feature: use refiners library for generation

BREAKING CHANGE

  - stable diffusion 1.5 + inpainting working
  - self-attention guidance working. improves image generation quality
  - tile-mode working
  - inpainting self-attention guidance working

disable/broken features:
  - sd 1.4, 2.0, 2.1
  - most of the samplers
  - pix2pix edit
  - most of the controlnets
  - memory management
  - python 3.8 support

wip

.github/workflows/ci.yaml

@@ -54,10 +54,11 @@ jobs:
         run: |
           black --diff --fast .
   test:
-    runs-on: ubuntu-latest
+    runs-on: macos-13-xlarge
     strategy:
+      fail-fast: false
       matrix:
-        python-version: ["3.8", "3.10"]
+        python-version: ["3.10"]
         subset: ["1/10", "2/10", "3/10", "4/10", "5/10", "6/10", "7/10", "8/10", "9/10", "10/10"]
     steps:
     - uses: actions/checkout@v3
@@ -69,7 +70,6 @@ jobs:
         cache-dependency-path: requirements-dev.txt
     - name: Install dependencies
       run: |
-        python -m pip install torch==1.13.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
         python -m pip install -r requirements-dev.txt .
     - name: Get current date
       id: date

Makefile

@@ -16,9 +16,9 @@ init: require_pyenv  ## Setup a dev environment for local development.
 	@echo -e "\033[0;32m ✔️  🐍 $(venv_name) virtualenv activated \033[0m"
 	pip install --upgrade pip pip-tools
 	pip-sync requirements-dev.txt
-	pip install -e . --no-deps
+	pip install -e .
 	# the compiled requirements don't included OS specific subdependencies so we trigger those this way
-	pip install `pip freeze | grep "^torch=="`
+	#pip install `pip freeze | grep "^torch=="`
 	@echo -e "\nEnvironment setup! ✨ 🍰 ✨ 🐍 \n\nCopy this path to tell PyCharm where your virtualenv is. You may have to click the refresh button in the pycharm file explorer.\n"
 	@echo -e "\033[0;32m"
 	@pyenv which python

imaginairy/__init__.py

@@ -2,6 +2,8 @@ import os
 
 # tells pytorch to allow MPS usage (for Mac M1 compatibility)
 os.putenv("PYTORCH_ENABLE_MPS_FALLBACK", "1")
+# use more memory than we should
+os.putenv("PYTORCH_MPS_HIGH_WATERMARK_RATIO", "0.0")
 
 import sys  # noqa
 

imaginairy/api.py

@@ -141,6 +141,7 @@ def imagine(
 ):
     import torch.nn
 
+    from imaginairy.api_refiners import _generate_single_image
     from imaginairy.schema import ImaginePrompt
     from imaginairy.utils import (
         check_torch_version,
@@ -190,7 +191,7 @@ def imagine(
             yield result
 
 
-def _generate_single_image(
+def _generate_single_image_compvis(
     prompt,
     debug_img_callback=None,
     progress_img_callback=None,
@@ -674,8 +675,10 @@ def _scale_latent(
 def _generate_composition_image(prompt, target_height, target_width, cutoff=512):
     from PIL import Image
 
+    from imaginairy.api_refiners import _generate_single_image
+
     if prompt.width <= cutoff and prompt.height <= cutoff:
-        return None
+        return None, None
 
     shrink_scale = calc_scale_to_fit_within(
         height=prompt.height,
@@ -713,7 +716,7 @@ def _generate_composition_image(prompt, target_height, target_width, cutoff=512)
         resample=Image.Resampling.LANCZOS,
     )
 
-    return img
+    return img, result.images["generated"]
 
 
 def prompt_normalized(prompt, length=130):

imaginairy/api_refiners.py

@@ -0,0 +1,433 @@
+import logging
+from typing import List, Optional
+
+from imaginairy import WeightedPrompt
+from imaginairy.config import CONTROLNET_CONFIG_SHORTCUTS
+from imaginairy.model_manager import load_controlnet_adapter
+
+logger = logging.getLogger(__name__)
+
+
+def _generate_single_image(
+    prompt,
+    debug_img_callback=None,
+    progress_img_callback=None,
+    progress_img_interval_steps=3,
+    progress_img_interval_min_s=0.1,
+    half_mode=None,
+    add_caption=False,
+    # controlnet, finetune, naive, auto
+    inpaint_method="finetune",
+    return_latent=False,
+):
+    import gc
+
+    import torch.nn
+    from PIL import ImageOps
+    from pytorch_lightning import seed_everything
+    from refiners.foundationals.latent_diffusion.schedulers import DDIM, DPMSolver
+    from tqdm import tqdm
+
+    from imaginairy.api import (
+        IMAGINAIRY_SAFETY_MODE,
+        _generate_composition_image,
+        combine_image,
+    )
+    from imaginairy.enhancers.clip_masking import get_img_mask
+    from imaginairy.enhancers.describe_image_blip import generate_caption
+    from imaginairy.enhancers.face_restoration_codeformer import enhance_faces
+    from imaginairy.enhancers.upscale_realesrgan import upscale_image
+    from imaginairy.img_utils import (
+        add_caption_to_image,
+        pillow_fit_image_within,
+        pillow_img_to_torch_image,
+        pillow_mask_to_latent_mask,
+    )
+    from imaginairy.log_utils import (
+        ImageLoggingContext,
+        log_img,
+        log_latent,
+    )
+    from imaginairy.model_manager import (
+        get_diffusion_model_refiners,
+        get_model_default_image_size,
+    )
+    from imaginairy.outpaint import outpaint_arg_str_parse, prepare_image_for_outpaint
+    from imaginairy.safety import create_safety_score
+    from imaginairy.samplers import SamplerName
+    from imaginairy.schema import ImaginePrompt, ImagineResult
+    from imaginairy.utils import get_device, randn_seeded
+
+    get_device()
+    gc.collect()
+    torch.cuda.empty_cache()
+    prompt = prompt.make_concrete_copy()
+
+    control_modes = []
+    control_inputs = prompt.control_inputs or []
+    control_inputs = control_inputs.copy()
+    for_inpainting = bool(prompt.mask_image or prompt.mask_prompt or prompt.outpaint)
+
+    if control_inputs:
+        control_modes = [c.mode for c in prompt.control_inputs]
+
+    sd = get_diffusion_model_refiners(
+        weights_location=prompt.model,
+        config_path=prompt.model_config_path,
+        control_weights_locations=tuple(control_modes),
+        half_mode=half_mode,
+        for_inpainting=for_inpainting and inpaint_method == "finetune",
+    )
+
+    seed_everything(prompt.seed)
+    downsampling_factor = 8
+    latent_channels = 4
+    batch_size = 1
+
+    mask_image = None
+    mask_image_orig = None
+    prompt = prompt.make_concrete_copy()
+
+    def latent_logger(latents):
+        progress_latents.append(latents)
+
+    with ImageLoggingContext(
+        prompt=prompt,
+        model=sd,
+        debug_img_callback=debug_img_callback,
+        progress_img_callback=progress_img_callback,
+        progress_img_interval_steps=progress_img_interval_steps,
+        progress_img_interval_min_s=progress_img_interval_min_s,
+        progress_latent_callback=latent_logger
+        if prompt.collect_progress_latents
+        else None,
+    ) as lc:
+        sd.set_tile_mode(prompt.tile_mode)
+
+        clip_text_embedding = _calc_conditioning(
+            positive_prompts=prompt.prompts,
+            negative_prompts=prompt.negative_prompt,
+            positive_conditioning=prompt.conditioning,
+            text_encoder=sd.clip_text_encoder,
+        )
+
+        result_images = {}
+        progress_latents = []
+        first_step = 0
+        mask_grayscale = None
+
+        shape = [
+            batch_size,
+            latent_channels,
+            prompt.height // downsampling_factor,
+            prompt.width // downsampling_factor,
+        ]
+
+        init_latent = None
+        if prompt.init_image:
+            starting_image = prompt.init_image
+            first_step = int((prompt.steps - 1) * prompt.init_image_strength)
+
+            if prompt.mask_prompt:
+                mask_image, mask_grayscale = get_img_mask(
+                    starting_image, prompt.mask_prompt, threshold=0.1
+                )
+            elif prompt.mask_image:
+                mask_image = prompt.mask_image.convert("L")
+
+            if prompt.outpaint:
+                outpaint_kwargs = outpaint_arg_str_parse(prompt.outpaint)
+                starting_image, mask_image = prepare_image_for_outpaint(
+                    starting_image, mask_image, **outpaint_kwargs
+                )
+
+            init_image = pillow_fit_image_within(
+                starting_image,
+                max_height=prompt.height,
+                max_width=prompt.width,
+            )
+            init_image_t = pillow_img_to_torch_image(init_image)
+            init_image_t = init_image_t.to(device=sd.device, dtype=sd.dtype)
+            init_latent = sd.lda.encode(init_image_t)
+            shape = init_latent.shape
+
+            log_latent(init_latent, "init_latent")
+
+            if mask_image is not None:
+                mask_image = pillow_fit_image_within(
+                    mask_image,
+                    max_height=prompt.height,
+                    max_width=prompt.width,
+                    convert="L",
+                )
+
+                log_img(mask_image, "init mask")
+
+                if prompt.mask_mode == ImaginePrompt.MaskMode.REPLACE:
+                    mask_image = ImageOps.invert(mask_image)
+
+                mask_image_orig = mask_image
+                log_img(mask_image, "latent_mask")
+                pillow_mask_to_latent_mask(
+                    mask_image, downsampling_factor=downsampling_factor
+                ).to(get_device())
+                # if inpaint_method == "controlnet":
+                #     result_images["control-inpaint"] = mask_image
+                #     control_inputs.append(
+                #         ControlNetInput(mode="inpaint", image=mask_image)
+                #     )
+
+        seed_everything(prompt.seed)
+
+        noise = randn_seeded(seed=prompt.seed, size=shape).to(
+            get_device(), dtype=sd.dtype
+        )
+        noised_latent = noise
+        controlnets = []
+
+        if control_modes:
+            control_strengths = []
+            from imaginairy.img_processors.control_modes import CONTROL_MODES
+
+            for control_input in control_inputs:
+                if control_input.image_raw is not None:
+                    control_image = control_input.image_raw
+                elif control_input.image is not None:
+                    control_image = control_input.image
+                control_image = control_image.convert("RGB")
+                log_img(control_image, "control_image_input")
+                control_image_input = pillow_fit_image_within(
+                    control_image,
+                    max_height=prompt.height,
+                    max_width=prompt.width,
+                )
+                control_image_input_t = pillow_img_to_torch_image(control_image_input)
+                control_image_input_t = control_image_input_t.to(get_device())
+
+                if control_input.image_raw is None:
+                    control_prep_function = CONTROL_MODES[control_input.mode]
+                    if control_input.mode == "inpaint":
+                        control_image_t = control_prep_function(
+                            control_image_input_t, init_image_t
+                        )
+                    else:
+                        control_image_t = control_prep_function(control_image_input_t)
+                else:
+                    control_image_t = (control_image_input_t + 1) / 2
+
+                control_image_disp = control_image_t * 2 - 1
+                result_images[f"control-{control_input.mode}"] = control_image_disp
+                log_img(control_image_disp, "control_image")
+
+                if len(control_image_t.shape) == 3:
+                    raise RuntimeError("Control image must be 4D")
+
+                if control_image_t.shape[1] != 3:
+                    raise RuntimeError("Control image must have 3 channels")
+
+                if (
+                    control_input.mode != "inpaint"
+                    and control_image_t.min() < 0
+                    or control_image_t.max() > 1
+                ):
+                    msg = f"Control image must be in [0, 1] but we received {control_image_t.min()} and {control_image_t.max()}"
+                    raise RuntimeError(msg)
+
+                if control_image_t.max() == control_image_t.min():
+                    msg = f"No control signal found in control image {control_input.mode}."
+                    raise RuntimeError(msg)
+
+                control_strengths.append(control_input.strength)
+
+                control_weights_path = CONTROLNET_CONFIG_SHORTCUTS.get(
+                    control_input.mode, None
+                ).weights_url
+
+                controlnet = load_controlnet_adapter(
+                    name=control_input.mode,
+                    control_weights_location=control_weights_path,
+                    target_unet=sd.unet,
+                    scale=control_input.strength,
+                )
+                controlnets.append((controlnet, control_image_t))
+
+        noise_step = None
+        if prompt.allow_compose_phase:
+            if prompt.init_image:
+                comp_image, comp_img_orig = _generate_composition_image(
+                    prompt=prompt,
+                    target_height=init_image.height,
+                    target_width=init_image.width,
+                    cutoff=get_model_default_image_size(prompt.model),
+                )
+            else:
+                comp_image, comp_img_orig = _generate_composition_image(
+                    prompt=prompt,
+                    target_height=prompt.height,
+                    target_width=prompt.width,
+                    cutoff=get_model_default_image_size(prompt.model),
+                )
+            if comp_image is not None:
+                result_images["composition"] = comp_img_orig
+                result_images["composition-upscaled"] = comp_image
+                # noise = noise[:, :, : comp_image.height, : comp_image.shape[3]]
+                comp_cutoff = 0.60
+                first_step = int((prompt.steps - 1) * comp_cutoff)
+                # noise_step = int(prompt.steps * max(comp_cutoff - 0.05, 0))
+                # noise_step = max(noise_step, 0)
+                # noise_step = min(noise_step, prompt.steps - 1)
+                log_img(comp_image, "comp_image")
+                comp_image_t = pillow_img_to_torch_image(comp_image)
+                comp_image_t = comp_image_t.to(sd.device, dtype=sd.dtype)
+                init_latent = sd.lda.encode(comp_image_t)
+        for controlnet, control_image_t in controlnets:
+            controlnet.set_controlnet_condition(
+                control_image_t.to(device=sd.device, dtype=sd.dtype)
+            )
+            controlnet.inject()
+        if prompt.sampler_type.lower() == SamplerName.K_DPMPP_2M:
+            sd.scheduler = DPMSolver(num_inference_steps=prompt.steps)
+        elif prompt.sampler_type.lower() == SamplerName.DDIM:
+            sd.scheduler = DDIM(num_inference_steps=prompt.steps)
+        else:
+            msg = f"Unknown sampler type: {prompt.sampler_type}"
+            raise ValueError(msg)
+        sd.scheduler.to(device=sd.device, dtype=sd.dtype)
+        sd.set_num_inference_steps(prompt.steps)
+        if hasattr(sd, "mask_latents"):
+            sd.set_inpainting_conditions(
+                target_image=init_image,
+                mask=ImageOps.invert(mask_image),
+                latents_size=shape[-2:],
+            )
+
+        if init_latent is not None:
+            print(
+                f"noise step: {noise_step} first step: {first_step} len steps: {len(sd.steps)}"
+            )
+            noise_step = noise_step if noise_step is not None else first_step
+            noised_latent = sd.scheduler.add_noise(
+                x=init_latent, noise=noise, step=sd.steps[noise_step]
+            )
+
+        x = noised_latent
+        x = x.to(device=sd.device, dtype=sd.dtype)
+
+        for step in tqdm(sd.steps[first_step:]):
+            log_latent(x, "noisy_latent")
+            x = sd(
+                x,
+                step=step,
+                clip_text_embedding=clip_text_embedding,
+                condition_scale=prompt.prompt_strength,
+            )
+
+        logger.debug("Decoding image")
+        gen_img = sd.lda.decode_latents(x)
+
+        if mask_image_orig and init_image:
+            result_images["pre-reconstitution"] = gen_img
+            mask_final = mask_image_orig.copy()
+            # mask_final = ImageOps.invert(mask_final)
+
+            log_img(mask_final, "reconstituting mask")
+            # gen_img = Image.composite(gen_img, init_image, mask_final)
+            gen_img = combine_image(
+                original_img=init_image,
+                generated_img=gen_img,
+                mask_img=mask_final,
+            )
+            log_img(gen_img, "reconstituted image")
+
+        upscaled_img = None
+        rebuilt_orig_img = None
+
+        if add_caption:
+            caption = generate_caption(gen_img)
+            logger.info(f"Generated caption: {caption}")
+
+        with lc.timing("safety-filter"):
+            safety_score = create_safety_score(
+                gen_img,
+                safety_mode=IMAGINAIRY_SAFETY_MODE,
+            )
+        if safety_score.is_filtered:
+            progress_latents.clear()
+        if not safety_score.is_filtered:
+            if prompt.fix_faces:
+                logger.info("Fixing 😊 's in 🖼  using CodeFormer...")
+                with lc.timing("face enhancement"):
+                    gen_img = enhance_faces(gen_img, fidelity=prompt.fix_faces_fidelity)
+            if prompt.upscale:
+                logger.info("Upscaling 🖼  using real-ESRGAN...")
+                with lc.timing("upscaling"):
+                    upscaled_img = upscale_image(gen_img)
+
+            # put the newly generated patch back into the original, full-size image
+            if prompt.mask_modify_original and mask_image_orig and starting_image:
+                logger.info("Combining inpainting with original image...")
+                img_to_add_back_to_original = upscaled_img if upscaled_img else gen_img
+                rebuilt_orig_img = combine_image(
+                    original_img=starting_image,
+                    generated_img=img_to_add_back_to_original,
+                    mask_img=mask_image_orig,
+                )
+
+            if prompt.caption_text:
+                caption_text = prompt.caption_text.format(prompt=prompt.prompt_text)
+                add_caption_to_image(gen_img, caption_text)
+
+        result = ImagineResult(
+            img=gen_img,
+            prompt=prompt,
+            upscaled_img=upscaled_img,
+            is_nsfw=safety_score.is_nsfw,
+            safety_score=safety_score,
+            modified_original=rebuilt_orig_img,
+            mask_binary=mask_image_orig,
+            mask_grayscale=mask_grayscale,
+            result_images=result_images,
+            timings={},
+            progress_latents=[],
+        )
+
+        _most_recent_result = result
+        logger.info(f"Image Generated. Timings: {result.timings_str()}")
+        for controlnet, _ in controlnets:
+            controlnet.eject()
+        gc.collect()
+        torch.cuda.empty_cache()
+        return result
+
+
+def _prompts_to_embeddings(prompts, text_encoder):
+    total_weight = sum(wp.weight for wp in prompts)
+    conditioning = sum(
+        text_encoder(wp.text) * (wp.weight / total_weight) for wp in prompts
+    )
+
+    return conditioning
+
+
+def _calc_conditioning(
+    positive_prompts: Optional[List[WeightedPrompt]],
+    negative_prompts: Optional[List[WeightedPrompt]],
+    positive_conditioning,
+    text_encoder,
+):
+    import torch
+
+    from imaginairy.log_utils import log_conditioning
+
+    # need to expand if doing batches
+    neutral_conditioning = _prompts_to_embeddings(negative_prompts, text_encoder)
+    log_conditioning(neutral_conditioning, "neutral conditioning")
+
+    if positive_conditioning is None:
+        positive_conditioning = _prompts_to_embeddings(positive_prompts, text_encoder)
+    log_conditioning(positive_conditioning, "positive conditioning")
+
+    clip_text_embedding = torch.cat(
+        tensors=(neutral_conditioning, positive_conditioning), dim=0
+    )
+    return clip_text_embedding

imaginairy/config.py

@@ -31,14 +31,14 @@ class ModelConfig:
 midas_url = "https://github.com/intel-isl/DPT/releases/download/1_0/dpt_hybrid-midas-501f0c75.pt"
 
