From 76f256fe8f844641f4e9b41f35c7dd2cba5090d6 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sun, 1 Jan 2023 11:08:39 +0300
Subject: Bump gradio version #YOLO

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'requirements.txt')

diff --git a/requirements.txt b/requirements.txt
index 5bed694e..e2c3876b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,7 +5,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.9
+gradio==3.15.0
 invisible-watermark
 numpy
 omegaconf
-- 
cgit v1.2.3


From 9a3b0ee960b0c61c4f60e3081ae6f2098533d393 Mon Sep 17 00:00:00 2001
From: hithereai <121192995+hithereai@users.noreply.github.com>
Date: Tue, 3 Jan 2023 11:22:06 +0200
Subject: update req.txt

The old 'opencv-python' package is very limiting in terms of optical flow - so I propose a package change to 'opencv-contrib-python', which has more cv2.optflow methods.

These are needed for optical flow trickery in auto1111 and its extensions, and it cannot be installed by an extension as only a single package of opencv needs to be installed for optical flow to work properly. Change of the main one is Inevitable.
---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'requirements.txt')

diff --git a/requirements.txt b/requirements.txt
index e2c3876b..4f09385f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,7 +9,7 @@ gradio==3.15.0
 invisible-watermark
 numpy
 omegaconf
-opencv-python
+opencv-contrib-python
 requests
 piexif
 Pillow
-- 
cgit v1.2.3


From d782a95967c9eea753df3333cd1954b6ec73eba0 Mon Sep 17 00:00:00 2001
From: brkirch <brkirch@users.noreply.github.com>
Date: Tue, 27 Dec 2022 08:50:55 -0500
Subject: Add Birch-san's sub-quadratic attention implementation

---
 README.md                          |   1 +
 modules/sd_hijack.py               |  15 ++-
 modules/sd_hijack_optimizations.py | 124 ++++++++++++++++++-----
 modules/shared.py                  |   4 +
 modules/sub_quadratic_attention.py | 201 +++++++++++++++++++++++++++++++++++++
 requirements.txt                   |   2 +-
 6 files changed, 312 insertions(+), 35 deletions(-)
 create mode 100644 modules/sub_quadratic_attention.py

(limited to 'requirements.txt')

diff --git a/README.md b/README.md
index 556000fb..1913caf3 100644
--- a/README.md
+++ b/README.md
@@ -139,6 +139,7 @@ The documentation was moved from this README over to the project's [wiki](https:
 - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
 - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
 - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
+- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san), Amin Rezaei (https://github.com/AminRezaei0x443)
 - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
 - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
 - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 690a9ec2..019a6f3f 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -7,8 +7,6 @@ from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet
 
-from modules.sd_hijack_optimizations import invokeAI_mps_available
-
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.modules.diffusionmodules.openaimodel
@@ -40,17 +38,16 @@ def apply_optimizations():
         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_sub_quad_attention:
+        print("Applying sub-quadratic cross attention optimization.")
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.sub_quad_attention_forward
+        ldm.modules.diffusionmodules.model.AttnBlock.forward = sd_hijack_optimizations.sub_quad_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()):
-        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("Applying v1 cross attention optimization.")
-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_v1
-        else:
-            print("Applying cross attention optimization (InvokeAI).")
-            ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
+        print("Applying cross attention optimization (InvokeAI).")
+        ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward_invokeAI
     elif not cmd_opts.disable_opt_split_attention and (cmd_opts.opt_split_attention or torch.cuda.is_available()):
         print("Applying cross attention optimization (Doggettx).")
         ldm.modules.attention.CrossAttention.forward = sd_hijack_optimizations.split_cross_attention_forward
diff --git a/modules/sd_hijack_optimizations.py b/modules/sd_hijack_optimizations.py
index 02c87f40..f5c153e8 100644
--- a/modules/sd_hijack_optimizations.py
+++ b/modules/sd_hijack_optimizations.py
@@ -1,7 +1,7 @@
 import math
 import sys
 import traceback
-import importlib
+import psutil
 
 import torch
 from torch import einsum
@@ -12,6 +12,8 @@ from einops import rearrange
 from modules import shared
 from modules.hypernetworks import hypernetwork
 
