mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-15 07:03:06 +03:00
Added initial lora support and removed redundant functions
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ynimmaga
parent
eb7b1a9af4
commit
c337bed5ff
@ -16,7 +16,7 @@ import modules.scripts as scripts
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import modules.shared as shared
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from modules import images, devices, extra_networks, generation_parameters_copypaste, masking, sd_samplers, sd_samplers_compvis, sd_samplers_kdiffusion, shared
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from modules.processing import StableDiffusionProcessing, Processed, apply_overlay, process_images, get_fixed_seed, program_version, StableDiffusionProcessingImg2Img, create_random_tensors
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from modules.processing import StableDiffusionProcessing, Processed, apply_overlay, process_images, get_fixed_seed, program_version, StableDiffusionProcessingImg2Img, create_random_tensors, create_infotext
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from modules.sd_models import list_models, CheckpointInfo
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from modules.sd_samplers_common import samples_to_image_grid, sample_to_image
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from modules.shared import Shared, cmd_opts, opts, state
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@ -47,7 +47,6 @@ from diffusers import (
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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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@ -95,6 +94,8 @@ def set_scheduler(sd_model, sampler_name):
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def get_diffusers_sd_model(sampler_name, enable_caching, openvino_device):
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global first_inference_global, sampler_name_global
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if (first_inference_global == 1):
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torch._dynamo.reset()
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torch._dynamo.config.verbose=True
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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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@ -103,8 +104,9 @@ def get_diffusers_sd_model(sampler_name, enable_caching, openvino_device):
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checkpoint_info = CheckpointInfo(checkpoint_path)
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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.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.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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@ -115,57 +117,13 @@ def get_diffusers_sd_model(sampler_name, enable_caching, openvino_device):
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if enable_caching:
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os.environ["OPENVINO_TORCH_MODEL_CACHING"] = "1"
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image = sd_model(warmup_prompt, num_inference_steps=1).images[0]
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print("warm up run complete")
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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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def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
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index = position_in_batch + iteration * p.batch_size
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clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
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enable_hr = getattr(p, 'enable_hr', False)
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token_merging_ratio = p.get_token_merging_ratio()
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token_merging_ratio_hr = p.get_token_merging_ratio(for_hr=True)
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uses_ensd = opts.eta_noise_seed_delta != 0
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if uses_ensd:
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uses_ensd = sd_samplers_common.is_sampler_using_eta_noise_seed_delta(p)
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generation_params = {
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"Steps": p.steps,
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"Sampler": p.sampler_name,
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"CFG scale": p.cfg_scale,
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"Image CFG scale": getattr(p, 'image_cfg_scale', None),
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"Seed": all_seeds[index],
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"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
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"Size": f"{p.width}x{p.height}",
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"Model hash": getattr(p, 'sd_model_hash', None),
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"Model": None,
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"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
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"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
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"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
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"Denoising strength": getattr(p, 'denoising_strength', None),
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"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
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"Clip skip": None if clip_skip <= 1 else clip_skip,
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"ENSD": opts.eta_noise_seed_delta if uses_ensd else None,
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"Token merging ratio": None if token_merging_ratio == 0 else token_merging_ratio,
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"Token merging ratio hr": None if not enable_hr or token_merging_ratio_hr == 0 else token_merging_ratio_hr,
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"Init image hash": getattr(p, 'init_img_hash', None),
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"RNG": opts.randn_source if opts.randn_source != "GPU" else None,
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"NGMS": None if p.s_min_uncond == 0 else p.s_min_uncond,
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**p.extra_generation_params,
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"Version": program_version() if opts.add_version_to_infotext else None,
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}
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generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
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negative_prompt_text = f"\nNegative prompt: {p.all_negative_prompts[index]}" if p.all_negative_prompts[index] else ""
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return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
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def init_new(self, all_prompts, all_seeds, all_subseeds):
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crop_region = None
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@ -290,7 +248,7 @@ def init_new(self, all_prompts, all_seeds, all_subseeds):
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self.init_latent = self.init_latent * self.mask
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def process_images_openvino(p: StableDiffusionProcessing) -> Processed:
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def process_images_openvino(p: StableDiffusionProcessing, sampler_name, enable_caching, openvino_device) -> 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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if type(p.prompt) == list:
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@ -354,19 +312,25 @@ def process_images_openvino(p: StableDiffusionProcessing) -> Processed:
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if len(p.prompts) == 0:
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break
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shared.sd_diffusers_model = get_diffusers_sd_model(sampler_name, enable_caching, openvino_device)
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extra_network_data = p.parse_extra_network_prompts()
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print("Extra network data: ", extra_network_data)
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if not p.disable_extra_networks:
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with devices.autocast():
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extra_networks.activate(p, p.extra_network_data)
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# TODO: support multiplier
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if ('lora' in modules.extra_networks.extra_network_registry):
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import lora
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for lora_model in lora.loaded_loras:
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shared.sd_diffusers_model.load_lora_weights(os.getcwd() + "/models/Lora/", weight_name=lora_model.name + ".safetensors")
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if p.scripts is not None:
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p.scripts.process_batch(p, batch_number=n, prompts=p.prompts, seeds=p.seeds, subseeds=p.subseeds)
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print("After process batch")
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# params.txt should be saved after scripts.process_batch, since the
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# infotext could be modified by that callback
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# Example: a wildcard processed by process_batch sets an extra model
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@ -375,14 +339,12 @@ def process_images_openvino(p: StableDiffusionProcessing) -> Processed:
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with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
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processed = Processed(p, [], p.seed, "")
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file.write(create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments=[], position_in_batch=0 % p.batch_size, iteration=0 // p.batch_size))
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print("After processed")
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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print("After shared state job")
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generator = [torch.Generator(device="cpu").manual_seed(s) for s in p.seeds]
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print("prompts: ", p.prompts)
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output = shared.sd_diffusers_model(
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prompt=p.prompts,
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negative_prompt=p.negative_prompts,
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@ -494,7 +456,6 @@ 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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@ -504,37 +465,27 @@ 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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global first_inference_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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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 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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warmup_status = gr.Textbox(label="Device", interactive=False, visible=False)
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def device_change(choice):
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global first_inference_global, warmed_up_global, openvino_device_global
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global first_inference_global, openvino_device_global
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if (openvino_device_global == choice):
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return gr.update(value="Device selected is " + choice)
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return gr.update(value="Device selected is " + choice, visible=True)
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else:
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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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return gr.update(value="Device changed to " + choice + ". Model will be re-compiled", visible=True)
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openvino_device.change(device_change, openvino_device, warmup_status)
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def warmup(run_warmup, openvino_device, enable_caching, sampler_name):
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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, openvino_device)
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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, openvino_device, enable_caching, sampler_name], 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, 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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global first_inference_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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@ -543,9 +494,6 @@ class Script(scripts.Script):
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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, openvino_device)
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if override_sampler:
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p.sampler_name = sampler_name
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else:
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@ -558,9 +506,9 @@ class Script(scripts.Script):
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sampler_name_global = p.sampler_name
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if self.is_txt2img:
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processed = process_images_openvino(p)
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processed = process_images_openvino(p, p.sampler_name, enable_caching, openvino_device)
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else:
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p.init = functools.partial(init_new, p)
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processed = process_images_openvino(p)
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processed = process_images_openvino(p, p.sampler_name, enable_caching, openvino_device)
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return processed
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