Revert "Add dalle2-laion to inference engine (jina-ai#72)" (jina-ai#73)

This reverts commit 3e2826d.

Dockerfile

@@ -24,14 +24,11 @@ RUN if [ -n "${APT_PACKAGES}" ]; then apt-get update && apt-get install --no-ins
     git clone --depth=1 https://github.com/JingyunLiang/SwinIR.git  && \
     git clone --depth=1 https://github.com/CompVis/latent-diffusion.git && \
     git clone --depth=1 https://github.com/hanxiao/glid-3-xl.git && \
-    git clone --depth=1 https://github.com/LAION-AI/dalle2-laion && \
     pip install jax[cuda11_cudnn82]==0.3.13 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html && \
     pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113 && \
-    pip install dalle2-pytorch==1.0.3 && \
     cd latent-diffusion && pip install --timeout=1000 -e . && cd - && \
     cd glid-3-xl && pip install --timeout=1000 -e . && cd - && \
     cd dalle-flow && pip install --timeout=1000 --compile -r requirements.txt && cd - && \
-    cd dalle2-laion && pip install --timeout=1000 -e . && cd - && \
     cd glid-3-xl && \
     wget -q https://dall-3.com/models/glid-3-xl/bert.pt &&  \
     wget -q https://dall-3.com/models/glid-3-xl/kl-f8.pt &&  \

README.md

@@ -14,7 +14,7 @@
 
 
 
-DALL·E Flow is an interactive workflow for generating high-definition images from text prompt. First, it leverages [DALL·E-Mega](https://github.com/borisdayma/dalle-mini) and [DALL·E2-LAION](https://github.com/LAION-AI/dalle2-laion) to generate image candidates, and then calls [CLIP-as-service](https://github.com/jina-ai/clip-as-service) to rank the candidates w.r.t. the prompt. The preferred candidate is fed to [GLID-3 XL](https://github.com/Jack000/glid-3-xl) for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via [SwinIR](https://github.com/JingyunLiang/SwinIR).
+DALL·E Flow is an interactive workflow for generating high-definition images from text prompt. First, it leverages [DALL·E-Mega](https://github.com/borisdayma/dalle-mini) to generate image candidates, and then calls [CLIP-as-service](https://github.com/jina-ai/clip-as-service) to rank the candidates w.r.t. the prompt. The preferred candidate is fed to [GLID-3 XL](https://github.com/Jack000/glid-3-xl) for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via [SwinIR](https://github.com/JingyunLiang/SwinIR).
 
 DALL·E Flow is built with [Jina](https://github.com/jina-ai/jina) in a client-server architecture, which gives it high scalability, non-blocking streaming, and a modern Pythonic interface. Client can interact with the server via gRPC/Websocket/HTTP with TLS.
 
@@ -29,7 +29,6 @@ DALL·E Flow is in client-server architecture.
 
 ## Updates
 
-- 🌟 **2022/7/29** Added dalle2-laion to inference engine
 - ⚠️ **2022/7/6** Demo server migration to AWS EKS for better availability and robustness, **server URL is now changing to `grpcs://dalle-flow.dev.jina.ai`**. All connections are now with TLS encryption, [please _reopen_ the notebook in Google Colab](https://colab.research.google.com/github/jina-ai/dalle-flow/blob/main/client.ipynb).
 - ⚠️ **2022/6/25** Unexpected downtime between 6/25 0:00 - 12:00 CET due to out of GPU quotas. The new server now has 2 GPUs, add healthcheck in client notebook.
 - **2022/6/3** Reduce default number of images to 2 per pathway, 4 for diffusion.
@@ -245,7 +244,6 @@ git clone https://github.com/jina-ai/dalle-flow.git
 git clone https://github.com/JingyunLiang/SwinIR.git
 git clone https://github.com/CompVis/latent-diffusion.git
 git clone https://github.com/hanxiao/glid-3-xl.git
-git clone https://github.com/LAION-AI/dalle2-laion
 ```
 
 You should have the following folder structure:
@@ -264,13 +262,6 @@ dalle/
 ```bash
 cd latent-diffusion && pip install -e . && cd -
 cd glid-3-xl && pip install -e . && cd -
-cd dalle2-laion && pip install -e . && cd -
-```
-
-Install DALLE2-pytorch:
-
-```bash
-pip install dalle2-pytorch==1.0.3
 ```
 
 There are couple models we need to download for GLID-3-XL:

flow.yml

@@ -13,16 +13,6 @@ executors:
       CUDA_VISIBLE_DEVICES: 0  # change this if you have multiple GPU
       XLA_PYTHON_CLIENT_ALLOCATOR: platform  # https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     replicas: 1  # change this if you have larger VRAM
-  - name: dalle2
-    uses: Dalle2Generator
-    timeout_ready: -1 # Give time to download
-    py_modules:
-      - executors/dalle2/executor.py
-    env:
-      CUDA_VISIBLE_DEVICES: 0  # change this if you have multiple GPU
-      XLA_PYTHON_CLIENT_ALLOCATOR: platform  # https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
-    replicas: 1  # change this if you have larger VRAM
-    needs: [gateway]
   - name: diffusion
     uses: GLID3Diffusion
     uses_with:
@@ -41,7 +31,7 @@ executors:
       clip_server: grpcs://demo-cas.jina.ai:2096
     py_modules:
       - executors/rerank/executor.py
-    needs: [dalle, dalle2, diffusion]
+    needs: [dalle, diffusion]
   - name: upscaler
     uses: SwinIRUpscaler
     py_modules: