Merge branch 'master' into fix-vram
This commit is contained in:
Jairo Correa
commit
b66aa334a9
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@ -4,6 +4,21 @@ global_progressbars = {}
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function check_progressbar(id_part, id_progressbar, id_progressbar_span, id_interrupt, id_preview, id_gallery){
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var progressbar = gradioApp().getElementById(id_progressbar)
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var interrupt = gradioApp().getElementById(id_interrupt)
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if(opts.show_progress_in_title && progressbar && progressbar.offsetParent){
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if(progressbar.innerText){
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let newtitle = 'Stable Diffusion - ' + progressbar.innerText
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if(document.title != newtitle){
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document.title = newtitle;
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}
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}else{
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let newtitle = 'Stable Diffusion'
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if(document.title != newtitle){
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document.title = newtitle;
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}
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}
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}
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if(progressbar!= null && progressbar != global_progressbars[id_progressbar]){
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global_progressbars[id_progressbar] = progressbar
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11
launch.py
11
launch.py
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@ -19,7 +19,7 @@ clip_package = os.environ.get('CLIP_PACKAGE', "git+https://github.com/openai/CLI
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stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "69ae4b35e0a0f6ee1af8bb9a5d0016ccb27e36dc")
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taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "a7ec1974d4ccb394c2dca275f42cd97490618924")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "f4e99857772fc3a126ba886aadf795a332774878")
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codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
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blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
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@ -86,6 +86,15 @@ def git_clone(url, dir, name, commithash=None):
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# TODO clone into temporary dir and move if successful
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if os.path.exists(dir):
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if commithash is None:
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return
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current_hash = run(f'"{git}" -C {dir} rev-parse HEAD', None, f"Couldn't determine {name}'s hash: {commithash}").strip()
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if current_hash == commithash:
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return
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run(f'"{git}" -C {dir} fetch', f"Fetching updates for {name}...", f"Couldn't fetch {name}")
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run(f'"{git}" -C {dir} checkout {commithash}', f"Checking out commint for {name} with hash: {commithash}...", f"Couldn't checkout commit {commithash} for {name}")
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return
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run(f'"{git}" clone "{url}" "{dir}"', f"Cloning {name} into {dir}...", f"Couldn't clone {name}")
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@ -11,9 +11,8 @@ import cv2
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from skimage import exposure
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking, lowvram
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from modules import devices, prompt_parser, masking, sd_samplers, lowvram
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from modules.sd_hijack import model_hijack
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from modules.sd_samplers import samplers, samplers_for_img2img
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.face_restoration
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@ -110,7 +109,7 @@ class Processed:
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self.width = p.width
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self.height = p.height
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self.sampler_index = p.sampler_index
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self.sampler = samplers[p.sampler_index].name
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self.sampler = sd_samplers.samplers[p.sampler_index].name
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self.cfg_scale = p.cfg_scale
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self.steps = p.steps
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self.batch_size = p.batch_size
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@ -265,7 +264,7 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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generation_params = {
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"Steps": p.steps,
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"Sampler": samplers[p.sampler_index].name,
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"Sampler": sd_samplers.samplers[p.sampler_index].name,
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"CFG scale": p.cfg_scale,
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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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@ -360,7 +359,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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#c = p.sd_model.get_learned_conditioning(prompts)
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with devices.autocast():
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uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt], p.steps)
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c = prompt_parser.get_learned_conditioning(shared.sd_model, prompts, p.steps)
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c = prompt_parser.get_multicond_learned_conditioning(shared.sd_model, prompts, p.steps)
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if len(model_hijack.comments) > 0:
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for comment in model_hijack.comments:
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@ -489,7 +488,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.firstphase_height_truncated = int(scale * self.height)
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
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self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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if not self.enable_hr:
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x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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@ -532,7 +531,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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shared.state.nextjob()
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self.sampler = samplers[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
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noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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# GC now before running the next img2img to prevent running out of memory
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@ -567,7 +567,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
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self.nmask = None
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def init(self, all_prompts, all_seeds, all_subseeds):
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self.sampler = samplers_for_img2img[self.sampler_index].constructor(self.sd_model)
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self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
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crop_region = None
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if self.image_mask is not None:
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@ -1,6 +1,6 @@
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import re
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from collections import namedtuple
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from typing import List
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import lark
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# a prompt like this: "fantasy landscape with a [mountain:lake:0.25] and [an oak:a christmas tree:0.75][ in foreground::0.6][ in background:0.25] [shoddy:masterful:0.5]"
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@ -97,10 +97,26 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
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ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
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ScheduledPromptBatch = namedtuple("ScheduledPromptBatch", ["shape", "schedules"])
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def get_learned_conditioning(model, prompts, steps):
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"""converts a list of prompts into a list of prompt schedules - each schedule is a list of ScheduledPromptConditioning, specifying the comdition (cond),
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and the sampling step at which this condition is to be replaced by the next one.
