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https://github.com/sd-webui/stable-diffusion-webui.git
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Merge pull request #1128 from codedealer/optimized-mode
fix: add optimized mode to streamlit
This commit is contained in:
Thomas Mello
GitHub
commit
066f9a31aa
@ -31,6 +31,7 @@ general:
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precision: "autocast"
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optimized: False
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optimized_turbo: False
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optimized_config: "optimizedSD/v1-inference.yaml"
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update_preview: True
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update_preview_frequency: 5
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@ -163,15 +163,15 @@ def img2img(prompt: str = '', init_info: any = None, init_info_mask: any = None,
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mask = torch.from_numpy(mask).to(st.session_state["device"])
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if st.session_state['defaults'].general.optimized:
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modelFS.to(st.session_state["device"] )
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st.session_state.modelFS.to(st.session_state["device"] )
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init_image = 2. * image - 1.
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init_image = init_image.to(st.session_state["device"])
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init_latent = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).get_first_stage_encoding((st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
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init_latent = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).get_first_stage_encoding((st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).encode_first_stage(init_image)) # move to latent space
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if st.session_state['defaults'].general.optimized:
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mem = torch.cuda.memory_allocated()/1e6
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modelFS.to("cpu")
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st.session_state.modelFS.to("cpu")
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while(torch.cuda.memory_allocated()/1e6 >= mem):
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time.sleep(1)
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@ -163,40 +163,33 @@ def load_models(continue_prev_run = False, use_GFPGAN=False, use_RealESRGAN=Fals
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if "RealESRGAN" in st.session_state:
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del st.session_state["RealESRGAN"]
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if "model" in st.session_state:
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if "model" in st.session_state and st.session_state["custom_model"] == custom_model:
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# TODO: check if the optimized mode was changed?
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print("Model already loaded")
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return
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else:
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try:
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del st.session_state["model"]
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del st.session_state.model
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del st.session_state.modelCS
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del st.session_state.modelFS
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except KeyError:
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pass
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config = OmegaConf.load(st.session_state["defaults"].general.default_model_config)
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# At this point the model is either
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# is not loaded yet or have been evicted:
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# load new model into memory
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st.session_state.custom_model = custom_model
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if custom_model == st.session_state["defaults"].general.default_model:
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model = load_model_from_config(config, st.session_state["defaults"].general.default_model_path)
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else:
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model = load_model_from_config(config, os.path.join("models","custom", f"{custom_model}.ckpt"))
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config, device, model, modelCS, modelFS = load_sd_model(custom_model)
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st.session_state["custom_model"] = custom_model
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st.session_state["device"] = torch.device(f"cuda:{defaults.general.gpu}") if torch.cuda.is_available() else torch.device("cpu")
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st.session_state["model"] = (model if st.session_state["defaults"].general.no_half else model.half()).to(st.session_state["device"] )
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else:
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config = OmegaConf.load(st.session_state["defaults"].general.default_model_config)
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st.session_state.device = device
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st.session_state.model = model
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st.session_state.modelCS = modelCS
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st.session_state.modelFS = modelFS
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if custom_model == st.session_state["defaults"].general.default_model:
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model = load_model_from_config(config, st.session_state["defaults"].general.default_model_path)
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else:
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model = load_model_from_config(config, os.path.join("models","custom", f"{custom_model}.ckpt"))
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st.session_state["custom_model"] = custom_model
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st.session_state["device"] = torch.device(f"cuda:{st.session_state['defaults'].general.gpu}") if torch.cuda.is_available() else torch.device("cpu")
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st.session_state["model"] = (model if st.session_state['defaults'].general.no_half else model.half()).to(st.session_state["device"] )
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print("Model loaded.")
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print("Model loaded.")
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def load_model_from_config(config, ckpt, verbose=False):
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@ -220,6 +213,7 @@ def load_model_from_config(config, ckpt, verbose=False):
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model.eval()
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return model
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def load_sd_from_config(ckpt, verbose=False):
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print(f"Loading model from {ckpt}")
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pl_sd = torch.load(ckpt, map_location="cpu")
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@ -245,7 +239,9 @@ class MemUsageMonitor(threading.Thread):
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print(f"[{self.name}] Unable to initialize NVIDIA management. No memory stats. \n")
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return
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print(f"[{self.name}] Recording max memory usage...\n")
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handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
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# Missing context
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#handle = pynvml.nvmlDeviceGetHandleByIndex(st.session_state['defaults'].general.gpu)
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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self.total = pynvml.nvmlDeviceGetMemoryInfo(handle).total
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while not self.stop_flag:
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m = pynvml.nvmlDeviceGetMemoryInfo(handle)
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@ -681,18 +677,26 @@ def try_loading_LDSR(model_name: str,checking=False):
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#try_loading_LDSR('model',checking=True)
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def load_SD_model():
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if st.session_state['defaults'].general.optimized:
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sd = load_sd_from_config(st.session_state['defaults'].general.default_model_path)
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# Loads Stable Diffusion model by name
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def load_sd_model(model_name: str) -> [any, any, any, any, any]:
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ckpt_path = st.session_state.defaults.general.default_model_path
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if model_name != st.session_state.defaults.general.default_model:
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ckpt_path = os.path.join("models", "custom", f"{model_name}.ckpt")
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if st.session_state.defaults.general.optimized:
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config = OmegaConf.load(st.session_state.defaults.general.optimized_config)
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sd = load_sd_from_config(ckpt_path)
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li, lo = [], []
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for key, v_ in sd.items():
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sp = key.split('.')