+from .sub_quadratic_attention import efficient_dot_product_attention
+
 
 if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
     try:
@@ -22,6 +24,19 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
         print(traceback.format_exc(), file=sys.stderr)
 
 
+def get_available_vram():
+    if shared.device.type == 'cuda':
+        stats = torch.cuda.memory_stats(shared.device)
+        mem_active = stats['active_bytes.all.current']
+        mem_reserved = stats['reserved_bytes.all.current']
+        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
+        mem_free_torch = mem_reserved - mem_active
+        mem_free_total = mem_free_cuda + mem_free_torch
+        return mem_free_total
+    else:
+        return psutil.virtual_memory().available
+
+
 # see https://github.com/basujindal/stable-diffusion/pull/117 for discussion
 def split_cross_attention_forward_v1(self, x, context=None, mask=None):
     h = self.heads
@@ -76,12 +91,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
-    stats = torch.cuda.memory_stats(q.device)
-    mem_active = stats['active_bytes.all.current']
-    mem_reserved = stats['reserved_bytes.all.current']
-    mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-    mem_free_torch = mem_reserved - mem_active
-    mem_free_total = mem_free_cuda + mem_free_torch
+    mem_free_total = get_available_vram()
 
     gb = 1024 ** 3
     tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
@@ -118,19 +128,8 @@ def split_cross_attention_forward(self, x, context=None, mask=None):
     return self.to_out(r2)
 
 
-def check_for_psutil():
-    try:
-        spec = importlib.util.find_spec('psutil')
-        return spec is not None
-    except ModuleNotFoundError:
-        return False
-
-invokeAI_mps_available = check_for_psutil()
-
 # -- Taken from https://github.com/invoke-ai/InvokeAI and modified --
-if invokeAI_mps_available:
-    import psutil
-    mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+mem_total_gb = psutil.virtual_memory().total // (1 << 30)
 
 def einsum_op_compvis(q, k, v):
     s = einsum('b i d, b j d -> b i j', q, k)
@@ -215,6 +214,70 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None):
 
 # -- End of code from https://github.com/invoke-ai/InvokeAI --
 
+
+# Based on Birch-san's modified implementation of sub-quadratic attention from https://github.com/Birch-san/diffusers/pull/1
+def sub_quad_attention_forward(self, x, context=None, mask=None):
+    assert mask is None, "attention-mask not currently implemented for SubQuadraticCrossAttnProcessor."
+
+    h = self.heads
+
+    q = self.to_q(x)
+    context = default(context, x)
+
+    context_k, context_v = hypernetwork.apply_hypernetwork(shared.loaded_hypernetwork, context)
+    k = self.to_k(context_k)
+    v = self.to_v(context_v)
+    del context, context_k, context_v, x
+
+    q = q.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+    k = k.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+    v = v.unflatten(-1, (h, -1)).transpose(1,2).flatten(end_dim=1)
+
+    x = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold_bytes=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
+
+    x = x.unflatten(0, (-1, h)).transpose(1,2).flatten(start_dim=2)
+
+    out_proj, dropout = self.to_out
+    x = out_proj(x)
+    x = dropout(x)
+
+    return x
+
+def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_size_min=None, chunk_threshold_bytes=None, use_checkpoint=True):
+    bytes_per_token = torch.finfo(q.dtype).bits//8
+    batch_x_heads, q_tokens, _ = q.shape
+    _, k_tokens, _ = k.shape
+    qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
+
+    available_vram = int(get_available_vram() * 0.9) if q.device.type == 'mps' else int(get_available_vram() * 0.7)
+
+    if chunk_threshold_bytes is None:
+        chunk_threshold_bytes = available_vram
+    elif chunk_threshold_bytes == 0:
+        chunk_threshold_bytes = None
+
+    if kv_chunk_size_min is None:
+        kv_chunk_size_min = chunk_threshold_bytes // (batch_x_heads * bytes_per_token * (k.shape[2] + v.shape[2]))
+    elif kv_chunk_size_min == 0:
+        kv_chunk_size_min = None
+
+    if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
+        # the big matmul fits into our memory limit; do everything in 1 chunk,
+        # i.e. send it down the unchunked fast-path
+        query_chunk_size = q_tokens
+        kv_chunk_size = k_tokens
+
+    return efficient_dot_product_attention(
+        q,
+        k,
+        v,
+        query_chunk_size=q_chunk_size,
+        kv_chunk_size=kv_chunk_size,
+        kv_chunk_size_min = kv_chunk_size_min,
+        use_checkpoint=use_checkpoint,
+    )
+
+
 def xformers_attention_forward(self, x, context=None, mask=None):
     h = self.heads
     q_in = self.to_q(x)
@@ -252,12 +315,7 @@ def cross_attention_attnblock_forward(self, x):
 