 MODEL_CONFIGS = [
-    ModelConfig(
-        description="Stable Diffusion 1.4",
-        short_name="SD-1.4",
-        config_path="configs/stable-diffusion-v1.yaml",
-        weights_url="https://huggingface.co/bstddev/sd-v1-4/resolve/77221977fa8de8ab8f36fac0374c120bd5b53287/sd-v1-4.ckpt",
-        default_image_size=512,
-        alias="sd14",
-    ),
+    # ModelConfig(
+    #     description="Stable Diffusion 1.4",
+    #     short_name="SD-1.4",
+    #     config_path="configs/stable-diffusion-v1.yaml",
+    #     weights_url="https://huggingface.co/bstddev/sd-v1-4/resolve/77221977fa8de8ab8f36fac0374c120bd5b53287/sd-v1-4.ckpt",
+    #     default_image_size=512,
+    #     alias="sd14",
+    # ),
     ModelConfig(
         description="Stable Diffusion 1.5",
         short_name="SD-1.5",
@@ -56,72 +56,72 @@ MODEL_CONFIGS = [
         default_image_size=512,
         alias="sd15in",
     ),
-    ModelConfig(
-        description="Stable Diffusion 2.0 - bad at making people",
-        short_name="SD-2.0",
-        config_path="configs/stable-diffusion-v2-inference.yaml",
-        weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt",
-        default_image_size=512,
-        alias="sd20",
-    ),
-    ModelConfig(
-        description="Stable Diffusion 2.0 - Inpainting",
-        short_name="SD-2.0-inpaint",
-        config_path="configs/stable-diffusion-v2-inpainting-inference.yaml",
-        weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.ckpt",
-        default_image_size=512,
-        alias="sd20in",
-    ),
-    ModelConfig(
-        description="Stable Diffusion 2.0 v - 768x768 - bad at making people",
-        short_name="SD-2.0-v",
-        config_path="configs/stable-diffusion-v2-inference-v.yaml",
-        weights_url="https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt",
-        default_image_size=768,
-        alias="sd20v",
-    ),
-    ModelConfig(
-        description="Stable Diffusion 2.0 - Depth",
-        short_name="SD-2.0-depth",
-        config_path="configs/stable-diffusion-v2-midas-inference.yaml",
-        weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-depth/resolve/main/512-depth-ema.ckpt",
-        default_image_size=512,
-        alias="sd20dep",
-    ),
-    ModelConfig(
-        description="Stable Diffusion 2.1",
-        short_name="SD-2.1",
-        config_path="configs/stable-diffusion-v2-inference.yaml",
-        weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt",
-        default_image_size=512,
-        alias="sd21",
-    ),
-    ModelConfig(
-        description="Stable Diffusion 2.1 - Inpainting",
-        short_name="SD-2.1-inpaint",
-        config_path="configs/stable-diffusion-v2-inpainting-inference.yaml",
-        weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.ckpt",
-        default_image_size=512,
-        alias="sd21in",
-    ),
-    ModelConfig(
-        description="Stable Diffusion 2.1 v - 768x768",
-        short_name="SD-2.1-v",
-        config_path="configs/stable-diffusion-v2-inference-v.yaml",
-        weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt",
-        default_image_size=768,
-        forced_attn_precision="fp32",
-        alias="sd21v",
-    ),
-    ModelConfig(
-        description="Instruct Pix2Pix - Photo Editing",
-        short_name="instruct-pix2pix",
-        config_path="configs/instruct-pix2pix.yaml",
-        weights_url="https://huggingface.co/imaginairy/instruct-pix2pix/resolve/ea0009b3d0d4888f410a40bd06d69516d0b5a577/instruct-pix2pix-00-22000-pruned.ckpt",
-        default_image_size=512,
-        default_negative_prompt="",
-        alias="edit",
-    ),
+    # ModelConfig(
+    #     description="Stable Diffusion 2.0 - bad at making people",
+    #     short_name="SD-2.0",
+    #     config_path="configs/stable-diffusion-v2-inference.yaml",
+    #     weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt",
+    #     default_image_size=512,
+    #     alias="sd20",
+    # ),
+    # ModelConfig(
+    #     description="Stable Diffusion 2.0 - Inpainting",
+    #     short_name="SD-2.0-inpaint",
+    #     config_path="configs/stable-diffusion-v2-inpainting-inference.yaml",
+    #     weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.ckpt",
+    #     default_image_size=512,
+    #     alias="sd20in",
+    # ),
+    # ModelConfig(
+    #     description="Stable Diffusion 2.0 v - 768x768 - bad at making people",
+    #     short_name="SD-2.0-v",
+    #     config_path="configs/stable-diffusion-v2-inference-v.yaml",
+    #     weights_url="https://huggingface.co/stabilityai/stable-diffusion-2/resolve/main/768-v-ema.ckpt",
+    #     default_image_size=768,
+    #     alias="sd20v",
+    # ),
+    # ModelConfig(
+    #     description="Stable Diffusion 2.0 - Depth",
+    #     short_name="SD-2.0-depth",
+    #     config_path="configs/stable-diffusion-v2-midas-inference.yaml",
+    #     weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-depth/resolve/main/512-depth-ema.ckpt",
+    #     default_image_size=512,
+    #     alias="sd20dep",
+    # ),
+    # ModelConfig(
+    #     description="Stable Diffusion 2.1",
+    #     short_name="SD-2.1",
+    #     config_path="configs/stable-diffusion-v2-inference.yaml",
+    #     weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/v2-1_512-ema-pruned.ckpt",
+    #     default_image_size=512,
+    #     alias="sd21",
+    # ),
+    # ModelConfig(
+    #     description="Stable Diffusion 2.1 - Inpainting",
+    #     short_name="SD-2.1-inpaint",
+    #     config_path="configs/stable-diffusion-v2-inpainting-inference.yaml",
+    #     weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.ckpt",
+    #     default_image_size=512,
+    #     alias="sd21in",
+    # ),
+    # ModelConfig(
+    #     description="Stable Diffusion 2.1 v - 768x768",
+    #     short_name="SD-2.1-v",
+    #     config_path="configs/stable-diffusion-v2-inference-v.yaml",
+    #     weights_url="https://huggingface.co/stabilityai/stable-diffusion-2-1/resolve/main/v2-1_768-ema-pruned.ckpt",
+    #     default_image_size=768,
+    #     forced_attn_precision="fp32",
+    #     alias="sd21v",
+    # ),
+    # ModelConfig(
+    #     description="Instruct Pix2Pix - Photo Editing",
+    #     short_name="instruct-pix2pix",
+    #     config_path="configs/instruct-pix2pix.yaml",
+    #     weights_url="https://huggingface.co/imaginairy/instruct-pix2pix/resolve/ea0009b3d0d4888f410a40bd06d69516d0b5a577/instruct-pix2pix-00-22000-pruned.ckpt",
+    #     default_image_size=512,
+    #     default_negative_prompt="",
+    #     alias="edit",
+    # ),
     ModelConfig(
         description="OpenJourney V1",
         short_name="openjourney-v1",
@@ -176,14 +176,14 @@ CONTROLNET_CONFIGS = [
         short_name="canny15",
         control_type="canny",
         config_path="configs/control-net-v15.yaml",
-        weights_url="https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/69fc48b9cbd98661f6d0288dc59b59a5ccb32a6b/control_v11p_sd15_canny.pth",
+        weights_url="https://huggingface.co/lllyasviel/control_v11p_sd15_canny/resolve/115a470d547982438f70198e353a921996e2e819/diffusion_pytorch_model.fp16.safetensors",
         alias="canny",
     ),
     ControlNetConfig(
         short_name="depth15",
         control_type="depth",
         config_path="configs/control-net-v15.yaml",
-        weights_url="https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/69fc48b9cbd98661f6d0288dc59b59a5ccb32a6b/control_v11f1p_sd15_depth.pth",
+        weights_url="https://huggingface.co/lllyasviel/control_v11f1p_sd15_depth/resolve/539f99181d33db39cf1af2e517cd8056785f0a87/diffusion_pytorch_model.fp16.safetensors",
         alias="depth",
     ),
     ControlNetConfig(
@@ -233,7 +233,7 @@ CONTROLNET_CONFIGS = [
         short_name="details15",
         control_type="details",
         config_path="configs/control-net-v15.yaml",
-        weights_url="https://huggingface.co/lllyasviel/ControlNet-v1-1/resolve/69fc48b9cbd98661f6d0288dc59b59a5ccb32a6b/control_v11f1e_sd15_tile.pth",
+        weights_url="https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile/resolve/3f877705c37010b7221c3d10743307d6b5b6efac/diffusion_pytorch_model.bin",
         alias="details",
     ),
     ControlNetConfig(
@@ -254,16 +254,17 @@ for m in CONTROLNET_CONFIGS:
     CONTROLNET_CONFIG_SHORTCUTS[m.short_name] = m
 
 SAMPLER_TYPE_OPTIONS = [
-    "plms",
+    # "plms",
     "ddim",
-    "k_dpm_fast",
-    "k_dpm_adaptive",
-    "k_lms",
-    "k_dpm_2",
-    "k_dpm_2_a",
-    "k_dpmpp_2m",
-    "k_dpmpp_2s_a",
-    "k_euler",
-    "k_euler_a",
-    "k_heun",
+    "k_dpmpp_2m"
+    # "k_dpm_fast",
+    # "k_dpm_adaptive",
+    # "k_lms",
+    # "k_dpm_2",
+    # "k_dpm_2_a",
+    # "k_dpmpp_2m",
+    # "k_dpmpp_2s_a",
+    # "k_euler",
+    # "k_euler_a",
+    # "k_heun",
 ]

imaginairy/img_utils.py

@@ -121,7 +121,7 @@ def model_latent_to_pillow_img(latent: torch.Tensor) -> PIL.Image.Image:
     if latent.shape[0] != 1:
         raise ValueError("Only batch size 1 supported")
     model = get_current_diffusion_model()
-    img_t = model.decode_first_stage(latent)
+    img_t = model.lda.decode(latent)
     return torch_img_to_pillow_img(img_t)
 
 

imaginairy/model_manager.py

@@ -3,7 +3,7 @@ import os
 import re
 import sys
 import urllib.parse
-from functools import wraps
+from functools import lru_cache, wraps
 
 import requests
 import torch
@@ -13,6 +13,7 @@ from huggingface_hub import (
     try_to_load_from_cache,
 )
 from omegaconf import OmegaConf
+from refiners.foundationals.latent_diffusion import SD1ControlnetAdapter, SD1UNet
 from safetensors.torch import load_file
 
 from imaginairy import config as iconfig
@@ -21,6 +22,7 @@ from imaginairy.modules import attention
 from imaginairy.paths import PKG_ROOT
 from imaginairy.utils import get_device, instantiate_from_config
 from imaginairy.utils.model_cache import memory_managed_model
+from imaginairy.weight_management.conversion import cast_weights
 
 logger = logging.getLogger(__name__)
 
@@ -232,6 +234,150 @@ def _get_diffusion_model(
     return diffusion_model
 
 
+def get_diffusion_model_refiners(
+    weights_location=iconfig.DEFAULT_MODEL,
+    config_path="configs/stable-diffusion-v1.yaml",
+    control_weights_locations=None,
+    half_mode=None,
+    for_inpainting=False,
+    for_training=False,
+):
+    """
+    Load a diffusion model.
+
+    Weights location may also be shortcut name, e.g. "SD-1.5"
+    """
+    try:
+        return _get_diffusion_model_refiners(
+            weights_location,
+            config_path,
+            half_mode,
+            for_inpainting,
+            control_weights_locations=control_weights_locations,
+            for_training=for_training,
+        )
+    except HuggingFaceAuthorizationError as e:
+        if for_inpainting:
+            logger.warning(
+                f"Failed to load inpainting model. Attempting to fall-back to standard model.   {e!s}"
+            )
+            return _get_diffusion_model_refiners(
+                iconfig.DEFAULT_MODEL,
+                config_path,
+                half_mode,
+                for_inpainting=False,
+                for_training=for_training,
+                control_weights_locations=control_weights_locations,
+            )
+        raise
+
+
+def _get_diffusion_model_refiners(
+    weights_location=iconfig.DEFAULT_MODEL,
+    config_path="configs/stable-diffusion-v1.yaml",
+    half_mode=None,
+    for_inpainting=False,
+    for_training=False,
+    control_weights_locations=None,
+    device=None,
+    dtype=torch.float16,
+):
+    """
+    Load a diffusion model.
+
+    Weights location may also be shortcut name, e.g. "SD-1.5"
+    """
+
+    sd = _get_diffusion_model_refiners_only(
+        weights_location=weights_location,
+        config_path=config_path,
+        for_inpainting=for_inpainting,
+        for_training=for_training,
+        device=device,
+        dtype=dtype,
+    )
+
+    return sd
+
+
+@lru_cache(maxsize=1)
+def _get_diffusion_model_refiners_only(
+    weights_location=iconfig.DEFAULT_MODEL,
+    config_path="configs/stable-diffusion-v1.yaml",
+    for_inpainting=False,
+    for_training=False,
+    control_weights_locations=None,
+    device=None,
+    dtype=torch.float16,
+):
+    """
+    Load a diffusion model.
+
+    Weights location may also be shortcut name, e.g. "SD-1.5"
+    """
+    from imaginairy.modules.refiners_sd import (
+        SD1AutoencoderSliced,
+        StableDiffusion_1,
+        StableDiffusion_1_Inpainting,
+    )
+
+    global MOST_RECENTLY_LOADED_MODEL
+
+    device = device or get_device()
+
+    (
+        model_config,
+        weights_location,
+        config_path,
+        control_weights_locations,
+    ) = resolve_model_paths(
+        weights_path=weights_location,
+        config_path=config_path,
+        control_weights_paths=control_weights_locations,
+        for_inpainting=for_inpainting,
+        for_training=for_training,
+    )
+    # some models need the attention calculated in float32
+    if model_config is not None:
+        attention.ATTENTION_PRECISION_OVERRIDE = model_config.forced_attn_precision
+    else:
+        attention.ATTENTION_PRECISION_OVERRIDE = "default"
+
+    (
+        vae_weights,
+        unet_weights,
+        text_encoder_weights,
+    ) = load_stable_diffusion_compvis_weights(weights_location)
+
+    if for_inpainting:
+        unet = SD1UNet(in_channels=9)
+        StableDiffusionCls = StableDiffusion_1_Inpainting
+    else:
+        unet = SD1UNet(in_channels=4)
+        StableDiffusionCls = StableDiffusion_1
+    logger.debug(f"Using class {StableDiffusionCls.__name__}")
+
+    sd = StableDiffusionCls(
+        device=device, dtype=dtype, lda=SD1AutoencoderSliced(), unet=unet
+    )
+    logger.debug("Loading VAE")
+    sd.lda.load_state_dict(vae_weights)
+
+    logger.debug("Loading text encoder")
+    sd.clip_text_encoder.load_state_dict(text_encoder_weights)
+
+    logger.debug("Loading UNet")
+    sd.unet.load_state_dict(unet_weights, strict=False)
+
+    logger.debug(f"'{weights_location}' Loaded")
+
+    MOST_RECENTLY_LOADED_MODEL = sd
+
+    sd.set_self_attention_guidance(enable=True)
+
+    return sd
+
+
 @memory_managed_model("stable-diffusion", memory_usage_mb=1951)
 def _load_diffusion_model(config_path, weights_location, half_mode, for_training):
     model_config = OmegaConf.load(f"{PKG_ROOT}/{config_path}")
@@ -250,6 +396,35 @@ def _load_diffusion_model(config_path, weights_location, half_mode, for_training
     return model
 
 
+def load_controlnet_adapter(
+    name,
+    control_weights_location,
+    target_unet,
+    scale=1.0,
+    half_mode=False,
+):
+    controlnet_state_dict = load_state_dict(
+        control_weights_location, half_mode=half_mode
+    )
+    controlnet_state_dict = cast_weights(
+        source_weights=controlnet_state_dict,
+        source_model_name="controlnet-1-1",
+        source_component_name="all",
+        source_format="diffusers",
+        dest_format="refiners",
+    )
+
+    for key in controlnet_state_dict:
+        controlnet_state_dict[key] = controlnet_state_dict[key].to(
+            device=target_unet.device, dtype=target_unet.dtype
+        )
+    adapter = SD1ControlnetAdapter(
+        target=target_unet, name=name, scale=scale, weights=controlnet_state_dict
+    )
+
+    return adapter
+
+
 @memory_managed_model("controlnet")
 def load_controlnet(control_weights_location, half_mode):
     controlnet_state_dict = load_state_dict(
@@ -447,3 +622,164 @@ def extract_huggingface_repo_commit_file_from_url(url):
     filepath = "/".join(path_components[4:])
 
     return repo, commit_hash, filepath
+
+
+def download_diffusers_weights(repo, sub, filename):
+    from imaginairy.model_manager import get_cached_url_path
+
+    url = f"https://huggingface.co/{repo}/resolve/main/{sub}/{filename}"
+    return get_cached_url_path(url, category="weights")
+
+
+@lru_cache
+def load_stable_diffusion_diffusers_weights(diffusers_repo, device=None):
+    from imaginairy.utils import get_device
+    from imaginairy.weight_management.conversion import cast_weights
+    from imaginairy.weight_management.utils import (
+        COMPONENT_NAMES,
+        FORMAT_NAMES,
+        MODEL_NAMES,
+    )
+
+    if device is None:
+        device = get_device()
+    vae_weights_path = download_diffusers_weights(
+        repo=diffusers_repo, sub="vae", filename="diffusion_pytorch_model.safetensors"
+    )
+    vae_weights = open_weights(vae_weights_path, device=device)
+    vae_weights = cast_weights(
+        source_weights=vae_weights,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.VAE,
+        source_format=FORMAT_NAMES.DIFFUSERS,
+        dest_format=FORMAT_NAMES.REFINERS,
+    )
+
+    unet_weights_path = download_diffusers_weights(
+        repo=diffusers_repo, sub="unet", filename="diffusion_pytorch_model.safetensors"
+    )
+    unet_weights = open_weights(unet_weights_path, device=device)
+    unet_weights = cast_weights(
+        source_weights=unet_weights,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.UNET,
+        source_format=FORMAT_NAMES.DIFFUSERS,
+        dest_format=FORMAT_NAMES.REFINERS,
+    )
+
+    text_encoder_weights_path = download_diffusers_weights(
+        repo=diffusers_repo, sub="text_encoder", filename="model.safetensors"
+    )
+    text_encoder_weights = open_weights(text_encoder_weights_path, device=device)
+    text_encoder_weights = cast_weights(
+        source_weights=text_encoder_weights,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
+        source_format=FORMAT_NAMES.DIFFUSERS,
+        dest_format=FORMAT_NAMES.REFINERS,
+    )
+
+    return vae_weights, unet_weights, text_encoder_weights
+
+
+def open_weights(filepath, device=None):
+    from imaginairy.utils import get_device
+
+    if device is None:
+        device = get_device()
+
+    if "safetensor" in filepath.lower():
+        from refiners.fluxion.utils import safe_open
+
+        with safe_open(path=filepath, framework="pytorch", device=device) as tensors:
+            state_dict = {key: tensors.get_tensor(key) for key in tensors}
+    else:
+        import torch
+
+        state_dict = torch.load(filepath, map_location=device)
+
+    while "state_dict" in state_dict:
+        state_dict = state_dict["state_dict"]
+
+    return state_dict
+
+
+@lru_cache
+def load_stable_diffusion_compvis_weights(weights_url):
+    from imaginairy.model_manager import get_cached_url_path
+    from imaginairy.utils import get_device
+    from imaginairy.weight_management.conversion import cast_weights
+    from imaginairy.weight_management.utils import (
+        COMPONENT_NAMES,
+        FORMAT_NAMES,
+        MODEL_NAMES,
+    )
+
+    weights_path = get_cached_url_path(weights_url, category="weights")
+    logger.info(f"Loading weights from {weights_path}")
+    state_dict = open_weights(weights_path, device=get_device())
+
+    text_encoder_prefix = "cond_stage_model."
+    cut_start = len(text_encoder_prefix)
+    text_encoder_state_dict = {
+        k[cut_start:]: v
+        for k, v in state_dict.items()
+        if k.startswith(text_encoder_prefix)
+    }
+    text_encoder_state_dict = cast_weights(
+        source_weights=text_encoder_state_dict,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
+        source_format=FORMAT_NAMES.COMPVIS,
+        dest_format=FORMAT_NAMES.DIFFUSERS,
+    )
+    text_encoder_state_dict = cast_weights(
+        source_weights=text_encoder_state_dict,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.TEXT_ENCODER,
+        source_format=FORMAT_NAMES.DIFFUSERS,
+        dest_format=FORMAT_NAMES.REFINERS,
+    )
+
+    vae_prefix = "first_stage_model."
+    cut_start = len(vae_prefix)
+    vae_state_dict = {
+        k[cut_start:]: v for k, v in state_dict.items() if k.startswith(vae_prefix)
+    }
+    vae_state_dict = cast_weights(
+        source_weights=vae_state_dict,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.VAE,
+        source_format=FORMAT_NAMES.COMPVIS,
+        dest_format=FORMAT_NAMES.DIFFUSERS,
+    )
+    vae_state_dict = cast_weights(
+        source_weights=vae_state_dict,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.VAE,
+        source_format=FORMAT_NAMES.DIFFUSERS,
+        dest_format=FORMAT_NAMES.REFINERS,
+    )
+
+    unet_prefix = "model."
+    cut_start = len(unet_prefix)
+    unet_state_dict = {
+        k[cut_start:]: v for k, v in state_dict.items() if k.startswith(unet_prefix)
+    }
+    unet_state_dict = cast_weights(
+        source_weights=unet_state_dict,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.UNET,
+        source_format=FORMAT_NAMES.COMPVIS,
+        dest_format=FORMAT_NAMES.DIFFUSERS,
+    )
+
+    unet_state_dict = cast_weights(
+        source_weights=unet_state_dict,
+        source_model_name=MODEL_NAMES.SD15,
+        source_component_name=COMPONENT_NAMES.UNET,
+        source_format=FORMAT_NAMES.DIFFUSERS,
+        dest_format=FORMAT_NAMES.REFINERS,
+    )
+
+    return vae_state_dict, unet_state_dict, text_encoder_state_dict

imaginairy/modules/refiners_sd.py

@@ -0,0 +1,184 @@
+import math
+from typing import Literal
+
+import torch
+from refiners.fluxion.layers.chain import ChainError
+from refiners.foundationals.latent_diffusion import (
+    StableDiffusion_1 as RefinerStableDiffusion_1,
+    StableDiffusion_1_Inpainting as RefinerStableDiffusion_1_Inpainting,
+)
+from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
+    SD1Autoencoder,
+)
+from torch import Tensor, nn
+from torch.nn import functional as F
+from torch.nn.modules.utils import _pair
+
+from imaginairy.feather_tile import rebuild_image, tile_image
+from imaginairy.modules.autoencoder import logger
+
+TileModeType = Literal["", "x", "y", "xy"]
+
+
+def _tile_mode_conv2d_conv_forward(
+    self, input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor  # noqa
+):
+    if self.padding_modeX == self.padding_modeY:
+        self.padding_mode = self.padding_modeX
+        return self._orig_conv_forward(input, weight, bias)
+
+    w1 = F.pad(input, self.paddingX, mode=self.padding_modeX)
+    del input
+
+    w2 = F.pad(w1, self.paddingY, mode=self.padding_modeY)
+    del w1
+
+    return F.conv2d(w2, weight, bias, self.stride, _pair(0), self.dilation, self.groups)
+
+
+class TileModeMixin(nn.Module):
+    def set_tile_mode(self, tile_mode: TileModeType = ""):
+        """
+        For creating seamless tile images.
+
+        Args:
+            tile_mode: One of "", "x", "y", "xy". If "x", the image will be tiled horizontally. If "y", the image will be
+                tiled vertically. If "xy", the image will be tiled both horizontally and vertically.
+        """
+
+        tile_x = "x" in tile_mode
+        tile_y = "y" in tile_mode
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if not hasattr(m, "_orig_conv_forward"):
+                    # patch with a function that can handle tiling in a single direction
+                    m._initial_padding_mode = m.padding_mode
+                    m._orig_conv_forward = m._conv_forward
+                    m._conv_forward = _tile_mode_conv2d_conv_forward.__get__(
+                        m, nn.Conv2d
+                    )
+                m.padding_modeX = "circular" if tile_x else "constant"
+                m.padding_modeY = "circular" if tile_y else "constant"
+                if m.padding_modeY == m.padding_modeX:
+                    m.padding_mode = m.padding_modeX
+                m.paddingX = (
+                    m._reversed_padding_repeated_twice[0],
+                    m._reversed_padding_repeated_twice[1],
+                    0,
+                    0,
+                )
+                m.paddingY = (
+                    0,
+                    0,
+                    m._reversed_padding_repeated_twice[2],
+                    m._reversed_padding_repeated_twice[3],
+                )
+
+
+class StableDiffusion_1(TileModeMixin, RefinerStableDiffusion_1):
+    pass
+
+
+class StableDiffusion_1_Inpainting(TileModeMixin, RefinerStableDiffusion_1_Inpainting):
+    def compute_self_attention_guidance(
+        self,
+        x: Tensor,
+        noise: Tensor,
+        step: int,
+        *,
+        clip_text_embedding: Tensor,
+        **kwargs: Tensor,
+    ) -> Tensor:
+        sag = self._find_sag_adapter()
+        assert sag is not None
+        assert self.mask_latents is not None
+        assert self.target_image_latents is not None
+
+        degraded_latents = sag.compute_degraded_latents(
+            scheduler=self.scheduler,
+            latents=x,
+            noise=noise,
+            step=step,
+            classifier_free_guidance=True,
+        )
+
+        negative_embedding, _ = clip_text_embedding.chunk(2)
+        timestep = self.scheduler.timesteps[step].unsqueeze(dim=0)
+        self.set_unet_context(
+            timestep=timestep, clip_text_embedding=negative_embedding, **kwargs
+        )
+        x = torch.cat(
+            tensors=(degraded_latents, self.mask_latents, self.target_image_latents),
+            dim=1,
+        )
+        degraded_noise = self.unet(x)
+
+        return sag.scale * (noise - degraded_noise)
+
+
+class SD1AutoencoderSliced(SD1Autoencoder):
+    max_chunk_size = 2048
+    min_chunk_size = 64
+
+    def decode(self, x):
+        while self.__class__.max_chunk_size > self.__class__.min_chunk_size:
+            if self.max_chunk_size**2 > x.shape[2] * x.shape[3]:
+                try:
+                    return self.decode_all_at_once(x)
+                except ChainError as e:
+                    if "OutOfMemoryError" not in str(e):
+                        raise
+                    self.__class__.max_chunk_size = (
+                        int(math.sqrt(x.shape[2] * x.shape[3])) // 2
+                    )
+                    logger.info(
+                        f"Ran out of memory. Trying tiled decode with chunk size {self.__class__.max_chunk_size}"
+                    )
+            else:
+                try:
+                    return self.decode_sliced(x, chunk_size=self.max_chunk_size)
+                except ChainError as e:
+                    if "OutOfMemoryError" not in str(e):
+                        raise
+                    self.__class__.max_chunk_size = self.max_chunk_size // 2
+                    self.__class__.max_chunk_size = max(
+                        self.__class__.max_chunk_size, self.__class__.min_chunk_size
+                    )
+                    logger.info(
+                        f"Ran out of memory. Trying tiled decode with chunk size {self.__class__.max_chunk_size}"
+                    )
+        raise RuntimeError("Could not decode image")
+
+    def decode_all_at_once(self, x: Tensor) -> Tensor:
+        decoder = self[1]
+        x = decoder(x / self.encoder_scale)
+        return x
+
+    def decode_sliced(self, x, chunk_size=128):
+        """
+        decodes the tensor in slices.
+
+        This results in image portions that don't exactly match, so we overlap, feather, and merge to reduce
+        (but not completely eliminate) impact.
+        """
+        b, c, h, w = x.size()
+        final_tensor = torch.zeros([1, 3, h * 8, w * 8], device=x.device)
+        for x_latent in x.split(1):
+            decoded_chunks = []
+            overlap_pct = 0.5
+            chunks = tile_image(
+                x_latent, tile_size=chunk_size, overlap_percent=overlap_pct
+            )
+
+            for latent_chunk in chunks:
+                # latent_chunk = self.post_quant_conv(latent_chunk)
+                dec = self.decode_all_at_once(latent_chunk)
+                decoded_chunks.append(dec)
+            final_tensor = rebuild_image(
+                decoded_chunks,
+                base_img=final_tensor,
+                tile_size=chunk_size * 8,
+                overlap_percent=overlap_pct,
+            )
+
+            return final_tensor

imaginairy/samplers/kdiff.py

@@ -64,7 +64,7 @@ class KDiffusionSampler(ImageSampler, ABC):
         super().__init__(model)
         denoiseer_cls = (
             StandardCompVisVDenoiser
-            if model.parameterization == "v"
+            if getattr(model, "parameterization", "") == "v"
             else StandardCompVisDenoiser
         )
         self.cv_denoiser = denoiseer_cls(model)

imaginairy/utils/__init__.py

@@ -21,7 +21,7 @@ def get_device() -> str:
         return "cuda"
 
     if torch.backends.mps.is_available():
-        return "mps:0"
+        return "mps"
 
     return "cpu"
 
@@ -250,5 +250,5 @@ def check_torch_version():
     """
     from packaging import version
 
-    if version.parse(torch.__version__) >= version.parse("2.0.0"):
-        raise RuntimeError("ImaginAIry is not compatible with torch>=2.0.0")
+    if version.parse(torch.__version__) < version.parse("2.0.0"):
+        raise RuntimeError("ImaginAIry is not compatible with torch<2.0.0")

imaginairy/weight_management/conversion.py

@@ -0,0 +1,113 @@
+import os.path
+from dataclasses import dataclass
+from functools import lru_cache
+from typing import TYPE_CHECKING
+
+from imaginairy.weight_management import utils
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+
+@dataclass
+class WeightMap:
+    model_name: str
+    component_name: str
+    source_format: str
+    dest_format: str
+
+    def __post_init__(self):
+        self.model_name = self.model_name.replace("_", "-")
+        self.component_name = self.component_name.replace("_", "-")
+        self.source_format = self.source_format.replace("_", "-")
+        self.dest_format = self.dest_format.replace("_", "-")
+
+        self._loaded_mapping_info = None
+
+    @property
+    def filename(self):
+        return f"{self.model_name}_{self.component_name}_{self.source_format}_TO_{self.dest_format}.json"
+
+    @property
+    def filepath(self):
+        return os.path.join(utils.WEIGHT_MAPS_PATH, self.filename)
+
+    @property
+    def _mapping_info(self):
+        if self._loaded_mapping_info is None:
+            import json
+
+            with open(self.filepath) as f:
+                self._loaded_mapping_info = json.load(f)
+        return self._loaded_mapping_info
+
+    @property
+    def mapping(self):
+        return self._mapping_info["mapping"]
+
+    @property
+    def source_aliases(self):
+        return self._mapping_info.get("source_aliases", {})
+
+    @property
+    def ignorable_prefixes(self):
+        return self._mapping_info.get("ignorable_prefixes", [])
+
+    @property
+    def reshapes(self):
+        return self._mapping_info.get("reshapes", {})
+
+    @property
+    def all_valid_prefixes(self):
+        return (
+            set(self.mapping.keys())
+            | set(self.source_aliases.keys())
+            | set(self.ignorable_prefixes)
+        )
+
+    def could_convert(self, source_weights):
+        source_keys = set(source_weights.keys())
+        return source_keys.issubset(self.all_valid_prefixes)
+
+    def cast_weights(self, source_weights):
+        converted_state_dict: dict[str, Tensor] = {}
+        for source_key in source_weights:
+            source_prefix, suffix = source_key.rsplit(sep=".", maxsplit=1)
+            # handle aliases
+            source_prefix = self.source_aliases.get(source_prefix, source_prefix)
+            try:
+                target_prefix = self.mapping[source_prefix]
+            except KeyError:
+                continue
+            target_key = ".".join([target_prefix, suffix])
+            converted_state_dict[target_key] = source_weights[source_key]
+
+        for key, new_shape in self.reshapes.items():
+            converted_state_dict[key] = converted_state_dict[key].reshape(new_shape)
+
+        return converted_state_dict
+
+
+@lru_cache(maxsize=None)
+def load_state_dict_conversion_maps():
+    import json
+
+    conversion_maps = {}
+    from importlib.resources import files
+
+    for file in files("imaginairy").joinpath("weight_conversion/maps").iterdir():
+        if file.is_file() and file.suffix == ".json":
+            conversion_maps[file.name] = json.loads(file.read_text())
+    return conversion_maps
+
+
+def cast_weights(
+    source_weights, source_model_name, source_component_name, source_format, dest_format
+):
+    weight_map = WeightMap(
+        model_name=source_model_name,
+        component_name=source_component_name,
+        source_format=source_format,
+        dest_format=dest_format,
+    )
+    return weight_map.cast_weights(source_weights)

imaginairy/weight_management/execution_trace.py

@@ -0,0 +1,283 @@
+import torch
+from transformers import CLIPTextModelWithProjection
+
+from imaginairy.model_manager import get_diffusion_model
+from imaginairy.utils import get_device
+from imaginairy.weight_management import utils
+
+
+def trace_execution_order(module, args, func_name=None):
+    """
+    Trace the execution order of a torch module and store full hierarchical state_dict paths.
+    :param module: The module to trace.
+    :param args: The arguments to pass to the module.
+    :return: A list of full hierarchical state_dict paths in the order they were used.
+    """
+    execution_order = []
+
+    hooks = []
+
+    def add_hooks(module, prefix=""):
+        for name, submodule in module.named_children():
+            # Construct the hierarchical name
+            module_full_name = f"{prefix}.{name}" if prefix else name
+
+            def log(mod, inp, out, module_full_name=module_full_name):
+                hook(mod, module_full_name)
+
+            hooks.append(submodule.register_forward_hook(log))
+
+            # Recursively add hooks to all child modules
+            add_hooks(submodule, module_full_name)
+
+    def hook(module, module_full_name):
+        # Retrieve state_dict and iterate over its items to get full paths
+        for name, param in module.named_parameters(recurse=False):
+            full_path = f"{module_full_name}.{name}"
+            execution_order.append(full_path)
+        for name, buffer in module.named_buffers(recurse=False):
+            print(name)
+            full_path = f"{module_full_name}.{name}"
+            execution_order.append(full_path)
+
+    # Initialize hooks
+    add_hooks(module)
+
+    # Execute the module
+    with torch.no_grad():
+        if func_name is not None:
+            getattr(module, func_name)(*args)
+        else:
+            module(*args)
+
+    # Remove hooks
+    for hook in hooks:
+        hook.remove()
+
+    return execution_order
+
+
+def trace_compvis_execution_order(device=None):
+    model = get_diffusion_model()._mmmw_load_model()
+
+    # vae
+    image_size = 256
+    img_in = torch.randn(1, 3, image_size, image_size).to(get_device())
+    vae_execution_order = trace_execution_order(
+        model.first_stage_model, (img_in,), func_name="encode_all_at_once"
+    )
+
+    latent_in = torch.randn(1, 4, 32, 32).to(get_device())
+    vae_execution_order.extend(
+        trace_execution_order(model.first_stage_model, (latent_in,), func_name="decode")
+    )
+    # text encoder model
+    text = "hello"
+    text_execution_order = trace_execution_order(model.cond_stage_model, (text,))
+
+    # unet
+    latent_in = torch.randn(1, 4, 32, 32).to(get_device())
+    text_embedding = [torch.randn(1, 77, 768).to(get_device())]
+    timestep = torch.tensor(data=[0]).to(get_device())
+    unet_execution_order = trace_execution_order(
+        model.model, (latent_in, timestep, text_embedding, text_embedding)
+    )
+
+    return vae_execution_order, text_execution_order, unet_execution_order
+
+
+def trace_sd15_diffusers_execution_order(device=None):
+    from diffusers import AutoencoderKL, UNet2DConditionModel
+
+    if device is None:
+        device = get_device()
+
+    # vae
+    image_size = 256
+    img_in = torch.randn(1, 3, image_size, image_size).to(device)
+    vae = AutoencoderKL.from_pretrained(
+        pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5", subfolder="vae"
+    ).to(device)
+    vae_execution_order = trace_execution_order(vae, (img_in,))
+
+    # text encoder model
+
+    text_encoder = CLIPTextModelWithProjection.from_pretrained(
+        pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5",
+        subfolder="text_encoder",
+    ).to(device)
+    tokens = torch.Tensor(
+        [
+            [
+                49406,
+                3306,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+                49407,
+            ]
+        ]
+    )
+    tokens = tokens.to(device).to(torch.int64)
+
+    text_execution_order = trace_execution_order(text_encoder, (tokens,))
+
+    # unet
+    latent_in = torch.randn(1, 4, 32, 32).to(device)
+    text_embedding = torch.randn(1, 77, 768).to(device)
+    timestep = torch.tensor(data=[0]).to(device)
+    unet = UNet2DConditionModel.from_pretrained(
+        pretrained_model_name_or_path="runwayml/stable-diffusion-v1-5", subfolder="unet"
+    ).to(device)
+    unet_execution_order = trace_execution_order(
+        unet, (latent_in, timestep, text_embedding)
+    )
+
+    return vae_execution_order, text_execution_order, unet_execution_order
+
+
+def calc_and_save_compvis_traces():
+    model_name = "stable-diffusion-1-5"
+    format_name = "compvis"
+
+    (
+        vae_execution_order,
+        text_execution_order,
+        unet_execution_order,
+    ) = trace_compvis_execution_order()
+
+    process_execution_order(
+        model_name=model_name,
+        format_name=format_name,
+        component_name="vae",
+        execution_order=vae_execution_order,
+    )
+    process_execution_order(
+        model_name=model_name,
+        format_name=format_name,
+        component_name="text",
+        execution_order=text_execution_order,
+    )
+    process_execution_order(
+        model_name=model_name,
+        format_name=format_name,
+        component_name="unet",
+        execution_order=unet_execution_order,
+    )
+
+
+def calc_and_save_sd15_diffusers_traces():
+    model_name = "stable-diffusion-1-5"
+    format_name = "diffusers"
+
+    (
+        vae_execution_order,
+        text_execution_order,
+        unet_execution_order,
+    ) = trace_sd15_diffusers_execution_order()
+
+    process_execution_order(
+        model_name=model_name,
+        format_name=format_name,
+        component_name="vae",
+        execution_order=vae_execution_order,
+    )
+    process_execution_order(
+        model_name=model_name,
+        format_name=format_name,
+        component_name="text",
+        execution_order=text_execution_order,
+    )
+    process_execution_order(
+        model_name=model_name,
+        format_name=format_name,
+        component_name="unet",
+        execution_order=unet_execution_order,
+    )
+
+
+def process_execution_order(model_name, component_name, format_name, execution_order):
+    prefixes = utils.prefixes_only(execution_order)
+    utils.save_model_info(
+        model_name,
+        component_name,
+        format_name,
+        "prefix-execution-order",
+        prefixes,
+    )
+
+
+if __name__ == "__main__":
+    calc_and_save_sd15_diffusers_traces()
+    calc_and_save_compvis_traces()