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Input:
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(model, ['a red crown', 'a [blue:green:5] jeweled crown'], 20)
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Output:
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[
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[
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ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0523, ..., -0.4901, -0.3066, 0.0674], ..., [ 0.3317, -0.5102, -0.4066, ..., 0.4119, -0.7647, -1.0160]], device='cuda:0'))
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],
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[
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ScheduledPromptConditioning(end_at_step=5, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.0192, 0.3867, -0.4644, ..., 0.1135, -0.3696, -0.4625]], device='cuda:0')),
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ScheduledPromptConditioning(end_at_step=20, cond=tensor([[-0.3886, 0.0229, -0.0522, ..., -0.4901, -0.3067, 0.0673], ..., [-0.7352, -0.4356, -0.7888, ..., 0.6994, -0.4312, -1.2593]], device='cuda:0'))
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]
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]
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"""
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res = []
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prompt_schedules = get_learned_conditioning_prompt_schedules(prompts, steps)
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@ -123,13 +139,75 @@ def get_learned_conditioning(model, prompts, steps):
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cache[prompt] = cond_schedule
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res.append(cond_schedule)
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return ScheduledPromptBatch((len(prompts),) + res[0][0].cond.shape, res)
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return res
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def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
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param = c.schedules[0][0].cond
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res = torch.zeros(c.shape, device=param.device, dtype=param.dtype)
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for i, cond_schedule in enumerate(c.schedules):
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re_AND = re.compile(r"\bAND\b")
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re_weight = re.compile(r"^(.*?)(?:\s*:\s*([-+]?(?:\d+\.?|\d*\.\d+)))?\s*$")
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def get_multicond_prompt_list(prompts):
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res_indexes = []
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prompt_flat_list = []
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prompt_indexes = {}
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for prompt in prompts:
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subprompts = re_AND.split(prompt)
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indexes = []
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for subprompt in subprompts:
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match = re_weight.search(subprompt)
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text, weight = match.groups() if match is not None else (subprompt, 1.0)
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weight = float(weight) if weight is not None else 1.0
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index = prompt_indexes.get(text, None)
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if index is None:
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index = len(prompt_flat_list)
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prompt_flat_list.append(text)
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prompt_indexes[text] = index
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indexes.append((index, weight))
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res_indexes.append(indexes)
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return res_indexes, prompt_flat_list, prompt_indexes
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class ComposableScheduledPromptConditioning:
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def __init__(self, schedules, weight=1.0):
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self.schedules: List[ScheduledPromptConditioning] = schedules
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self.weight: float = weight
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class MulticondLearnedConditioning:
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def __init__(self, shape, batch):
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self.shape: tuple = shape # the shape field is needed to send this object to DDIM/PLMS
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self.batch: List[List[ComposableScheduledPromptConditioning]] = batch
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def get_multicond_learned_conditioning(model, prompts, steps) -> MulticondLearnedConditioning:
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"""same as get_learned_conditioning, but returns a list of ScheduledPromptConditioning along with the weight objects for each prompt.
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For each prompt, the list is obtained by splitting the prompt using the AND separator.
