CUDA error on regular Stable Diffusion

.dockerignore

@@ -0,0 +1,3 @@
+*Dockerfile*
+docker-compose.yml
+.git

.github/FUNDING.yml

@@ -0,0 +1,13 @@
+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: basuj
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
+custom: ['https://paypal.me/basuj']

Dockerfile

@@ -0,0 +1,47 @@
+FROM continuumio/miniconda3:4.12.0 AS build
+
+# Step for image utility dependencies.
+RUN apt update \
+ && apt install --no-install-recommends -y git \
+ && apt-get clean
+
+COPY . /root/stable-diffusion/
+
+# Step to install dependencies with conda
+RUN eval "$(conda shell.bash hook)" \
+ && conda install -c conda-forge conda-pack \
+ && conda env create -f /root/stable-diffusion/environment.yaml \
+ && conda activate ldm \
+ && pip install gradio==3.1.7 \
+ && conda activate base  
+
+# Step to zip and conda environment to "venv" folder
+RUN conda pack --ignore-missing-files --ignore-editable-packages -n ldm -o /tmp/env.tar \
+ && mkdir /venv \
+ && cd /venv \
+ && tar xf /tmp/env.tar \
+ && rm /tmp/env.tar
+
+FROM nvidia/cuda:11.8.0-base-ubuntu22.04 as runtime
+
+ARG OPTIMIZED_FILE=txt2img_gradio.py
+WORKDIR /root/stable-diffusion
+
+COPY --from=build /venv /venv
+COPY --from=build /root/stable-diffusion /root/stable-diffusion
+
+RUN mkdir -p /output /root/stable-diffusion/outputs \
+ && ln -s /data /root/stable-diffusion/models/ldm/stable-diffusion-v1 \
+ && ln -s /output /root/stable-diffusion/outputs/txt2img-samples
+
+ENV PYTHONUNBUFFERED=1
+ENV GRADIO_SERVER_NAME=0.0.0.0
+ENV GRADIO_SERVER_PORT=7860 
+ENV APP_MAIN_FILE=${OPTIMIZED_FILE}
+EXPOSE 7860
+
+VOLUME ["/root/.cache", "/data", "/output"]
+
+SHELL ["/bin/bash", "-c"]
+ENTRYPOINT ["/root/stable-diffusion/docker-bootstrap.sh"]
+CMD python optimizedSD/${APP_MAIN_FILE}

docker-bootstrap.sh

@@ -0,0 +1,6 @@
+#!/bin/bash
+set -e
+source /venv/bin/activate
+update-ca-certificates --fresh
+export SSL_CERT_DIR=/etc/ssl/certs
+exec "$@"

docker-compose.yml

@@ -0,0 +1,21 @@
+version: "3.9"
+services:
+  sd:
+    build: .
+    ports:
+      - "7860:7860"
+    volumes:
+      - ../sd-data:/data
+      - ../sd-output:/output
+      - sd-cache:/root/.cache
+    environment:
+      - APP_MAIN_FILE=txt2img_gradio.py
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+volumes:
+  sd-cache:

environment.yaml

@@ -8,7 +8,7 @@ dependencies:
   - cudatoolkit=11.3
   - pytorch=1.11.0
   - torchvision=0.12.0
-  - numpy=1.19.2
+  - numpy=1.20.3
   - pip:
     - albumentations==0.4.3
     - opencv-python==4.1.2.30

ldm/modules/diffusionmodules/model.py

@@ -2,6 +2,7 @@
 import math
 import torch
 import torch.nn as nn
+from torch.nn.functional import silu
 import numpy as np
 from einops import rearrange
 
@@ -30,11 +31,6 @@ def get_timestep_embedding(timesteps, embedding_dim):
     return emb
 
 
-def nonlinearity(x):
-    # swish
-    return x*torch.sigmoid(x)
-
-
 def Normalize(in_channels, num_groups=32):
     return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
 
@@ -121,14 +117,14 @@ def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
     def forward(self, x, temb):
         h = x
         h = self.norm1(h)
-        h = nonlinearity(h)
+        h = silu(h)
         h = self.conv1(h)
 
         if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+            h = h + self.temb_proj(silu(temb))[:,:,None,None]
 
         h = self.norm2(h)
-        h = nonlinearity(h)
+        h = silu(h)
         h = self.dropout(h)
         h = self.conv2(h)
 
@@ -323,7 +319,7 @@ def forward(self, x, t=None, context=None):
             assert t is not None
             temb = get_timestep_embedding(t, self.ch)
             temb = self.temb.dense[0](temb)
-            temb = nonlinearity(temb)
+            temb = silu(temb)
             temb = self.temb.dense[1](temb)
         else:
             temb = None
@@ -357,7 +353,7 @@ def forward(self, x, t=None, context=None):
 
         # end
         h = self.norm_out(h)
-        h = nonlinearity(h)
+        h = silu(h)
         h = self.conv_out(h)
         return h
 
@@ -454,7 +450,7 @@ def forward(self, x):
 
         # end
         h = self.norm_out(h)
-        h = nonlinearity(h)
+        h = silu(h)
         h = self.conv_out(h)
         return h
 
@@ -561,7 +557,7 @@ def forward(self, z):
             return h
 
         h = self.norm_out(h)
-        h = nonlinearity(h)
+        h = silu(h)
         h = self.conv_out(h)
         if self.tanh_out:
             h = torch.tanh(h)
@@ -599,7 +595,7 @@ def forward(self, x):
                 x = layer(x)
 
         h = self.norm_out(x)
-        h = nonlinearity(h)
+        h = silu(h)
         x = self.conv_out(h)
         return x
 
@@ -647,7 +643,7 @@ def forward(self, x):
             if i_level != self.num_resolutions - 1:
                 h = self.upsample_blocks[k](h)
         h = self.norm_out(h)
-        h = nonlinearity(h)
+        h = silu(h)
         h = self.conv_out(h)
         return h
 
@@ -823,7 +819,7 @@ def forward(self,x):
         z_fs = self.encode_with_pretrained(x)
         z = self.proj_norm(z_fs)
         z = self.proj(z)
-        z = nonlinearity(z)
+        z = silu(z)
 
         for submodel, downmodel in zip(self.model,self.downsampler):
             z = submodel(z,temb=None)

optimizedSD/LICENSE

@@ -0,0 +1,80 @@
+Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
+
+CreativeML Open RAIL-M
+dated August 22, 2022
+
+Section I: PREAMBLE
+
+Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation.
+
+Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations.
+
+In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation.
+
+Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). We believe in the intersection between open and responsible AI development; thus, this License aims to strike a balance between both in order to enable responsible open-science in the field of AI.
+
+This License governs the use of the model (and its derivatives) and is informed by the model card associated with the model.
+
+NOW THEREFORE, You and Licensor agree as follows:
+
+1. Definitions
+
+- "License" means the terms and conditions for use, reproduction, and Distribution as defined in this document.
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+Attachment A
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+Use Restrictions
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+You agree not to use the Model or Derivatives of the Model:
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