         h_ = torch.zeros_like(k, device=q.device)
 
-        stats = torch.cuda.memory_stats(q.device)
-        mem_active = stats['active_bytes.all.current']
-        mem_reserved = stats['reserved_bytes.all.current']
-        mem_free_cuda, _ = torch.cuda.mem_get_info(torch.cuda.current_device())
-        mem_free_torch = mem_reserved - mem_active
-        mem_free_total = mem_free_cuda + mem_free_torch
+        mem_free_total = get_available_vram()
 
         tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size()
         mem_required = tensor_size * 2.5
@@ -312,3 +370,19 @@ def xformers_attnblock_forward(self, x):
         return x + out
     except NotImplementedError:
         return cross_attention_attnblock_forward(self, x)
+
+def sub_quad_attnblock_forward(self, x):
+    h_ = x
+    h_ = self.norm(h_)
+    q = self.q(h_)
+    k = self.k(h_)
+    v = self.v(h_)
+    b, c, h, w = q.shape
+    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    out = sub_quad_attention(q, k, v, q_chunk_size=shared.cmd_opts.sub_quad_q_chunk_size, kv_chunk_size=shared.cmd_opts.sub_quad_kv_chunk_size, chunk_threshold_bytes=shared.cmd_opts.sub_quad_chunk_threshold, use_checkpoint=self.training)
+    out = rearrange(out, 'b (h w) c -> b c h w', h=h)
+    out = self.proj_out(out)
+    return x + out
diff --git a/modules/shared.py b/modules/shared.py
index d4ddeea0..487a7792 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -56,6 +56,10 @@ parser.add_argument("--xformers", action='store_true', help="enable xformers for
 parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
 parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything")
 parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.")
+parser.add_argument("--opt-sub-quad-attention", action='store_true', help="enable memory efficient sub-quadratic cross-attention layer optimization")
+parser.add_argument("--sub-quad-q-chunk-size", type=int, help="query chunk size for the sub-quadratic cross-attention layer optimization to use", default=1024)
+parser.add_argument("--sub-quad-kv-chunk-size", type=int, help="kv chunk size for the sub-quadratic cross-attention layer optimization to use", default=None)
+parser.add_argument("--sub-quad-chunk-threshold", type=int, help="the size threshold in bytes for the sub-quadratic cross-attention layer optimization to use chunking", default=None)
 parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.")
 parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find")
 parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization")
diff --git a/modules/sub_quadratic_attention.py b/modules/sub_quadratic_attention.py
new file mode 100644
index 00000000..b11dc1c7
--- /dev/null
+++ b/modules/sub_quadratic_attention.py
@@ -0,0 +1,201 @@
+# original source:
+#   https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py
+# license:
+#   unspecified
+# credit:
+#   Amin Rezaei (original author)
+#   Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks)
+# implementation of:
+#   Self-attention Does Not Need O(n2) Memory":
+#   https://arxiv.org/abs/2112.05682v2
+
+from functools import partial
+import torch
+from torch import Tensor
+from torch.utils.checkpoint import checkpoint
+import math
+from typing import Optional, NamedTuple, Protocol, List
+
+def dynamic_slice(
+    x: Tensor,
+    starts: List[int],
+    sizes: List[int],
+) -> Tensor:
+    slicing = [slice(start, start + size) for start, size in zip(starts, sizes)]
+    return x[slicing]
+
+class AttnChunk(NamedTuple):
+    exp_values: Tensor
+    exp_weights_sum: Tensor
+    max_score: Tensor
+
+class SummarizeChunk(Protocol):
+    @staticmethod
+    def __call__(
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+    ) -> AttnChunk: ...
+
+class ComputeQueryChunkAttn(Protocol):
+    @staticmethod
+    def __call__(
+        query: Tensor,
+        key: Tensor,
+        value: Tensor,
+    ) -> Tensor: ...
+
+def _summarize_chunk(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    scale: float,
+) -> AttnChunk:
+    attn_weights = torch.baddbmm(
+        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+        query,
+        key.transpose(1,2),
+        alpha=scale,
+        beta=0,
+    )
+    max_score, _ = torch.max(attn_weights, -1, keepdim=True)
+    max_score = max_score.detach()
+    exp_weights = torch.exp(attn_weights - max_score)
+    exp_values = torch.bmm(exp_weights, value)
+    max_score = max_score.squeeze(-1)
+    return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score)
+
+def _query_chunk_attention(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    summarize_chunk: SummarizeChunk,
+    kv_chunk_size: int,
+) -> Tensor:
+    batch_x_heads, k_tokens, k_channels_per_head = key.shape
+    _, _, v_channels_per_head = value.shape
+
