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| import torch | |
| import requests | |
| import urllib.request | |
| import streamlit as st | |
| if torch.cuda.is_available(): | |
| device = 'cuda' | |
| else: | |
| device = 'cpu' | |
| validT2IModelTypes = ["KandinskyPipeline", "StableDiffusionPipeline", "DiffusionPipeline", "StableDiffusionXLPipeline", | |
| "LatentConsistencyModelPipeline"] | |
| def check_if_model_exists(repoName): | |
| modelLoaded = None | |
| huggingFaceURL = "https://huggingface.co/" + repoName + "/raw/main/model_index.json" | |
| response = requests.get(huggingFaceURL).status_code | |
| if response != 200: | |
| return None | |
| else: | |
| # modelLoaded = huggingFaceURL | |
| return huggingFaceURL | |
| def get_model_info(modelURL): | |
| modelType = None | |
| try: | |
| with urllib.request.urlopen(modelURL) as f: | |
| modelType = str(f.read()).split(',\\n')[0].split(':')[1].replace('"', '').strip() | |
| except urllib.error.URLError as e: | |
| st.write(e.reason) | |
| return modelType | |
| # Definitely need to work on these functions to consider adaptors | |
| # currently only works if there is a model index json file | |
| def import_model(modelID, modelType): | |
| T2IModel = None | |
| if modelType in validT2IModelTypes: | |
| if modelType == 'StableDiffusionXLPipeline': | |
| from diffusers import StableDiffusionXLPipeline | |
| T2IModel = StableDiffusionXLPipeline.from_pretrained(modelID, torch_dtype=torch.float16) | |
| elif modelType == 'LatentConsistencyModelPipeline': | |
| from diffusers import DiffusionPipeline | |
| T2IModel = DiffusionPipeline.from_pretrained(modelID, torch_dtype=torch.float16) | |
| else: | |
| from diffusers import AutoPipelineForText2Image | |
| T2IModel = AutoPipelineForText2Image.from_pretrained(modelID, torch_dtype=torch.float16) | |
| T2IModel.to("cuda") | |
| return T2IModel | |