Merge branch 'master' into master
zhaohu xing
3 years ago
committed by
GitHub
commit
0831ab476c
@ -0,0 +1,31 @@
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name: Run basic features tests on CPU with empty SD model
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on:
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- push
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- pull_request
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jobs:
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test:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout Code
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uses: actions/checkout@v3
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- name: Set up Python 3.10
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uses: actions/setup-python@v4
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with:
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python-version: 3.10.6
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- uses: actions/cache@v3
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with:
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path: ~/.cache/pip
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key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
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restore-keys: ${{ runner.os }}-pip-
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- name: Run tests
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run: python launch.py --tests basic_features --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
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- name: Upload main app stdout-stderr
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uses: actions/upload-artifact@v3
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if: always()
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with:
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name: stdout-stderr
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path: |
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test/stdout.txt
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test/stderr.txt
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@ -0,0 +1,98 @@
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import html
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import sys
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import threading
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import traceback
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import time
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from modules import shared
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queue_lock = threading.Lock()
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def wrap_queued_call(func):
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def f(*args, **kwargs):
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with queue_lock:
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res = func(*args, **kwargs)
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return res
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return f
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def wrap_gradio_gpu_call(func, extra_outputs=None):
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def f(*args, **kwargs):
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shared.state.begin()
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with queue_lock:
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res = func(*args, **kwargs)
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shared.state.end()
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return res
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return wrap_gradio_call(f, extra_outputs=extra_outputs, add_stats=True)
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def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
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def f(*args, extra_outputs_array=extra_outputs, **kwargs):
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run_memmon = shared.opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled and add_stats
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if run_memmon:
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shared.mem_mon.monitor()
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t = time.perf_counter()
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try:
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res = list(func(*args, **kwargs))
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except Exception as e:
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# When printing out our debug argument list, do not print out more than a MB of text
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max_debug_str_len = 131072 # (1024*1024)/8
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print("Error completing request", file=sys.stderr)
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argStr = f"Arguments: {str(args)} {str(kwargs)}"
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print(argStr[:max_debug_str_len], file=sys.stderr)
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if len(argStr) > max_debug_str_len:
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print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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shared.state.job = ""
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shared.state.job_count = 0
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if extra_outputs_array is None:
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extra_outputs_array = [None, '']
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res = extra_outputs_array + [f"<div class='error'>{html.escape(type(e).__name__+': '+str(e))}</div>"]
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shared.state.skipped = False
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shared.state.interrupted = False
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shared.state.job_count = 0
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if not add_stats:
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return tuple(res)
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elapsed = time.perf_counter() - t
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elapsed_m = int(elapsed // 60)
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elapsed_s = elapsed % 60
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elapsed_text = f"{elapsed_s:.2f}s"
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if elapsed_m > 0:
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elapsed_text = f"{elapsed_m}m "+elapsed_text
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if run_memmon:
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mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