imaginairy/weight_management/generate_conversion_maps.py

@@ -0,0 +1,57 @@
+import itertools
+import json
+import os
+from collections import defaultdict
+
+from imaginairy.weight_management.utils import WEIGHT_INFO_PATH, WEIGHT_MAPS_PATH
+
+
+def generate_conversion_maps():
+    execution_orders_map = defaultdict(dict)
+    for filename in os.listdir(WEIGHT_INFO_PATH):
+        if not filename.endswith("prefix-execution-order.json"):
+            continue
+
+        base_name = filename.split(".", 1)[0]
+        model_name, component_name, format_name = base_name.split("_")
+        execution_orders_map[(model_name, component_name)][format_name] = filename
+
+    for (model_name, component_name), format_lookup in execution_orders_map.items():
+        if len(format_lookup) <= 1:
+            continue
+
+        formats = list(format_lookup.keys())
+        for format_a, format_b in itertools.permutations(formats, 2):
+            filename_a = format_lookup[format_a]
+            filename_b = format_lookup[format_b]
+            with open(os.path.join(WEIGHT_INFO_PATH, filename_a)) as f:
+                execution_order_a = json.load(f)
+            with open(os.path.join(WEIGHT_INFO_PATH, filename_b)) as f:
+                execution_order_b = json.load(f)
+
+            mapping_filename = (
+                f"{model_name}_{component_name}_{format_a}_TO_{format_b}.json"
+            )
+            mapping_filepath = os.path.join(WEIGHT_MAPS_PATH, mapping_filename)
+            print(f"Creating {mapping_filename}...")
+            if os.path.exists(mapping_filepath):
+                continue
+
+            if len(execution_order_a) != len(execution_order_b):
+                print(
+                    f"Could not create {mapping_filename} - Execution orders for {format_a} and {format_b} have different lengths"
+                )
+                continue
+
+            mapping = dict(zip(execution_order_a, execution_order_b))
+            mapping_info = {
+                "mapping": mapping,
+                "source_aliases": {},
+                "ignorable_prefixes": [],
+            }
+            with open(mapping_filepath, "w") as f:
+                json.dump(mapping_info, f, indent=2)
+
+
+if __name__ == "__main__":
+    generate_conversion_maps()

imaginairy/weight_management/generate_weight_info.py

@@ -0,0 +1,137 @@
+import safetensors
+
+from imaginairy.model_manager import (
+    get_cached_url_path,
+    open_weights,
+    resolve_model_paths,
+)
+from imaginairy.weight_management import utils
+from imaginairy.weight_management.pattern_collapse import find_state_dict_key_patterns
+from imaginairy.weight_management.utils import save_model_info
+
+
+def save_compvis_patterns():
+    (
+        model_metadata,
+        weights_url,
+        config_path,
+        control_weights_paths,
+    ) = resolve_model_paths(
+        weights_path="openjourney-v1",
+    )
+    weights_path = get_cached_url_path(weights_url, category="weights")
+
+    with safetensors.safe_open(weights_path, "pytorch") as f:
+        weights_keys = f.keys()
+
+    text_encoder_prefix = "cond_stage_model.transformer.text_model"
+    text_encoder_keys = [k for k in weights_keys if k.startswith(text_encoder_prefix)]
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.TEXT_ENCODER,
+        format_name=utils.FORMAT_NAMES.COMPVIS,
+        weights_keys=text_encoder_keys,
+    )
+
+    vae_prefix = "first_stage_model"
+    vae_keys = [k for k in weights_keys if k.startswith(vae_prefix)]
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.VAE,
+        format_name=utils.FORMAT_NAMES.COMPVIS,
+        weights_keys=vae_keys,
+    )
+
+    unet_prefix = "model.diffusion_model"
+    unet_keys = [k for k in weights_keys if k.startswith(unet_prefix)]
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.UNET,
+        format_name=utils.FORMAT_NAMES.COMPVIS,
+        weights_keys=unet_keys,
+    )
+
+
+def save_diffusers_patterns():
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.VAE,
+        format_name=utils.FORMAT_NAMES.DIFFUSERS,
+        weights_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/vae/diffusion_pytorch_model.fp16.safetensors",
+    )
+
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.UNET,
+        format_name=utils.FORMAT_NAMES.DIFFUSERS,
+        weights_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/unet/diffusion_pytorch_model.fp16.safetensors",
+    )
+
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.TEXT_ENCODER,
+        format_name=utils.FORMAT_NAMES.DIFFUSERS,
+        weights_url="https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/text_encoder/model.fp16.safetensors",
+    )
+
+
+def save_lora_patterns():
+    filepath = "/Users/bryce/projects/sandbox-img-gen/refiners/weights/pytorch_lora_weights-refiners.safetensors"
+    state_dict = open_weights(filepath, device="cpu")
+
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.LORA,
+        format_name=utils.FORMAT_NAMES.REFINERS,
+        weights_keys=list(state_dict.keys()),
+    )
+
+    save_weight_info(
+        model_name=utils.MODEL_NAMES.SD15,
+        component_name=utils.COMPONENT_NAMES.LORA,
+        format_name=utils.FORMAT_NAMES.DIFFUSERS,
+        weights_url="https://huggingface.co/pcuenq/pokemon-lora/resolve/main/pytorch_lora_weights.bin",
+    )
+
+
+def save_weight_info(
+    model_name, component_name, format_name, weights_url=None, weights_keys=None
+):
+    if weights_keys is None and weights_url is None:
+        msg = "Either weights_keys or weights_url must be provided"
+        raise ValueError(msg)
+
+    if weights_keys is None:
+        weights_path = get_cached_url_path(weights_url, category="weights")
+
+        state_dict = open_weights(weights_path, device="cpu")
+        weights_keys = list(state_dict.keys())
+
+    # prefixes = utils.prefixes_only(weights_keys)
+
+    save_model_info(
+        model_name=model_name,
+        component_name=component_name,
+        format_name=format_name,
+        info_type="weights_keys",
+        data=weights_keys,
+    )
+
+    patterns = find_state_dict_key_patterns(weights_keys)
+    save_model_info(
+        model_name=model_name,
+        component_name=component_name,
+        format_name=format_name,
+        info_type="patterns",
+        data=patterns,
+    )
+
+
+def save_patterns():
+    save_lora_patterns()
+    # save_compvis_patterns()
+    # save_diffusers_patterns()
+
+
+if __name__ == "__main__":
+    save_patterns()

imaginairy/weight_management/maps/controlnet-1-1_all_diffusers_TO_refiners.json

@@ -0,0 +1,195 @@
+{
+  "mapping": {
+    "time_embedding.linear_1": "TimestepEncoder.RangeEncoder.Linear_1",
+    "time_embedding.linear_2": "TimestepEncoder.RangeEncoder.Linear_2",
+    "down_blocks.2.resnets.0.time_emb_proj": "DownBlocks.Chain_8.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "down_blocks.2.attentions.0.transformer_blocks.0.attn1.to_out.0": "DownBlocks.Chain_8.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Linear",
+    "down_blocks.2.attentions.0.transformer_blocks.0.attn2.to_out.0": "DownBlocks.Chain_8.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Linear",
+    "down_blocks.2.resnets.1.time_emb_proj": "DownBlocks.Chain_9.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "down_blocks.2.attentions.1.transformer_blocks.0.attn1.to_out.0": "DownBlocks.Chain_9.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Linear",
+    "down_blocks.2.attentions.1.transformer_blocks.0.attn2.to_out.0": "DownBlocks.Chain_9.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Linear",
+    "down_blocks.3.resnets.0.time_emb_proj": "DownBlocks.Chain_11.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "down_blocks.3.resnets.1.time_emb_proj": "DownBlocks.Chain_12.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "mid_block.resnets.0.time_emb_proj": "MiddleBlock.ResidualBlock_1.Chain.RangeAdapter2d.Chain.Linear",
+    "mid_block.attentions.0.transformer_blocks.0.attn1.to_out.0": "MiddleBlock.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Linear",
+    "mid_block.attentions.0.transformer_blocks.0.attn2.to_out.0": "MiddleBlock.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Linear",
+    "mid_block.resnets.1.time_emb_proj": "MiddleBlock.ResidualBlock_2.Chain.RangeAdapter2d.Chain.Linear",
+    "conv_in": "DownBlocks.Chain_1.Conv2d",
+    "controlnet_cond_embedding.conv_in": "DownBlocks.Chain_1.Residual.ConditionEncoder.Chain_1.Conv2d",
+    "controlnet_cond_embedding.blocks.0": "DownBlocks.Chain_1.Residual.ConditionEncoder.Chain_2.Conv2d_1",
+    "controlnet_cond_embedding.blocks.1": "DownBlocks.Chain_1.Residual.ConditionEncoder.Chain_2.Conv2d_2",
+    "controlnet_cond_embedding.blocks.2": "DownBlocks.Chain_1.Residual.ConditionEncoder.Chain_3.Conv2d_1",
+    "controlnet_cond_embedding.blocks.3": "DownBlocks.Chain_1.Residual.ConditionEncoder.Chain_3.Conv2d_2",
+    "controlnet_cond_embedding.blocks.4": "DownBlocks.Chain_1.Residual.ConditionEncoder.Chain_4.Conv2d_1",
+    "controlnet_cond_embedding.blocks.5": "DownBlocks.Chain_1.Residual.ConditionEncoder.Chain_4.Conv2d_2",
+    "controlnet_cond_embedding.conv_out": "DownBlocks.Chain_1.Residual.ConditionEncoder.Conv2d",
+    "down_blocks.0.resnets.0.norm1": "DownBlocks.Chain_2.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.0.resnets.0.norm2": "DownBlocks.Chain_2.ResidualBlock.Chain.GroupNorm_2",
+    "down_blocks.0.attentions.0.norm": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_1.GroupNorm",
+    "down_blocks.0.resnets.1.norm1": "DownBlocks.Chain_3.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.0.resnets.1.norm2": "DownBlocks.Chain_3.ResidualBlock.Chain.GroupNorm_2",
+    "down_blocks.0.attentions.1.norm": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_1.GroupNorm",
+    "down_blocks.1.resnets.0.norm1": "DownBlocks.Chain_5.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.0.resnets.0.conv1": "DownBlocks.Chain_2.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.0.resnets.0.conv2": "DownBlocks.Chain_2.ResidualBlock.Chain.Conv2d",
+    "down_blocks.0.resnets.1.conv1": "DownBlocks.Chain_3.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.0.resnets.1.conv2": "DownBlocks.Chain_3.ResidualBlock.Chain.Conv2d",
+    "down_blocks.0.downsamplers.0.conv": "DownBlocks.Chain_4.Downsample.Conv2d",
+    "down_blocks.0.resnets.0.time_emb_proj": "DownBlocks.Chain_2.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.2": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_2",
+    "down_blocks.0.resnets.1.time_emb_proj": "DownBlocks.Chain_3.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "down_blocks.0.attentions.1.transformer_blocks.0.ff.net.2": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_2",
+    "controlnet_down_blocks.0": "DownBlocks.Chain_1.Passthrough.Conv2d",
+    "down_blocks.0.attentions.0.proj_in": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_1.Conv2d",
+    "down_blocks.0.attentions.0.proj_out": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_3.Conv2d",
+    "controlnet_down_blocks.1": "DownBlocks.Chain_2.Passthrough.Conv2d",
+    "down_blocks.0.attentions.1.proj_in": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_1.Conv2d",
+    "down_blocks.0.attentions.1.proj_out": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_3.Conv2d",
+    "controlnet_down_blocks.2": "DownBlocks.Chain_3.Passthrough.Conv2d",
+    "controlnet_down_blocks.3": "DownBlocks.Chain_4.Passthrough.Conv2d",
+    "down_blocks.0.attentions.0.transformer_blocks.0.norm1": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.LayerNorm",
+    "down_blocks.0.attentions.0.transformer_blocks.0.norm2": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.LayerNorm",
+    "down_blocks.0.attentions.0.transformer_blocks.0.norm3": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.LayerNorm",
+    "down_blocks.0.attentions.1.transformer_blocks.0.norm1": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.LayerNorm",
+    "down_blocks.0.attentions.1.transformer_blocks.0.norm2": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.LayerNorm",
+    "down_blocks.0.attentions.1.transformer_blocks.0.norm3": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.LayerNorm",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_q": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_1",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_k": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_2",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_v": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_3",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_q": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_1",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn1.to_q": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_1",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn1.to_k": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_2",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn1.to_v": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_3",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_q": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_1",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn1.to_out.0": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Linear",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_out.0": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Linear",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn1.to_out.0": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Linear",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_out.0": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Linear",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_k": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_2",
+    "down_blocks.0.attentions.0.transformer_blocks.0.attn2.to_v": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_3",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_k": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_2",
+    "down_blocks.0.attentions.1.transformer_blocks.0.attn2.to_v": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_3",
+    "down_blocks.0.attentions.0.transformer_blocks.0.ff.net.0.proj": "DownBlocks.Chain_2.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_1",
+    "down_blocks.0.attentions.1.transformer_blocks.0.ff.net.0.proj": "DownBlocks.Chain_3.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_1",
+    "down_blocks.1.resnets.0.conv1": "DownBlocks.Chain_5.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.1.resnets.0.time_emb_proj": "DownBlocks.Chain_5.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "down_blocks.1.resnets.1.time_emb_proj": "DownBlocks.Chain_6.ResidualBlock.Chain.RangeAdapter2d.Chain.Linear",
+    "down_blocks.1.resnets.0.norm2": "DownBlocks.Chain_5.ResidualBlock.Chain.GroupNorm_2",
+    "down_blocks.1.attentions.0.norm": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_1.GroupNorm",
+    "down_blocks.1.resnets.1.norm1": "DownBlocks.Chain_6.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.1.resnets.1.norm2": "DownBlocks.Chain_6.ResidualBlock.Chain.GroupNorm_2",
+    "down_blocks.1.attentions.1.norm": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_1.GroupNorm",
+    "down_blocks.2.resnets.0.norm1": "DownBlocks.Chain_8.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.1.resnets.0.conv2": "DownBlocks.Chain_5.ResidualBlock.Chain.Conv2d",
+    "down_blocks.1.resnets.1.conv1": "DownBlocks.Chain_6.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.1.resnets.1.conv2": "DownBlocks.Chain_6.ResidualBlock.Chain.Conv2d",
+    "down_blocks.1.downsamplers.0.conv": "DownBlocks.Chain_7.Downsample.Conv2d",
+    "down_blocks.1.resnets.0.conv_shortcut": "DownBlocks.Chain_5.ResidualBlock.Conv2d",
+    "down_blocks.1.attentions.0.proj_in": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_1.Conv2d",
+    "down_blocks.1.attentions.0.proj_out": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_3.Conv2d",
+    "controlnet_down_blocks.4": "DownBlocks.Chain_5.Passthrough.Conv2d",
+    "down_blocks.1.attentions.1.proj_in": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_1.Conv2d",
+    "down_blocks.1.attentions.1.proj_out": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_3.Conv2d",
+    "controlnet_down_blocks.5": "DownBlocks.Chain_6.Passthrough.Conv2d",
+    "controlnet_down_blocks.6": "DownBlocks.Chain_7.Passthrough.Conv2d",
+    "down_blocks.1.attentions.0.transformer_blocks.0.norm1": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.LayerNorm",
+    "down_blocks.1.attentions.0.transformer_blocks.0.norm2": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.LayerNorm",
+    "down_blocks.1.attentions.0.transformer_blocks.0.norm3": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.LayerNorm",
+    "down_blocks.1.attentions.1.transformer_blocks.0.norm1": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.LayerNorm",
+    "down_blocks.1.attentions.1.transformer_blocks.0.norm2": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.LayerNorm",
+    "down_blocks.1.attentions.1.transformer_blocks.0.norm3": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.LayerNorm",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_1",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_k": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_2",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_v": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_3",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_q": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_1",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_q": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_1",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_k": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_2",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_v": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Distribute.Linear_3",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_q": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_1",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_out.0": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Linear",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_out.0": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Linear",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn1.to_out.0": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_1.SelfAttention.Linear",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_out.0": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Linear",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_2",
+    "down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_v": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_3",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_k": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_2",
+    "down_blocks.1.attentions.1.transformer_blocks.0.attn2.to_v": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_2.Attention.Distribute.Linear_3",
+    "down_blocks.1.attentions.0.transformer_blocks.0.ff.net.0.proj": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_1",
+    "down_blocks.1.attentions.1.transformer_blocks.0.ff.net.0.proj": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_1",
+    "down_blocks.1.attentions.0.transformer_blocks.0.ff.net.2": "DownBlocks.Chain_5.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_2",
+    "down_blocks.1.attentions.1.transformer_blocks.0.ff.net.2": "DownBlocks.Chain_6.CLIPLCrossAttention.Chain_2.CrossAttentionBlock.Residual_3.Linear_2",
+    "down_blocks.2.resnets.0.conv1": "DownBlocks.Chain_8.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.2.resnets.0.norm2": "DownBlocks.Chain_8.ResidualBlock.Chain.GroupNorm_2",
+    "down_blocks.2.attentions.0.norm": "DownBlocks.Chain_8.CLIPLCrossAttention.Chain_1.GroupNorm",
+    "down_blocks.2.resnets.1.norm1": "DownBlocks.Chain_9.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.2.resnets.1.norm2": "DownBlocks.Chain_9.ResidualBlock.Chain.GroupNorm_2",
+    "down_blocks.2.attentions.1.norm": "DownBlocks.Chain_9.CLIPLCrossAttention.Chain_1.GroupNorm",
+    "down_blocks.3.resnets.0.norm1": "DownBlocks.Chain_11.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.3.resnets.0.norm2": "DownBlocks.Chain_11.ResidualBlock.Chain.GroupNorm_2",
+    "down_blocks.3.resnets.1.norm1": "DownBlocks.Chain_12.ResidualBlock.Chain.GroupNorm_1",
+    "down_blocks.3.resnets.1.norm2": "DownBlocks.Chain_12.ResidualBlock.Chain.GroupNorm_2",
+    "mid_block.resnets.0.norm1": "MiddleBlock.ResidualBlock_1.Chain.GroupNorm_1",
+    "mid_block.resnets.0.norm2": "MiddleBlock.ResidualBlock_1.Chain.GroupNorm_2",
+    "mid_block.attentions.0.norm": "MiddleBlock.CLIPLCrossAttention.Chain_1.GroupNorm",
+    "mid_block.resnets.1.norm1": "MiddleBlock.ResidualBlock_2.Chain.GroupNorm_1",
+    "mid_block.resnets.1.norm2": "MiddleBlock.ResidualBlock_2.Chain.GroupNorm_2",
+    "down_blocks.2.resnets.0.conv2": "DownBlocks.Chain_8.ResidualBlock.Chain.Conv2d",
+    "down_blocks.2.resnets.1.conv1": "DownBlocks.Chain_9.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.2.resnets.1.conv2": "DownBlocks.Chain_9.ResidualBlock.Chain.Conv2d",
+    "down_blocks.2.downsamplers.0.conv": "DownBlocks.Chain_10.Downsample.Conv2d",
+    "down_blocks.3.resnets.0.conv1": "DownBlocks.Chain_11.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.3.resnets.0.conv2": "DownBlocks.Chain_11.ResidualBlock.Chain.Conv2d",
+    "down_blocks.3.resnets.1.conv1": "DownBlocks.Chain_12.ResidualBlock.Chain.RangeAdapter2d.Conv2d",
+    "down_blocks.3.resnets.1.conv2": "DownBlocks.Chain_12.ResidualBlock.Chain.Conv2d",
+    "mid_block.resnets.0.conv1": "MiddleBlock.ResidualBlock_1.Chain.RangeAdapter2d.Conv2d",
+    "mid_block.resnets.0.conv2": "MiddleBlock.ResidualBlock_1.Chain.Conv2d",
+    "mid_block.resnets.1.conv1": "MiddleBlock.ResidualBlock_2.Chain.RangeAdapter2d.Conv2d",
+    "mid_block.resnets.1.conv2": "MiddleBlock.ResidualBlock_2.Chain.Conv2d",
+    "down_blocks.2.resnets.0.conv_shortcut": "DownBlocks.Chain_8.ResidualBlock.Conv2d",
+    "down_blocks.2.attentions.0.proj_in": "DownBlocks.Chain_8.CLIPLCrossAttention.Chain_1.Conv2d",
+    "down_blocks.2.attentions.0.proj_out": "DownBlocks.Chain_8.CLIPLCrossAttention.Chain_3.Conv2d",
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+}

imaginairy/weight_management/maps/stable-diffusion-1-5_text_compvis_TO_diffusers.json

@@ -0,0 +1,106 @@
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+    "transformer.text_model.final_layer_norm": "text_model.final_layer_norm"
+  },
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+  "ignorable_prefixes": []
+}

imaginairy/weight_management/maps/stable-diffusion-1-5_text_diffusers_TO_compvis.json

@@ -0,0 +1,106 @@
+{
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+}

imaginairy/weight_management/maps/stable-diffusion-1-5_text_diffusers_TO_refiners.json

@@ -0,0 +1,107 @@
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+}

imaginairy/weight_management/maps/stable-diffusion-1-5_unet_compvis_TO_diffusers.json

@@ -0,0 +1,397 @@
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imaginairy/weight_management/maps/stable-diffusion-1-5_unet_diffusers_TO_compvis.json

@@ -0,0 +1,397 @@
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+}

imaginairy/weight_management/maps/stable-diffusion-1-5_unet_diffusers_TO_refiners.json

@@ -0,0 +1,397 @@
+{
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+}

imaginairy/weight_management/maps/stable-diffusion-1-5_vae_compvis_TO_diffusers.json

@@ -0,0 +1,130 @@
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+}

imaginairy/weight_management/maps/stable-diffusion-1-5_vae_diffusers_TO_compvis.json

@@ -0,0 +1,130 @@
+{
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+    "decoder.conv_out": "decoder.conv_out"
+  },
+  "source_aliases": {},
+  "ignorable_prefixes": []
+}

imaginairy/weight_management/maps/stable-diffusion-1-5_vae_diffusers_TO_refiners.json

@@ -0,0 +1,149 @@
+{
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+    "decoder.up_blocks.1.resnets.2.conv1": "Decoder.Chain_1.Chain_3.Resnet_3.Chain.Conv2d_1",
+    "decoder.up_blocks.1.resnets.2.conv2": "Decoder.Chain_1.Chain_3.Resnet_3.Chain.Conv2d_2",
+    "decoder.up_blocks.1.upsamplers.0.conv": "Decoder.Chain_1.Chain_3.Upsample.Conv2d",
+    "encoder.down_blocks.2.resnets.0.conv_shortcut": "Encoder.Chain_1.Chain_3.Resnet_1.Conv2d",
+    "encoder.mid_block.attentions.0.to_q": "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Distribute.Linear_1",
+    "encoder.mid_block.attentions.0.to_k": "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Distribute.Linear_2",
+    "encoder.mid_block.attentions.0.to_v": "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Distribute.Linear_3",
+    "encoder.mid_block.attentions.0.to_out.0": "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Linear",
+    "decoder.mid_block.attentions.0.to_q": "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Distribute.Linear_1",
+    "decoder.mid_block.attentions.0.to_k": "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Distribute.Linear_2",
+    "decoder.mid_block.attentions.0.to_v": "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Distribute.Linear_3",
+    "decoder.mid_block.attentions.0.to_out.0": "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Linear",
+    "encoder.conv_out": "Encoder.Chain_2.Conv2d",
+    "quant_conv": "Encoder.Chain_3.Conv2d",
+    "post_quant_conv": "Decoder.Conv2d_1",
+    "decoder.conv_in": "Decoder.Conv2d_2",
+    "decoder.up_blocks.2.resnets.0.conv1": "Decoder.Chain_1.Chain_4.Resnet_1.Chain.Conv2d_1",
+    "decoder.up_blocks.2.resnets.0.conv_shortcut": "Decoder.Chain_1.Chain_4.Resnet_1.Conv2d",
+    "decoder.up_blocks.3.resnets.0.conv1": "Decoder.Chain_1.Chain_5.Resnet_1.Chain.Conv2d_1",
+    "decoder.up_blocks.3.resnets.0.conv_shortcut": "Decoder.Chain_1.Chain_5.Resnet_1.Conv2d",
+    "decoder.conv_out": "Decoder.Chain_2.Conv2d"
+  },
+  "source_aliases": {
+    "encoder.mid_block.attentions.0.value": "encoder.mid_block.attentions.0.to_v",
+    "decoder.mid_block.attentions.0.value": "decoder.mid_block.attentions.0.to_v",
+    "decoder.mid_block.attentions.0.proj_attn": "decoder.mid_block.attentions.0.to_out.0",
+    "encoder.mid_block.attentions.0.proj_attn": "encoder.mid_block.attentions.0.to_out.0",
+    "encoder.mid_block.attentions.0.key": "encoder.mid_block.attentions.0.to_k",
+    "decoder.mid_block.attentions.0.key": "decoder.mid_block.attentions.0.to_k",
+    "decoder.mid_block.attentions.0.query": "decoder.mid_block.attentions.0.to_q",
+    "encoder.mid_block.attentions.0.query": "encoder.mid_block.attentions.0.to_q"
+  },
+  "reshapes": {
+    "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Distribute.Linear_1.weight": [512, 512],
+    "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Distribute.Linear_2.weight": [512, 512],
+    "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Distribute.Linear_3.weight": [512, 512],
+    "Encoder.Chain_1.Chain_5.Residual.SelfAttention2d.Linear.weight": [512, 512],
+    "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Distribute.Linear_1.weight": [512, 512],
+    "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Distribute.Linear_2.weight": [512, 512],
+    "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Distribute.Linear_3.weight": [512, 512],
+    "Decoder.Chain_1.Chain_1.Residual.SelfAttention2d.Linear.weight": [512, 512]
+  }
+}

imaginairy/weight_management/pattern_collapse.py

@@ -0,0 +1,111 @@
+def find_state_dict_key_patterns(patterns):
+    """Given a list of state_dict keys, collapse similar keys into patterns.
+
+    For example, if the keys are:
+
+    foo.bar.0.baz
+    foo.bar.1.baz
+
+    Then the pattern will be:
+
+    foo.bar.(0|1).baz
+
+    """
+    prev_pattern_count = len(patterns) + 1
+
+    # keep running the pattern collapse function until the list of patterns doesn't get any smaller
+    while prev_pattern_count > len(patterns):
+        prev_pattern_count = len(patterns)
+        prev_pattern_count_sub = len(patterns) + 1
+        while prev_pattern_count_sub > len(patterns):
+            prev_pattern_count_sub = len(patterns)
+            patterns = _collapse_patterns(patterns)
+        prev_pattern_count_sub = len(patterns) + 1
+        while prev_pattern_count_sub > len(patterns):
+            prev_pattern_count_sub = len(patterns)
+            patterns = _collapse_patterns(patterns, reverse_sort=True)
+
+    return patterns
+
+
+def prefix_only(k):
+    return k.rsplit(".", 1)[0]
+
+
+def nested_dict_from_keys(keys):
+    output = {}
+    for key in keys:
+        parts = key.split(".")
+        # Start from the root of the output and iteratively go deeper
+        current_level = output
+        for part in parts:
+            # If the key doesn't exist at the current level, create a new dict
+            if part not in current_level:
+                current_level[part] = {}
+            # Go one level deeper
+            current_level = current_level[part]
+    return output
+
+
+def _collapse_patterns(keys, reverse_sort=False):
+    keys = keys.copy()
+    keys = [k.split(".") for k in keys]
+    if reverse_sort:
+        keys.sort(key=lambda k: (len(k), list(reversed(str(k)))))
+    else:
+        keys.sort(key=lambda k: (len(k), k))
+    new_key_patterns = []
+    curr_key = None
+    for k in keys:
+        if curr_key is None:
+            curr_key = k
+            continue
+        single_diff_index = get_single_difference(curr_key, k)
+        if single_diff_index is None:
+            new_key_patterns.append(curr_key)
+            curr_key = k
+        else:
+            cur_part_val = curr_key[single_diff_index]
+            key_part_val = k[single_diff_index]
+            if "(" in key_part_val:
+                key_vals = key_part_val.strip("()").split("|")
+            else:
+                key_vals = [key_part_val]
+            if "(" in cur_part_val:
+                vals = cur_part_val.strip("()").split("|")
+            else:
+                vals = [cur_part_val]
+            vals.extend(key_vals)
+            vals.sort()
+            try:
+                vals = [int(v) for v in vals]
+                vals.sort()
+                vals = [str(v) for v in vals]
+            except ValueError:
+                pass
+            new_cur_part_val = "(" + "|".join(vals) + ")"
+            curr_key[single_diff_index] = new_cur_part_val
+    new_key_patterns.append(curr_key)
+    new_key_patterns = [".".join(p) for p in new_key_patterns]
+    new_key_patterns.sort()
+    return new_key_patterns
+
+
+def get_single_difference(a, b):
+    """
+    Given two list of strings, if only a single string differs between the two lists, return the index of the differing string.
+    """
+    if len(a) != len(b):
+        return None
+    diff_count = 0
+    diff_index = None
+    for i, (asub, bsub) in enumerate(zip(a, b)):
+        if asub != bsub:
+            diff_count += 1
+            diff_index = i
+        if diff_count > 1:
+            break
+
+    if diff_count == 1:
+        return diff_index
+    return None

imaginairy/weight_management/utils.py

@@ -0,0 +1,48 @@
+import os.path
+
+_base_dir = os.path.dirname(os.path.realpath(__file__))
+
+WEIGHT_MAPS_PATH = os.path.join(_base_dir, "maps")
+WEIGHT_INFO_PATH = os.path.join(_base_dir, "weight_info")
+
+
+class MODEL_NAMES:
+    SD15 = "stable-diffusion-1-5"
+
+
+class COMPONENT_NAMES:
+    VAE = "vae"
+    TEXT_ENCODER = "text"
+    UNET = "unet"
+    LORA = "lora"
+
+
+class FORMAT_NAMES:
+    COMPVIS = "compvis"
+    DIFFUSERS = "diffusers"
+    REFINERS = "refiners"
+
+
+def save_model_info(model_name, component_name, format_name, info_type, data):
+    import json
+
+    model_name = model_name.replace("_", "-")
+    component_name = component_name.replace("_", "-")
+    format_name = format_name.replace("_", "-")
+    filename = os.path.join(
+        WEIGHT_INFO_PATH,
+        f"{model_name}_{component_name}_{format_name}.{info_type}.json",
+    )
+    with open(filename, "w") as f:
+        f.write(json.dumps(data, indent=2))
+
+
+def prefixes_only(keys):
+    new_keys = []
+    prev_key = None
+    for k in keys:
+        new_key = k.rsplit(".", 1)[0]
+        if new_key != prev_key:
+            new_keys.append(new_key)
+        prev_key = new_key
+    return new_keys

imaginairy/weight_management/weight_info/stable-diffusion-1-5_lora_diffusers.patterns.json

@@ -0,0 +1,5 @@
+[
+  "down_blocks.(0|1|2).attentions.(0|1).transformer_blocks.0.(attn1|attn2).processor.(to_k_lora|to_out_lora|to_q_lora|to_v_lora).(down|up).weight",
+  "mid_block.attentions.0.transformer_blocks.0.(attn1|attn2).processor.(to_k_lora|to_out_lora|to_q_lora|to_v_lora).(down|up).weight",
+  "up_blocks.(1|2|3).attentions.(0|1|2).transformer_blocks.0.(attn1|attn2).processor.(to_k_lora|to_out_lora|to_q_lora|to_v_lora).(down|up).weight"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_lora_diffusers.weights_keys.json

@@ -0,0 +1,258 @@
+[
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_q_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_q_lora.up.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_k_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_k_lora.up.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_v_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_v_lora.up.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_out_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor.to_out_lora.up.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_q_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_q_lora.up.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_k_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_k_lora.up.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_v_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_v_lora.up.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_out_lora.down.weight",
+  "down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor.to_out_lora.up.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_q_lora.down.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_q_lora.up.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_k_lora.down.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_k_lora.up.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_v_lora.down.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_v_lora.up.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_out_lora.down.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn1.processor.to_out_lora.up.weight",
+  "down_blocks.0.attentions.1.transformer_blocks.0.attn2.processor.to_q_lora.down.weight",
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imaginairy/weight_management/weight_info/stable-diffusion-1-5_text_compvis.patterns.json

@@ -0,0 +1,7 @@
+[
+  "cond_stage_model.transformer.text_model.(embeddings|final_layer_norm)",
+  "cond_stage_model.transformer.text_model.embeddings.(position_embedding|token_embedding)",
+  "cond_stage_model.transformer.text_model.encoder.layers.(0|1|2|3|4|5|6|7|8|9|10|11).(layer_norm1|layer_norm2)",
+  "cond_stage_model.transformer.text_model.encoder.layers.(0|1|2|3|4|5|6|7|8|9|10|11).mlp.(fc1|fc2)",
+  "cond_stage_model.transformer.text_model.encoder.layers.(0|1|2|3|4|5|6|7|8|9|10|11).self_attn.(k_proj|out_proj|q_proj|v_proj)"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_text_compvis.prefix-execution-order.json

@@ -0,0 +1,102 @@
+[
+  "transformer.text_model.embeddings.token_embedding",
+  "transformer.text_model.embeddings.position_embedding",
+  "transformer.text_model.embeddings",
+  "transformer.text_model.encoder.layers.0.layer_norm1",
+  "transformer.text_model.encoder.layers.0.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.0.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.0.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.0.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.0.layer_norm2",
+  "transformer.text_model.encoder.layers.0.mlp.fc1",
+  "transformer.text_model.encoder.layers.0.mlp.fc2",
+  "transformer.text_model.encoder.layers.1.layer_norm1",
+  "transformer.text_model.encoder.layers.1.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.1.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.1.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.1.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.1.layer_norm2",
+  "transformer.text_model.encoder.layers.1.mlp.fc1",
+  "transformer.text_model.encoder.layers.1.mlp.fc2",
+  "transformer.text_model.encoder.layers.2.layer_norm1",
+  "transformer.text_model.encoder.layers.2.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.2.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.2.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.2.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.2.layer_norm2",
+  "transformer.text_model.encoder.layers.2.mlp.fc1",
+  "transformer.text_model.encoder.layers.2.mlp.fc2",
+  "transformer.text_model.encoder.layers.3.layer_norm1",
+  "transformer.text_model.encoder.layers.3.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.3.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.3.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.3.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.3.layer_norm2",
+  "transformer.text_model.encoder.layers.3.mlp.fc1",
+  "transformer.text_model.encoder.layers.3.mlp.fc2",
+  "transformer.text_model.encoder.layers.4.layer_norm1",
+  "transformer.text_model.encoder.layers.4.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.4.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.4.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.4.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.4.layer_norm2",
+  "transformer.text_model.encoder.layers.4.mlp.fc1",
+  "transformer.text_model.encoder.layers.4.mlp.fc2",
+  "transformer.text_model.encoder.layers.5.layer_norm1",
+  "transformer.text_model.encoder.layers.5.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.5.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.5.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.5.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.5.layer_norm2",
+  "transformer.text_model.encoder.layers.5.mlp.fc1",
+  "transformer.text_model.encoder.layers.5.mlp.fc2",
+  "transformer.text_model.encoder.layers.6.layer_norm1",
+  "transformer.text_model.encoder.layers.6.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.6.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.6.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.6.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.6.layer_norm2",
+  "transformer.text_model.encoder.layers.6.mlp.fc1",
+  "transformer.text_model.encoder.layers.6.mlp.fc2",
+  "transformer.text_model.encoder.layers.7.layer_norm1",
+  "transformer.text_model.encoder.layers.7.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.7.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.7.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.7.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.7.layer_norm2",
+  "transformer.text_model.encoder.layers.7.mlp.fc1",
+  "transformer.text_model.encoder.layers.7.mlp.fc2",
+  "transformer.text_model.encoder.layers.8.layer_norm1",
+  "transformer.text_model.encoder.layers.8.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.8.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.8.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.8.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.8.layer_norm2",
+  "transformer.text_model.encoder.layers.8.mlp.fc1",
+  "transformer.text_model.encoder.layers.8.mlp.fc2",
+  "transformer.text_model.encoder.layers.9.layer_norm1",
+  "transformer.text_model.encoder.layers.9.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.9.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.9.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.9.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.9.layer_norm2",
+  "transformer.text_model.encoder.layers.9.mlp.fc1",
+  "transformer.text_model.encoder.layers.9.mlp.fc2",
+  "transformer.text_model.encoder.layers.10.layer_norm1",
+  "transformer.text_model.encoder.layers.10.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.10.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.10.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.10.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.10.layer_norm2",
+  "transformer.text_model.encoder.layers.10.mlp.fc1",
+  "transformer.text_model.encoder.layers.10.mlp.fc2",
+  "transformer.text_model.encoder.layers.11.layer_norm1",
+  "transformer.text_model.encoder.layers.11.self_attn.q_proj",
+  "transformer.text_model.encoder.layers.11.self_attn.k_proj",
+  "transformer.text_model.encoder.layers.11.self_attn.v_proj",
+  "transformer.text_model.encoder.layers.11.self_attn.out_proj",
+  "transformer.text_model.encoder.layers.11.layer_norm2",
+  "transformer.text_model.encoder.layers.11.mlp.fc1",
+  "transformer.text_model.encoder.layers.11.mlp.fc2",
+  "transformer.text_model.final_layer_norm"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_text_compvis.prefixes.json

@@ -0,0 +1,102 @@
+[
+  "cond_stage_model.transformer.text_model.embeddings.position_embedding",
+  "cond_stage_model.transformer.text_model.embeddings",
+  "cond_stage_model.transformer.text_model.embeddings.token_embedding",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.0.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.1.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.10.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.11.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.2.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.3.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.4.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.5.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.6.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.7.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.8.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm1",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.layer_norm2",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc1",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.mlp.fc2",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.k_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.out_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.q_proj",
+  "cond_stage_model.transformer.text_model.encoder.layers.9.self_attn.v_proj",
+  "cond_stage_model.transformer.text_model.final_layer_norm"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_text_diffusers.patterns.json

@@ -0,0 +1,7 @@
+[
+  "text_model.(embeddings|final_layer_norm)",
+  "text_model.embeddings.(position_embedding|token_embedding)",
+  "text_model.encoder.layers.(0|1|2|3|4|5|6|7|8|9|10|11).(layer_norm1|layer_norm2)",
+  "text_model.encoder.layers.(0|1|2|3|4|5|6|7|8|9|10|11).mlp.(fc1|fc2)",
+  "text_model.encoder.layers.(0|1|2|3|4|5|6|7|8|9|10|11).self_attn.(k_proj|out_proj|q_proj|v_proj)"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_text_diffusers.prefix-execution-order.json

@@ -0,0 +1,102 @@
+[
+  "text_model.embeddings.token_embedding",
+  "text_model.embeddings.position_embedding",
+  "text_model.embeddings",
+  "text_model.encoder.layers.0.layer_norm1",
+  "text_model.encoder.layers.0.self_attn.q_proj",
+  "text_model.encoder.layers.0.self_attn.k_proj",
+  "text_model.encoder.layers.0.self_attn.v_proj",
+  "text_model.encoder.layers.0.self_attn.out_proj",
+  "text_model.encoder.layers.0.layer_norm2",
+  "text_model.encoder.layers.0.mlp.fc1",
+  "text_model.encoder.layers.0.mlp.fc2",
+  "text_model.encoder.layers.1.layer_norm1",
+  "text_model.encoder.layers.1.self_attn.q_proj",
+  "text_model.encoder.layers.1.self_attn.k_proj",
+  "text_model.encoder.layers.1.self_attn.v_proj",
+  "text_model.encoder.layers.1.self_attn.out_proj",
+  "text_model.encoder.layers.1.layer_norm2",
+  "text_model.encoder.layers.1.mlp.fc1",
+  "text_model.encoder.layers.1.mlp.fc2",
+  "text_model.encoder.layers.2.layer_norm1",
+  "text_model.encoder.layers.2.self_attn.q_proj",
+  "text_model.encoder.layers.2.self_attn.k_proj",
+  "text_model.encoder.layers.2.self_attn.v_proj",
+  "text_model.encoder.layers.2.self_attn.out_proj",
+  "text_model.encoder.layers.2.layer_norm2",
+  "text_model.encoder.layers.2.mlp.fc1",
+  "text_model.encoder.layers.2.mlp.fc2",
+  "text_model.encoder.layers.3.layer_norm1",
+  "text_model.encoder.layers.3.self_attn.q_proj",
+  "text_model.encoder.layers.3.self_attn.k_proj",
+  "text_model.encoder.layers.3.self_attn.v_proj",
+  "text_model.encoder.layers.3.self_attn.out_proj",
+  "text_model.encoder.layers.3.layer_norm2",
+  "text_model.encoder.layers.3.mlp.fc1",
+  "text_model.encoder.layers.3.mlp.fc2",
+  "text_model.encoder.layers.4.layer_norm1",
+  "text_model.encoder.layers.4.self_attn.q_proj",
+  "text_model.encoder.layers.4.self_attn.k_proj",
+  "text_model.encoder.layers.4.self_attn.v_proj",
+  "text_model.encoder.layers.4.self_attn.out_proj",
+  "text_model.encoder.layers.4.layer_norm2",
+  "text_model.encoder.layers.4.mlp.fc1",
+  "text_model.encoder.layers.4.mlp.fc2",
+  "text_model.encoder.layers.5.layer_norm1",
+  "text_model.encoder.layers.5.self_attn.q_proj",
+  "text_model.encoder.layers.5.self_attn.k_proj",
+  "text_model.encoder.layers.5.self_attn.v_proj",
+  "text_model.encoder.layers.5.self_attn.out_proj",
+  "text_model.encoder.layers.5.layer_norm2",
+  "text_model.encoder.layers.5.mlp.fc1",
+  "text_model.encoder.layers.5.mlp.fc2",
+  "text_model.encoder.layers.6.layer_norm1",
+  "text_model.encoder.layers.6.self_attn.q_proj",
+  "text_model.encoder.layers.6.self_attn.k_proj",
+  "text_model.encoder.layers.6.self_attn.v_proj",
+  "text_model.encoder.layers.6.self_attn.out_proj",
+  "text_model.encoder.layers.6.layer_norm2",
+  "text_model.encoder.layers.6.mlp.fc1",
+  "text_model.encoder.layers.6.mlp.fc2",
+  "text_model.encoder.layers.7.layer_norm1",
+  "text_model.encoder.layers.7.self_attn.q_proj",
+  "text_model.encoder.layers.7.self_attn.k_proj",
+  "text_model.encoder.layers.7.self_attn.v_proj",
+  "text_model.encoder.layers.7.self_attn.out_proj",
+  "text_model.encoder.layers.7.layer_norm2",
+  "text_model.encoder.layers.7.mlp.fc1",
+  "text_model.encoder.layers.7.mlp.fc2",
+  "text_model.encoder.layers.8.layer_norm1",
+  "text_model.encoder.layers.8.self_attn.q_proj",
+  "text_model.encoder.layers.8.self_attn.k_proj",
+  "text_model.encoder.layers.8.self_attn.v_proj",
+  "text_model.encoder.layers.8.self_attn.out_proj",
+  "text_model.encoder.layers.8.layer_norm2",
+  "text_model.encoder.layers.8.mlp.fc1",
+  "text_model.encoder.layers.8.mlp.fc2",
+  "text_model.encoder.layers.9.layer_norm1",
+  "text_model.encoder.layers.9.self_attn.q_proj",
+  "text_model.encoder.layers.9.self_attn.k_proj",
+  "text_model.encoder.layers.9.self_attn.v_proj",
+  "text_model.encoder.layers.9.self_attn.out_proj",
+  "text_model.encoder.layers.9.layer_norm2",
+  "text_model.encoder.layers.9.mlp.fc1",
+  "text_model.encoder.layers.9.mlp.fc2",
+  "text_model.encoder.layers.10.layer_norm1",
+  "text_model.encoder.layers.10.self_attn.q_proj",
+  "text_model.encoder.layers.10.self_attn.k_proj",
+  "text_model.encoder.layers.10.self_attn.v_proj",
+  "text_model.encoder.layers.10.self_attn.out_proj",
+  "text_model.encoder.layers.10.layer_norm2",
+  "text_model.encoder.layers.10.mlp.fc1",
+  "text_model.encoder.layers.10.mlp.fc2",
+  "text_model.encoder.layers.11.layer_norm1",
+  "text_model.encoder.layers.11.self_attn.q_proj",
+  "text_model.encoder.layers.11.self_attn.k_proj",
+  "text_model.encoder.layers.11.self_attn.v_proj",
+  "text_model.encoder.layers.11.self_attn.out_proj",
+  "text_model.encoder.layers.11.layer_norm2",
+  "text_model.encoder.layers.11.mlp.fc1",
+  "text_model.encoder.layers.11.mlp.fc2",
+  "text_model.final_layer_norm"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_text_diffusers.prefixes.json

@@ -0,0 +1,102 @@
+[
+  "text_model.embeddings.position_embedding",
+  "text_model.embeddings",
+  "text_model.embeddings.token_embedding",
+  "text_model.encoder.layers.0.layer_norm1",
+  "text_model.encoder.layers.0.layer_norm2",
+  "text_model.encoder.layers.0.mlp.fc1",
+  "text_model.encoder.layers.0.mlp.fc2",
+  "text_model.encoder.layers.0.self_attn.k_proj",
+  "text_model.encoder.layers.0.self_attn.out_proj",
+  "text_model.encoder.layers.0.self_attn.q_proj",
+  "text_model.encoder.layers.0.self_attn.v_proj",
+  "text_model.encoder.layers.1.layer_norm1",
+  "text_model.encoder.layers.1.layer_norm2",
+  "text_model.encoder.layers.1.mlp.fc1",
+  "text_model.encoder.layers.1.mlp.fc2",
+  "text_model.encoder.layers.1.self_attn.k_proj",
+  "text_model.encoder.layers.1.self_attn.out_proj",
+  "text_model.encoder.layers.1.self_attn.q_proj",
+  "text_model.encoder.layers.1.self_attn.v_proj",
+  "text_model.encoder.layers.10.layer_norm1",
+  "text_model.encoder.layers.10.layer_norm2",
+  "text_model.encoder.layers.10.mlp.fc1",
+  "text_model.encoder.layers.10.mlp.fc2",
+  "text_model.encoder.layers.10.self_attn.k_proj",
+  "text_model.encoder.layers.10.self_attn.out_proj",
+  "text_model.encoder.layers.10.self_attn.q_proj",
+  "text_model.encoder.layers.10.self_attn.v_proj",
+  "text_model.encoder.layers.11.layer_norm1",
+  "text_model.encoder.layers.11.layer_norm2",
+  "text_model.encoder.layers.11.mlp.fc1",
+  "text_model.encoder.layers.11.mlp.fc2",
+  "text_model.encoder.layers.11.self_attn.k_proj",
+  "text_model.encoder.layers.11.self_attn.out_proj",
+  "text_model.encoder.layers.11.self_attn.q_proj",
+  "text_model.encoder.layers.11.self_attn.v_proj",
+  "text_model.encoder.layers.2.layer_norm1",
+  "text_model.encoder.layers.2.layer_norm2",
+  "text_model.encoder.layers.2.mlp.fc1",
+  "text_model.encoder.layers.2.mlp.fc2",
+  "text_model.encoder.layers.2.self_attn.k_proj",
+  "text_model.encoder.layers.2.self_attn.out_proj",
+  "text_model.encoder.layers.2.self_attn.q_proj",
+  "text_model.encoder.layers.2.self_attn.v_proj",
+  "text_model.encoder.layers.3.layer_norm1",
+  "text_model.encoder.layers.3.layer_norm2",
+  "text_model.encoder.layers.3.mlp.fc1",
+  "text_model.encoder.layers.3.mlp.fc2",
+  "text_model.encoder.layers.3.self_attn.k_proj",
+  "text_model.encoder.layers.3.self_attn.out_proj",
+  "text_model.encoder.layers.3.self_attn.q_proj",
+  "text_model.encoder.layers.3.self_attn.v_proj",
+  "text_model.encoder.layers.4.layer_norm1",
+  "text_model.encoder.layers.4.layer_norm2",
+  "text_model.encoder.layers.4.mlp.fc1",
+  "text_model.encoder.layers.4.mlp.fc2",
+  "text_model.encoder.layers.4.self_attn.k_proj",
+  "text_model.encoder.layers.4.self_attn.out_proj",
+  "text_model.encoder.layers.4.self_attn.q_proj",
+  "text_model.encoder.layers.4.self_attn.v_proj",
+  "text_model.encoder.layers.5.layer_norm1",
+  "text_model.encoder.layers.5.layer_norm2",
+  "text_model.encoder.layers.5.mlp.fc1",
+  "text_model.encoder.layers.5.mlp.fc2",
+  "text_model.encoder.layers.5.self_attn.k_proj",
+  "text_model.encoder.layers.5.self_attn.out_proj",
+  "text_model.encoder.layers.5.self_attn.q_proj",
+  "text_model.encoder.layers.5.self_attn.v_proj",
+  "text_model.encoder.layers.6.layer_norm1",
+  "text_model.encoder.layers.6.layer_norm2",
+  "text_model.encoder.layers.6.mlp.fc1",
+  "text_model.encoder.layers.6.mlp.fc2",
+  "text_model.encoder.layers.6.self_attn.k_proj",
+  "text_model.encoder.layers.6.self_attn.out_proj",
+  "text_model.encoder.layers.6.self_attn.q_proj",
+  "text_model.encoder.layers.6.self_attn.v_proj",
+  "text_model.encoder.layers.7.layer_norm1",
+  "text_model.encoder.layers.7.layer_norm2",
+  "text_model.encoder.layers.7.mlp.fc1",
+  "text_model.encoder.layers.7.mlp.fc2",
+  "text_model.encoder.layers.7.self_attn.k_proj",
+  "text_model.encoder.layers.7.self_attn.out_proj",
+  "text_model.encoder.layers.7.self_attn.q_proj",
+  "text_model.encoder.layers.7.self_attn.v_proj",
+  "text_model.encoder.layers.8.layer_norm1",
+  "text_model.encoder.layers.8.layer_norm2",
+  "text_model.encoder.layers.8.mlp.fc1",
+  "text_model.encoder.layers.8.mlp.fc2",
+  "text_model.encoder.layers.8.self_attn.k_proj",
+  "text_model.encoder.layers.8.self_attn.out_proj",
+  "text_model.encoder.layers.8.self_attn.q_proj",
+  "text_model.encoder.layers.8.self_attn.v_proj",
+  "text_model.encoder.layers.9.layer_norm1",
+  "text_model.encoder.layers.9.layer_norm2",
+  "text_model.encoder.layers.9.mlp.fc1",
+  "text_model.encoder.layers.9.mlp.fc2",
+  "text_model.encoder.layers.9.self_attn.k_proj",
+  "text_model.encoder.layers.9.self_attn.out_proj",
+  "text_model.encoder.layers.9.self_attn.q_proj",
+  "text_model.encoder.layers.9.self_attn.v_proj",
+  "text_model.final_layer_norm"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_unet_compvis.patterns.json

@@ -0,0 +1,42 @@
+[
+  "model.diffusion_model.(input_blocks|output_blocks).(1|2|4|5|7|8|10|11).0.emb_layers.1",
+  "model.diffusion_model.(input_blocks|output_blocks).(1|2|4|5|7|8|10|11).0.in_layers.(0|2)",
+  "model.diffusion_model.(input_blocks|output_blocks).(1|2|4|5|7|8|10|11).0.out_layers.(0|3)",
+  "model.diffusion_model.(input_blocks|output_blocks).(4|5|7|8).1.transformer_blocks.0.(attn1|attn2).to_out.0",
+  "model.diffusion_model.(input_blocks|output_blocks).(4|5|7|8).1.transformer_blocks.0.ff.net.2",
+  "model.diffusion_model.(input_blocks|output_blocks).(4|7).0.skip_connection",
+  "model.diffusion_model.(out|time_embed).(0|2)",
+  "model.diffusion_model.input_blocks.(1|2|4|5|7|8).1.(norm|proj_in|proj_out)",
+  "model.diffusion_model.input_blocks.(1|2|4|5|7|8).1.transformer_blocks.0.(attn1|attn2).(to_k|to_q|to_v)",
+  "model.diffusion_model.input_blocks.(1|2|4|5|7|8).1.transformer_blocks.0.(norm1|norm2|norm3)",
+  "model.diffusion_model.input_blocks.(1|2|4|5|7|8).1.transformer_blocks.0.ff.net.0.proj",
+  "model.diffusion_model.input_blocks.(3|6|9).0.op",
+  "model.diffusion_model.input_blocks.0.0",
+  "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.(attn1|attn2).to_out.0",
+  "model.diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2",
+  "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.(attn1|attn2).to_out.0",
+  "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.ff.net.2",
+  "model.diffusion_model.middle_block.(0|2).emb_layers.1",
+  "model.diffusion_model.middle_block.(0|2).in_layers.(0|2)",
+  "model.diffusion_model.middle_block.(0|2).out_layers.(0|3)",
+  "model.diffusion_model.middle_block.1.(norm|proj_in|proj_out)",
+  "model.diffusion_model.middle_block.1.transformer_blocks.0.(attn1|attn2).(to_k|to_q|to_v)",
+  "model.diffusion_model.middle_block.1.transformer_blocks.0.(attn1|attn2).to_out.0",
+  "model.diffusion_model.middle_block.1.transformer_blocks.0.(norm1|norm2|norm3)",
+  "model.diffusion_model.middle_block.1.transformer_blocks.0.ff.net.0.proj",
+  "model.diffusion_model.middle_block.1.transformer_blocks.0.ff.net.2",
+  "model.diffusion_model.output_blocks.(0|1|2|3|5|6|8|9|10|11).0.skip_connection",
+  "model.diffusion_model.output_blocks.(0|3|6|9).0.emb_layers.1",
+  "model.diffusion_model.output_blocks.(0|3|6|9).0.in_layers.(0|2)",
+  "model.diffusion_model.output_blocks.(0|3|6|9).0.out_layers.(0|3)",
+  "model.diffusion_model.output_blocks.(3|4|5|6|7|8|9|10|11).1.(norm|proj_in|proj_out)",
+  "model.diffusion_model.output_blocks.(3|4|5|6|7|8|9|10|11).1.transformer_blocks.0.(attn1|attn2).(to_k|to_q|to_v)",
+  "model.diffusion_model.output_blocks.(3|4|5|6|7|8|9|10|11).1.transformer_blocks.0.(norm1|norm2|norm3)",
+  "model.diffusion_model.output_blocks.(3|4|5|6|7|8|9|10|11).1.transformer_blocks.0.ff.net.0.proj",
+  "model.diffusion_model.output_blocks.(3|6|9|10).1.transformer_blocks.0.(attn1|attn2).to_out.0",
+  "model.diffusion_model.output_blocks.(3|6|9|10).1.transformer_blocks.0.ff.net.2",
+  "model.diffusion_model.output_blocks.(5|8).2.conv",
+  "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.(attn1|attn2).to_out.0",
+  "model.diffusion_model.output_blocks.11.1.transformer_blocks.0.ff.net.2",
+  "model.diffusion_model.output_blocks.2.1.conv"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_unet_compvis.prefix-execution-order.json

@@ -0,0 +1,393 @@
+[
+  "diffusion_model.time_embed.0",
+  "diffusion_model.time_embed.2",
+  "diffusion_model.input_blocks.0.0",
+  "diffusion_model.input_blocks.1.0.in_layers.0",
+  "diffusion_model.input_blocks.1.0.in_layers.2",
+  "diffusion_model.input_blocks.1.0.emb_layers.1",
+  "diffusion_model.input_blocks.1.0.out_layers.0",
+  "diffusion_model.input_blocks.1.0.out_layers.3",
+  "diffusion_model.input_blocks.1.1.norm",
+  "diffusion_model.input_blocks.1.1.proj_in",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.norm1",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_q",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_k",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_v",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn1.to_out.0",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.norm2",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_q",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_k",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_v",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.attn2.to_out.0",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.norm3",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.0.proj",
+  "diffusion_model.input_blocks.1.1.transformer_blocks.0.ff.net.2",
+  "diffusion_model.input_blocks.1.1.proj_out",
+  "diffusion_model.input_blocks.2.0.in_layers.0",
+  "diffusion_model.input_blocks.2.0.in_layers.2",
+  "diffusion_model.input_blocks.2.0.emb_layers.1",
+  "diffusion_model.input_blocks.2.0.out_layers.0",
+  "diffusion_model.input_blocks.2.0.out_layers.3",
+  "diffusion_model.input_blocks.2.1.norm",
+  "diffusion_model.input_blocks.2.1.proj_in",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.norm1",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_q",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_k",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_v",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn1.to_out.0",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.norm2",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_q",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_v",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_out.0",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.norm3",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.ff.net.0.proj",
+  "diffusion_model.input_blocks.2.1.transformer_blocks.0.ff.net.2",
+  "diffusion_model.input_blocks.2.1.proj_out",
+  "diffusion_model.input_blocks.3.0.op",
+  "diffusion_model.input_blocks.4.0.in_layers.0",
+  "diffusion_model.input_blocks.4.0.in_layers.2",
+  "diffusion_model.input_blocks.4.0.emb_layers.1",
+  "diffusion_model.input_blocks.4.0.out_layers.0",
+  "diffusion_model.input_blocks.4.0.out_layers.3",
+  "diffusion_model.input_blocks.4.0.skip_connection",
+  "diffusion_model.input_blocks.4.1.norm",
+  "diffusion_model.input_blocks.4.1.proj_in",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.norm1",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_q",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_k",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_v",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn1.to_out.0",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.norm2",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_q",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_v",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_out.0",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.norm3",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.0.proj",
+  "diffusion_model.input_blocks.4.1.transformer_blocks.0.ff.net.2",
+  "diffusion_model.input_blocks.4.1.proj_out",
+  "diffusion_model.input_blocks.5.0.in_layers.0",
+  "diffusion_model.input_blocks.5.0.in_layers.2",
+  "diffusion_model.input_blocks.5.0.emb_layers.1",
+  "diffusion_model.input_blocks.5.0.out_layers.0",
+  "diffusion_model.input_blocks.5.0.out_layers.3",
+  "diffusion_model.input_blocks.5.1.norm",
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imaginairy/weight_management/weight_info/stable-diffusion-1-5_unet_diffusers.patterns.json

@@ -0,0 +1,43 @@
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imaginairy/weight_management/weight_info/stable-diffusion-1-5_unet_diffusers.prefix-execution-order.json

@@ -0,0 +1,393 @@
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imaginairy/weight_management/weight_info/stable-diffusion-1-5_unet_diffusers.prefixes.json

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+  "up_blocks.3.attentions.1.proj_in",
+  "up_blocks.3.attentions.1.proj_out",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn1.to_k",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn1.to_out.0",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn1.to_q",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn1.to_v",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_k",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_out.0",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_q",
+  "up_blocks.3.attentions.1.transformer_blocks.0.attn2.to_v",
+  "up_blocks.3.attentions.1.transformer_blocks.0.ff.net.0.proj",
+  "up_blocks.3.attentions.1.transformer_blocks.0.ff.net.2",
+  "up_blocks.3.attentions.1.transformer_blocks.0.norm1",
+  "up_blocks.3.attentions.1.transformer_blocks.0.norm2",
+  "up_blocks.3.attentions.1.transformer_blocks.0.norm3",
+  "up_blocks.3.attentions.2.norm",
+  "up_blocks.3.attentions.2.proj_in",
+  "up_blocks.3.attentions.2.proj_out",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_k",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_out.0",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_q",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn1.to_v",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_k",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_out.0",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_q",
+  "up_blocks.3.attentions.2.transformer_blocks.0.attn2.to_v",
+  "up_blocks.3.attentions.2.transformer_blocks.0.ff.net.0.proj",
+  "up_blocks.3.attentions.2.transformer_blocks.0.ff.net.2",
+  "up_blocks.3.attentions.2.transformer_blocks.0.norm1",
+  "up_blocks.3.attentions.2.transformer_blocks.0.norm2",
+  "up_blocks.3.attentions.2.transformer_blocks.0.norm3",
+  "up_blocks.3.resnets.0.conv1",
+  "up_blocks.3.resnets.0.conv2",
+  "up_blocks.3.resnets.0.conv_shortcut",
+  "up_blocks.3.resnets.0.norm1",
+  "up_blocks.3.resnets.0.norm2",
+  "up_blocks.3.resnets.0.time_emb_proj",
+  "up_blocks.3.resnets.1.conv1",
+  "up_blocks.3.resnets.1.conv2",
+  "up_blocks.3.resnets.1.conv_shortcut",
+  "up_blocks.3.resnets.1.norm1",
+  "up_blocks.3.resnets.1.norm2",
+  "up_blocks.3.resnets.1.time_emb_proj",
+  "up_blocks.3.resnets.2.conv1",
+  "up_blocks.3.resnets.2.conv2",
+  "up_blocks.3.resnets.2.conv_shortcut",
+  "up_blocks.3.resnets.2.norm1",
+  "up_blocks.3.resnets.2.norm2",
+  "up_blocks.3.resnets.2.time_emb_proj"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_vae_compvis.patterns.json

@@ -0,0 +1,14 @@
+[
+  "first_stage_model.(decoder|encoder).(conv_in|conv_out|norm_out)",
+  "first_stage_model.(decoder|encoder).mid.(block_1|block_2).(conv1|conv2|norm1|norm2)",
+  "first_stage_model.(decoder|encoder).mid.attn_1.(k|norm|proj_out|q|v)",
+  "first_stage_model.(post_quant_conv|quant_conv)",
+  "first_stage_model.decoder.up.(0|1).block.(1|2).(conv1|conv2|norm1|norm2)",
+  "first_stage_model.decoder.up.(0|1).block.0.(conv1|conv2|nin_shortcut|norm1|norm2)",
+  "first_stage_model.decoder.up.(1|2|3).upsample.conv",
+  "first_stage_model.decoder.up.(2|3).block.(0|1|2).(conv1|conv2|norm1|norm2)",
+  "first_stage_model.encoder.down.(0|1|2).downsample.conv",
+  "first_stage_model.encoder.down.(0|3).block.(0|1).(conv1|conv2|norm1|norm2)",
+  "first_stage_model.encoder.down.(1|2).block.0.(conv1|conv2|nin_shortcut|norm1|norm2)",
+  "first_stage_model.encoder.down.(1|2).block.1.(conv1|conv2|norm1|norm2)"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_vae_compvis.prefix-execution-order.json

@@ -0,0 +1,126 @@
+[
+  "encoder.conv_in",
+  "encoder.down.0.block.0.norm1",
+  "encoder.down.0.block.0.conv1",
+  "encoder.down.0.block.0.norm2",
+  "encoder.down.0.block.0.conv2",
+  "encoder.down.0.block.1.norm1",
+  "encoder.down.0.block.1.conv1",
+  "encoder.down.0.block.1.norm2",
+  "encoder.down.0.block.1.conv2",
+  "encoder.down.0.downsample.conv",
+  "encoder.down.1.block.0.norm1",
+  "encoder.down.1.block.0.conv1",
+  "encoder.down.1.block.0.norm2",
+  "encoder.down.1.block.0.conv2",
+  "encoder.down.1.block.0.nin_shortcut",
+  "encoder.down.1.block.1.norm1",
+  "encoder.down.1.block.1.conv1",
+  "encoder.down.1.block.1.norm2",
+  "encoder.down.1.block.1.conv2",
+  "encoder.down.1.downsample.conv",
+  "encoder.down.2.block.0.norm1",
+  "encoder.down.2.block.0.conv1",
+  "encoder.down.2.block.0.norm2",
+  "encoder.down.2.block.0.conv2",
+  "encoder.down.2.block.0.nin_shortcut",
+  "encoder.down.2.block.1.norm1",
+  "encoder.down.2.block.1.conv1",
+  "encoder.down.2.block.1.norm2",
+  "encoder.down.2.block.1.conv2",
+  "encoder.down.2.downsample.conv",
+  "encoder.down.3.block.0.norm1",
+  "encoder.down.3.block.0.conv1",
+  "encoder.down.3.block.0.norm2",
+  "encoder.down.3.block.0.conv2",
+  "encoder.down.3.block.1.norm1",
+  "encoder.down.3.block.1.conv1",
+  "encoder.down.3.block.1.norm2",
+  "encoder.down.3.block.1.conv2",
+  "encoder.mid.block_1.norm1",
+  "encoder.mid.block_1.conv1",
+  "encoder.mid.block_1.norm2",
+  "encoder.mid.block_1.conv2",
+  "encoder.mid.attn_1.norm",
+  "encoder.mid.attn_1.q",
+  "encoder.mid.attn_1.k",
+  "encoder.mid.attn_1.v",
+  "encoder.mid.attn_1.proj_out",
+  "encoder.mid.block_2.norm1",
+  "encoder.mid.block_2.conv1",
+  "encoder.mid.block_2.norm2",
+  "encoder.mid.block_2.conv2",
+  "encoder.norm_out",
+  "encoder.conv_out",
+  "quant_conv",
+  "post_quant_conv",
+  "decoder.conv_in",
+  "decoder.mid.block_1.norm1",
+  "decoder.mid.block_1.conv1",
+  "decoder.mid.block_1.norm2",
+  "decoder.mid.block_1.conv2",
+  "decoder.mid.attn_1.norm",
+  "decoder.mid.attn_1.q",
+  "decoder.mid.attn_1.k",
+  "decoder.mid.attn_1.v",
+  "decoder.mid.attn_1.proj_out",
+  "decoder.mid.block_2.norm1",
+  "decoder.mid.block_2.conv1",
+  "decoder.mid.block_2.norm2",
+  "decoder.mid.block_2.conv2",
+  "decoder.up.3.block.0.norm1",
+  "decoder.up.3.block.0.conv1",
+  "decoder.up.3.block.0.norm2",
+  "decoder.up.3.block.0.conv2",
+  "decoder.up.3.block.1.norm1",
+  "decoder.up.3.block.1.conv1",
+  "decoder.up.3.block.1.norm2",
+  "decoder.up.3.block.1.conv2",
+  "decoder.up.3.block.2.norm1",
+  "decoder.up.3.block.2.conv1",
+  "decoder.up.3.block.2.norm2",
+  "decoder.up.3.block.2.conv2",
+  "decoder.up.3.upsample.conv",
+  "decoder.up.2.block.0.norm1",
+  "decoder.up.2.block.0.conv1",
+  "decoder.up.2.block.0.norm2",
+  "decoder.up.2.block.0.conv2",
+  "decoder.up.2.block.1.norm1",
+  "decoder.up.2.block.1.conv1",
+  "decoder.up.2.block.1.norm2",
+  "decoder.up.2.block.1.conv2",
+  "decoder.up.2.block.2.norm1",
+  "decoder.up.2.block.2.conv1",
+  "decoder.up.2.block.2.norm2",
+  "decoder.up.2.block.2.conv2",
+  "decoder.up.2.upsample.conv",
+  "decoder.up.1.block.0.norm1",
+  "decoder.up.1.block.0.conv1",
+  "decoder.up.1.block.0.norm2",
+  "decoder.up.1.block.0.conv2",
+  "decoder.up.1.block.0.nin_shortcut",
+  "decoder.up.1.block.1.norm1",
+  "decoder.up.1.block.1.conv1",
+  "decoder.up.1.block.1.norm2",
+  "decoder.up.1.block.1.conv2",
+  "decoder.up.1.block.2.norm1",
+  "decoder.up.1.block.2.conv1",
+  "decoder.up.1.block.2.norm2",
+  "decoder.up.1.block.2.conv2",
+  "decoder.up.1.upsample.conv",
+  "decoder.up.0.block.0.norm1",
+  "decoder.up.0.block.0.conv1",
+  "decoder.up.0.block.0.norm2",
+  "decoder.up.0.block.0.conv2",
+  "decoder.up.0.block.0.nin_shortcut",
+  "decoder.up.0.block.1.norm1",
+  "decoder.up.0.block.1.conv1",
+  "decoder.up.0.block.1.norm2",
+  "decoder.up.0.block.1.conv2",
+  "decoder.up.0.block.2.norm1",
+  "decoder.up.0.block.2.conv1",
+  "decoder.up.0.block.2.norm2",
+  "decoder.up.0.block.2.conv2",
+  "decoder.norm_out",
+  "decoder.conv_out"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_vae_compvis.prefixes.json

@@ -0,0 +1,126 @@
+[
+  "first_stage_model.decoder.conv_in",
+  "first_stage_model.decoder.conv_out",
+  "first_stage_model.decoder.mid.attn_1.k",
+  "first_stage_model.decoder.mid.attn_1.norm",
+  "first_stage_model.decoder.mid.attn_1.proj_out",
+  "first_stage_model.decoder.mid.attn_1.q",
+  "first_stage_model.decoder.mid.attn_1.v",
+  "first_stage_model.decoder.mid.block_1.conv1",
+  "first_stage_model.decoder.mid.block_1.conv2",
+  "first_stage_model.decoder.mid.block_1.norm1",
+  "first_stage_model.decoder.mid.block_1.norm2",
+  "first_stage_model.decoder.mid.block_2.conv1",
+  "first_stage_model.decoder.mid.block_2.conv2",
+  "first_stage_model.decoder.mid.block_2.norm1",
+  "first_stage_model.decoder.mid.block_2.norm2",
+  "first_stage_model.decoder.norm_out",
+  "first_stage_model.decoder.up.0.block.0.conv1",
+  "first_stage_model.decoder.up.0.block.0.conv2",
+  "first_stage_model.decoder.up.0.block.0.nin_shortcut",
+  "first_stage_model.decoder.up.0.block.0.norm1",
+  "first_stage_model.decoder.up.0.block.0.norm2",
+  "first_stage_model.decoder.up.0.block.1.conv1",
+  "first_stage_model.decoder.up.0.block.1.conv2",
+  "first_stage_model.decoder.up.0.block.1.norm1",
+  "first_stage_model.decoder.up.0.block.1.norm2",
+  "first_stage_model.decoder.up.0.block.2.conv1",
+  "first_stage_model.decoder.up.0.block.2.conv2",
+  "first_stage_model.decoder.up.0.block.2.norm1",
+  "first_stage_model.decoder.up.0.block.2.norm2",
+  "first_stage_model.decoder.up.1.block.0.conv1",
+  "first_stage_model.decoder.up.1.block.0.conv2",
+  "first_stage_model.decoder.up.1.block.0.nin_shortcut",
+  "first_stage_model.decoder.up.1.block.0.norm1",
+  "first_stage_model.decoder.up.1.block.0.norm2",
+  "first_stage_model.decoder.up.1.block.1.conv1",
+  "first_stage_model.decoder.up.1.block.1.conv2",
+  "first_stage_model.decoder.up.1.block.1.norm1",
+  "first_stage_model.decoder.up.1.block.1.norm2",
+  "first_stage_model.decoder.up.1.block.2.conv1",
+  "first_stage_model.decoder.up.1.block.2.conv2",
+  "first_stage_model.decoder.up.1.block.2.norm1",
+  "first_stage_model.decoder.up.1.block.2.norm2",
+  "first_stage_model.decoder.up.1.upsample.conv",
+  "first_stage_model.decoder.up.2.block.0.conv1",
+  "first_stage_model.decoder.up.2.block.0.conv2",
+  "first_stage_model.decoder.up.2.block.0.norm1",
+  "first_stage_model.decoder.up.2.block.0.norm2",
+  "first_stage_model.decoder.up.2.block.1.conv1",
+  "first_stage_model.decoder.up.2.block.1.conv2",
+  "first_stage_model.decoder.up.2.block.1.norm1",
+  "first_stage_model.decoder.up.2.block.1.norm2",
+  "first_stage_model.decoder.up.2.block.2.conv1",
+  "first_stage_model.decoder.up.2.block.2.conv2",
+  "first_stage_model.decoder.up.2.block.2.norm1",
+  "first_stage_model.decoder.up.2.block.2.norm2",
+  "first_stage_model.decoder.up.2.upsample.conv",
+  "first_stage_model.decoder.up.3.block.0.conv1",
+  "first_stage_model.decoder.up.3.block.0.conv2",
+  "first_stage_model.decoder.up.3.block.0.norm1",
+  "first_stage_model.decoder.up.3.block.0.norm2",
+  "first_stage_model.decoder.up.3.block.1.conv1",
+  "first_stage_model.decoder.up.3.block.1.conv2",
+  "first_stage_model.decoder.up.3.block.1.norm1",
+  "first_stage_model.decoder.up.3.block.1.norm2",
+  "first_stage_model.decoder.up.3.block.2.conv1",
+  "first_stage_model.decoder.up.3.block.2.conv2",
+  "first_stage_model.decoder.up.3.block.2.norm1",
+  "first_stage_model.decoder.up.3.block.2.norm2",
+  "first_stage_model.decoder.up.3.upsample.conv",
+  "first_stage_model.encoder.conv_in",
+  "first_stage_model.encoder.conv_out",
+  "first_stage_model.encoder.down.0.block.0.conv1",
+  "first_stage_model.encoder.down.0.block.0.conv2",
+  "first_stage_model.encoder.down.0.block.0.norm1",
+  "first_stage_model.encoder.down.0.block.0.norm2",
+  "first_stage_model.encoder.down.0.block.1.conv1",
+  "first_stage_model.encoder.down.0.block.1.conv2",
+  "first_stage_model.encoder.down.0.block.1.norm1",
+  "first_stage_model.encoder.down.0.block.1.norm2",
+  "first_stage_model.encoder.down.0.downsample.conv",
+  "first_stage_model.encoder.down.1.block.0.conv1",
+  "first_stage_model.encoder.down.1.block.0.conv2",
+  "first_stage_model.encoder.down.1.block.0.nin_shortcut",
+  "first_stage_model.encoder.down.1.block.0.norm1",
+  "first_stage_model.encoder.down.1.block.0.norm2",
+  "first_stage_model.encoder.down.1.block.1.conv1",
+  "first_stage_model.encoder.down.1.block.1.conv2",
+  "first_stage_model.encoder.down.1.block.1.norm1",
+  "first_stage_model.encoder.down.1.block.1.norm2",
+  "first_stage_model.encoder.down.1.downsample.conv",
+  "first_stage_model.encoder.down.2.block.0.conv1",
+  "first_stage_model.encoder.down.2.block.0.conv2",
+  "first_stage_model.encoder.down.2.block.0.nin_shortcut",
+  "first_stage_model.encoder.down.2.block.0.norm1",
+  "first_stage_model.encoder.down.2.block.0.norm2",
+  "first_stage_model.encoder.down.2.block.1.conv1",
+  "first_stage_model.encoder.down.2.block.1.conv2",
+  "first_stage_model.encoder.down.2.block.1.norm1",
+  "first_stage_model.encoder.down.2.block.1.norm2",
+  "first_stage_model.encoder.down.2.downsample.conv",
+  "first_stage_model.encoder.down.3.block.0.conv1",
+  "first_stage_model.encoder.down.3.block.0.conv2",
+  "first_stage_model.encoder.down.3.block.0.norm1",
+  "first_stage_model.encoder.down.3.block.0.norm2",
+  "first_stage_model.encoder.down.3.block.1.conv1",
+  "first_stage_model.encoder.down.3.block.1.conv2",
+  "first_stage_model.encoder.down.3.block.1.norm1",
+  "first_stage_model.encoder.down.3.block.1.norm2",
+  "first_stage_model.encoder.mid.attn_1.k",
+  "first_stage_model.encoder.mid.attn_1.norm",
+  "first_stage_model.encoder.mid.attn_1.proj_out",
+  "first_stage_model.encoder.mid.attn_1.q",
+  "first_stage_model.encoder.mid.attn_1.v",
+  "first_stage_model.encoder.mid.block_1.conv1",
+  "first_stage_model.encoder.mid.block_1.conv2",
+  "first_stage_model.encoder.mid.block_1.norm1",
+  "first_stage_model.encoder.mid.block_1.norm2",
+  "first_stage_model.encoder.mid.block_2.conv1",
+  "first_stage_model.encoder.mid.block_2.conv2",
+  "first_stage_model.encoder.mid.block_2.norm1",
+  "first_stage_model.encoder.mid.block_2.norm2",
+  "first_stage_model.encoder.norm_out",
+  "first_stage_model.post_quant_conv",
+  "first_stage_model.quant_conv"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_vae_diffusers.patterns.json

