fix to tokens lenght, addend embs generator, add new features to edit the embedding before the generation using text
This commit is contained in:
MalumaDev
parent
bb57f30c2d
commit
37d7ffb415
6 changed files with 302 additions and 96 deletions
78
modules/aesthetic_clip.py
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78
modules/aesthetic_clip.py
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@ -0,0 +1,78 @@
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import itertools
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import os
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from pathlib import Path
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import html
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import gc
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import gradio as gr
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import torch
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from PIL import Image
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from modules import shared
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from modules.shared import device, aesthetic_embeddings
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from transformers import CLIPModel, CLIPProcessor
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from tqdm.auto import tqdm
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def get_all_images_in_folder(folder):
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return [os.path.join(folder, f) for f in os.listdir(folder) if
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os.path.isfile(os.path.join(folder, f)) and check_is_valid_image_file(f)]
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def check_is_valid_image_file(filename):
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return filename.lower().endswith(('.png', '.jpg', '.jpeg'))
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def batched(dataset, total, n=1):
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for ndx in range(0, total, n):
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yield [dataset.__getitem__(i) for i in range(ndx, min(ndx + n, total))]
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def iter_to_batched(iterable, n=1):
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it = iter(iterable)
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while True:
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chunk = tuple(itertools.islice(it, n))
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if not chunk:
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return
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yield chunk
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def generate_imgs_embd(name, folder, batch_size):
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# clipModel = CLIPModel.from_pretrained(
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# shared.sd_model.cond_stage_model.clipModel.name_or_path
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# )
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model = CLIPModel.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path).to(device)
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processor = CLIPProcessor.from_pretrained(shared.sd_model.cond_stage_model.clipModel.name_or_path)
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with torch.no_grad():
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embs = []
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for paths in tqdm(iter_to_batched(get_all_images_in_folder(folder), batch_size),
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desc=f"Generating embeddings for {name}"):
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if shared.state.interrupted:
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break
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inputs = processor(images=[Image.open(path) for path in paths], return_tensors="pt").to(device)
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outputs = model.get_image_features(**inputs).cpu()
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embs.append(torch.clone(outputs))
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inputs.to("cpu")
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del inputs, outputs
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embs = torch.cat(embs, dim=0).mean(dim=0, keepdim=True)
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# The generated embedding will be located here
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path = str(Path(shared.cmd_opts.aesthetic_embeddings_dir) / f"{name}.pt")
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torch.save(embs, path)
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model = model.cpu()
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del model
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del processor
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del embs
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gc.collect()
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torch.cuda.empty_cache()
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res = f"""
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Done generating embedding for {name}!
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Hypernetwork saved to {html.escape(path)}
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"""
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shared.update_aesthetic_embeddings()
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return gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding",
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value=sorted(aesthetic_embeddings.keys())[0] if len(
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aesthetic_embeddings) > 0 else None), res, ""
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@ -20,7 +20,6 @@ import modules.images as images
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import modules.styles
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import logging
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_f = 8
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@ -52,8 +51,13 @@ def get_correct_sampler(p):
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elif isinstance(p, modules.processing.StableDiffusionProcessingImg2Img):
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return sd_samplers.samplers_for_img2img
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class StableDiffusionProcessing:
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1, subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True, sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512, restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False, extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt="", styles=None, seed=-1,
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subseed=-1, subseed_strength=0, seed_resize_from_h=-1, seed_resize_from_w=-1, seed_enable_extras=True,
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sampler_index=0, batch_size=1, n_iter=1, steps=50, cfg_scale=7.0, width=512, height=512,
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restore_faces=False, tiling=False, do_not_save_samples=False, do_not_save_grid=False,
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extra_generation_params=None, overlay_images=None, negative_prompt=None, eta=None):
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self.sd_model = sd_model
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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@ -104,7 +108,8 @@ class StableDiffusionProcessing:
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class Processed:
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def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
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def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None,
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all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None):
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self.images = images_list
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self.prompt = p.prompt
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self.negative_prompt = p.negative_prompt
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@ -141,7 +146,8 @@ class Processed:
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self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
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self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
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self.seed = int(self.seed if type(self.seed) != list else self.seed[0])
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self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
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self.subseed = int(
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self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
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self.all_prompts = all_prompts or [self.prompt]
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self.all_seeds = all_seeds or [self.seed]
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@ -181,39 +187,43 @@ class Processed:
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return json.dumps(obj)
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def infotext(self, p: StableDiffusionProcessing, index):
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return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
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def infotext(self, p: StableDiffusionProcessing, index):
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return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[],
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position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
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# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
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def slerp(val, low, high):
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low_norm = low/torch.norm(low, dim=1, keepdim=True)
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high_norm = high/torch.norm(high, dim=1, keepdim=True)
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dot = (low_norm*high_norm).sum(1)
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low_norm = low / torch.norm(low, dim=1, keepdim=True)
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high_norm = high / torch.norm(high, dim=1, keepdim=True)
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dot = (low_norm * high_norm).sum(1)
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if dot.mean() > 0.9995:
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return low * val + high * (1 - val)
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omega = torch.acos(dot)
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so = torch.sin(omega)
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res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
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res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
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return res
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def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
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def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0,
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p=None):
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xs = []
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# if we have multiple seeds, this means we are working with batch size>1; this then
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# enables the generation of additional tensors with noise that the sampler will use during its processing.