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active_peak = mem_stats['active_peak']
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reserved_peak = mem_stats['reserved_peak']
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sys_peak = mem_stats['system_peak']
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sys_total = mem_stats['total']
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sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
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vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
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else:
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vram_html = ''
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# last item is always HTML
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res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
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return tuple(res)
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return f
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@ -0,0 +1,10 @@
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from torch.utils.checkpoint import checkpoint
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def BasicTransformerBlock_forward(self, x, context=None):
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return checkpoint(self._forward, x, context)
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def AttentionBlock_forward(self, x):
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return checkpoint(self._forward, x)
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def ResBlock_forward(self, x, emb):
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return checkpoint(self._forward, x, emb)
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@ -0,0 +1,301 @@
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import math
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import torch
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from modules import prompt_parser, devices
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from modules.shared import opts
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def get_target_prompt_token_count(token_count):
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return math.ceil(max(token_count, 1) / 75) * 75
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class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
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def __init__(self, wrapped, hijack):
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super().__init__()
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self.wrapped = wrapped
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self.hijack = hijack
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def tokenize(self, texts):
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raise NotImplementedError
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def encode_with_transformers(self, tokens):
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raise NotImplementedError
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def encode_embedding_init_text(self, init_text, nvpt):
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raise NotImplementedError
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def tokenize_line(self, line, used_custom_terms, hijack_comments):
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if opts.enable_emphasis:
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parsed = prompt_parser.parse_prompt_attention(line)
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else:
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parsed = [[line, 1.0]]
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tokenized = self.tokenize([text for text, _ in parsed])
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fixes = []
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remade_tokens = []
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multipliers = []
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last_comma = -1
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for tokens, (text, weight) in zip(tokenized, parsed):
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i = 0
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while i < len(tokens):
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token = tokens[i]
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embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
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if token == self.comma_token:
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last_comma = len(remade_tokens)
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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:
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last_comma += 1
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reloc_tokens = remade_tokens[last_comma:]
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reloc_mults = multipliers[last_comma:]
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remade_tokens = remade_tokens[:last_comma]
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length = len(remade_tokens)
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rem = int(math.ceil(length / 75)) * 75 - length
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remade_tokens += [self.id_end] * rem + reloc_tokens
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multipliers = multipliers[:last_comma] + [1.0] * rem + reloc_mults
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if embedding is None:
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remade_tokens.append(token)
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multipliers.append(weight)
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i += 1
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else:
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emb_len = int(embedding.vec.shape[0])
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iteration = len(remade_tokens) // 75
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if (len(remade_tokens) + emb_len) // 75 != iteration:
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rem = (75 * (iteration + 1) - len(remade_tokens))
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remade_tokens += [self.id_end] * rem
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multipliers += [1.0] * rem
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iteration += 1
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fixes.append((iteration, (len(remade_tokens) % 75, embedding)))
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remade_tokens += [0] * emb_len
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multipliers += [weight] * emb_len