+    def chunk_scanner(chunk_idx: int) -> AttnChunk:
+        key_chunk = dynamic_slice(
+            key,
+            (0, chunk_idx, 0),
+            (batch_x_heads, kv_chunk_size, k_channels_per_head)
+        )
+        value_chunk = dynamic_slice(
+            value,
+            (0, chunk_idx, 0),
+            (batch_x_heads, kv_chunk_size, v_channels_per_head)
+        )
+        return summarize_chunk(query, key_chunk, value_chunk)
+
+    chunks: List[AttnChunk] = [
+        chunk_scanner(chunk) for chunk in torch.arange(0, k_tokens, kv_chunk_size)
+    ]
+    acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks)))
+    chunk_values, chunk_weights, chunk_max = acc_chunk
+
+    global_max, _ = torch.max(chunk_max, 0, keepdim=True)
+    max_diffs = torch.exp(chunk_max - global_max)
+    chunk_values *= torch.unsqueeze(max_diffs, -1)
+    chunk_weights *= max_diffs
+
+    all_values = chunk_values.sum(dim=0)
+    all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0)
+    return all_values / all_weights
+
+# TODO: refactor CrossAttention#get_attention_scores to share code with this
+def _get_attention_scores_no_kv_chunking(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    scale: float,
+) -> Tensor:
+    attn_scores = torch.baddbmm(
+        torch.empty(1, 1, 1, device=query.device, dtype=query.dtype),
+        query,
+        key.transpose(1,2),
+        alpha=scale,
+        beta=0,
+    )
+    attn_probs = attn_scores.softmax(dim=-1)
+    del attn_scores
+    hidden_states_slice = torch.bmm(attn_probs, value)
+    return hidden_states_slice
+
+class ScannedChunk(NamedTuple):
+    chunk_idx: int
+    attn_chunk: AttnChunk
+
+def efficient_dot_product_attention(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    query_chunk_size=1024,
+    kv_chunk_size: Optional[int] = None,
+    kv_chunk_size_min: Optional[int] = None,
+    use_checkpoint=True,
+):
+    """Computes efficient dot-product attention given query, key, and value.
+      This is efficient version of attention presented in
+      https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements.
+      Args:
+        query: queries for calculating attention with shape of
+          `[batch * num_heads, tokens, channels_per_head]`.
+        key: keys for calculating attention with shape of
+          `[batch * num_heads, tokens, channels_per_head]`.
+        value: values to be used in attention with shape of
+          `[batch * num_heads, tokens, channels_per_head]`.
+        query_chunk_size: int: query chunks size
+        kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens)
+        kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done).
+        use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference)
+      Returns:
+        Output of shape `[batch * num_heads, query_tokens, channels_per_head]`.
+      """
+    batch_x_heads, q_tokens, q_channels_per_head = query.shape
+    _, k_tokens, _ = key.shape
+    scale = q_channels_per_head ** -0.5
+
+    kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens)
+    if kv_chunk_size_min is not None:
+        kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min)
+
+    def get_query_chunk(chunk_idx: int) -> Tensor:
+        return dynamic_slice(
+            query,
+            (0, chunk_idx, 0),
+            (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head)
+        )
+    
+    summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale)
+    summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk
+    compute_query_chunk_attn: ComputeQueryChunkAttn = partial(
+        _get_attention_scores_no_kv_chunking,
+        scale=scale
+    ) if k_tokens <= kv_chunk_size else (
+        # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw)
+        partial(
+            _query_chunk_attention,
+            kv_chunk_size=kv_chunk_size,
+            summarize_chunk=summarize_chunk,
+        )
+    )
+
+    if q_tokens <= query_chunk_size:
+        # fast-path for when there's just 1 query chunk
+        return compute_query_chunk_attn(
+            query=query,
+            key=key,
+            value=value,
+        )
+    
+    # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
+    # and pass slices to be mutated, instead of torch.cat()ing the returned slices
+    res = torch.cat([
+        compute_query_chunk_attn(
+            query=get_query_chunk(i * query_chunk_size),
+            key=key,
+            value=value,
+        ) for i in range(math.ceil(q_tokens / query_chunk_size))
+    ], dim=1)
+    return res
diff --git a/requirements.txt b/requirements.txt
index 5bed694e..0dbea322 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,4 +30,4 @@ inflection
 GitPython
 torchsde
 safetensors
-psutil; sys_platform == 'darwin'
+psutil
-- 
cgit v1.2.3