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# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
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# produce the same images as with two batches [100], [101].
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if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
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if p is not None and p.sampler is not None and (
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len(seeds) > 1 and opts.enable_batch_seeds or opts.eta_noise_seed_delta > 0):
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sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
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else:
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sampler_noises = None
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for i, seed in enumerate(seeds):
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noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
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noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (
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shape[0], seed_resize_from_h // 8, seed_resize_from_w // 8)
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subnoise = None
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if subseeds is not None:
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@ -241,7 +251,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
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dx = max(-dx, 0)
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dy = max(-dy, 0)
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x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
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x[:, ty:ty + h, tx:tx + w] = noise[:, dy:dy + h, dx:dx + w]
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noise = x
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if sampler_noises is not None:
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@ -293,14 +303,20 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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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 if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
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"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
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"Hypernet": (None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(':', '')),
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"Model hash": getattr(p, 'sd_model_hash',
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None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
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"Model": (
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None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(
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',', '').replace(':', '')),
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"Hypernet": (
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None if shared.loaded_hypernetwork is None else shared.loaded_hypernetwork.name.replace(',', '').replace(
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':', '')),
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"Batch size": (None if p.batch_size < 2 else p.batch_size),
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"Batch pos": (None if p.batch_size < 2 else position_in_batch),
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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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"Seed resize from": (
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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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"Eta": (None if p.sampler is None or p.sampler.eta == p.sampler.default_eta else p.sampler.eta),
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"Clip skip": None if clip_skip <= 1 else clip_skip,
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@ -309,7 +325,8 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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generation_params.update(p.extra_generation_params)
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generation_params_text = ", ".join([k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
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generation_params_text = ", ".join(
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[k if k == v else f'{k}: {v}' for k, v in generation_params.items() if v is not None])
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negative_prompt_text = "\nNegative prompt: " + p.negative_prompt if p.negative_prompt else ""
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@ -317,7 +334,9 @@ def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments, iteration
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def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0,
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aesthetic_imgs=None,aesthetic_slerp=False) -> Processed:
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aesthetic_imgs=None, aesthetic_slerp=False, aesthetic_imgs_text="",
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aesthetic_slerp_angle=0.15,
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aesthetic_text_negative=False) -> 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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aesthetic_lr = float(aesthetic_lr)
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@ -385,7 +404,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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for n in range(p.n_iter):
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if state.skipped:
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state.skipped = False
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if state.interrupted:
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break
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@ -396,16 +415,19 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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if (len(prompts) == 0):
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break
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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 = 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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with devices.autocast():
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if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
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shared.sd_model.cond_stage_model.set_aesthetic_params(0, 0, 0)
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shared.sd_model.cond_stage_model.set_aesthetic_params()
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uc = prompt_parser.get_learned_conditioning(shared.sd_model, len(prompts) * [p.negative_prompt],
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p.steps)
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if hasattr(shared.sd_model.cond_stage_model, "set_aesthetic_params"):
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shared.sd_model.cond_stage_model.set_aesthetic_params(aesthetic_lr, aesthetic_weight,
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aesthetic_steps, aesthetic_imgs,aesthetic_slerp)
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aesthetic_steps, aesthetic_imgs,
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aesthetic_slerp, aesthetic_imgs_text,
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aesthetic_slerp_angle,
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aesthetic_text_negative)
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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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@ -413,13 +435,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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comments[comment] = 1
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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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shared.state.job = f"Batch {n + 1} out of {p.n_iter}"
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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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samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds,
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subseed_strength=p.subseed_strength)
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if state.interrupted or state.skipped:
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# if we are interrupted, 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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@ -445,7 +467,9 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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if p.restore_faces:
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if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
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images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
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images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i],
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opts.samples_format, info=infotext(n, i), p=p,
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suffix="-before-face-restoration")
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devices.torch_gc()
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@ -456,7 +480,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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if p.color_corrections is not None and i < len(p.color_corrections):
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if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
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images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
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images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
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info=infotext(n, i), p=p, suffix="-before-color-correction")
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image = apply_color_correction(p.color_corrections[i], image)
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if p.overlay_images is not None and i < len(p.overlay_images):
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@ -474,7 +499,8 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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image = image.convert('RGB')
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if opts.samples_save and not p.do_not_save_samples:
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images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
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images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format,
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info=infotext(n, i), p=p)
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text = infotext(n, i)
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infotexts.append(text)
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@ -482,7 +508,7 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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image.info["parameters"] = text
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output_images.append(image)
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del x_samples_ddim
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del x_samples_ddim
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devices.torch_gc()
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@ -504,10 +530,13 @@ def process_images(p: StableDiffusionProcessing, aesthetic_lr=0, aesthetic_weigh
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index_of_first_image = 1
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if opts.grid_save:
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images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
|
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images.save_image(grid, p.outpath_grids, "grid", all_seeds[0], all_prompts[0], opts.grid_format,
|
||||
info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
|
||||
|
||||
devices.torch_gc()
|
||||
return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds, index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
return Processed(p, output_images, all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]),
|
||||
subseed=all_subseeds[0], all_prompts=all_prompts, all_seeds=all_seeds, all_subseeds=all_subseeds,
|
||||
index_of_first_image=index_of_first_image, infotexts=infotexts)
|
||||
|
||||
|
||||
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
||||
|
|
@ -543,25 +572,34 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
|
||||
|
||||
if not self.enable_hr:
|
||||
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)
|
||||
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)
|
||||
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
|
||||
return samples
|
||||
|
||||
x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_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)
|
||||
x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_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)
|
||||
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning)
|
||||
|
||||
truncate_x = (self.firstphase_width - self.firstphase_width_truncated) // opt_f
|
||||
truncate_y = (self.firstphase_height - self.firstphase_height_truncated) // opt_f
|
||||
|
||||
samples = samples[:, :, truncate_y//2:samples.shape[2]-truncate_y//2, truncate_x//2:samples.shape[3]-truncate_x//2]
|
||||
samples = samples[:, :, truncate_y // 2:samples.shape[2] - truncate_y // 2,
|
||||
truncate_x // 2:samples.shape[3] - truncate_x // 2]
|
||||
|
||||
if self.scale_latent:
|
||||
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
|
||||
samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f),
|
||||
mode="bilinear")
|
||||
else:
|
||||
decoded_samples = decode_first_stage(self.sd_model, samples)
|
||||
|
||||
if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None":
|
||||
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width), mode="bilinear")
|
||||
decoded_samples = torch.nn.functional.interpolate(decoded_samples, size=(self.height, self.width),
|
||||
mode="bilinear")
|
||||
else:
|
||||
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
|
||||
|
||||
|
|
@ -585,13 +623,16 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers, self.sampler_index, self.sd_model)
|
||||
|
||||
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)
|
||||
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)
|
||||
|
||||
# GC now before running the next img2img to prevent running out of memory
|
||||
x = None
|
||||
devices.torch_gc()
|
||||
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.steps)
|
||||
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning,
|
||||
steps=self.steps)
|
||||
|
||||
return samples
|
||||
|
||||
|
|
@ -599,7 +640,9 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|||
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
||||
sampler = None
|
||||
|
||||
def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4, inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0, **kwargs):
|
||||
def __init__(self, init_images=None, resize_mode=0, denoising_strength=0.75, mask=None, mask_blur=4,
|
||||
inpainting_fill=0, inpaint_full_res=True, inpaint_full_res_padding=0, inpainting_mask_invert=0,
|
||||
**kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
self.init_images = init_images
|
||||
|
|
@ -607,7 +650,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
self.denoising_strength: float = denoising_strength
|
||||
self.init_latent = None
|
||||
self.image_mask = mask
|
||||
#self.image_unblurred_mask = None
|
||||
# self.image_unblurred_mask = None
|
||||
self.latent_mask = None
|
||||
self.mask_for_overlay = None
|
||||
self.mask_blur = mask_blur
|
||||
|
|
@ -619,7 +662,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
self.nmask = None
|
||||
|
||||
def init(self, all_prompts, all_seeds, all_subseeds):
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index, self.sd_model)
|
||||
self.sampler = sd_samplers.create_sampler_with_index(sd_samplers.samplers_for_img2img, self.sampler_index,
|
||||
self.sd_model)
|
||||
crop_region = None
|
||||
|
||||
if self.image_mask is not None:
|
||||
|
|
@ -628,7 +672,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
if self.inpainting_mask_invert:
|
||||
self.image_mask = ImageOps.invert(self.image_mask)
|
||||
|
||||
#self.image_unblurred_mask = self.image_mask
|
||||
# self.image_unblurred_mask = self.image_mask
|
||||
|
||||
if self.mask_blur > 0:
|
||||
self.image_mask = self.image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
|
||||
|
|
@ -642,7 +686,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
|
||||
mask = mask.crop(crop_region)
|
||||
self.image_mask = images.resize_image(2, mask, self.width, self.height)
|
||||
self.paste_to = (x1, y1, x2-x1, y2-y1)
|
||||
self.paste_to = (x1, y1, x2 - x1, y2 - y1)
|
||||
else:
|
||||
self.image_mask = images.resize_image(self.resize_mode, self.image_mask, self.width, self.height)
|
||||
np_mask = np.array(self.image_mask)
|
||||
|
|
@ -665,7 +709,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
|
||||
if self.image_mask is not None:
|
||||
image_masked = Image.new('RGBa', (image.width, image.height))
|
||||
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
|
||||
image_masked.paste(image.convert("RGBA").convert("RGBa"),
|
||||
mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
|
||||
|
||||
self.overlay_images.append(image_masked.convert('RGBA'))
|
||||
|
||||
|
|
@ -714,12 +759,17 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|||
|
||||
# this needs to be fixed to be done in sample() using actual seeds for batches
|
||||
if self.inpainting_fill == 2:
|
||||
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
|
||||
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:],
|
||||
all_seeds[
|
||||
0:self.init_latent.shape[
|
||||
0]]) * self.nmask
|
||||
elif self.inpainting_fill == 3:
|
||||
self.init_latent = self.init_latent * self.mask
|
||||
|
||||
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):
|
||||
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)
|
||||
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)
|
||||
|
||||
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning)
|
||||
|
||||
|
|
|
|||
|
|
@ -14,7 +14,8 @@ from modules.sd_hijack_optimizations import invokeAI_mps_available
|
|||
|
||||
import ldm.modules.attention
|
||||
import ldm.modules.diffusionmodules.model
|
||||
from transformers import CLIPVisionModel, CLIPModel
|
||||
from tqdm import trange
|
||||
from transformers import CLIPVisionModel, CLIPModel, CLIPTokenizer
|
||||
import torch.optim as optim
|
||||
import copy
|
||||
|
||||
|
|
@ -22,21 +23,25 @@ attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
|
|||
diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
|
||||
diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
|
||||
|
||||
|
||||
def apply_optimizations():
|
||||
undo_optimizations()
|
||||
|
||||
ldm.modules.diffusionmodules.model.nonlinearity = silu
|
||||
|
||||
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
|
||||
if cmd_opts.force_enable_xformers or (cmd_opts.xformers and shared.xformers_available and torch.version.cuda and (
|
||||
6, 0) <= torch.cuda.get_device_capability(shared.device) <= (8, 6)):
|
||||
print("Applying xformers cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.xformers_attention_forward
|
||||
ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.xformers_attnblock_forward
|
||||
elif cmd_opts.opt_split_attention_v1:
|
||||
print("Applying v1 cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
|
||||
elif not cmd_opts.disable_opt_split_attention and (
|
||||
cmd_opts.opt_split_attention_invokeai or not torch.cuda.is_available()):
|
||||
if not invokeAI_mps_available and shared.device.type == 'mps':
|
||||
print("The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
|
||||
print(
|
||||
"The InvokeAI cross attention optimization for MPS requires the psutil package which is not installed.")
|
||||
print("Applying v1 cross attention optimization.")
|
||||
ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
|
||||
else:
|
||||
|
|
@ -112,14 +117,16 @@ class StableDiffusionModelHijack:
|
|||
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
|
||||
return remade_batch_tokens[0], token_count, get_target_prompt_token_count(token_count)
|
||||
|
||||
|
||||
def slerp(low, high, val):
|
||||
low_norm = low/torch.norm(low, dim=1, keepdim=True)
|
||||
high_norm = high/torch.norm(high, dim=1, keepdim=True)
|
||||
omega = torch.acos((low_norm*high_norm).sum(1))
|
||||
low_norm = low / torch.norm(low, dim=1, keepdim=True)
|
||||
high_norm = high / torch.norm(high, dim=1, keepdim=True)
|
||||
omega = torch.acos((low_norm * high_norm).sum(1))
|
||||
so = torch.sin(omega)
|
||||
res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
|
||||
res = (torch.sin((1.0 - val) * omega) / so).unsqueeze(1) * low + (torch.sin(val * omega) / so).unsqueeze(1) * high
|
||||
return res
|
||||
|
||||
|
||||
class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
||||
def __init__(self, wrapped, hijack):
|
||||
super().__init__()
|
||||
|
|
@ -128,6 +135,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
self.wrapped.transformer.name_or_path
|
||||
)
|
||||
del self.clipModel.vision_model
|
||||
self.tokenizer = CLIPTokenizer.from_pretrained(self.wrapped.transformer.name_or_path)
|
||||
self.hijack: StableDiffusionModelHijack = hijack
|
||||
self.tokenizer = wrapped.tokenizer
|
||||
# self.vision = CLIPVisionModel.from_pretrained(self.wrapped.transformer.name_or_path).eval()
|
||||
|
|
@ -139,7 +147,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
|
||||
self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
|
||||
|
||||
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
|
||||
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if
|
||||
'(' in k or ')' in k or '[' in k or ']' in k]
|
||||
for text, ident in tokens_with_parens:
|
||||
mult = 1.0
|
||||
for c in text:
|
||||
|
|
@ -155,8 +164,13 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
if mult != 1.0:
|
||||
self.token_mults[ident] = mult
|
||||
|
||||
def set_aesthetic_params(self, aesthetic_lr, aesthetic_weight, aesthetic_steps, image_embs_name=None,
|
||||
aesthetic_slerp=True):
|
||||
def set_aesthetic_params(self, aesthetic_lr=0, aesthetic_weight=0, aesthetic_steps=0, image_embs_name=None,
|
||||
aesthetic_slerp=True, aesthetic_imgs_text="",
|
||||
aesthetic_slerp_angle=0.15,
|
||||
aesthetic_text_negative=False):
|
||||
self.aesthetic_imgs_text = aesthetic_imgs_text
|
||||
self.aesthetic_slerp_angle = aesthetic_slerp_angle
|
||||
self.aesthetic_text_negative = aesthetic_text_negative
|
||||
self.slerp = aesthetic_slerp
|
||||
self.aesthetic_lr = aesthetic_lr
|
||||
self.aesthetic_weight = aesthetic_weight
|
||||
|
|
@ -180,7 +194,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
else:
|
||||
parsed = [[line, 1.0]]
|
||||
|
||||
tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)["input_ids"]
|
||||
tokenized = self.wrapped.tokenizer([text for text, _ in parsed], truncation=False, add_special_tokens=False)[
|
||||
"input_ids"]
|
||||
|
||||
fixes = []
|
||||
remade_tokens = []
|
||||
|
|
@ -196,18 +211,20 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
|
||||
if token == self.comma_token:
|
||||
last_comma = len(remade_tokens)
|
||||
elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens), 1) % 75 == 0 and last_comma != -1 and len(remade_tokens) - last_comma <= opts.comma_padding_backtrack:
|
||||
elif opts.comma_padding_backtrack != 0 and max(len(remade_tokens),
|
||||
1) % 75 == 0 and last_comma != -1 and len(
|
||||
remade_tokens) - last_comma <= opts.comma_padding_backtrack:
|
||||
last_comma += 1
|
||||
reloc_tokens = remade_tokens[last_comma:]
|
||||
reloc_mults = multipliers[last_comma:]
|
||||
|
||||
remade_tokens = remade_tokens[:last_comma]
|
||||
length = len(remade_tokens)
|
||||
|
||||
|
||||
rem = int(math.ceil(length / 75)) * 75 - length
|
||||
remade_tokens += [id_end] * rem + reloc_tokens
|
||||
multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
|
||||
|
||||
|
||||
if embedding is None:
