Update multimodalart/latentdiffusion

.stignore

@@ -0,0 +1,101 @@
+.git
+# Byte-compiled / optimized / DLL files
+__pycache__
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build
+develop-eggs
+dist
+downloads
+eggs
+.eggs
+lib
+lib64
+parts
+sdist
+var
+wheels
+pip-wheel-metadata
+share/python-wheels
+*.egg-info
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build
+
+# PyBuilder
+target
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env
+venv
+ENV
+env.bak
+venv.bak
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mypy
+.mypy_cache
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre

app-diffusers.py

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+"""See https://huggingface.co/fusing/latent-diffusion-text2im-large."""
+from logzero import logger
+from install import install
+try:
+    import gradio as gr
+except ModuleNotFoundError:
+        try:
+            install("gradio")
+            import gradio as gr
+        except Exception as exc:
+            logger.error(exc)
+            raise SystemExit(1)
+
+import PIL
+from diffusers import DiffusionPipeline
+
+ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
+
+generator = torch.manual_seed(42)
+
+examples = ["A street sign that reads Huggingface", "A painting of a squirrel eating a burger"]
+
+prompt_ = "A painting of a squirrel eating a burger"
+
+def fn(prompt=prompt_):
+  image = ldm(
+    [prompt],
+    generator=generator,
+    eta=0.3,
+    guidance_scale=6.0,
+    num_inference_steps=50,
+  )
+
+  image_processed = image.cpu().permute(0, 2, 3, 1)
+  image_processed = image_processed  * 255.
+  image_processed = image_processed.numpy().astype(np.uint8)
+  image_pil = PIL.Image.fromarray(image_processed[0])
+
+  # save image
+  # image_pil.save("test.png")
+  return image_pil
+
+iface = gr.Interface(
+  fn=fn,
+  inputs="text",
+  outputs="image",
+  examples=examples,
+  live=True,
+)
+iface.launch()
+
+# gr.Interface.load("fusing/latent-diffusion-text2im-large", examples=examples).launch()

app.py

@@ -1,41 +1,13 @@
-"""See https://huggingface.co/fusing/latent-diffusion-text2im-large."""
+"""See https://huggingface.co/spaces/Gradio-Blocks/Story-to-video/blob/main/app.py."""
 import gradio as gr
-import PIL
-from diffusers import DiffusionPipeline
 
-ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
+# from PIL import Image
+# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
+# import requests
+# import torch
 
-generator = torch.manual_seed(42)
+image_gen = gr.Interface.load("spaces/multimodalart/latentdiffusion")
 
-examples = ["A street sign that reads Huggingface", "A painting of a squirrel eating a burger"]
+# image_bytes = image_gen(senten, steps, width, height, num_images, diversity)
 
-prompt_ = "A painting of a squirrel eating a burger"
-
-def fn(prompt=prompt_):
-  image = ldm(
-    [prompt], 
-    generator=generator, 
-    eta=0.3, 
-    guidance_scale=6.0, 
-    num_inference_steps=50,
-  )
-  
-  image_processed = image.cpu().permute(0, 2, 3, 1)
-  image_processed = image_processed  * 255.
-  image_processed = image_processed.numpy().astype(np.uint8)
-  image_pil = PIL.Image.fromarray(image_processed[0])
-  
-  # save image
-  # image_pil.save("test.png")
-  return image_pil 
-  
-iface = gr.Interface(
-  fn=fn, 
-  inputs="text", 
-  outputs="image",
-  examples=examples,
-  live=True,
-)
-iface.launch()
-
-# gr.Interface.load("fusing/latent-diffusion-text2im-large", examples=examples).launch()
+image_gen.launch()

