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https://github.com/openvinotoolkit/stable-diffusion-webui.git
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HiRes Latent upscaler
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likholat
parent
44006297e0
commit
fe48c58433
@ -53,6 +53,7 @@ from diffusers import (
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StableDiffusionXLImg2ImgPipeline,
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StableDiffusionXLInpaintPipeline,
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ControlNetModel,
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StableDiffusionLatentUpscalePipeline,
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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@ -535,6 +536,23 @@ class NoWatermark:
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def apply_watermark(self, img):
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return img
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def get_diffusers_upscaler(upscaler: str):
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torch._dynamo.reset()
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openvino_clear_caches()
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model_name = "stabilityai/sd-x2-latent-upscaler"
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print("OpenVINO Script: loading upscaling model: " + model_name)
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sd_model = StableDiffusionLatentUpscalePipeline.from_pretrained(model_name, torch_dtype=torch.float32)
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sd_model.safety_checker = None
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sd_model.cond_stage_key = functools.partial(cond_stage_key, shared.sd_model)
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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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shared.sd_diffusers_model = sd_model
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del sd_model
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return shared.sd_diffusers_model
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def get_diffusers_sd_model(model_config, vae_ckpt, sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac):
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if (model_state.recompile == 1):
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model_state.partition_id = 0
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@ -770,7 +788,8 @@ def init_new(self, all_prompts, all_seeds, all_subseeds):
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else:
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raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
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def process_images_openvino(p: StableDiffusionProcessing, model_config, vae_ckpt, sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac) -> Processed:
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def process_images_openvino(p: StableDiffusionProcessing, model_config, vae_ckpt, sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac) -> Processed:
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"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
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"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
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if (mode == 0 and p.enable_hr):
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@ -1092,6 +1111,23 @@ def process_images_openvino(p: StableDiffusionProcessing, model_config, vae_ckpt
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devices.torch_gc()
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# Hight resolutuon mode
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if override_hires:
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if upscaler == "Latent":
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model_state.mode = -1
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shared.sd_diffusers_model = get_diffusers_upscaler(upscaler)
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img_idx = slice(len(output_images)) if p.batch_size == 1 else slice(1, len(output_images))
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output_images[img_idx] = shared.sd_diffusers_model(
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image=output_images[img_idx],
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prompt=p.prompts,
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negative_prompt=p.negative_prompts,
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num_inference_steps=hires_steps,
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guidance_scale=p.cfg_scale,
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generator=generator,
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callback = callback,
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callback_steps = 1,
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).images
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res = Processed(
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p,
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images_list=output_images,
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@ -1102,7 +1138,8 @@ def process_images_openvino(p: StableDiffusionProcessing, model_config, vae_ckpt
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index_of_first_image=index_of_first_image,
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infotexts=infotexts,
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)
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if override_hires:
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res.info = res.info + f", Hires upscaler: {upscaler}, Denoising strength: {d_strength}"
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res.info = res.info + ", Warm up time: " + str(round(warmup_duration, 2)) + " secs "
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if (generation_rate >= 1.0):
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@ -1116,6 +1153,9 @@ def process_images_openvino(p: StableDiffusionProcessing, model_config, vae_ckpt
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return res
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def on_change(mode):
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return gr.update(visible=mode)
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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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@ -1170,6 +1210,12 @@ class Script(scripts.Script):
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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", "Heun", "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 on disk for faster model load in subsequent launches (Recommended)", value=True, elem_id=self.elem_id("enable_caching"))
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override_hires = gr.Checkbox(label="Override the Hires.fix selection from the main UI (Recommended as only below upscalers have been validated for OpenVINO)", value=False, visible=self.is_txt2img)
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with gr.Group(visible=False) as hires:
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with gr.Row():
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upscaler = gr.Dropdown(label="Upscaler", choices=["Latent"], value="Latent")
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hires_steps = gr.Slider(1, 150, value=10, step=1, label="Steps")
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d_strength = gr.Slider(0, 1, value=0.5, step=0.01, label="Strength")
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warmup_status = gr.Textbox(label="Device", interactive=False, visible=False)
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vae_status = gr.Textbox(label="VAE", interactive=False, visible=False)
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gr.Markdown(
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@ -1184,6 +1230,8 @@ class Script(scripts.Script):
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So it's normal for the first inference after a settings change to be slower, while subsequent inferences use the optimized compiled model and run faster.
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""")
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override_hires.change(on_change, override_hires, hires)
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def device_change(choice):
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if (model_state.device == choice):
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return gr.update(value="Device selected is " + choice, visible=True)
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@ -1206,9 +1254,9 @@ class Script(scripts.Script):
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else:
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model_state.refiner_ckpt = choice
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refiner_ckpt.change(refiner_ckpt_change, refiner_ckpt)
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return [model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, is_xl_ckpt, refiner_ckpt, refiner_frac]
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return [model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, is_xl_ckpt, refiner_ckpt, refiner_frac]
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def run(self, p, model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, is_xl_ckpt, refiner_ckpt, refiner_frac):
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def run(self, p, model_config, vae_ckpt, openvino_device, override_sampler, sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, is_xl_ckpt, refiner_ckpt, refiner_frac):
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os.environ["OPENVINO_TORCH_BACKEND_DEVICE"] = str(openvino_device)
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if enable_caching:
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@ -1225,14 +1273,12 @@ class Script(scripts.Script):
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mode = 0
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if self.is_txt2img:
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mode = 0
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processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
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processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
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else:
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if p.image_mask is None:
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mode = 1
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else:
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mode = 2
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p.init = functools.partial(init_new, p)
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processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
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processed = process_images_openvino(p, model_config, vae_ckpt, p.sampler_name, enable_caching, override_hires, upscaler, hires_steps, d_strength, openvino_device, mode, is_xl_ckpt, refiner_ckpt, refiner_frac)
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return processed
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