From 6eb72fd13f34d94d5459290dd1a0bf0e9ddeda82 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 14 Jan 2023 13:38:10 +0300
Subject: bump gradio to 3.16.1

---
 modules/ui.py             | 13 ++++++-----
 requirements.txt          |  2 +-
 requirements_versions.txt |  2 +-
 style.css                 | 57 ++++++++++++++++++++++++++++++++---------------
 webui.py                  |  3 +--
 5 files changed, 49 insertions(+), 28 deletions(-)

(limited to 'requirements.txt')

diff --git a/modules/ui.py b/modules/ui.py
index e86a624b..202e84e5 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -605,7 +605,7 @@ def create_ui():
                 setup_progressbar(progressbar, txt2img_preview, 'txt2img')
 
         with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel', elem_id="txt2img_settings"):
+            with gr.Column(variant='compact', elem_id="txt2img_settings"):
                 for category in ordered_ui_categories():
                     if category == "sampler":
                         steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
@@ -794,7 +794,7 @@ def create_ui():
                 setup_progressbar(progressbar, img2img_preview, 'img2img')
 
         with FormRow().style(equal_height=False):
-            with gr.Column(variant='panel', elem_id="img2img_settings"):
+            with gr.Column(variant='compact', elem_id="img2img_settings"):
                 with gr.Tabs(elem_id="mode_img2img"):
                     with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
                         init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
@@ -1026,7 +1026,7 @@ def create_ui():
 
     with gr.Blocks(analytics_enabled=False) as extras_interface:
         with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
+            with gr.Column(variant='compact'):
                 with gr.Tabs(elem_id="mode_extras"):
                     with gr.TabItem('Single Image', elem_id="extras_single_tab"):
                         extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
@@ -1127,8 +1127,8 @@ def create_ui():
 
