opparco

{
    "name": "",
    "path": "share/Stable-diffusion/darkSushi-Corneos7thHeaven-0.3.safetensors",
    "vae_path": "",
    "motion_module": "models/motion-module/mm_sd_v14.ckpt",
    "compile": false,
    "tensor_interpolation_slerp": true,
    "seed": [
        2174415336
    ],

opparco

--- Ultra.ini	2023-09-01 08:57:51.408400700 +0900
+++ Ultra-Performance.ini	2023-09-03 06:43:22.568596100 +0900
@@ -1,6 +1,6 @@
 [Display]
 bVolumetricLightingEnable=1
-bDepthOfFieldEnable=1
+bDepthOfFieldEnable=0
 
 [Decals]
 uMaxDecals=100

opparco

diff --git a/scripts/animatediff.py b/scripts/animatediff.py
index 9696b5a..37e1705 100644
--- a/scripts/animatediff.py
+++ b/scripts/animatediff.py
@@ -137,6 +137,22 @@ class AnimateDiffScript(scripts.Script):
             p.batch_size = video_length
             self.inject_motion_modules(p, model)
 
+            beta_start = 0.00085
+            beta_end = 0.012

opparco

diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
index c6cfd57..2569ea1 100644
--- a/ldm/models/diffusion/ddim.py
+++ b/ldm/models/diffusion/ddim.py
@@ -25,12 +25,24 @@ class DDIMSampler(object):
         self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
                                                   num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
         alphas_cumprod = self.model.alphas_cumprod
+
+        beta_start = 0.00085

opparco

diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index d2176c9..3735e62 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -13,6 +13,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 if (EMSCRIPTEN)
 else()
     add_subdirectory(main)
+    add_subdirectory(gptneox-wip)
     add_subdirectory(quantize)

opparco

#
# debug tokenizer of lmsys/vicuna-13b-v1.3
#

from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-13b-v1.3")

def encode_decode(string: str):

opparco

#
# correct vocab of matsuo-lab/weblab-10b
#

vocab = {}

# 95 -> (none)
# 96 -> \xa1
# ...
# 107 -> \xac

opparco

from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b")
file_path = "hiragana.txt"
with open(file_path, "w", encoding="utf-8") as o:
    o.write("# unicode decoded [num-ids] id, ...\n")
    for i in range(0x3040, 0x309F + 1):
        ids = tokenizer.encode(chr(i), add_special_tokens=False)
        decoded = tokenizer.decode(ids)

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from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("matsuo-lab/weblab-10b")

dot = tokenizer.encode(".")
print(dot)
# [15]

nemureru = tokenizer.encode("眠れる")

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#
filename = '../grammars/japanese.gbnf'

print(f"overwrite {filename}")
with open(filename, 'w', encoding='utf-8') as f:
    f.write("""#
root ::= char+ ([ \\t\\n] char+)*
char ::= [\u3000-\u303F\u3040-\u309F\u30A0-\u30FF\u4E00-\u9FFF\uFF21-\uFF3A\uFF41-\uFF5A\uFF10-\uFF19]""")