update

update

Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>

.github/workflows/build_docker_images.yml

@@ -25,17 +25,17 @@ jobs:
     steps:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v1
-      
+
       - name: Check out code
         uses: actions/checkout@v3
-      
+
       - name: Find Changed Dockerfiles
         id: file_changes
         uses: jitterbit/get-changed-files@v1
         with:
           format: 'space-delimited'
           token: ${{ secrets.GITHUB_TOKEN }}
-      
+
       - name: Build Changed Docker Images
         run: |
           CHANGED_FILES="${{ steps.file_changes.outputs.all }}"
@@ -52,7 +52,7 @@ jobs:
   build-and-push-docker-images:
     runs-on: [ self-hosted, intel-cpu, 8-cpu, ci ]
     if: github.event_name != 'pull_request'
-    
+
     permissions:
       contents: read
       packages: write
@@ -69,6 +69,7 @@ jobs:
           - diffusers-flax-tpu
           - diffusers-onnxruntime-cpu
           - diffusers-onnxruntime-cuda
+          - diffusers-doc-builder
 
     steps:
       - name: Checkout repository

docker/diffusers-doc-builder/Dockerfile

@@ -0,0 +1,50 @@
+FROM ubuntu:20.04
+LABEL maintainer="Hugging Face"
+LABEL repository="diffusers"
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+RUN apt-get -y update \
+    && apt-get install -y software-properties-common \
+    && add-apt-repository ppa:deadsnakes/ppa
+
+RUN apt install -y bash \
+                   build-essential \
+                   git \
+                   git-lfs \
+                   curl \
+                   ca-certificates \
+                   libsndfile1-dev \
+                   python3.10 \
+                   python3-pip \
+                   libgl1 \
+                   python3.10-venv && \
+    rm -rf /var/lib/apt/lists
+
+# make sure to use venv
+RUN python3.10 -m venv /opt/venv
+ENV PATH="/opt/venv/bin:$PATH"
+
+# pre-install the heavy dependencies (these can later be overridden by the deps from setup.py)
+RUN python3.10 -m pip install --no-cache-dir --upgrade pip uv==0.1.11 && \
+    python3.10 -m uv pip install --no-cache-dir \
+        torch \
+        torchvision \
+        torchaudio \
+        invisible_watermark \
+        --extra-index-url https://download.pytorch.org/whl/cpu && \
+    python3.10 -m uv pip install --no-cache-dir \
+        accelerate \
+        datasets \
+        hf-doc-builder \
+        huggingface-hub \
+        Jinja2 \
+        librosa \
+        numpy \
+        scipy \
+        tensorboard \
+        transformers \
+        matplotlib \
+        setuptools==69.5.1
+
+CMD ["/bin/bash"]

tests/models/unets/test_models_unet_stable_cascade.py

@@ -1,191 +0,0 @@
-# coding=utf-8
-# Copyright 2024 HuggingFace Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import gc
-import unittest
-
-import torch
-
-from diffusers import StableCascadeUNet
-from diffusers.utils import logging
-from diffusers.utils.testing_utils import (
-    enable_full_determinism,
-    numpy_cosine_similarity_distance,
-    require_torch_gpu,
-    slow,
-)
-from diffusers.utils.torch_utils import randn_tensor
-
-
-logger = logging.get_logger(__name__)
-
-enable_full_determinism()
-
-
-@slow
-class StableCascadeUNetModelSlowTests(unittest.TestCase):
-    def tearDown(self) -> None:
-        super().tearDown()
-        gc.collect()
-        torch.cuda.empty_cache()
-
-    def test_stable_cascade_unet_prior_single_file_components(self):
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url)
-
-        single_file_unet_config = single_file_unet.config
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade-prior", subfolder="prior", variant="bf16")
-        unet_config = unet.config
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in single_file_unet_config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-
-            assert unet_config[param_name] == param_value
-
-    def test_stable_cascade_unet_decoder_single_file_components(self):
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b_bf16.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url)
-
-        single_file_unet_config = single_file_unet.config
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        unet = StableCascadeUNet.from_pretrained("stabilityai/stable-cascade", subfolder="decoder", variant="bf16")
-        unet_config = unet.config
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in single_file_unet_config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-
-            assert unet_config[param_name] == param_value
-
-    def test_stable_cascade_unet_config_loading(self):
-        config = StableCascadeUNet.load_config(
-            pretrained_model_name_or_path="diffusers/stable-cascade-configs", subfolder="prior"
-        )
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
-
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url, config=config)
-        single_file_unet_config = single_file_unet.config
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        PARAMS_TO_IGNORE = ["torch_dtype", "_name_or_path", "_use_default_values", "_diffusers_version"]
-        for param_name, param_value in config.items():
-            if param_name in PARAMS_TO_IGNORE:
-                continue
-
-            assert single_file_unet_config[param_name] == param_value
-
-    @require_torch_gpu
-    def test_stable_cascade_unet_single_file_prior_forward_pass(self):
-        dtype = torch.bfloat16
-        generator = torch.Generator("cpu")
-
-        model_inputs = {
-            "sample": randn_tensor((1, 16, 24, 24), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "timestep_ratio": torch.tensor([1]).to("cuda", dtype),
-            "clip_text_pooled": randn_tensor((1, 1, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "clip_text": randn_tensor((1, 77, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "clip_img": randn_tensor((1, 1, 768), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "pixels": randn_tensor((1, 3, 8, 8), generator=generator.manual_seed(0)).to("cuda", dtype),
-        }
-
-        unet = StableCascadeUNet.from_pretrained(
-            "stabilityai/stable-cascade-prior",
-            subfolder="prior",
-            revision="refs/pr/2",
-            variant="bf16",
-            torch_dtype=dtype,
-        )
-        unet.to("cuda")
-        with torch.no_grad():
-            prior_output = unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_c_bf16.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url, torch_dtype=dtype)
-        single_file_unet.to("cuda")
-        with torch.no_grad():
-            prior_single_file_output = single_file_unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        max_diff = numpy_cosine_similarity_distance(prior_output.flatten(), prior_single_file_output.flatten())
-        assert max_diff < 8e-3
-
-    @require_torch_gpu
-    def test_stable_cascade_unet_single_file_decoder_forward_pass(self):
-        dtype = torch.float32
-        generator = torch.Generator("cpu")
-
-        model_inputs = {
-            "sample": randn_tensor((1, 4, 256, 256), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "timestep_ratio": torch.tensor([1]).to("cuda", dtype),
-            "clip_text": randn_tensor((1, 77, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "clip_text_pooled": randn_tensor((1, 1, 1280), generator=generator.manual_seed(0)).to("cuda", dtype),
-            "pixels": randn_tensor((1, 3, 8, 8), generator=generator.manual_seed(0)).to("cuda", dtype),
-        }
-
-        unet = StableCascadeUNet.from_pretrained(
-            "stabilityai/stable-cascade",
-            subfolder="decoder",
-            revision="refs/pr/44",
-            torch_dtype=dtype,
-        )
-        unet.to("cuda")
-        with torch.no_grad():
-            prior_output = unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        single_file_url = "https://huggingface.co/stabilityai/stable-cascade/blob/main/stage_b.safetensors"
-        single_file_unet = StableCascadeUNet.from_single_file(single_file_url, torch_dtype=dtype)
-        single_file_unet.to("cuda")
-        with torch.no_grad():
-            prior_single_file_output = single_file_unet(**model_inputs).sample.float().cpu().numpy()
-
-        # Remove UNet from GPU memory before loading the single file UNet model
-        del single_file_unet
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        max_diff = numpy_cosine_similarity_distance(prior_output.flatten(), prior_single_file_output.flatten())
-        assert max_diff < 1e-4