     with gr.Blocks(analytics_enabled=False) as modelmerger_interface:
         with gr.Row().style(equal_height=False):
-            with gr.Column(variant='panel'):
-                gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
+            with gr.Column(variant='compact'):
+                gr.HTML(value="<p style='margin-bottom: 2.5em'>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
 
                 with FormRow():
                     primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
@@ -1150,7 +1150,8 @@ def create_ui():
 
                 config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
 
-                modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
+                with gr.Row():
+                    modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
 
             with gr.Column(variant='panel'):
                 submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
diff --git a/requirements.txt b/requirements.txt
index e1dbf8e5..6cdea781 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,7 +5,7 @@ fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
-gradio==3.15.0
+gradio==3.16.1
 invisible-watermark
 numpy
 omegaconf
diff --git a/requirements_versions.txt b/requirements_versions.txt
index d2899292..cc06d2b4 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -3,7 +3,7 @@ transformers==4.19.2
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
-gradio==3.15.0
+gradio==3.16.1
 numpy==1.23.3
 Pillow==9.4.0
 realesrgan==0.3.0
diff --git a/style.css b/style.css
index ffd6307f..14b15191 100644
--- a/style.css
+++ b/style.css
@@ -20,7 +20,7 @@
     padding-right: 0.25em;
     margin: 0.1em 0;
     opacity: 0%;
-    cursor: default;	
+    cursor: default;
 }
 
 .output-html p {margin: 0 0.5em;}
@@ -221,7 +221,10 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500,  label.block s
 
 .dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
     background-color: rgb(31, 41, 55);
-    box-shadow: 6px 0 6px 0px rgb(31, 41, 55), -6px 0 6px 0px rgb(31, 41, 55);
+    box-shadow: none;
+    border: 1px solid rgba(128, 128, 128, 0.1);
+    border-radius: 6px;
+    padding: 0.1em 0.5em;
 }
 
 #txt2img_column_batch, #img2img_column_batch{
@@ -371,7 +374,7 @@ input[type="range"]{
     grid-area: tile;
 }
 
-.modalClose, 
+.modalClose,
 .modalZoom,
 .modalTileImage {
   color: white;
@@ -509,29 +512,20 @@ input[type="range"]{
 }
 
 #quicksettings > div{
-    border: none;
-    background: none;
-    flex: unset;
-    gap: 1em;
-}
-
-#quicksettings > div > div{
-    max-width: 32em;
+    max-width: 24em;
     min-width: 24em;
     padding: 0;
+    border: none;
+    box-shadow: none;
+    background: none;
 }
 
-#quicksettings > div > div > div > div > label > span {
+#quicksettings > div > div > div > label > span {
     position: relative;
     margin-right: 9em;
     margin-bottom: -1em;
 }
 
-#quicksettings > div > div > label > span {
-    position: relative;
-    margin-bottom: -1em;
-}
-
 canvas[key="mask"] {
     z-index: 12 !important;
     filter: invert();
@@ -666,7 +660,10 @@ footer {
     font-weight: bold;
 }
 
-#txt2img_checkboxes > div > div{
+#txt2img_checkboxes, #img2img_checkboxes{
+    margin-bottom: 0.5em;
+}
+#txt2img_checkboxes > div > div, #img2img_checkboxes > div > div{
     flex: 0;
     white-space: nowrap;
     min-width: auto;
@@ -676,6 +673,30 @@ footer {
     opacity: 0.5;
 }
 
+.gr-compact {
+    border: none;
+    padding-top: 1em;
+}
+
+.dark .gr-compact{
+    background-color: rgb(31 41 55 / var(--tw-bg-opacity));
+    margin-left: 0.8em;
+}
+
+.gr-compact > *{
+    margin-top: 0.5em !important;
+}
+
+.gr-compact .gr-block, .gr-compact .gr-form{
+    border: none;
+    box-shadow: none;
+}
+
+.gr-compact .gr-box{
+    border-radius: .5rem !important;
+    border-width: 1px !important;
+}
+
 /* The following handles localization for right-to-left (RTL) languages like Arabic.
 The rtl media type will only be activated by the logic in javascript/localization.js.
 If you change anything above, you need to make sure it is RTL compliant by just running
diff --git a/webui.py b/webui.py
index 1fff80da..84159515 100644
--- a/webui.py
+++ b/webui.py
@@ -157,7 +157,7 @@ def webui():
 
         shared.demo = modules.ui.create_ui()
 
