org").text
+    except requests.RequestException:
+        public_ip = "Not Available"
+
+    print(f"Internal URL: http://{internal_ip}:8000")
+    print(f"Public URL: http://{public_ip}:8000")
+
+    uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run("app:app", host="0.0.0.0", port=8000, reload=True)