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used_custom_terms.append((embedding.name, embedding.checksum()))
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i += embedding_length_in_tokens
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token_count = len(remade_tokens)
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prompt_target_length = get_target_prompt_token_count(token_count)
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tokens_to_add = prompt_target_length - len(remade_tokens)
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remade_tokens = remade_tokens + [self.id_end] * tokens_to_add
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multipliers = multipliers + [1.0] * tokens_to_add
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return remade_tokens, fixes, multipliers, token_count
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def process_text(self, texts):
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used_custom_terms = []
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remade_batch_tokens = []
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hijack_comments = []
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hijack_fixes = []
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token_count = 0
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cache = {}
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batch_multipliers = []
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for line in texts:
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if line in cache:
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remade_tokens, fixes, multipliers = cache[line]
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else:
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remade_tokens, fixes, multipliers, current_token_count = self.tokenize_line(line, used_custom_terms, hijack_comments)
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token_count = max(current_token_count, token_count)
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cache[line] = (remade_tokens, fixes, multipliers)
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remade_batch_tokens.append(remade_tokens)
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hijack_fixes.append(fixes)
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batch_multipliers.append(multipliers)
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return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
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def process_text_old(self, texts):
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id_start = self.id_start
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id_end = self.id_end
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maxlen = self.wrapped.max_length # you get to stay at 77
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used_custom_terms = []
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remade_batch_tokens = []
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hijack_comments = []
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hijack_fixes = []
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token_count = 0
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cache = {}
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batch_tokens = self.tokenize(texts)
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batch_multipliers = []
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for tokens in batch_tokens:
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tuple_tokens = tuple(tokens)
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if tuple_tokens in cache:
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remade_tokens, fixes, multipliers = cache[tuple_tokens]
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else:
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fixes = []
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remade_tokens = []
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multipliers = []
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mult = 1.0
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i = 0
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while i < len(tokens):
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token = tokens[i]
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embedding, embedding_length_in_tokens = self.hijack.embedding_db.find_embedding_at_position(tokens, i)
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mult_change = self.token_mults.get(token) if opts.enable_emphasis else None
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if mult_change is not None:
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mult *= mult_change
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i += 1
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elif embedding is None:
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remade_tokens.append(token)
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multipliers.append(mult)
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i += 1
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else:
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emb_len = int(embedding.vec.shape[0])
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fixes.append((len(remade_tokens), embedding))
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remade_tokens += [0] * emb_len
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multipliers += [mult] * emb_len
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used_custom_terms.append((embedding.name, embedding.checksum()))
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i += embedding_length_in_tokens
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if len(remade_tokens) > maxlen - 2:
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vocab = {v: k for k, v in self.wrapped.tokenizer.get_vocab().items()}
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ovf = remade_tokens[maxlen - 2:]
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overflowing_words = [vocab.get(int(x), "") for x in ovf]
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overflowing_text = self.wrapped.tokenizer.convert_tokens_to_string(''.join(overflowing_words))
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hijack_comments.append(f"Warning: too many input tokens; some ({len(overflowing_words)}) have been truncated:\n{overflowing_text}\n")
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token_count = len(remade_tokens)
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remade_tokens = remade_tokens + [id_end] * (maxlen - 2 - len(remade_tokens))
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remade_tokens = [id_start] + remade_tokens[0:maxlen - 2] + [id_end]
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cache[tuple_tokens] = (remade_tokens, fixes, multipliers)