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if(sp[0]) == 'model':
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if('input_blocks' in sp):
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if (sp[0]) == 'model':
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if 'input_blocks' in sp:
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li.append(key)
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elif('middle_block' in sp):
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elif 'middle_block' in sp:
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li.append(key)
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elif('time_embed' in sp):
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elif 'time_embed' in sp:
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li.append(key)
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else:
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lo.append(key)
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@ -701,14 +705,14 @@ def load_SD_model():
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for key in lo:
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sd['model2.' + key[6:]] = sd.pop(key)
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config = OmegaConf.load("optimizedSD/v1-inference.yaml")
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device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu")
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device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
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if torch.cuda.is_available() else torch.device("cpu")
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model = instantiate_from_config(config.modelUNet)
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_, _ = model.load_state_dict(sd, strict=False)
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model.cuda()
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model.eval()
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model.turbo = st.session_state['defaults'].general.optimized_turbo
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model.turbo = st.session_state.defaults.general.optimized_turbo
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modelCS = instantiate_from_config(config.modelCondStage)
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_, _ = modelCS.load_state_dict(sd, strict=False)
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@ -721,22 +725,25 @@ def load_SD_model():
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del sd
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if not st.session_state['defaults'].general.no_half:
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if not st.session_state.defaults.general.no_half:
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model = model.half()
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modelCS = modelCS.half()
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modelFS = modelFS.half()
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return model,modelCS,modelFS,device, config
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return config, device, model, modelCS, modelFS
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else:
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config = OmegaConf.load(st.session_state['defaults'].general.default_model_config)
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model = load_model_from_config(config, st.session_state['defaults'].general.default_model_path)
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config = OmegaConf.load(st.session_state.defaults.general.default_model_config)
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model = load_model_from_config(config, ckpt_path)
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device = torch.device(f"cuda:{opt.gpu}") if torch.cuda.is_available() else torch.device("cpu")
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model = (model if st.session_state['defaults'].general.no_half else model.half()).to(device)
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return model, device,config
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device = torch.device(f"cuda:{st.session_state.defaults.general.gpu}") \
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if torch.cuda.is_available() else torch.device("cpu")
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model = (model if st.session_state.defaults.general.no_half
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else model.half()).to(device)
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#
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return config, device, model, None, None
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#
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# @codedealer: No usages
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def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='RealESRGAN_x4plus'):
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#get global variables
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global_vars = globals()
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@ -750,8 +757,8 @@ def ModelLoader(models,load=False,unload=False,imgproc_realesrgan_model_name='Re
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if m == 'model':
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del global_vars[m+'FS']
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del global_vars[m+'CS']
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if m =='model':
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m='Stable Diffusion'
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if m == 'model':
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m = 'Stable Diffusion'
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print('Unloaded ' + m)
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if load:
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for m in models:
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@ -792,11 +799,11 @@ def generation_callback(img, i=0):
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# It can probably be done in a better way for someone who knows what they're doing. I don't.
