Merge branch 'main' into fit-init-img

add a --fit option to limit the size of the initial image to the
maximum boundaries specified by width and height.

README-Mac-MPS.md

@@ -12,8 +12,7 @@ issue](https://github.com/CompVis/stable-diffusion/issues/25), and generally on
 
 You have to have macOS 12.3 Monterey or later. Anything earlier than that won't work.
 
-BTW, I haven't tested any of this on Intel Macs but I have read that one person
+Tested on a 2022 Macbook M2 Air with 10-core GPU and 24 GB unified memory.
-got it to work.
 
 How to:
 
@@ -22,24 +21,23 @@ git clone https://github.com/lstein/stable-diffusion.git
 cd stable-diffusion
 
 mkdir -p models/ldm/stable-diffusion-v1/
-ln -s /path/to/ckpt/sd-v1-1.ckpt models/ldm/stable-diffusion-v1/model.ckpt
+PATH_TO_CKPT="$HOME/Documents/stable-diffusion-v-1-4-original"  # or wherever yours is.
+ln -s "$PATH_TO_CKPT/sd-v1-4.ckpt" models/ldm/stable-diffusion-v1/model.ckpt
 
-conda env create -f environment-mac.yaml
+CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac.yaml
 conda activate ldm
 
 python scripts/preload_models.py
-python scripts/orig_scripts/txt2img.py --prompt "a photograph of an astronaut riding a horse" --plms
+python scripts/dream.py --full_precision  # half-precision requires autocast and won't work
 ```
 
-We have not gotten lstein's dream.py to work yet.
+After you follow all the instructions and run dream.py you might get several errors. Here's the errors I've seen and found solutions for.
-
-After you follow all the instructions and run txt2img.py you might get several errors. Here's the errors I've seen and found solutions for.
 
 ### Is it slow?
 
 Be sure to specify 1 sample and 1 iteration.
 
-	python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
+	python ./scripts/orig_scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
 
 ### Doesn't work anymore?
 
@@ -94,10 +92,6 @@ get quick feedback.
 
 	python ./scripts/txt2img.py --prompt "ocean" --ddim_steps 5 --n_samples 1 --n_iter 1
 
-### MAC: torch._C' has no attribute '_cuda_resetPeakMemoryStats' #234
-
-We haven't fixed gotten dream.py to work on Mac yet.
-
 ### OSError: Can't load tokenizer for 'openai/clip-vit-large-patch14'...
 
 	python scripts/preload_models.py
@@ -108,7 +102,7 @@ Example error.
 