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multipliers = multipliers + [1.0] * (maxlen - 2 - len(multipliers))
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multipliers = [1.0] + multipliers[0:maxlen - 2] + [1.0]
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remade_batch_tokens.append(remade_tokens)
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hijack_fixes.append(fixes)
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batch_multipliers.append(multipliers)
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return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
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def forward(self, text):
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use_old = opts.use_old_emphasis_implementation
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if use_old:
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
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else:
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batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text(text)
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self.hijack.comments += hijack_comments
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if len(used_custom_terms) > 0:
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self.hijack.comments.append("Used embeddings: " + ", ".join([f'{word} [{checksum}]' for word, checksum in used_custom_terms]))
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if use_old:
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self.hijack.fixes = hijack_fixes
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return self.process_tokens(remade_batch_tokens, batch_multipliers)
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z = None
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i = 0
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while max(map(len, remade_batch_tokens)) != 0:
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rem_tokens = [x[75:] for x in remade_batch_tokens]
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rem_multipliers = [x[75:] for x in batch_multipliers]
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self.hijack.fixes = []
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for unfiltered in hijack_fixes:
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fixes = []
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for fix in unfiltered:
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if fix[0] == i:
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fixes.append(fix[1])
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self.hijack.fixes.append(fixes)
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tokens = []
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multipliers = []
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for j in range(len(remade_batch_tokens)):
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if len(remade_batch_tokens[j]) > 0:
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tokens.append(remade_batch_tokens[j][:75])
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multipliers.append(batch_multipliers[j][:75])
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else:
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tokens.append([self.id_end] * 75)
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multipliers.append([1.0] * 75)
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z1 = self.process_tokens(tokens, multipliers)
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z = z1 if z is None else torch.cat((z, z1), axis=-2)
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remade_batch_tokens = rem_tokens
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batch_multipliers = rem_multipliers
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i += 1
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return z
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def process_tokens(self, remade_batch_tokens, batch_multipliers):
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if not opts.use_old_emphasis_implementation:
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remade_batch_tokens = [[self.id_start] + x[:75] + [self.id_end] for x in remade_batch_tokens]
|
||||
batch_multipliers = [[1.0] + x[:75] + [1.0] for x in batch_multipliers]
|
||||
|
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tokens = torch.asarray(remade_batch_tokens).to(devices.device)
|
||||
|
||||
if self.id_end != self.id_pad:
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for batch_pos in range(len(remade_batch_tokens)):
|
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index = remade_batch_tokens[batch_pos].index(self.id_end)
|
||||
tokens[batch_pos, index+1:tokens.shape[1]] = self.id_pad
|
||||
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z = self.encode_with_transformers(tokens)
|
||||
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||||
# restoring original mean is likely not correct, but it seems to work well to prevent artifacts that happen otherwise
|
||||
batch_multipliers_of_same_length = [x + [1.0] * (75 - len(x)) for x in batch_multipliers]
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||||
batch_multipliers = torch.asarray(batch_multipliers_of_same_length).to(devices.device)
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||||
original_mean = z.mean()
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||||
z *= batch_multipliers.reshape(batch_multipliers.shape + (1,)).expand(z.shape)
|
||||
new_mean = z.mean()
|
||||
z *= original_mean / new_mean
|
||||
|
||||
return z
|
||||
|
||||
|
||||
class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
|
||||
def __init__(self, wrapped, hijack):
|
||||
super().__init__(wrapped, hijack)
|
||||
self.tokenizer = wrapped.tokenizer
|
||||
self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
|
||||
|
||||
self.token_mults = {}
|
||||
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:
|
||||
if c == '[':
|
||||
mult /= 1.1
|
||||
if c == ']':
|
||||
mult *= 1.1
|
||||
if c == '(':
|
||||
mult *= 1.1
|
||||
if c == ')':
|
||||
mult /= 1.1
|
||||
|
||||
if mult != 1.0:
|
||||
self.token_mults[ident] = mult
|
||||
|
||||
self.id_start = self.wrapped.tokenizer.bos_token_id
|
||||
self.id_end = self.wrapped.tokenizer.eos_token_id
|
||||
self.id_pad = self.id_end
|
||||
|
||||
def tokenize(self, texts):
|
||||
tokenized = self.wrapped.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
|
||||
|
||||
return tokenized
|
||||
|
||||
def encode_with_transformers(self, tokens):
|
||||
outputs = self.wrapped.transformer(input_ids=tokens, output_hidden_states=-opts.CLIP_stop_at_last_layers)
|
||||
|
||||
if opts.CLIP_stop_at_last_layers > 1:
|
||||
z = outputs.hidden_states[-opts.CLIP_stop_at_last_layers]
|
||||
z = self.wrapped.transformer.text_model.final_layer_norm(z)
