Spaces:
Paused
Paused
| 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) | |
| 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) | |