 ```
 ...
-NotImplementedError: The operator 'aten::index.Tensor' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on [https://github.com/pytorch/pytorch/issues/77764](https://github.com/pytorch/pytorch/issues/77764). As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
+NotImplementedError: The operator 'aten::_index_put_impl_' is not current implemented for the MPS device. If you want this op to be added in priority during the prototype phase of this feature, please comment on [https://github.com/pytorch/pytorch/issues/77764](https://github.com/pytorch/pytorch/issues/77764). As a temporary fix, you can set the environment variable `PYTORCH_ENABLE_MPS_FALLBACK=1` to use the CPU as a fallback for this op. WARNING: this will be slower than running natively on MPS.
 ```
 
 The lstein branch includes this fix in [environment-mac.yaml](https://github.com/lstein/stable-diffusion/blob/main/environment-mac.yaml).
@@ -137,27 +131,18 @@ still working on it.
 
 	OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.
 
-There are several things you can do. First, you could use something
+You are likely using an Intel package by mistake. Be sure to run conda with
-besides Anaconda like miniforge. I read a lot of things online telling
+the environment variable `CONDA_SUBDIR=osx-arm64`, like so:
-people to use something else, but I am stuck with Anaconda for other
-reasons.
 
-Or you can try this.
+`CONDA_SUBDIR=osx-arm64 conda install ...`
 
-	export KMP_DUPLICATE_LIB_OK=True
+This error happens with Anaconda on Macs when the Intel-only `mkl` is pulled in by
+a dependency. [nomkl](https://stackoverflow.com/questions/66224879/what-is-the-nomkl-python-package-used-for)
+is a metapackage designed to prevent this, by making it impossible to install
+`mkl`, but if your environment is already broken it may not work. 
 
-Or this (which takes forever on my computer and didn't work anyway).
+Do *not* use `os.environ['KMP_DUPLICATE_LIB_OK']='True'` or equivalents as this
-
+masks the underlying issue of using Intel packages.
-	conda install nomkl
-
-This error happens with Anaconda on Macs, and
-[nomkl](https://stackoverflow.com/questions/66224879/what-is-the-nomkl-python-package-used-for)
-is supposed to fix the issue (it isn't a module but a fix of some
-sort). [There's more
-suggestions](https://stackoverflow.com/questions/53014306/error-15-initializing-libiomp5-dylib-but-found-libiomp5-dylib-already-initial),
-like uninstalling tensorflow and reinstalling. I haven't tried them.
-
-Since I switched to miniforge I haven't seen the error.
 
 ### Not enough memory.
 
@@ -226,4 +211,8 @@ What? Intel? On an Apple Silicon?
 	The processor must support the Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) instructions.
 	The processor must support the Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.
 
-This was actually the issue that I couldn't solve until I switched to miniforge.
+This is due to the Intel `mkl` package getting picked up when you try to install
+something that depends on it-- Rosetta can translate some Intel instructions but
+not the specialized ones here. To avoid this, make sure to use the environment
+variable `CONDA_SUBDIR=osx-arm64`, which restricts the Conda environment to only
+use ARM packages, and use `nomkl` as described above.

README.md

@@ -740,7 +740,7 @@ and [Tesseract Cat](https://github.com/TesseractCat)
 
 Original portions of the software are Copyright (c) 2020 Lincoln D. Stein (https://github.com/lstein)
 
-#Further Reading
+# Further Reading
 
 Please see the original README for more information on this software
-and underlying algorithm, located in the file README-CompViz.md.
+and underlying algorithm, located in the file [README-CompViz.md](README-CompViz.md).

environment-mac.yaml

@@ -1,33 +1,57 @@
 name: ldm
 channels:
-  - apple
-  - conda-forge
   - pytorch-nightly
-  - defaults
+  - conda-forge
 dependencies:
-  - python=3.10.4
+  - python==3.9.13
-  - pip=22.1.2
+  - pip==22.2.2
+ 
+  # pytorch-nightly, left unpinned
   - pytorch
+  - torchmetrics
   - torchvision
-  - numpy=1.23.1
+
+  # I suggest to keep the other deps sorted for convenience.
+  # If you wish to upgrade to 3.10, try to run this:
+  #
+  # ```shell
+  # CONDA_CMD=conda
+  # sed -E 's/python==3.9.13/python==3.10.5/;s/ldm/ldm-3.10/;21,99s/- ([^=]+)==.+/- \1/' environment-mac.yaml > /tmp/environment-mac-updated.yml
+  # CONDA_SUBDIR=osx-arm64 $CONDA_CMD env create -f /tmp/environment-mac-updated.yml && $CONDA_CMD list -n ldm-3.10 | awk ' {print "  - " $1 "==" $2;} '
+  # ```
+  #
+  # Unfortunately, as of 2022-08-31, this fails at the pip stage.
+  - albumentations==1.2.1
+  - coloredlogs==15.0.1
+  - einops==0.4.1
+  - grpcio==1.46.4
+  - humanfriendly
+  - imageio-ffmpeg==0.4.7
+  - imageio==2.21.2
+  - imgaug==0.4.0
+  - kornia==0.6.7
+  - mpmath==1.2.1
+  - nomkl
+  - numpy==1.23.2
+  - omegaconf==2.1.1
+  - onnx==1.12.0
+  - onnxruntime==1.12.1
+  - opencv==4.6.0
+  - pudb==2022.1
+  - pytorch-lightning==1.6.5
+  - scipy==1.9.1
+  - streamlit==1.12.2
+  - sympy==1.10.1
+  - tensorboard==2.9.0