example.py

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+import torch
+from diffusers import UNetUnconditionalModel, DDIMScheduler
+import PIL.Image
+import numpy as np
+import tqdm
+
+torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# 1. Load models
+scheduler = DDIMScheduler.from_config("fusing/ddpm-celeba-hq", tensor_format="pt")
+unet = UNetUnconditionalModel.from_pretrained("fusing/ddpm-celeba-hq", ddpm=True).to(torch_device)
+
+# 2. Sample gaussian noise
+generator = torch.manual_seed(23)
+generator = torch.manual_seed(43)
+unet.image_size = unet.resolution
+image = torch.randn(
+   (1, unet.in_channels, unet.image_size, unet.image_size),
+   generator=generator,
+)
+image = image.to(torch_device)
+
+# 3. Denoise
+num_inference_steps = 50
+num_inference_steps = 5
+eta = 0.0  # <- deterministic sampling
+scheduler.set_timesteps(num_inference_steps)
+
+for t in tqdm.tqdm(scheduler.timesteps):
+    # 1. predict noise residual
+    with torch.no_grad():
+        residual = unet(image, t)["sample"]
+
+    prev_image = scheduler.step(residual, t, image, eta)["prev_sample"]
+
+    # 3. set current image to prev_image: x_t -> x_t-1
+    image = prev_image
+
+# 4. process image to PIL
+image_processed = image.cpu().permute(0, 2, 3, 1)
+image_processed = (image_processed + 1.0) * 127.5
+image_processed = image_processed.numpy().astype(np.uint8)
+image_pil = PIL.Image.fromarray(image_processed[0])
+
+# 5. save image
+image_pil.save("generated_image.png")

example1.py

@@ -0,0 +1,31 @@
+"""https://pypi.org/project/diffusers/.
+
+!pip install git+https://github.com/patil-suraj/transformers@ldm-bert
+!git clone https://github.com/huggingface/diffusers
+!cd diffusers && pip install -e .
+!pip install git+file:///content/diffusers
+!pip install torch
+
+https://github.com/CompVis/latent-diffusion/blob/main/scripts/txt2img.py
+
+https://medium.com/tag/diffusion-models
+
+ !pip install einops
+    
+"""
+from diffusers import DiffusionPipeline
+
+ldm = DiffusionPipeline.from_pretrained("fusing/latent-diffusion-text2im-large")
+
+generator = torch.manual_seed(42)
+
+prompt = "A painting of a squirrel eating a burger"
+image = ldm([prompt], generator=generator, eta=0.3, guidance_scale=6.0, num_inference_steps=50)
+
+image_processed = image.cpu().permute(0, 2, 3, 1)
+image_processed = image_processed  * 255.
+image_processed = image_processed.numpy().astype(np.uint8)
+image_pil = PIL.Image.fromarray(image_processed[0])
+
+# save image
+image_pil.save("test.png")

install-sw.sh

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+# pip install pipx
+# pipx install poetry
+# pipx ensurepath
+# source ~/.bashrc
+
+# curl -sSL https://install.python-poetry.org | python3 -
+# -C- continue -S show error -o output
+curl -sSL -C- -o install-poetry.py  https://install.python-poetry.org
+python install-poetry.py
+rm install-poetry.py
+echo export PATH=~/.local/bin:$PATH > ~/.bashrc
+source ~/.bashrc
+# ~/.local/bin/poetry install
+
+wget -c https://deb.nodesource.com/setup_14.x
+bash setup_14.x
+apt-get install -y nodejs
+npm install -g npm@latest
+npm install -g nodemon
+rm setup_14.x
+
+# apt upate  # alerady done in apt-get install -y nodejs
+apt install byobu -y > /dev/null 2>&1

install-sw1.sh

@@ -0,0 +1,25 @@
+# pip install pipx
+# pipx install poetry
+# pipx ensurepath
+# source ~/.bashrc
+
+# curl -sSL https://install.python-poetry.org | python3 -
+# -C- continue -S show error -o output
+curl -sSL -C- -o install-poetry.py  https://install.python-poetry.org
+python install-poetry.py
+rm install-poetry.py
+echo export PATH=~/.local/bin:$PATH > ~/.bashrc
+source ~/.bashrc
+# ~/.local/bin/poetry install
+
+wget -c https://deb.nodesource.com/setup_12.x
+bash setup_12.x
+apt-get install -y nodejs
+npm install -g npm@latest
+npm install -g nodemon
+rm setup_12.x
+
+# apt update  # alerady done in apt-get install -y nodejs
+apt install byobu -y > /dev/null 2>&1
+byobu-enable
+byobu