|
||||
else:
|
||||
z = outputs.last_hidden_state
|
||||
|
||||
return z
|
||||
|
||||
def encode_embedding_init_text(self, init_text, nvpt):
|
||||
embedding_layer = self.wrapped.transformer.text_model.embeddings
|
||||
ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
|
||||
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
|
||||
|
||||
return embedded
|
||||
@ -0,0 +1,37 @@
|
||||
import open_clip.tokenizer
|
||||
import torch
|
||||
|
||||
from modules import sd_hijack_clip, devices
|
||||
from modules.shared import opts
|
||||
|
||||
tokenizer = open_clip.tokenizer._tokenizer
|
||||
|
||||
|
||||
class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
|
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def __init__(self, wrapped, hijack):
|
||||
super().__init__(wrapped, hijack)
|
||||
|
||||
self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
|
||||
self.id_start = tokenizer.encoder["<start_of_text>"]
|
||||
self.id_end = tokenizer.encoder["<end_of_text>"]
|
||||
self.id_pad = 0
|
||||
|
||||
def tokenize(self, texts):
|
||||
assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
|
||||
|
||||
tokenized = [tokenizer.encode(text) for text in texts]
|
||||
|
||||
return tokenized
|
||||
|
||||
def encode_with_transformers(self, tokens):
|
||||
# set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
|
||||
z = self.wrapped.encode_with_transformer(tokens)
|
||||
|
||||
return z
|
||||
|
||||
def encode_embedding_init_text(self, init_text, nvpt):
|
||||
ids = tokenizer.encode(init_text)
|
||||
ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
|
||||
embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
|
||||
|
||||
return embedded
|
||||
@ -0,0 +1,62 @@
|
||||
import os
|
||||
import tempfile
|
||||
from collections import namedtuple
|
||||
|
||||
import gradio as gr
|
||||
|
||||
from PIL import PngImagePlugin
|
||||
|
||||
from modules import shared
|
||||
|
||||
|
||||
Savedfile = namedtuple("Savedfile", ["name"])
|
||||
|
||||
|
||||
def save_pil_to_file(pil_image, dir=None):
|
||||
already_saved_as = getattr(pil_image, 'already_saved_as', None)
|
||||
if already_saved_as and os.path.isfile(already_saved_as):
|
||||
shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(os.path.dirname(already_saved_as))}
|
||||
file_obj = Savedfile(already_saved_as)
|
||||
return file_obj
|
||||
|
||||
if shared.opts.temp_dir != "":
|
||||
dir = shared.opts.temp_dir
|
||||
|
||||
use_metadata = False
|
||||
metadata = PngImagePlugin.PngInfo()
|
||||
for key, value in pil_image.info.items():
|
||||
if isinstance(key, str) and isinstance(value, str):
|
||||
metadata.add_text(key, value)
|
||||
use_metadata = True
|
||||
|
||||
file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
|
||||
pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
|
||||
return file_obj
|
||||
|
||||
|
||||
# override save to file function so that it also writes PNG info
|
||||
gr.processing_utils.save_pil_to_file = save_pil_to_file
|
||||
|
||||
|
||||
def on_tmpdir_changed():
|
||||
if shared.opts.temp_dir == "" or shared.demo is None:
|
||||
return
|
||||
|
||||
os.makedirs(shared.opts.temp_dir, exist_ok=True)
|
||||
|
||||
shared.demo.temp_dirs = shared.demo.temp_dirs | {os.path.abspath(shared.opts.temp_dir)}
|
||||
|
||||
|
||||
def cleanup_tmpdr():
|
||||
temp_dir = shared.opts.temp_dir
|
||||
if temp_dir == "" or not os.path.isdir(temp_dir):
|
||||
return
|
||||
|
||||
for root, dirs, files in os.walk(temp_dir, topdown=False):
|
||||
for name in files:
|
||||
_, extension = os.path.splitext(name)
|
||||
if extension != ".png":
|
||||
continue
|
||||
|
||||
filename = os.path.join(root, name)
|
||||
os.remove(filename)
|
||||
@ -0,0 +1,47 @@
|
||||
import unittest
|
||||
import requests
|
||||
|
||||
|
||||
class TestTxt2ImgWorking(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.url_txt2img = "http://localhost:7860/sdapi/v1/txt2img"
|
||||
self.simple_txt2img = {
|
||||
"enable_hr": False,
|
||||
"denoising_strength": 0,
|
||||
"firstphase_width": 0,
|
||||
"firstphase_height": 0,
|
||||
"prompt": "example prompt",
|
||||
"styles": [],
|
||||
"seed": -1,
|
||||
"subseed": -1,
|
||||
"subseed_strength": 0,
|
||||
"seed_resize_from_h": -1,
|
||||
"seed_resize_from_w": -1,
|
||||
"batch_size": 1,
|
||||
"n_iter": 1,
|
||||
"steps": 3,
|
||||
"cfg_scale": 7,
|
||||
"width": 64,
|
||||
"height": 64,
|
||||
"restore_faces": False,
|
||||
"tiling": False,
|
||||
"negative_prompt": "",
|
||||
"eta": 0,
|
||||
"s_churn": 0,
|
||||
"s_tmax": 0,
|
||||
"s_tmin": 0,
|
||||
"s_noise": 1,
|
||||
"sampler_index": "Euler a"
|
||||
}
|
||||
|
||||
def test_txt2img_with_restore_faces_performed(self):
|
||||
self.simple_txt2img["restore_faces"] = True
|
||||
self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)
|
||||
|
||||
|
||||
class TestTxt2ImgCorrectness(unittest.TestCase):
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Binary file not shown.
@ -0,0 +1,70 @@
|
||||
model:
|
||||
base_learning_rate: 1.0e-04
|
||||
target: ldm.models.diffusion.ddpm.LatentDiffusion
|
||||
params:
|
||||
linear_start: 0.00085
|
||||
linear_end: 0.0120
|
||||
num_timesteps_cond: 1
|
||||
log_every_t: 200
|
||||
timesteps: 1000
|
||||
first_stage_key: "jpg"
|
||||
cond_stage_key: "txt"
|
||||
image_size: 64
|
||||
channels: 4
|
||||
cond_stage_trainable: false # Note: different from the one we trained before
|
||||
conditioning_key: crossattn
|
||||
monitor: val/loss_simple_ema
|
||||
scale_factor: 0.18215
|
||||
use_ema: False
|
||||
|
||||
scheduler_config: # 10000 warmup steps
|
||||
target: ldm.lr_scheduler.LambdaLinearScheduler
|
||||
params:
|
||||
warm_up_steps: [ 10000 ]
|
||||
cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
|
||||
f_start: [ 1.e-6 ]
|
||||
f_max: [ 1. ]
|
||||
f_min: [ 1. ]
|
||||
|
||||
unet_config:
|
||||
target: ldm.modules.diffusionmodules.openaimodel.UNetModel
|
||||
params:
|
||||
image_size: 32 # unused
|
||||
in_channels: 4
|
||||
out_channels: 4
|
||||
model_channels: 320
|
||||
attention_resolutions: [ 4, 2, 1 ]
|
||||
num_res_blocks: 2
|
||||
channel_mult: [ 1, 2, 4, 4 ]
|
||||
num_heads: 8
|
||||
use_spatial_transformer: True
|
||||
transformer_depth: 1
|
||||
context_dim: 768
|
||||
use_checkpoint: True
|
||||
legacy: False
|
||||
|
||||
first_stage_config:
|
||||
target: ldm.models.autoencoder.AutoencoderKL
|
||||
params:
|
||||
embed_dim: 4
|
||||
monitor: val/rec_loss
|
||||
ddconfig:
|
||||
double_z: true
|
||||
z_channels: 4
|
||||
resolution: 256
|
||||
in_channels: 3
|
||||
out_ch: 3
|
||||
ch: 128
|
||||
ch_mult:
|
||||
- 1
|
||||
- 2
|
||||
- 4
|
||||
- 4
|
||||
num_res_blocks: 2
|
||||
attn_resolutions: []
|
||||
dropout: 0.0
|
||||
lossconfig:
|
||||
target: torch.nn.Identity
|
||||
|
||||
cond_stage_config:
|
||||
target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
|
||||
Loading…
Reference in New Issue