Merge branch 'master' into cpu-cmdline-opt
This commit is contained in:
brkirch
GitHub
commit
e9e2a7ec9a
8 changed files with 109 additions and 80 deletions
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@ -69,10 +69,14 @@ def setup_model(dirname):
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self.net = net
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self.face_helper = face_helper
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self.net.to(devices.device_codeformer)
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return net, face_helper
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def send_model_to(self, device):
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self.net.to(device)
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self.face_helper.face_det.to(device)
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self.face_helper.face_parse.to(device)
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def restore(self, np_image, w=None):
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np_image = np_image[:, :, ::-1]
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@ -82,6 +86,8 @@ def setup_model(dirname):
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if self.net is None or self.face_helper is None:
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return np_image
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self.send_model_to(devices.device_codeformer)
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self.face_helper.clean_all()
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self.face_helper.read_image(np_image)
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self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
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@ -113,8 +119,10 @@ def setup_model(dirname):
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if original_resolution != restored_img.shape[0:2]:
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restored_img = cv2.resize(restored_img, (0, 0), fx=original_resolution[1]/restored_img.shape[1], fy=original_resolution[0]/restored_img.shape[0], interpolation=cv2.INTER_LINEAR)
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self.face_helper.clean_all()
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if shared.opts.face_restoration_unload:
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self.net.to(devices.cpu)
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self.send_model_to(devices.cpu)
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return restored_img
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@ -1,3 +1,5 @@
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import contextlib
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import torch
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from modules import errors
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@ -56,3 +58,11 @@ def randn_without_seed(shape):
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return torch.randn(shape, device=device)
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def autocast():
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from modules import shared
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if dtype == torch.float32 or shared.cmd_opts.precision == "full":
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return contextlib.nullcontext()
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return torch.autocast("cuda")
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@ -36,23 +36,33 @@ def gfpgann():
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else:
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print("Unable to load gfpgan model!")
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return None
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model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None, device=devices.device_gfpgan)
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model.gfpgan.to(devices.device_gfpgan)
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model = gfpgan_constructor(model_path=model_file, upscale=1, arch='clean', channel_multiplier=2, bg_upsampler=None)
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loaded_gfpgan_model = model
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return model
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def send_model_to(model, device):
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model.gfpgan.to(device)
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model.face_helper.face_det.to(device)
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model.face_helper.face_parse.to(device)
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def gfpgan_fix_faces(np_image):
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model = gfpgann()
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if model is None:
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return np_image
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send_model_to(model, devices.device_gfpgan)
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np_image_bgr = np_image[:, :, ::-1]
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cropped_faces, restored_faces, gfpgan_output_bgr = model.enhance(np_image_bgr, has_aligned=False, only_center_face=False, paste_back=True)
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np_image = gfpgan_output_bgr[:, :, ::-1]
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model.face_helper.clean_all()
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if shared.opts.face_restoration_unload:
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model.gfpgan.to(devices.cpu)
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send_model_to(model, devices.cpu)
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return np_image
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@ -1,4 +1,3 @@
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import contextlib
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import json
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import math
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import os
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@ -330,9 +329,8 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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infotexts = []
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output_images = []
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precision_scope = torch.autocast if cmd_opts.precision == "autocast" else contextlib.nullcontext
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ema_scope = (contextlib.nullcontext if cmd_opts.lowvram else p.sd_model.ema_scope)
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with torch.no_grad(), precision_scope("cuda"), ema_scope():
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with torch.no_grad():
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p.init(all_prompts, all_seeds, all_subseeds)
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if state.job_count == -1:
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@ -351,8 +349,9 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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#uc = p.sd_model.get_learned_conditioning(len(prompts) * [p.negative_prompt])
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#c = p.sd_model.get_learned_conditioning(prompts)
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uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
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c = prompt_parser.get_learned_conditioning(prompts, p.steps)
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with devices.autocast():
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uc = prompt_parser.get_learned_conditioning(len(prompts) * [p.negative_prompt], p.steps)
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c = prompt_parser.get_learned_conditioning(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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@ -361,13 +360,17 @@ def process_images(p: StableDiffusionProcessing) -> 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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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
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with devices.autocast():
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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength)
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if state.interrupted:
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# if we are interruped, sample returns just noise
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# use the image collected previously in sampler loop
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samples_ddim = shared.state.current_latent
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samples_ddim = samples_ddim.to(devices.dtype)
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x_samples_ddim = p.sd_model.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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@ -386,6 +389,7 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
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devices.torch_gc()
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x_sample = modules.face_restoration.restore_faces(x_sample)
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devices.torch_gc()
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image = Image.fromarray(x_sample)
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@ -1,20 +1,11 @@
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import re
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from collections import namedtuple
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import torch
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from lark import Lark, Transformer, Visitor
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import functools
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import modules.shared as shared
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re_prompt = re.compile(r'''
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(.*?)
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\[
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([^]:]+):
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(?:([^]:]*):)?
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([0-9]*\.?[0-9]+)
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]
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(.+)
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''', re.X)
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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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# will be represented with prompt_schedule like this (assuming steps=100):
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# [25, 'fantasy landscape with a mountain and an oak in foreground shoddy']
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@ -25,61 +16,57 @@ re_prompt = re.compile(r'''
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def get_learned_conditioning_prompt_schedules(prompts, steps):
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res = []
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cache = {}
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for prompt in prompts:
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prompt_schedule: list[list[str | int]] = [[steps, ""]]
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cached = cache.get(prompt, None)
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if cached is not None:
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res.append(cached)
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continue
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for m in re_prompt.finditer(prompt):
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plaintext = m.group(1) if m.group(5) is None else m.group(5)
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concept_from = m.group(2)
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concept_to = m.group(3)
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if concept_to is None:
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concept_to = concept_from
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concept_from = ""
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swap_position = float(m.group(4)) if m.group(4) is not None else None
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if swap_position is not None:
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if swap_position < 1:
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swap_position = swap_position * steps
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swap_position = int(min(swap_position, steps))
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swap_index = None
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found_exact_index = False
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for i in range(len(prompt_schedule)):
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end_step = prompt_schedule[i][0]
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prompt_schedule[i][1] += plaintext
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if swap_position is not None and swap_index is None:
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if swap_position == end_step:
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swap_index = i
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found_exact_index = True
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if swap_position < end_step:
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swap_index = i
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if swap_index is not None:
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if not found_exact_index:
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prompt_schedule.insert(swap_index, [swap_position, prompt_schedule[swap_index][1]])
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for i in range(len(prompt_schedule)):
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end_step = prompt_schedule[i][0]
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must_replace = swap_position < end_step
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prompt_schedule[i][1] += concept_to if must_replace else concept_from
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res.append(prompt_schedule)
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cache[prompt] = prompt_schedule
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#for t in prompt_schedule:
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# print(t)
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return res
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grammar = r"""
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start: prompt
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prompt: (emphasized | scheduled | weighted | plain)*
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!emphasized: "(" prompt ")"
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| "(" prompt ":" prompt ")"
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| "[" prompt "]"
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scheduled: "[" (prompt ":")? prompt ":" NUMBER "]"
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!weighted: "{" weighted_item ("|" weighted_item)* "}"
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!weighted_item: prompt (":" prompt)?
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plain: /([^\\\[\](){}:|]|\\.)+/
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%import common.SIGNED_NUMBER -> NUMBER
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"""
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parser = Lark(grammar, parser='lalr')
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def collect_steps(steps, tree):
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l = [steps]
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class CollectSteps(Visitor):
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def scheduled(self, tree):
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tree.children[-1] = float(tree.children[-1])
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if tree.children[-1] < 1:
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tree.children[-1] *= steps
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tree.children[-1] = min(steps, int(tree.children[-1]))
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l.append(tree.children[-1])
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CollectSteps().visit(tree)
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return sorted(set(l))
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def at_step(step, tree):
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class AtStep(Transformer):
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def scheduled(self, args):
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if len(args) == 2:
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before, after, when = (), *args
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else:
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before, after, when = args
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yield before if step <= when else after
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def start(self, args):
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def flatten(x):
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if type(x) == str:
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yield x
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else:
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for gen in x:
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yield from flatten(gen)
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return ''.join(flatten(args[0]))
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def plain(self, args):
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yield args[0].value
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def __default__(self, data, children, meta):
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for child in children:
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yield from child
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return AtStep().transform(tree)
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@functools.cache
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def get_schedule(prompt):
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tree = parser.parse(prompt)
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return [[t, at_step(t, tree)] for t in collect_steps(steps, tree)]
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return [get_schedule(prompt) for prompt in prompts]
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ScheduledPromptConditioning = namedtuple("ScheduledPromptConditioning", ["end_at_step", "cond"])
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@ -386,14 +386,22 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
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outputs=[seed, dummy_component]
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)
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def update_token_counter(text, steps):
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prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
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try:
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prompt_schedules = get_learned_conditioning_prompt_schedules([text], steps)
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except Exception:
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# a parsing error can happen here during typing, and we don't want to bother the user with
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# messages related to it in console
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prompt_schedules = [[[steps, text]]]
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flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
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prompts = [prompt_text for step,prompt_text in flat_prompts]
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prompts = [prompt_text for step, prompt_text in flat_prompts]
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tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
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style_class = ' class="red"' if (token_count > max_length) else ""
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return f"<span {style_class}>{token_count}/{max_length}</span>"
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def create_toprow(is_img2img):
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id_part = "img2img" if is_img2img else "txt2img"
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@ -22,3 +22,4 @@ clean-fid
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resize-right
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torchdiffeq
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kornia
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lark
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@ -21,3 +21,4 @@ clean-fid==0.1.29
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resize-right==0.0.2
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torchdiffeq==0.2.3
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kornia==0.6.7
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lark==1.1.2
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