okteto-up.bat

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+okteto up 

okteto.yml

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+name: gradio-cmat
+
+# The build section defines how to build the images of
+# your development environment
+# More info: https://www.okteto.com/docs/reference/manifest/#build
+# build:
+#   my-service:
+#     context: .
+
+# The deploy section defines how to deploy your development environment
+# More info: https://www.okteto.com/docs/reference/manifest/#deploy
+# deploy:
+  # commands:
+    # - name: Deploy
+      # command: echo 'Replace this line with the proper 'helm'
+
+#  or 'kubectl' commands to deploy your development environment'
+
+# The dependencies section defines other git repositories to be
+# deployed as part of your development environment
+# More info: https://www.okteto.com/docs/reference/manifest/#dependencies
+# dependencies:
+#   - https://github.com/okteto/sample
+# The dev section defines how to activate a development container
+# More info: https://www.okteto.com/docs/reference/manifest/#dev
+dev:
+  gradio-cmat:
+    # image: okteto/dev:latest
+    # image: python:3.8.13-bullseye
+    # image: simbachain/poetry-3.8
+    image: python:3.8
+    command: bash
+    workdir: /usr/src/app
+    sync:
+      - .:/usr/src/app
+    environment:
+      - name=$USER
+    forward:
+      - 7861:7861
+      - 7860:7860
+      - 8501:8501
+    reverse:
+      - 9000:9000
+    autocreate: true

poetry.lock

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+[[package]]
+name = "colorama"
+version = "0.4.5"
+description = "Cross-platform colored terminal text."
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
+[[package]]
+name = "install"
+version = "1.3.5"
+description = "Install packages from within code"
+category = "main"
+optional = false
+python-versions = ">=2.7, >=3.5"
+
+[[package]]
+name = "logzero"
+version = "1.7.0"
+description = "Robust and effective logging for Python 2 and 3"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+
+[metadata]
+lock-version = "1.1"
+python-versions = "^3.8"
+content-hash = "99de593375f55736457752f5abe46a79ef864a037b1bd84592a3983d44b410c2"
+
+[metadata.files]
+colorama = [
+    {file = "colorama-0.4.5-py2.py3-none-any.whl", hash = "sha256:854bf444933e37f5824ae7bfc1e98d5bce2ebe4160d46b5edf346a89358e99da"},
+    {file = "colorama-0.4.5.tar.gz", hash = "sha256:e6c6b4334fc50988a639d9b98aa429a0b57da6e17b9a44f0451f930b6967b7a4"},
+]
+install = [
+    {file = "install-1.3.5-py3-none-any.whl", hash = "sha256:0d3fadf4aa62c95efe8d34757c8507eb46177f86c016c21c6551eafc6a53d5a9"},
+    {file = "install-1.3.5.tar.gz", hash = "sha256:e67c8a0be5ccf8cb4ffa17d090f3a61b6e820e6a7e21cd1d2c0f7bc59b18e647"},
+]
+logzero = [
+    {file = "logzero-1.7.0-py2.py3-none-any.whl", hash = "sha256:23eb1f717a2736f9ab91ca0d43160fd2c996ad49ae6bad34652d47aba908769d"},
+    {file = "logzero-1.7.0.tar.gz", hash = "sha256:7f73ddd3ae393457236f081ffebd044a3aa2e423a47ae6ddb5179ab90d0ad082"},
+]

pyproject.toml

@@ -0,0 +1,16 @@
+[tool.poetry]
+name = "app"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <you@example.com>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+logzero = "^1.7.0"
+install = "^1.3.5"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

requirements.txt → requirements-diffusers.txt

@@ -3,6 +3,7 @@
 
 # git+https://github.com/huggingface/diffusers.git@v0.0.2-release
 # diffusers
+
 git+https://github.com/huggingface/diffusers
 
 # https://github.com/huggingface/diffusers/issues/82
@@ -12,7 +13,13 @@ git+https://github.com/huggingface/diffusers
 # pip install git+file:///content/diffusers
 # pip install torch
 
-# git+https://github.com/patil-suraj/transformers.git@ldm-bert
+    https://hub.fastgit.xyz/author/repo
+    https://hub.fastgit.org/author/repo
+
+git+https://github.com/patil-suraj/transformers.git@ldm-bert
+torch
 
 opencv-python
-transformers 
+transformers
+install
+logzero