-        app, local_url, share_url = shared.demo.queue(default_enabled=False).launch(
+        app, local_url, share_url = shared.demo.launch(
             share=cmd_opts.share,
             server_name=server_name,
             server_port=cmd_opts.port,
@@ -185,7 +185,6 @@ def webui():
             create_api(app)
 
         modules.script_callbacks.app_started_callback(shared.demo, app)
-        modules.script_callbacks.app_started_callback(shared.demo, app)
 
         wait_on_server(shared.demo)
         print('Restarting UI...')
-- 
cgit v1.2.3


From 28189985e6f56dc725938a3f0e4d2462dad74bc5 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 24 Jan 2023 20:24:27 +0300
Subject: remove fairscale requirement, add fake fairscale to make BLIP not
 complain about it

---
 modules/interrogate.py    | 11 +++++++++--
 requirements.txt          |  1 -
 requirements_versions.txt |  1 -
 3 files changed, 9 insertions(+), 4 deletions(-)

(limited to 'requirements.txt')

diff --git a/modules/interrogate.py b/modules/interrogate.py
index 236e6983..9f063197 100644
--- a/modules/interrogate.py
+++ b/modules/interrogate.py
@@ -82,9 +82,16 @@ class InterrogateModels:
 
         return self.loaded_categories
 
+    def create_fake_fairscale(self):
+        class FakeFairscale:
+            def checkpoint_wrapper(self):
+                pass
+
+        sys.modules["fairscale.nn.checkpoint.checkpoint_activations"] = FakeFairscale
+
     def load_blip_model(self):
-        with paths.Prioritize("BLIP"):
-            import models.blip
+        create_fake_fairscale()
+        import models.blip
 
         files = modelloader.load_models(
             model_path=os.path.join(paths.models_path, "BLIP"),
diff --git a/requirements.txt b/requirements.txt
index ef5e3472..a4be1ec3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,7 +1,6 @@
 blendmodes
 accelerate
 basicsr
-fairscale==0.4.4
 fonts
 font-roboto
 gfpgan
diff --git a/requirements_versions.txt b/requirements_versions.txt
index f97ad765..135908be 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -14,7 +14,6 @@ scikit-image==0.19.2
 fonts
 font-roboto
 timm==0.6.7
-fairscale==0.4.9
 piexif==1.1.3
 einops==0.4.1
 jsonmerge==1.8.0
-- 
cgit v1.2.3


From bd9b55ee908c43fb1b654b3a3a1320545023ce1c Mon Sep 17 00:00:00 2001
From: Kyle <zerouex@gmail.com>
Date: Wed, 25 Jan 2023 09:41:41 -0500
Subject: Update requirements transformers==4.25.1

Update requirement for transformers to version 4.25.1 to allow instruct-pix2pix demo code to work
---
 requirements.txt          | 2 +-
 requirements_versions.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

(limited to 'requirements.txt')

diff --git a/requirements.txt b/requirements.txt
index a4be1ec3..6d53f089 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -16,7 +16,7 @@ pytorch_lightning==1.7.7
 realesrgan
 scikit-image>=0.19
 timm==0.4.12
-transformers==4.19.2
+transformers==4.25.1
 torch
 einops
 jsonmerge
diff --git a/requirements_versions.txt b/requirements_versions.txt
index 135908be..eaa08806 100644
--- a/requirements_versions.txt
+++ b/requirements_versions.txt
@@ -1,5 +1,5 @@
 blendmodes==2022
-transformers==4.19.2
+transformers==4.25.1
 accelerate==0.12.0
 basicsr==1.4.2
 gfpgan==1.3.8
-- 
cgit v1.2.3