mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 07:03:06 +03:00
UI modifications and fixes in img2img
This commit is contained in:
ynimmaga
parent
40de419561
commit
f1b5c72725
@ -1,3 +1,6 @@
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#Copyright (C) 2023 Intel Corporation
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#SPDX-License-Identifier: AGPL-3.0
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import math
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import cv2
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import os
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@ -7,6 +10,7 @@ import gradio as gr
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import numpy as np
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import openvino.frontend.pytorch.torchdynamo.backend
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import modules
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import modules.paths as paths
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import modules.scripts as scripts
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import modules.shared as shared
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@ -40,8 +44,10 @@ from diffusers import (
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UniPCMultistepScheduler,
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)
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first_inference=1
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sampler_name_global="Euler a"
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first_inference_global = 1
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sampler_name_global = "Euler a"
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openvino_device_global = "CPU"
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warmed_up_global = 0
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def sd_diffusers_model(self):
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import modules.sd_models
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@ -86,9 +92,9 @@ def set_scheduler(sd_model, sampler_name):
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return sd_model.scheduler
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def get_diffusers_sd_model(sampler_name, exclude_warmup, enable_caching):
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global first_inference, sampler_name_global
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if (first_inference == 1):
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def get_diffusers_sd_model(sampler_name, enable_caching):
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global first_inference_global, sampler_name_global
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if (first_inference_global == 1):
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curr_dir_path = os.getcwd()
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model_path = "/models/Stable-diffusion/"
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checkpoint_name = shared.opts.sd_model_checkpoint.split(" ")[0]
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@ -98,16 +104,17 @@ def get_diffusers_sd_model(sampler_name, exclude_warmup, enable_caching):
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sd_model.sd_checkpoint_info = checkpoint_info
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sd_model.sd_model_hash = checkpoint_info.calculate_shorthash()
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sd_model.unet = torch.compile(sd_model.unet, backend="openvino")
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sd_model.safety_checker = None
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sd_model.scheduler = set_scheduler(sd_model, sampler_name)
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sd_model.unet = torch.compile(sd_model.unet, backend="openvino")
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sd_model.vae.decode = torch.compile(sd_model.vae.decode, backend="openvino")
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sampler_name_global = sampler_name
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if (exclude_warmup):
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warmup_prompt = "a dog walking in a park"
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image = sd_model(warmup_prompt, num_inference_steps=1).images[0]
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first_inference = 0
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warmup_prompt = "a dog walking in a park"
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image = sd_model(warmup_prompt, num_inference_steps=1).images[0]
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first_inference_global = 0
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shared.sd_diffusers_model = sd_model
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del sd_model
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shared.sd_diffusers_model.cond_stage_key = functools.partial(cond_stage_key, shared.sd_diffusers_model)
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return shared.sd_diffusers_model
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@ -260,7 +267,7 @@ def init_new(self, all_prompts, all_seeds, all_subseeds):
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self.init_latent = shared.sd_diffusers_model.vae.encode(image).latent_dist.sample()
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if self.resize_mode == 3:
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self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
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self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // 8, self.width // 8), mode="bilinear")
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if image_mask is not None:
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init_mask = latent_mask
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@ -484,6 +491,7 @@ def process_images_openvino(p: StableDiffusionProcessing) -> Processed:
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return res
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warm_up_triggered_global = 0
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class Script(scripts.Script):
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def title(self):
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return "Accelerate with OpenVINO"
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@ -493,35 +501,44 @@ class Script(scripts.Script):
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def ui(self, is_img2img):
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core = Core()
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global first_inference_global, warmed_up_global, warm_up_triggered_global
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openvino_device = gr.Dropdown(label="Select a device", choices=[device for device in core.available_devices], value="CPU")
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sampler_name = gr.Dropdown(label="Select a sampling method", choices=["Euler a", "Euler", "LMS", "Huen", "DPM++ 2M", "LMS Karras", "DPM++ 2M Karras", "DDIM", "PLMS"], value="Euler a")
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exclude_warmup = gr.Checkbox(label="Run a warm up iteration to pre-load the model (Recommended for performance measurements)", value=True, elem_id=self.elem_id("exclude_warmup"))
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run_warmup = gr.Button("Warm up run")
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warmup_status = gr.Textbox()
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def warmup(exclude_warmup):
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shared.sd_diffusers_model = get_diffusers_sd_model(sampler_name, exclude_warmup, enable_caching)
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return "Warm up run completed"
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run_warmup.click(warmup, exclude_warmup, warmup_status)
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warmup_run_status = gr.Markdown("""Warm up run complete""", visible=False)
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#def change_warmup(choice):
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# if choice == True:
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# shared.sd_diffusers_model = get_diffusers_sd_model(sampler_name, exclude_warmup, enable_caching)
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# return gr.update(visible=True)
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# else:
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# return gr.update(visible=False)
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#exclude_warmup.change(fn=change_warmup, inputs=exclude_warmup, outputs=warmup_run_status, show_progress=True)
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override_sampler = gr.Checkbox(label="Override the sampling selection from the main UI (Recommended as only below sampling methods have been validated for OpenVINO)", value=True)
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sampler_name = gr.Radio(label="Select a sampling method", choices=["Euler a", "Euler", "LMS", "Huen", "DPM++ 2M", "LMS Karras", "DPM++ 2M Karras", "DDIM", "PLMS"], value="Euler a")
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enable_caching = gr.Checkbox(label="Cache the compiled models for faster model load in subsequent launches (Recommended)", value=True, elem_id=self.elem_id("enable_caching"))
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run_warmup = gr.Button("Run a warmup iteration (recommended)")
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warmup_status = gr.Textbox(label="Status of the warm up iteration", interactive=False)
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def device_change(choice):
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global first_inference_global, warmed_up_global
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warmed_up_global = 0
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first_inference_global = 1
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return gr.update(value="Device changed to " + choice + ". Press the button to run a new warmup iteration")
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openvino_device.change(device_change, openvino_device, warmup_status)
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return [openvino_device, sampler_name, exclude_warmup, enable_caching]
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def warmup(run_warmup):
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global first_inference_global, warmed_up_global
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first_inference_global = 1
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shared.sd_diffusers_model = get_diffusers_sd_model(sampler_name, enable_caching)
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warmed_up_global = 1
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return gr.update(value="Warm up run complete")
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run_warmup.click(warmup, run_warmup, warmup_status)
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return [openvino_device, override_sampler, sampler_name, warmup_status, enable_caching]
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def run(self, p, openvino_device, sampler_name, exclude_warmup, enable_caching):
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global sampler_name_global, first_inference
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if (exclude_warmup == False):
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shared.sd_diffusers_model = get_diffusers_sd_model(sampler_name, exclude_warmup, enable_caching)
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def run(self, p, openvino_device, override_sampler, sampler_name, warmup_status, enable_caching):
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global first_inference_global, warmed_up_global, sampler_name_global, openvino_device_global
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os.environ["OPENVINO_DEVICE"] = str(openvino_device)
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if enable_caching:
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os.environ["OPENVINO_TORCH_MODEL_CACHING"] = "1"
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if (openvino_device_global != openvino_device):
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first_inference_global = 1
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openvino_device_global = openvino_device
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if (warmed_up_global == 0):
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shared.sd_diffusers_model = get_diffusers_sd_model(sampler_name, enable_caching)
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if (sampler_name_global != sampler_name):
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print("Sampler name: ", sampler_name)
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shared.sd_diffusers_model.scheduler = set_scheduler(shared.sd_diffusers_model, sampler_name)
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sampler_name_global = sampler_name
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