+  - transformers==4.21.2
   - pip:
-    - albumentations==0.4.6
+    - invisible-watermark
-    - opencv-python==4.6.0.66
+    - test-tube
-    - pudb==2019.2
+    - tokenizers
-    - imageio==2.9.0
+    - torch-fidelity
-    - imageio-ffmpeg==0.4.2
+    - -e git+https://github.com/huggingface/diffusers.git@v0.2.4#egg=diffusers
-    - pytorch-lightning==1.4.2
-    - omegaconf==2.1.1
-    - test-tube>=0.7.5
-    - streamlit==1.12.0
-    - pillow==9.2.0
-    - einops==0.3.0
-    - torch-fidelity==0.3.0
-    - transformers==4.19.2
-    - torchmetrics==0.6.0
-    - kornia==0.6.0
-    - -e git+https://github.com/openai/CLIP.git@main#egg=clip
     - -e git+https://github.com/CompVis/taming-transformers.git@master#egg=taming-transformers
+    - -e git+https://github.com/openai/CLIP.git@main#egg=clip
     - -e git+https://github.com/lstein/k-diffusion.git@master#egg=k-diffusion
     - -e .
 variables:

ldm/simplet2i.py

@@ -27,7 +27,6 @@ from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.plms import PLMSSampler
 from ldm.models.diffusion.ksampler import KSampler
 from ldm.dream.pngwriter import PngWriter
-from ldm.dream.image_util import InitImageResizer
 from ldm.dream.devices import choose_torch_device
 
 """Simplified text to image API for stable diffusion/latent diffusion
@@ -159,7 +158,7 @@ class T2I:
 
         # for VRAM usage statistics
         self.session_peakmem = torch.cuda.max_memory_allocated() if self.device == 'cuda' else None
-            
+
         if seed is None:
             self.seed = self._new_seed()
         else:
@@ -178,7 +177,8 @@ class T2I:
         outputs = []
         for image, seed in results:
             name = f'{prefix}.{seed}.png'
-            path = pngwriter.save_image_and_prompt_to_png(image, f'{prompt} -S{seed}', name)
+            path = pngwriter.save_image_and_prompt_to_png(
+                image, f'{prompt} -S{seed}', name)
             outputs.append([path, seed])
         return outputs
 
@@ -276,7 +276,8 @@ class T2I:
             self._set_sampler()
 
         tic = time.time()
-        torch.cuda.torch.cuda.reset_peak_memory_stats()
+        if torch.cuda.is_available():
+            torch.cuda.reset_peak_memory_stats()
         results = list()
 
         try:
@@ -487,7 +488,8 @@ class T2I:
         uc = self.model.get_learned_conditioning([''])
 
         # get weighted sub-prompts
-        weighted_subprompts = T2I._split_weighted_subprompts(prompt, skip_normalize)
+        weighted_subprompts = T2I._split_weighted_subprompts(
+            prompt, skip_normalize)
 
         if len(weighted_subprompts) > 1:
             # i dont know if this is correct.. but it works
@@ -530,7 +532,7 @@ class T2I:
         if self.model is None:
             seed_everything(self.seed)
             try:
-                config      = OmegaConf.load(self.config)
+                config = OmegaConf.load(self.config)
                 self.device = self._get_device()
                 model = self._load_model_from_config(config, self.weights)
                 if self.embedding_path is not None:
@@ -673,18 +675,20 @@ class T2I:
             $               # else, if no ':' then match end of line
             )               # end non-capture group
         """, re.VERBOSE)
-        parsed_prompts = [(match.group("prompt").replace("\\:", ":"), float(match.group("weight") or 1)) for match in re.finditer(prompt_parser, text)]
+        parsed_prompts = [(match.group("prompt").replace("\\:", ":"), float(
+            match.group("weight") or 1)) for match in re.finditer(prompt_parser, text)]
         if skip_normalize:
             return parsed_prompts
         weight_sum = sum(map(lambda x: x[1], parsed_prompts))
         if weight_sum == 0:
-            print("Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
+            print(
+                "Warning: Subprompt weights add up to zero. Discarding and using even weights instead.")
             equal_weight = 1 / len(parsed_prompts)
             return [(x[0], equal_weight) for x in parsed_prompts]
         return [(x[0], x[1] / weight_sum) for x in parsed_prompts]
-        
+
-    # shows how the prompt is tokenized 
+    # shows how the prompt is tokenized
-    # usually tokens have '</w>' to indicate end-of-word, 
+    # usually tokens have '</w>' to indicate end-of-word,
     # but for readability it has been replaced with ' '
     def _log_tokenization(self, text):
         if not self.log_tokenization:
@@ -721,4 +725,8 @@ class T2I:
             height = h
             width  = w
             resize_needed = True
+
+        if (width * height) > (self.width * self.height):
+            print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
+
         return width, height, resize_needed

scripts/orig_scripts/img2img.py

@@ -200,7 +200,7 @@ def main():
     config = OmegaConf.load(f"{opt.config}")
     model = load_model_from_config(config, f"{opt.ckpt}")
 
-    device = choose_torch_device()
+    device = torch.device(choose_torch_device())
     model = model.to(device)
 
     if opt.plms: