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216
lollms/services/tti/diffusers_client/lollms_diffusers_client.py
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| Original file line number | Diff line number | Diff line change |
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| # Title LollmsDiffusers | ||
| # Licence: MIT | ||
| # Author : Paris Neo | ||
| # All rights are reserved | ||
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| from pathlib import Path | ||
| import sys | ||
| from lollms.app import LollmsApplication | ||
| from lollms.utilities import PackageManager, check_and_install_torch, find_next_available_filename, install_cuda, check_torch_version | ||
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| import sys | ||
| import requests | ||
| from typing import List, Dict, Any | ||
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| from ascii_colors import ASCIIColors, trace_exception | ||
| from lollms.paths import LollmsPaths | ||
| from lollms.tti import LollmsTTI | ||
| from lollms.utilities import git_pull | ||
| from tqdm import tqdm | ||
| from PIL import Image | ||
| import threading | ||
| import base64 | ||
| from PIL import Image | ||
| import io | ||
| import pipmaster as pm | ||
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| def adjust_dimensions(value: int) -> int: | ||
| """Adjusts the given value to be divisible by 8.""" | ||
| return (value // 8) * 8 | ||
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| def download_file(url, folder_path, local_filename): | ||
| # Make sure 'folder_path' exists | ||
| folder_path.mkdir(parents=True, exist_ok=True) | ||
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| with requests.get(url, stream=True) as r: | ||
| r.raise_for_status() | ||
| total_size = int(r.headers.get('content-length', 0)) | ||
| progress_bar = tqdm(total=total_size, unit='B', unit_scale=True) | ||
| with open(folder_path / local_filename, 'wb') as f: | ||
| for chunk in r.iter_content(chunk_size=8192): | ||
| f.write(chunk) | ||
| progress_bar.update(len(chunk)) | ||
| progress_bar.close() | ||
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| return local_filename | ||
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| def install_diffusers(lollms_app:LollmsApplication): | ||
| root_dir = lollms_app.lollms_paths.personal_path | ||
| shared_folder = root_dir/"shared" | ||
| diffusers_folder = shared_folder / "diffusers" | ||
| diffusers_folder.mkdir(exist_ok=True, parents=True) | ||
| models_dir = diffusers_folder / "models" | ||
| models_dir.mkdir(parents=True, exist_ok=True) | ||
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| PackageManager.reinstall("diffusers") | ||
| PackageManager.reinstall("xformers") | ||
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| def upgrade_diffusers(lollms_app:LollmsApplication): | ||
| PackageManager.install_or_update("diffusers") | ||
| PackageManager.install_or_update("xformers") | ||
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| class LollmsDiffusersClient(LollmsTTI): | ||
| has_controlnet = False | ||
| def __init__( | ||
| self, | ||
| app:LollmsApplication, | ||
| wm = "Artbot", | ||
| base_url:str="http://localhost:8593" | ||
| ): | ||
| super().__init__("diffusers_client",app) | ||
| self.ready = False | ||
| # Get the current directory | ||
| lollms_paths = app.lollms_paths | ||
| root_dir = lollms_paths.personal_path | ||
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| self.base_url = base_url | ||
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| self.wm = wm | ||
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| shared_folder = root_dir/"shared" | ||
| self.diffusers_folder = shared_folder / "diffusers" | ||
| self.output_dir = root_dir / "outputs/diffusers" | ||
| self.models_dir = self.diffusers_folder / "models" | ||
| self.output_dir.mkdir(parents=True, exist_ok=True) | ||
| self.models_dir.mkdir(parents=True, exist_ok=True) | ||
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| ASCIIColors.green(" _ _ _ _ _ __ __ ") | ||
| ASCIIColors.green(" | | | | | | (_)/ _|/ _| ") | ||
| ASCIIColors.green(" | | ___ | | |_ __ ___ ___ __| |_| |_| |_ _ _ ___ ___ _ __ ___ ") | ||
| ASCIIColors.green(" | | / _ \| | | '_ ` _ \/ __| / _` | | _| _| | | / __|/ _ \ '__/ __| ") | ||
| ASCIIColors.green(" | |___| (_) | | | | | | | \__ \| (_| | | | | | | |_| \__ \ __/ | \__ \ ") | ||
| ASCIIColors.green(" |______\___/|_|_|_| |_| |_|___/ \__,_|_|_| |_| \__,_|___/\___|_| |___/ ") | ||
| ASCIIColors.green(" ______ ") | ||
| ASCIIColors.green(" |______| ") | ||
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| @staticmethod | ||
| def verify(app:LollmsApplication): | ||
| # Clone repository | ||
| root_dir = app.lollms_paths.personal_path | ||
| shared_folder = root_dir/"shared" | ||
| diffusers_folder = shared_folder / "diffusers" | ||
| return diffusers_folder.exists() | ||
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| def get(app:LollmsApplication): | ||
| root_dir = app.lollms_paths.personal_path | ||
| shared_folder = root_dir/"shared" | ||
| diffusers_folder = shared_folder / "diffusers" | ||
| diffusers_script_path = diffusers_folder / "lollms_diffusers.py" | ||
| git_pull(diffusers_folder) | ||
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| if diffusers_script_path.exists(): | ||
| ASCIIColors.success("lollms_diffusers found.") | ||
| ASCIIColors.success("Loading source file...",end="") | ||
| # use importlib to load the module from the file path | ||
| from lollms.services.tti.diffusers.lollms_diffusers import LollmsDiffusers | ||
| ASCIIColors.success("ok") | ||
| return LollmsDiffusers | ||
| def paint( | ||
| self, | ||
| positive_prompt, | ||
| negative_prompt="", | ||
| sampler_name="", | ||
| seed=-1, | ||
| scale=7.5, | ||
| steps=20, | ||
| img2img_denoising_strength=0.9, | ||
| width=512, | ||
| height=512, | ||
| restore_faces=True, | ||
| output_path=None | ||
| ): | ||
| url = f"{self.base_url}/generate-image" | ||
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| payload = { | ||
| "positive_prompt": positive_prompt, | ||
| "negative_prompt": negative_prompt, | ||
| "sampler_name": sampler_name, | ||
| "seed": seed, | ||
| "scale": scale, | ||
| "steps": steps, | ||
| "width": width, | ||
| "height": height, | ||
| "restore_faces": restore_faces | ||
| } | ||
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| try: | ||
| response = requests.post(url, json=payload) | ||
| response.raise_for_status() | ||
| result = response.json() | ||
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| # Assuming the server returns the image path | ||
| server_image_path = result['image_path'] | ||
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| # If output_path is not provided, use the server's image path | ||
| if output_path is None: | ||
| output_path = server_image_path | ||
| else: | ||
| # Copy the image from server path to output_path | ||
| # This part needs to be implemented based on how you want to handle file transfer | ||
| pass | ||
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| return { | ||
| "image_path": output_path, | ||
| "prompt": result['prompt'], | ||
| "negative_prompt": result['negative_prompt'] | ||
| } | ||
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| except requests.exceptions.RequestException as e: | ||
| print(f"An error occurred: {e}") | ||
| return None | ||
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| def save_image(self, image_data, output_path): | ||
| image = Image.open(io.BytesIO(base64.b64decode(image_data))) | ||
| image.save(output_path) | ||
| print(f"Image saved to {output_path}") | ||
|
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| def paint_from_images(self, positive_prompt: str, | ||
| images: List[str], | ||
| negative_prompt: str = "", | ||
| sampler_name="", | ||
| seed=-1, | ||
| scale=7.5, | ||
| steps=20, | ||
| img2img_denoising_strength=0.9, | ||
| width=512, | ||
| height=512, | ||
| restore_faces=True, | ||
| output_path=None | ||
| ) -> List[Dict[str, str]]: | ||
| import torch | ||
| if sampler_name!="": | ||
| sc = self.get_scheduler_by_name(sampler_name) | ||
| if sc: | ||
| self.model.scheduler = sc | ||
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| if output_path is None: | ||
| output_path = self.output_dir | ||
| if seed!=-1: | ||
| generator = torch.Generator("cuda").manual_seed(seed) | ||
| image = self.model(positive_prompt, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps, generator=generator).images[0] | ||
| else: | ||
| image = self.model(positive_prompt, negative_prompt=negative_prompt, height=height, width=width, guidance_scale=scale, num_inference_steps=steps).images[0] | ||
| output_path = Path(output_path) | ||
| fn = find_next_available_filename(output_path,"diff_img_") | ||
| # Save the image | ||
| image.save(fn) | ||
| return fn, {"prompt":positive_prompt, "negative_prompt":negative_prompt} | ||
|
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