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https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
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"""
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res_indexes, prompt_flat_list, prompt_indexes = get_multicond_prompt_list(prompts)
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learned_conditioning = get_learned_conditioning(model, prompt_flat_list, steps)
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res = []
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for indexes in res_indexes:
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res.append([ComposableScheduledPromptConditioning(learned_conditioning[i], weight) for i, weight in indexes])
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return MulticondLearnedConditioning(shape=(len(prompts),), batch=res)
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def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_step):
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param = c[0][0].cond
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res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
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for i, cond_schedule in enumerate(c):
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target_index = 0
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for current, (end_at, cond) in enumerate(cond_schedule):
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if current_step <= end_at:
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@ -140,6 +218,30 @@ def reconstruct_cond_batch(c: ScheduledPromptBatch, current_step):
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return res
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def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
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param = c.batch[0][0].schedules[0].cond
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tensors = []
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conds_list = []
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for batch_no, composable_prompts in enumerate(c.batch):
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conds_for_batch = []
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for cond_index, composable_prompt in enumerate(composable_prompts):
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target_index = 0
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for current, (end_at, cond) in enumerate(composable_prompt.schedules):
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if current_step <= end_at:
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target_index = current
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break
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conds_for_batch.append((len(tensors), composable_prompt.weight))
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tensors.append(composable_prompt.schedules[target_index].cond)
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conds_list.append(conds_for_batch)
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return conds_list, torch.stack(tensors).to(device=param.device, dtype=param.dtype)
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re_attention = re.compile(r"""
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\\\(|
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\\\)|
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|
|
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|
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@ -13,31 +13,57 @@ from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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|
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|
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases'])
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SamplerData = namedtuple('SamplerData', ['name', 'constructor', 'aliases', 'options'])
|
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|
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samplers_k_diffusion = [
|
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('Euler a', 'sample_euler_ancestral', ['k_euler_a']),
|
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('Euler', 'sample_euler', ['k_euler']),
|
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('LMS', 'sample_lms', ['k_lms']),
|
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('Heun', 'sample_heun', ['k_heun']),
|
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('DPM2', 'sample_dpm_2', ['k_dpm_2']),
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a']),
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('DPM fast', 'sample_dpm_fast', ['k_dpm_fast']),
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('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad']),
|
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('Euler a', 'sample_euler_ancestral', ['k_euler_a'], {}),
|
||||
('Euler', 'sample_euler', ['k_euler'], {}),
|
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('LMS', 'sample_lms', ['k_lms'], {}),
|
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('Heun', 'sample_heun', ['k_heun'], {}),
|
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('DPM2', 'sample_dpm_2', ['k_dpm_2'], {}),
|
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('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {}),
|
||||
('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {}),
|
||||
('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {}),
|
||||
('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras'}),
|
||||
('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras'}),
|
||||
]
|
||||
|
||||
samplers_data_k_diffusion = [
|
||||
SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases)
|
||||
for label, funcname, aliases in samplers_k_diffusion
|
||||
SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
|
||||
for label, funcname, aliases, options in samplers_k_diffusion
|
||||
if hasattr(k_diffusion.sampling, funcname)
|
||||
]
|
||||
|
||||
samplers = [
|
||||
all_samplers = [
|
||||
*samplers_data_k_diffusion,
|
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SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), []),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), []),
|
||||
SamplerData('DDIM', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.ddim.DDIMSampler, model), [], {}),
|
||||
SamplerData('PLMS', lambda model: VanillaStableDiffusionSampler(ldm.models.diffusion.plms.PLMSSampler, model), [], {}),
|
||||
]
|
||||
samplers_for_img2img = [x for x in samplers if x.name not in ['PLMS', 'DPM fast', 'DPM adaptive']]
|
||||
|
||||
samplers = []
|
||||
samplers_for_img2img = []
|
||||
|
||||
|
||||
def create_sampler_with_index(list_of_configs, index, model):
|
||||
config = list_of_configs[index]
|
||||
sampler = config.constructor(model)
|
||||
sampler.config = config
|
||||
|
||||
return sampler
|
||||
|
||||
|
||||
def set_samplers():
|
||||
global samplers, samplers_for_img2img
|
||||
|
||||
hidden = set(opts.hide_samplers)
|
||||
hidden_img2img = set(opts.hide_samplers + ['PLMS', 'DPM fast', 'DPM adaptive'])
|
||||
|
||||
samplers = [x for x in all_samplers if x.name not in hidden]
|
||||
samplers_for_img2img = [x for x in all_samplers if x.name not in hidden_img2img]
|
||||
|
||||
|
||||
set_samplers()
|
||||
|
||||
sampler_extra_params = {
|
||||
'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
|
||||
|
|
@ -104,14 +130,18 @@ class VanillaStableDiffusionSampler:
|
|||
self.step = 0
|
||||
self.eta = None
|
||||
self.default_eta = 0.0
|
||||
self.config = None
|
||||
|
||||
def number_of_needed_noises(self, p):
|
||||
return 0
|
||||
|
||||
def p_sample_ddim_hook(self, x_dec, cond, ts, unconditional_conditioning, *args, **kwargs):
|
||||
cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
|
||||
|
||||
assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
|
||||
cond = tensor
|
||||
|
||||
if self.mask is not None:
|
||||
img_orig = self.sampler.model.q_sample(self.init_latent, ts)
|
||||
x_dec = img_orig * self.mask + self.nmask * x_dec
|
||||
|
|
@ -183,19 +213,31 @@ class CFGDenoiser(torch.nn.Module):
|
|||
self.step = 0
|
||||
|
||||
def forward(self, x, sigma, uncond, cond, cond_scale):
|
||||
cond = prompt_parser.reconstruct_cond_batch(cond, self.step)
|
||||
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
|
||||
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
|
||||
|
||||
batch_size = len(conds_list)
|
||||
repeats = [len(conds_list[i]) for i in range(batch_size)]
|
||||
|
||||
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
|
||||
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
|
||||
cond_in = torch.cat([tensor, uncond])
|
||||
|
||||
if shared.batch_cond_uncond:
|
||||
x_in = torch.cat([x] * 2)
|
||||
sigma_in = torch.cat([sigma] * 2)
|
||||
cond_in = torch.cat([uncond, cond])
|
||||
uncond, cond = self.inner_model(x_in, sigma_in, cond=cond_in).chunk(2)
|
||||
denoised = uncond + (cond - uncond) * cond_scale
|
||||
x_out = self.inner_model(x_in, sigma_in, cond=cond_in)
|
||||
else:
|
||||
uncond = self.inner_model(x, sigma, cond=uncond)
|
||||
cond = self.inner_model(x, sigma, cond=cond)
|
||||
denoised = uncond + (cond - uncond) * cond_scale
|
||||
x_out = torch.zeros_like(x_in)
|
||||
for batch_offset in range(0, x_out.shape[0], batch_size):
|
||||
a = batch_offset
|
||||
b = a + batch_size
|
||||
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=cond_in[a:b])
|
||||
|
||||
denoised_uncond = x_out[-batch_size:]
|
||||
denoised = torch.clone(denoised_uncond)
|
||||
|
||||
for i, conds in enumerate(conds_list):
|
||||
for cond_index, weight in conds:
|
||||
denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
|
||||
|
||||
if self.mask is not None:
|
||||
denoised = self.init_latent * self.mask + self.nmask * denoised
|
||||
|
|
@ -250,6 +292,7 @@ class KDiffusionSampler:
|
|||
self.stop_at = None
|
||||
self.eta = None
|
||||
self.default_eta = 1.0
|
||||
self.config = None
|
||||
|
||||
def callback_state(self, d):
|
||||
store_latent(d["denoised"])
|
||||
|
|
@ -314,9 +357,12 @@ class KDiffusionSampler:
|
|||
steps = steps or p.steps
|
||||
|
||||
if p.sampler_noise_scheduler_override:
|
||||
sigmas = p.sampler_noise_scheduler_override(steps)
|
||||
sigmas = p.sampler_noise_scheduler_override(steps)
|
||||
elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
|
||||
sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device)
|
||||
else:
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
sigmas = self.model_wrap.get_sigmas(steps)
|
||||
|
||||
x = x * sigmas[0]
|
||||
|
||||
extra_params_kwargs = self.initialize(p)
|
||||
|
|
|
|||
|
|
@ -13,6 +13,7 @@ import modules.memmon
|
|||
import modules.sd_models
|
||||
import modules.styles
|
||||
import modules.devices as devices
|
||||
from modules import sd_samplers
|
||||
from modules.paths import script_path, sd_path
|
||||
|
||||
sd_model_file = os.path.join(script_path, 'model.ckpt')
|
||||
|
|
@ -55,7 +56,7 @@ parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide dire
|
|||
parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json'))
|
||||
parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option")
|
||||
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="color-sketch")
|
||||
parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor")
|
||||
parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last")
|
||||
parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv'))
|
||||
parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False)
|
||||
|
|
@ -235,17 +236,20 @@ options_templates.update(options_section(('ui', "User interface"), {
|
|||
"font": OptionInfo("", "Font for image grids that have text"),
|
||||
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
|
||||
"js_modal_lightbox_initialy_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
|
||||
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
|
||||
}))
|
||||
|
||||
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"hide_samplers": OptionInfo([], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in sd_samplers.all_samplers]}),
|
||||
"eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
|
||||
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
|
||||
}))
|
||||
|
||||
|
||||
class Options:
|
||||
data = None
|
||||
data_labels = options_templates
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
import os
|
||||
from PIL import Image, ImageOps
|
||||
import platform
|
||||
import sys
|
||||
import tqdm
|
||||
|
||||
from modules import shared, images
|
||||
|
|
@ -10,7 +12,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
|
|||
src = os.path.abspath(process_src)
|
||||
dst = os.path.abspath(process_dst)
|
||||
|
||||
assert src != dst, 'same directory specified as source and desitnation'
|
||||
assert src != dst, 'same directory specified as source and destination'
|
||||
|
||||
os.makedirs(dst, exist_ok=True)
|
||||
|
||||
|
|
@ -25,6 +27,7 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
|
|||
def save_pic_with_caption(image, index):
|
||||
if process_caption:
|
||||
caption = "-" + shared.interrogator.generate_caption(image)
|
||||
caption = sanitize_caption(os.path.join(dst, f"{index:05}-{subindex[0]}"), caption, ".png")
|
||||
else:
|
||||
caption = filename
|
||||
caption = os.path.splitext(caption)[0]
|
||||
|
|
@ -75,3 +78,27 @@ def preprocess(process_src, process_dst, process_flip, process_split, process_ca
|
|||
|
||||
if process_caption:
|
||||
shared.interrogator.send_blip_to_ram()
|
||||
|
||||
def sanitize_caption(base_path, original_caption, suffix):
|
||||
operating_system = platform.system().lower()
|
||||
if (operating_system == "windows"):
|
||||
invalid_path_characters = "\\/:*?\"<>|"
|
||||
max_path_length = 259
|
||||
else:
|
||||
invalid_path_characters = "/" #linux/macos
|
||||
max_path_length = 1023
|
||||
caption = original_caption
|
||||
for invalid_character in invalid_path_characters:
|
||||
caption = caption.replace(invalid_character, "")
|
||||
fixed_path_length = len(base_path) + len(suffix)
|
||||
if fixed_path_length + len(caption) <= max_path_length:
|
||||
return caption
|
||||
caption_tokens = caption.split()
|
||||
new_caption = ""
|
||||
for token in caption_tokens:
|
||||
last_caption = new_caption
|
||||
new_caption = new_caption + token + " "
|
||||
if (len(new_caption) + fixed_path_length - 1 > max_path_length):
|
||||
break
|
||||
print(f"\nPath will be too long. Truncated caption: {original_caption}\nto: {last_caption}", file=sys.stderr)
|
||||
return last_caption.strip()
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ import modules.gfpgan_model
|
|||
import modules.codeformer_model
|
||||
import modules.styles
|
||||
import modules.generation_parameters_copypaste
|
||||
from modules.prompt_parser import get_learned_conditioning_prompt_schedules
|
||||
from modules import prompt_parser
|
||||
from modules.images import apply_filename_pattern, get_next_sequence_number
|
||||
import modules.textual_inversion.ui
|
||||
|
||||
|
|
@ -394,7 +394,9 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
|
|||
|
||||
def update_token_counter(text, steps):
|
||||
try:
|
||||
prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
|
||||
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
|
||||
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
|
||||
|
||||
except Exception:
|
||||
# a parsing error can happen here during typing, and we don't want to bother the user with
|
||||
# messages related to it in console
|
||||
|
|
@ -1210,6 +1212,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
)
|
||||
|
||||
def request_restart():
|
||||
shared.state.interrupt()
|
||||
settings_interface.gradio_ref.do_restart = True
|
||||
|
||||
restart_gradio.click(
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ import gradio as gr
|
|||
|
||||
from modules import processing, shared, sd_samplers, prompt_parser
|
||||
from modules.processing import Processed
|
||||
from modules.sd_samplers import samplers
|
||||
from modules.shared import opts, cmd_opts, state
|
||||
|
||||
import torch
|
||||
|
|
@ -159,7 +158,7 @@ class Script(scripts.Script):
|
|||
|
||||
combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
|
||||
|
||||
sampler = samplers[p.sampler_index].constructor(p.sd_model)
|
||||
sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, p.sampler_index, p.sd_model)
|
||||
|
||||
sigmas = sampler.model_wrap.get_sigmas(p.steps)
|
||||
|
||||
|
|
|
|||
|
|
@ -85,8 +85,11 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
|
|||
src_dist = np.absolute(src_fft)
|
||||
src_phase = src_fft / src_dist
|
||||
|
||||
# create a generator with a static seed to make outpainting deterministic / only follow global seed
|
||||
rng = np.random.default_rng(0)
|
||||
|
||||
noise_window = _get_gaussian_window(width, height, mode=1) # start with simple gaussian noise
|
||||
noise_rgb = np.random.random_sample((width, height, num_channels))
|
||||
noise_rgb = rng.random((width, height, num_channels))
|
||||
noise_grey = (np.sum(noise_rgb, axis=2) / 3.)
|
||||
noise_rgb *= color_variation # the colorfulness of the starting noise is blended to greyscale with a parameter
|
||||
for c in range(num_channels):
|
||||
|
|
|
|||
|
|
@ -1,8 +1,9 @@
|
|||
from collections import namedtuple
|
||||
from copy import copy
|
||||
from itertools import permutations
|
||||
from itertools import permutations, chain
|
||||
import random
|
||||
|
||||
import csv
|
||||
from io import StringIO
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
|
||||
|
|
@ -168,7 +169,6 @@ re_range_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d
|
|||
re_range_count = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\[(\d+)\s*\])?\s*")
|
||||
re_range_count_float = re.compile(r"\s*([+-]?\s*\d+(?:.\d*)?)\s*-\s*([+-]?\s*\d+(?:.\d*)?)(?:\s*\[(\d+(?:.\d*)?)\s*\])?\s*")
|
||||
|
||||
|
||||
class Script(scripts.Script):
|
||||
def title(self):
|
||||
return "X/Y plot"
|
||||
|
|
@ -197,7 +197,7 @@ class Script(scripts.Script):
|
|||
if opt.label == 'Nothing':
|
||||
return [0]
|
||||
|
||||
valslist = [x.strip() for x in vals.split(",")]
|
||||
valslist = list(map(str.strip,chain.from_iterable(csv.reader(StringIO(vals)))))
|
||||
|
||||
if opt.type == int:
|
||||
valslist_ext = []
|
||||
|
|
|
|||
7
webui.py
7
webui.py
|
|
@ -2,11 +2,12 @@ import os
|
|||
import threading
|
||||
import time
|
||||
import importlib
|
||||
from modules import devices
|
||||
from modules.paths import script_path
|
||||
import signal
|
||||
import threading
|
||||
|
||||
from modules.paths import script_path
|
||||
|
||||
from modules import devices, sd_samplers
|
||||
import modules.codeformer_model as codeformer
|
||||
import modules.extras
|
||||
import modules.face_restoration
|
||||
|
|
@ -109,6 +110,8 @@ def webui():
|
|||
time.sleep(0.5)
|
||||
break
|
||||
|
||||
sd_samplers.set_samplers()
|
||||
|
||||
print('Reloading Custom Scripts')
|
||||
modules.scripts.reload_scripts(os.path.join(script_path, "scripts"))
|
||||
print('Reloading modules: modules.ui')
|
||||
|
|
|
|||
Loading…
Reference in New Issue