|
||||
remade_tokens.append(token)
|
||||
multipliers.append(weight)
|
||||
|
|
@ -248,7 +265,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
if line in cache:
|
||||
remade_tokens, fixes, multipliers = cache[line]
|
||||
else:
|
||||
remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
|
||||
remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms,
|
||||
hijack_comments)
|
||||
token_count = max(current_token_count, token_count)
|
||||
|
||||
cache[line] = (remade_tokens, fixes, multipliers)
|
||||
|
|
@ -259,7 +277,6 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
|
||||
def process_text_old(self, text):
|
||||
id_start = self.wrapped.tokenizer.bos_token_id
|
||||
id_end = self.wrapped.tokenizer.eos_token_id
|
||||
|
|
@ -289,7 +306,8 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
while i < len(tokens):
|
||||
token = tokens[i]
|
||||
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
|
||||
embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens,
|
||||
i)
|
||||
|
||||
mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
|
||||
if mult_change is not None:
|
||||
|
|
@ -312,11 +330,12 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
ovf = remade_tokens[maxlen - 2:]
|
||||
overflowing_words = [vocab.get(int(x), "") for x in ovf]
|
||||
overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
|
||||
hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
hijack_comments.append(
|
||||
f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
|
||||
|
||||
token_count = len(remade_tokens)
|
||||
remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen-2] + [id_end]
|
||||
remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
|
||||
cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
|
||||
|
||||
multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
|
||||
|
|
@ -326,23 +345,26 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
hijack_fixes.append(fixes)
|
||||
batch_multipliers.append(multipliers)
|
||||
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
|
||||
|
||||
|
||||
def forward(self, text):
|
||||
use_old = opts.use_old_emphasis_implementation
|
||||
if use_old:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(
|
||||
text)
|
||||
else:
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
|
||||
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(
|
||||
text)
|
||||
|
||||
self.hijack.comments += hijack_comments
|
||||
|
||||
if len(used_custom_terms) > 0:
|
||||
self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
self.hijack.comments.append(
|
||||
"Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
|
||||
|
||||
if use_old:
|
||||
self.hijack.fixes = hijack_fixes
|
||||
return self.process_tokens(remade_batch_tokens, batch_multipliers)
|
||||
|
||||
|
||||
z = None
|
||||
i = 0
|
||||
while max(map(len, remade_batch_tokens)) != 0:
|
||||
|
|
@ -356,7 +378,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
if fix[0] == i:
|
||||
fixes.append(fix[1])
|
||||
self.hijack.fixes.append(fixes)
|
||||
|
||||
|
||||
tokens = []
|
||||
multipliers = []
|
||||
for j in range(len(remade_batch_tokens)):
|
||||
|
|
@ -378,19 +400,30 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
remade_batch_tokens]
|
||||
|
||||
tokens = torch.asarray(remade_batch_tokens).to(device)
|
||||
|
||||
model = copy.deepcopy(self.clipModel).to(device)
|
||||
model.requires_grad_(True)
|
||||
if self.aesthetic_imgs_text is not None and len(self.aesthetic_imgs_text) > 0:
|
||||
text_embs_2 = model.get_text_features(
|
||||
**self.tokenizer([self.aesthetic_imgs_text], padding=True, return_tensors="pt").to(device))
|
||||
if self.aesthetic_text_negative:
|
||||
text_embs_2 = self.image_embs - text_embs_2
|
||||
text_embs_2 /= text_embs_2.norm(dim=-1, keepdim=True)
|
||||
img_embs = slerp(self.image_embs, text_embs_2, self.aesthetic_slerp_angle)
|
||||
else:
|
||||
img_embs = self.image_embs
|
||||
|
||||
with torch.enable_grad():
|
||||
model = copy.deepcopy(self.clipModel).to(device)
|
||||
model.requires_grad_(True)
|
||||
|
||||
# We optimize the model to maximize the similarity
|