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#print (img,isinstance(img, torch.Tensor))
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if isinstance(img, torch.Tensor):
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x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).decode_first_stage(img)
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x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(img)
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else:
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# When using the k Diffusion samplers they return a dict instead of a tensor that look like this:
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# {'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}
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x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).decode_first_stage(img["denoised"])
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x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(img["denoised"])
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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@ -1025,10 +1032,10 @@ def draw_prompt_matrix(im, width, height, all_prompts):
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def check_prompt_length(prompt, comments):
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"""this function tests if prompt is too long, and if so, adds a message to comments"""
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tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).cond_stage_model.tokenizer
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max_length = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).cond_stage_model.max_length
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tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer
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max_length = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.max_length
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info = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length,
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info = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer([prompt], truncation=True, max_length=max_length,
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return_overflowing_tokens=True, padding="max_length", return_tensors="pt")
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ovf = info['overflowing_tokens'][0]
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overflowing_count = ovf.shape[0]
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@ -1322,9 +1329,9 @@ def process_images(
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print(prompt)
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if st.session_state['defaults'].general.optimized:
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modelCS.to(st.session_state['defaults'].general.gpu)
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st.session_state.modelCS.to(st.session_state['defaults'].general.gpu)
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uc = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).get_learned_conditioning(len(prompts) * [""])
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uc = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(len(prompts) * [""])
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if isinstance(prompts, tuple):
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prompts = list(prompts)
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@ -1338,16 +1345,16 @@ def process_images(
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c = torch.zeros_like(uc) # i dont know if this is correct.. but it works
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for i in range(0, len(weighted_subprompts)):
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# note if alpha negative, it functions same as torch.sub
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c = torch.add(c, (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1])
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c = torch.add(c, (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(weighted_subprompts[i][0]), alpha=weighted_subprompts[i][1])
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else: # just behave like usual
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c = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelCS).get_learned_conditioning(prompts)
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c = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).get_learned_conditioning(prompts)
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shape = [opt_C, height // opt_f, width // opt_f]
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if st.session_state['defaults'].general.optimized:
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mem = torch.cuda.memory_allocated()/1e6
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modelCS.to("cpu")
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st.session_state.modelCS.to("cpu")
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while(torch.cuda.memory_allocated()/1e6 >= mem):
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time.sleep(1)
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@ -1376,9 +1383,9 @@ def process_images(
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samples_ddim = func_sample(init_data=init_data, x=x, conditioning=c, unconditional_conditioning=uc, sampler_name=sampler_name)
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if st.session_state['defaults'].general.optimized:
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modelFS.to(st.session_state['defaults'].general.gpu)
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st.session_state.modelFS.to(st.session_state['defaults'].general.gpu)
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x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else modelFS).decode_first_stage(samples_ddim)
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x_samples_ddim = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelFS).decode_first_stage(samples_ddim)
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x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
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for i, x_sample in enumerate(x_samples_ddim):
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@ -1512,7 +1519,7 @@ def process_images(
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if st.session_state['defaults'].general.optimized:
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mem = torch.cuda.memory_allocated()/1e6
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modelFS.to("cpu")
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st.session_state.modelFS.to("cpu")
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while(torch.cuda.memory_allocated()/1e6 >= mem):
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time.sleep(1)
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@ -162,16 +162,26 @@ def layout():
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cfg_scale = st.slider("CFG (Classifier Free Guidance Scale):", min_value=1.0, max_value=30.0, value=st.session_state['defaults'].txt2img.cfg_scale, step=0.5, help="How strongly the image should follow the prompt.")
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seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2img.seed, help=" The seed to use, if left blank a random seed will be generated.")
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batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2img.batch_count, step=1, help="How many iterations or batches of images to generate in total.")
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#batch_size = st.slider("Batch size", min_value=1, max_value=250, value=defaults.txt2img.batch_size, step=1,
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#help="How many images are at once in a batch.\
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#It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
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#Default: 1")
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bs_slider_max_value = 5
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if st.session_state.defaults.general.optimized:
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bs_slider_max_value = 100
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batch_size = st.slider(
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"Batch size",
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min_value=1,
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max_value=bs_slider_max_value,
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value=st.session_state.defaults.txt2img.batch_size,
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step=1,
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help="How many images are at once in a batch.\
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It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
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Default: 1")
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with st.expander("Preview Settings"):
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st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2img.update_preview,
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help="If enabled the image preview will be updated during the generation instead of at the end. \
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You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
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By default this is enabled and the frequency is set to 1 step.")
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help="If enabled the image preview will be updated during the generation instead of at the end. \
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You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
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By default this is enabled and the frequency is set to 1 step.")
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st.session_state["update_preview_frequency"] = st.text_input("Update Image Preview Frequency", value=st.session_state['defaults'].txt2img.update_preview_frequency,
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help="Frequency in steps at which the the preview image is updated. By default the frequency \
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@ -244,9 +254,9 @@ def layout():
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load_models(False, use_GFPGAN, use_RealESRGAN, RealESRGAN_model)
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try:
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output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, RealESRGAN_model, batch_count, 1,
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output_images, seeds, info, stats = txt2img(prompt, st.session_state.sampling_steps, sampler_name, RealESRGAN_model, batch_count, batch_size,
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cfg_scale, seed, height, width, separate_prompts, normalize_prompt_weights, save_individual_images,
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save_grid, group_by_prompt, save_as_jpg, use_GFPGAN, use_RealESRGAN, RealESRGAN_model, fp=defaults.general.fp,
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save_grid, group_by_prompt, save_as_jpg, use_GFPGAN, use_RealESRGAN, RealESRGAN_model, fp=st.session_state.defaults.general.fp,
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variant_amount=variant_amount, variant_seed=variant_seed, write_info_files=write_info_files)
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message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
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