||||
optimizer = optim.Adam(
|
||||
model.text_model.parameters(), lr=self.aesthetic_lr
|
||||
)
|
||||
|
||||
for i in range(self.aesthetic_steps):
|
||||
for i in trange(self.aesthetic_steps, desc="Aesthetic optimization"):
|
||||
text_embs = model.get_text_features(input_ids=tokens)
|
||||
text_embs = text_embs / text_embs.norm(dim=-1, keepdim=True)
|
||||
sim = text_embs @ self.image_embs.T
|
||||
sim = text_embs @ img_embs.T
|
||||
loss = -sim
|
||||
optimizer.zero_grad()
|
||||
loss.mean().backward()
|
||||
|
|
@ -405,6 +438,7 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
model.cpu()
|
||||
del model
|
||||
|
||||
zn = torch.concat([zn for i in range(z.shape[1] // 77)], 1)
|
||||
if self.slerp:
|
||||
z = slerp(z, zn, self.aesthetic_weight)
|
||||
else:
|
||||
|
|
@ -413,15 +447,16 @@ class FrozenCLIPEmbedderWithCustomWords(torch.nn.Module):
|
|||
remade_batch_tokens = rem_tokens
|
||||
batch_multipliers = rem_multipliers
|
||||
i += 1
|
||||
|
||||
|
||||
return z
|
||||
|
||||
|
||||
|
||||
def process_tokens(self, remade_batch_tokens, batch_multipliers):
|
||||
if not opts.use_old_emphasis_implementation:
|
||||
remade_batch_tokens = [[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in remade_batch_tokens]
|
||||
remade_batch_tokens = [
|
||||
[self.wrapped.tokenizer.bos_token_id] + x[:75] + [self.wrapped.tokenizer.eos_token_id] for x in
|
||||
remade_batch_tokens]
|
||||
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
|
||||
|
||||
|
||||
tokens = torch.asarray(remade_batch_tokens).to(device)
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
|
||||
|
||||
|
|
@ -461,8 +496,8 @@ class EmbeddingsWithFixes(torch.nn.Module):
|
|||
for fixes, tensor in zip(batch_fixes, inputs_embeds):
|
||||
for offset, embedding in fixes:
|
||||
emb = embedding.vec
|
||||
emb_len = min(tensor.shape[0]-offset-1, emb.shape[0])
|
||||
tensor = torch.cat([tensor[0:offset+1], emb[0:emb_len], tensor[offset+1+emb_len:]])
|
||||
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
|
||||
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]])
|
||||
|
||||
vecs.append(tensor)
|
||||
|
||||
|
|
|
|||
|
|
@ -95,6 +95,10 @@ loaded_hypernetwork = None
|
|||
aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
|
||||
os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
|
||||
|
||||
def update_aesthetic_embeddings():
|
||||
global aesthetic_embeddings
|
||||
aesthetic_embeddings = {f.replace(".pt",""): os.path.join(cmd_opts.aesthetic_embeddings_dir, f) for f in
|
||||
os.listdir(cmd_opts.aesthetic_embeddings_dir) if f.endswith(".pt")}
|
||||
|
||||
def reload_hypernetworks():
|
||||
global hypernetworks
|
||||
|
|
|
|||
|
|
@ -13,7 +13,11 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
|
|||
aesthetic_lr=0,
|
||||
aesthetic_weight=0, aesthetic_steps=0,
|
||||
aesthetic_imgs=None,
|
||||
aesthetic_slerp=False, *args):
|
||||
aesthetic_slerp=False,
|
||||
aesthetic_imgs_text="",
|
||||
aesthetic_slerp_angle=0.15,
|
||||
aesthetic_text_negative=False,
|
||||
*args):
|
||||
p = StableDiffusionProcessingTxt2Img(
|
||||
sd_model=shared.sd_model,
|
||||
outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
|
||||
|
|
@ -47,7 +51,9 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
|
|||
processed = modules.scripts.scripts_txt2img.run(p, *args)
|
||||
|
||||
if processed is None:
|
||||
processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp)
|
||||
processed = process_images(p, aesthetic_lr, aesthetic_weight, aesthetic_steps, aesthetic_imgs, aesthetic_slerp,aesthetic_imgs_text,
|
||||
aesthetic_slerp_angle,
|
||||
aesthetic_text_negative)
|
||||
|
||||
shared.total_tqdm.clear()
|
||||
|
||||
|
|
|
|||
|
|
@ -41,6 +41,7 @@ from modules import prompt_parser
|
|||
from modules.images import save_image
|
||||
import modules.textual_inversion.ui
|
||||
import modules.hypernetworks.ui
|
||||
import modules.aesthetic_clip
|
||||
|
||||
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
|
||||
mimetypes.init()
|
||||
|
|
@ -449,7 +450,7 @@ def create_toprow(is_img2img):
|
|||
with gr.Row():
|
||||
negative_prompt = gr.Textbox(label="Negative prompt", elem_id="negative_prompt", show_label=False, placeholder="Negative prompt", lines=2)
|
||||
with gr.Column(scale=1, elem_id="roll_col"):
|
||||
sh = gr.Button(elem_id="sh", visible=True)
|
||||
sh = gr.Button(elem_id="sh", visible=True)
|
||||
|
||||
with gr.Column(scale=1, elem_id="style_neg_col"):
|
||||
prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())), visible=len(shared.prompt_styles.styles) > 1)
|
||||
|
|
@ -536,9 +537,13 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
|
||||
|
||||
with gr.Group():
|
||||
aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
|
||||
aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.7)
|
||||
aesthetic_steps = gr.Slider(minimum=0, maximum=50, step=1, label="Aesthetic steps", value=50)
|
||||
aesthetic_lr = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.0001")
|
||||
aesthetic_weight = gr.Slider(minimum=0, maximum=1, step=0.01, label="Aesthetic weight", value=0.9)
|
||||
aesthetic_steps = gr.Slider(minimum=0, maximum=256, step=1, label="Aesthetic steps", value=5)
|
||||
with gr.Row():
|
||||
aesthetic_imgs_text = gr.Textbox(label='Aesthetic text for imgs', placeholder="This text is used to rotate the feature space of the imgs embs", value="")
|
||||
aesthetic_slerp_angle = gr.Slider(label='Slerp angle',minimum=0, maximum=1, step=0.01, value=0.1)
|
||||
aesthetic_text_negative = gr.Checkbox(label="Is negative text", value=False)
|
||||
|
||||
aesthetic_imgs = gr.Dropdown(sorted(aesthetic_embeddings.keys()), label="Imgs embedding", value=sorted(aesthetic_embeddings.keys())[0] if len(aesthetic_embeddings) > 0 else None)
|
||||
aesthetic_slerp = gr.Checkbox(label="Slerp interpolation", value=False)
|
||||
|
|
@ -617,7 +622,10 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
aesthetic_weight,
|
||||
aesthetic_steps,
|
||||
aesthetic_imgs,
|
||||
aesthetic_slerp
|
||||
aesthetic_slerp,
|
||||
aesthetic_imgs_text,
|
||||
aesthetic_slerp_angle,
|
||||
aesthetic_text_negative
|
||||
] + custom_inputs,
|
||||
outputs=[
|
||||
txt2img_gallery,
|
||||
|
|
@ -721,7 +729,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
|
||||
with gr.Row():
|
||||
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
|
||||
inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
|
||||
inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=1024, step=4, value=32)
|
||||
|
||||
with gr.TabItem('Batch img2img', id='batch'):
|
||||
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
||||
|
|
@ -1071,6 +1079,17 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
with gr.Column():
|
||||
create_embedding = gr.Button(value="Create embedding", variant='primary')
|
||||
|
||||
with gr.Tab(label="Create images embedding"):
|
||||
new_embedding_name_ae = gr.Textbox(label="Name")
|
||||
process_src_ae = gr.Textbox(label='Source directory')
|
||||
batch_ae = gr.Slider(minimum=1, maximum=1024, step=1, label="Batch size", value=256)
|
||||
with gr.Row():
|
||||
with gr.Column(scale=3):
|
||||
gr.HTML(value="")
|
||||
|
||||
with gr.Column():
|
||||
create_embedding_ae = gr.Button(value="Create images embedding", variant='primary')
|
||||
|
||||
with gr.Tab(label="Create hypernetwork"):
|
||||
new_hypernetwork_name = gr.Textbox(label="Name")
|
||||
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
|
||||
|
|
@ -1139,7 +1158,7 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
fn=modules.textual_inversion.ui.create_embedding,
|
||||
inputs=[
|
||||
new_embedding_name,
|
||||
initialization_text,
|
||||
process_src,
|
||||
nvpt,
|
||||
],
|
||||
outputs=[
|
||||
|
|
@ -1149,6 +1168,20 @@ def create_ui(wrap_gradio_gpu_call):
|
|||
]
|
||||
)
|
||||
|
||||
create_embedding_ae.click(
|
||||
fn=modules.aesthetic_clip.generate_imgs_embd,
|
||||
inputs=[
|
||||
new_embedding_name_ae,
|
||||
process_src_ae,
|
||||
batch_ae
|
||||
],
|
||||
outputs=[
|
||||
aesthetic_imgs,
|
||||
ti_output,
|
||||
ti_outcome,
|
||||
]
|
||||
)
|
||||
|
||||
create_hypernetwork.click(
|
||||
fn=modules.hypernetworks.ui.create_hypernetwork,
|
||||
inputs=[
|
||||
|
|
|
|||
Loading…
Reference in a new issue