@@ -0,0 +1,15 @@
+[
+  "(decoder|encoder).(conv_in|conv_norm_out|conv_out)",
+  "(decoder|encoder).mid_block.attentions.0.(group_norm|to_k|to_q|to_v)",
+  "(decoder|encoder).mid_block.attentions.0.to_out.0",
+  "(decoder|encoder).mid_block.resnets.(0|1).(conv1|conv2|norm1|norm2)",
+  "(post_quant_conv|quant_conv)",
+  "decoder.up_blocks.(0|1).resnets.(0|1|2).(conv1|conv2|norm1|norm2)",
+  "decoder.up_blocks.(0|1|2).upsamplers.0.conv",
+  "decoder.up_blocks.(2|3).resnets.(1|2).(conv1|conv2|norm1|norm2)",
+  "decoder.up_blocks.(2|3).resnets.0.(conv1|conv2|conv_shortcut|norm1|norm2)",
+  "encoder.down_blocks.(0|1|2).downsamplers.0.conv",
+  "encoder.down_blocks.(0|3).resnets.(0|1).(conv1|conv2|norm1|norm2)",
+  "encoder.down_blocks.(1|2).resnets.0.(conv1|conv2|conv_shortcut|norm1|norm2)",
+  "encoder.down_blocks.(1|2).resnets.1.(conv1|conv2|norm1|norm2)"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_vae_diffusers.prefix-execution-order.json

@@ -0,0 +1,126 @@
+[
+  "encoder.conv_in",
+  "encoder.down_blocks.0.resnets.0.norm1",
+  "encoder.down_blocks.0.resnets.0.conv1",
+  "encoder.down_blocks.0.resnets.0.norm2",
+  "encoder.down_blocks.0.resnets.0.conv2",
+  "encoder.down_blocks.0.resnets.1.norm1",
+  "encoder.down_blocks.0.resnets.1.conv1",
+  "encoder.down_blocks.0.resnets.1.norm2",
+  "encoder.down_blocks.0.resnets.1.conv2",
+  "encoder.down_blocks.0.downsamplers.0.conv",
+  "encoder.down_blocks.1.resnets.0.norm1",
+  "encoder.down_blocks.1.resnets.0.conv1",
+  "encoder.down_blocks.1.resnets.0.norm2",
+  "encoder.down_blocks.1.resnets.0.conv2",
+  "encoder.down_blocks.1.resnets.0.conv_shortcut",
+  "encoder.down_blocks.1.resnets.1.norm1",
+  "encoder.down_blocks.1.resnets.1.conv1",
+  "encoder.down_blocks.1.resnets.1.norm2",
+  "encoder.down_blocks.1.resnets.1.conv2",
+  "encoder.down_blocks.1.downsamplers.0.conv",
+  "encoder.down_blocks.2.resnets.0.norm1",
+  "encoder.down_blocks.2.resnets.0.conv1",
+  "encoder.down_blocks.2.resnets.0.norm2",
+  "encoder.down_blocks.2.resnets.0.conv2",
+  "encoder.down_blocks.2.resnets.0.conv_shortcut",
+  "encoder.down_blocks.2.resnets.1.norm1",
+  "encoder.down_blocks.2.resnets.1.conv1",
+  "encoder.down_blocks.2.resnets.1.norm2",
+  "encoder.down_blocks.2.resnets.1.conv2",
+  "encoder.down_blocks.2.downsamplers.0.conv",
+  "encoder.down_blocks.3.resnets.0.norm1",
+  "encoder.down_blocks.3.resnets.0.conv1",
+  "encoder.down_blocks.3.resnets.0.norm2",
+  "encoder.down_blocks.3.resnets.0.conv2",
+  "encoder.down_blocks.3.resnets.1.norm1",
+  "encoder.down_blocks.3.resnets.1.conv1",
+  "encoder.down_blocks.3.resnets.1.norm2",
+  "encoder.down_blocks.3.resnets.1.conv2",
+  "encoder.mid_block.resnets.0.norm1",
+  "encoder.mid_block.resnets.0.conv1",
+  "encoder.mid_block.resnets.0.norm2",
+  "encoder.mid_block.resnets.0.conv2",
+  "encoder.mid_block.attentions.0.group_norm",
+  "encoder.mid_block.attentions.0.to_q",
+  "encoder.mid_block.attentions.0.to_k",
+  "encoder.mid_block.attentions.0.to_v",
+  "encoder.mid_block.attentions.0.to_out.0",
+  "encoder.mid_block.resnets.1.norm1",
+  "encoder.mid_block.resnets.1.conv1",
+  "encoder.mid_block.resnets.1.norm2",
+  "encoder.mid_block.resnets.1.conv2",
+  "encoder.conv_norm_out",
+  "encoder.conv_out",
+  "quant_conv",
+  "post_quant_conv",
+  "decoder.conv_in",
+  "decoder.mid_block.resnets.0.norm1",
+  "decoder.mid_block.resnets.0.conv1",
+  "decoder.mid_block.resnets.0.norm2",
+  "decoder.mid_block.resnets.0.conv2",
+  "decoder.mid_block.attentions.0.group_norm",
+  "decoder.mid_block.attentions.0.to_q",
+  "decoder.mid_block.attentions.0.to_k",
+  "decoder.mid_block.attentions.0.to_v",
+  "decoder.mid_block.attentions.0.to_out.0",
+  "decoder.mid_block.resnets.1.norm1",
+  "decoder.mid_block.resnets.1.conv1",
+  "decoder.mid_block.resnets.1.norm2",
+  "decoder.mid_block.resnets.1.conv2",
+  "decoder.up_blocks.0.resnets.0.norm1",
+  "decoder.up_blocks.0.resnets.0.conv1",
+  "decoder.up_blocks.0.resnets.0.norm2",
+  "decoder.up_blocks.0.resnets.0.conv2",
+  "decoder.up_blocks.0.resnets.1.norm1",
+  "decoder.up_blocks.0.resnets.1.conv1",
+  "decoder.up_blocks.0.resnets.1.norm2",
+  "decoder.up_blocks.0.resnets.1.conv2",
+  "decoder.up_blocks.0.resnets.2.norm1",
+  "decoder.up_blocks.0.resnets.2.conv1",
+  "decoder.up_blocks.0.resnets.2.norm2",
+  "decoder.up_blocks.0.resnets.2.conv2",
+  "decoder.up_blocks.0.upsamplers.0.conv",
+  "decoder.up_blocks.1.resnets.0.norm1",
+  "decoder.up_blocks.1.resnets.0.conv1",
+  "decoder.up_blocks.1.resnets.0.norm2",
+  "decoder.up_blocks.1.resnets.0.conv2",
+  "decoder.up_blocks.1.resnets.1.norm1",
+  "decoder.up_blocks.1.resnets.1.conv1",
+  "decoder.up_blocks.1.resnets.1.norm2",
+  "decoder.up_blocks.1.resnets.1.conv2",
+  "decoder.up_blocks.1.resnets.2.norm1",
+  "decoder.up_blocks.1.resnets.2.conv1",
+  "decoder.up_blocks.1.resnets.2.norm2",
+  "decoder.up_blocks.1.resnets.2.conv2",
+  "decoder.up_blocks.1.upsamplers.0.conv",
+  "decoder.up_blocks.2.resnets.0.norm1",
+  "decoder.up_blocks.2.resnets.0.conv1",
+  "decoder.up_blocks.2.resnets.0.norm2",
+  "decoder.up_blocks.2.resnets.0.conv2",
+  "decoder.up_blocks.2.resnets.0.conv_shortcut",
+  "decoder.up_blocks.2.resnets.1.norm1",
+  "decoder.up_blocks.2.resnets.1.conv1",
+  "decoder.up_blocks.2.resnets.1.norm2",
+  "decoder.up_blocks.2.resnets.1.conv2",
+  "decoder.up_blocks.2.resnets.2.norm1",
+  "decoder.up_blocks.2.resnets.2.conv1",
+  "decoder.up_blocks.2.resnets.2.norm2",
+  "decoder.up_blocks.2.resnets.2.conv2",
+  "decoder.up_blocks.2.upsamplers.0.conv",
+  "decoder.up_blocks.3.resnets.0.norm1",
+  "decoder.up_blocks.3.resnets.0.conv1",
+  "decoder.up_blocks.3.resnets.0.norm2",
+  "decoder.up_blocks.3.resnets.0.conv2",
+  "decoder.up_blocks.3.resnets.0.conv_shortcut",
+  "decoder.up_blocks.3.resnets.1.norm1",
+  "decoder.up_blocks.3.resnets.1.conv1",
+  "decoder.up_blocks.3.resnets.1.norm2",
+  "decoder.up_blocks.3.resnets.1.conv2",
+  "decoder.up_blocks.3.resnets.2.norm1",
+  "decoder.up_blocks.3.resnets.2.conv1",
+  "decoder.up_blocks.3.resnets.2.norm2",
+  "decoder.up_blocks.3.resnets.2.conv2",
+  "decoder.conv_norm_out",
+  "decoder.conv_out"
+]

imaginairy/weight_management/weight_info/stable-diffusion-1-5_vae_diffusers.prefixes.json

@@ -0,0 +1,126 @@
+[
+  "decoder.conv_in",
+  "decoder.conv_norm_out",
+  "decoder.conv_out",
+  "decoder.mid_block.attentions.0.group_norm",
+  "decoder.mid_block.attentions.0.to_k",
+  "decoder.mid_block.attentions.0.to_out.0",
+  "decoder.mid_block.attentions.0.to_q",
+  "decoder.mid_block.attentions.0.to_v",
+  "decoder.mid_block.resnets.0.conv1",
+  "decoder.mid_block.resnets.0.conv2",
+  "decoder.mid_block.resnets.0.norm1",
+  "decoder.mid_block.resnets.0.norm2",
+  "decoder.mid_block.resnets.1.conv1",
+  "decoder.mid_block.resnets.1.conv2",
+  "decoder.mid_block.resnets.1.norm1",
+  "decoder.mid_block.resnets.1.norm2",
+  "decoder.up_blocks.0.resnets.0.conv1",
+  "decoder.up_blocks.0.resnets.0.conv2",
+  "decoder.up_blocks.0.resnets.0.norm1",
+  "decoder.up_blocks.0.resnets.0.norm2",
+  "decoder.up_blocks.0.resnets.1.conv1",
+  "decoder.up_blocks.0.resnets.1.conv2",
+  "decoder.up_blocks.0.resnets.1.norm1",
+  "decoder.up_blocks.0.resnets.1.norm2",
+  "decoder.up_blocks.0.resnets.2.conv1",
+  "decoder.up_blocks.0.resnets.2.conv2",
+  "decoder.up_blocks.0.resnets.2.norm1",
+  "decoder.up_blocks.0.resnets.2.norm2",
+  "decoder.up_blocks.0.upsamplers.0.conv",
+  "decoder.up_blocks.1.resnets.0.conv1",
+  "decoder.up_blocks.1.resnets.0.conv2",
+  "decoder.up_blocks.1.resnets.0.norm1",
+  "decoder.up_blocks.1.resnets.0.norm2",
+  "decoder.up_blocks.1.resnets.1.conv1",
+  "decoder.up_blocks.1.resnets.1.conv2",
+  "decoder.up_blocks.1.resnets.1.norm1",
+  "decoder.up_blocks.1.resnets.1.norm2",
+  "decoder.up_blocks.1.resnets.2.conv1",
+  "decoder.up_blocks.1.resnets.2.conv2",
+  "decoder.up_blocks.1.resnets.2.norm1",
+  "decoder.up_blocks.1.resnets.2.norm2",
+  "decoder.up_blocks.1.upsamplers.0.conv",
+  "decoder.up_blocks.2.resnets.0.conv1",
+  "decoder.up_blocks.2.resnets.0.conv2",
+  "decoder.up_blocks.2.resnets.0.conv_shortcut",
+  "decoder.up_blocks.2.resnets.0.norm1",
+  "decoder.up_blocks.2.resnets.0.norm2",
+  "decoder.up_blocks.2.resnets.1.conv1",
+  "decoder.up_blocks.2.resnets.1.conv2",
+  "decoder.up_blocks.2.resnets.1.norm1",
+  "decoder.up_blocks.2.resnets.1.norm2",
+  "decoder.up_blocks.2.resnets.2.conv1",
+  "decoder.up_blocks.2.resnets.2.conv2",
+  "decoder.up_blocks.2.resnets.2.norm1",
+  "decoder.up_blocks.2.resnets.2.norm2",
+  "decoder.up_blocks.2.upsamplers.0.conv",
+  "decoder.up_blocks.3.resnets.0.conv1",
+  "decoder.up_blocks.3.resnets.0.conv2",
+  "decoder.up_blocks.3.resnets.0.conv_shortcut",
+  "decoder.up_blocks.3.resnets.0.norm1",
+  "decoder.up_blocks.3.resnets.0.norm2",
+  "decoder.up_blocks.3.resnets.1.conv1",
+  "decoder.up_blocks.3.resnets.1.conv2",
+  "decoder.up_blocks.3.resnets.1.norm1",
+  "decoder.up_blocks.3.resnets.1.norm2",
+  "decoder.up_blocks.3.resnets.2.conv1",
+  "decoder.up_blocks.3.resnets.2.conv2",
+  "decoder.up_blocks.3.resnets.2.norm1",
+  "decoder.up_blocks.3.resnets.2.norm2",
+  "encoder.conv_in",
+  "encoder.conv_norm_out",
+  "encoder.conv_out",
+  "encoder.down_blocks.0.downsamplers.0.conv",
+  "encoder.down_blocks.0.resnets.0.conv1",
+  "encoder.down_blocks.0.resnets.0.conv2",
+  "encoder.down_blocks.0.resnets.0.norm1",
+  "encoder.down_blocks.0.resnets.0.norm2",
+  "encoder.down_blocks.0.resnets.1.conv1",
+  "encoder.down_blocks.0.resnets.1.conv2",
+  "encoder.down_blocks.0.resnets.1.norm1",
+  "encoder.down_blocks.0.resnets.1.norm2",
+  "encoder.down_blocks.1.downsamplers.0.conv",
+  "encoder.down_blocks.1.resnets.0.conv1",
+  "encoder.down_blocks.1.resnets.0.conv2",
+  "encoder.down_blocks.1.resnets.0.conv_shortcut",
+  "encoder.down_blocks.1.resnets.0.norm1",
+  "encoder.down_blocks.1.resnets.0.norm2",
+  "encoder.down_blocks.1.resnets.1.conv1",
+  "encoder.down_blocks.1.resnets.1.conv2",
+  "encoder.down_blocks.1.resnets.1.norm1",
+  "encoder.down_blocks.1.resnets.1.norm2",
+  "encoder.down_blocks.2.downsamplers.0.conv",
+  "encoder.down_blocks.2.resnets.0.conv1",
+  "encoder.down_blocks.2.resnets.0.conv2",
+  "encoder.down_blocks.2.resnets.0.conv_shortcut",
+  "encoder.down_blocks.2.resnets.0.norm1",
+  "encoder.down_blocks.2.resnets.0.norm2",
+  "encoder.down_blocks.2.resnets.1.conv1",
+  "encoder.down_blocks.2.resnets.1.conv2",
+  "encoder.down_blocks.2.resnets.1.norm1",
+  "encoder.down_blocks.2.resnets.1.norm2",
+  "encoder.down_blocks.3.resnets.0.conv1",
+  "encoder.down_blocks.3.resnets.0.conv2",
+  "encoder.down_blocks.3.resnets.0.norm1",
+  "encoder.down_blocks.3.resnets.0.norm2",
+  "encoder.down_blocks.3.resnets.1.conv1",
+  "encoder.down_blocks.3.resnets.1.conv2",
+  "encoder.down_blocks.3.resnets.1.norm1",
+  "encoder.down_blocks.3.resnets.1.norm2",
+  "encoder.mid_block.attentions.0.group_norm",
+  "encoder.mid_block.attentions.0.to_k",
+  "encoder.mid_block.attentions.0.to_out.0",
+  "encoder.mid_block.attentions.0.to_q",
+  "encoder.mid_block.attentions.0.to_v",
+  "encoder.mid_block.resnets.0.conv1",
+  "encoder.mid_block.resnets.0.conv2",
+  "encoder.mid_block.resnets.0.norm1",
+  "encoder.mid_block.resnets.0.norm2",
+  "encoder.mid_block.resnets.1.conv1",
+  "encoder.mid_block.resnets.1.conv2",
+  "encoder.mid_block.resnets.1.norm1",
+  "encoder.mid_block.resnets.1.norm2",
+  "post_quant_conv",
+  "quant_conv"
+]

requirements-dev.txt

@@ -1,13 +1,14 @@
+#
 # This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
 #    pip-compile --output-file=requirements-dev.txt requirements-dev.in setup.py
 #
-aiohttp==3.8.5
+aiohttp==3.9.0
     # via fsspec
 aiosignal==1.3.1
     # via aiohttp
-annotated-types==0.5.0
+annotated-types==0.6.0
     # via pydantic
 antlr4-python3-runtime==4.9.3
     # via omegaconf
@@ -19,14 +20,12 @@ async-timeout==4.0.3
     # via aiohttp
 attrs==23.1.0
     # via aiohttp
-black==23.9.1
+black==23.11.0
     # via -r requirements-dev.in
-certifi==2023.7.22
+certifi==2023.11.17
+    # via requests
+charset-normalizer==3.3.2
     # via requests
-charset-normalizer==3.2.0
-    # via
-    #   aiohttp
-    #   requests
 click==8.1.7
     # via
     #   black
@@ -34,21 +33,21 @@ click==8.1.7
     #   click-shell
     #   imaginAIry (setup.py)
     #   uvicorn
-click-help-colors==0.9.2
+click-help-colors==0.9.4
     # via imaginAIry (setup.py)
 click-shell==2.1
     # via imaginAIry (setup.py)
-contourpy==1.1.1
+contourpy==1.2.0
     # via matplotlib
-coverage==7.3.1
+coverage==7.3.2
     # via -r requirements-dev.in
-cycler==0.12.0
+cycler==0.12.1
     # via matplotlib
-diffusers==0.21.4
+diffusers==0.23.1
     # via imaginAIry (setup.py)
-einops==0.6.1
+einops==0.7.0
     # via imaginAIry (setup.py)
-exceptiongroup==1.1.3
+exceptiongroup==1.2.0
     # via
     #   anyio
     #   pytest
@@ -56,69 +55,82 @@ facexlib==0.3.0
     # via imaginAIry (setup.py)
 fairscale==0.4.13
     # via imaginAIry (setup.py)
-fastapi==0.103.2
+fastapi==0.104.1
     # via imaginAIry (setup.py)
-filelock==3.12.4
+filelock==3.13.1
     # via
     #   diffusers
     #   huggingface-hub
+    #   torch
     #   transformers
 filterpy==1.4.5
     # via facexlib
-fonttools==4.43.0
+fonttools==4.45.0
     # via matplotlib
 frozenlist==1.4.0
     # via
     #   aiohttp
     #   aiosignal
-fsspec[http]==2023.9.2
+fsspec[http]==2023.10.0
     # via
     #   huggingface-hub
     #   pytorch-lightning
-ftfy==6.1.1
+    #   torch
+ftfy==6.1.3
     # via
     #   imaginAIry (setup.py)
     #   open-clip-torch
 h11==0.14.0
     # via uvicorn
-huggingface-hub==0.17.3
+huggingface-hub==0.19.4
     # via
     #   diffusers
     #   open-clip-torch
     #   timm
+    #   tokenizers
     #   transformers
 idna==3.4
     # via
     #   anyio
     #   requests
     #   yarl
-imageio==2.31.4
+imageio==2.33.0
     # via imaginAIry (setup.py)
 importlib-metadata==6.8.0
     # via diffusers
 iniconfig==2.0.0
     # via pytest
+jaxtyping==0.2.23
+    # via refiners
+jinja2==3.1.2
+    # via torch
 kiwisolver==1.4.5
     # via matplotlib
 kornia==0.7.0
     # via imaginAIry (setup.py)
-lightning-utilities==0.9.0
+lightning-utilities==0.10.0
     # via
     #   pytorch-lightning
     #   torchmetrics
-llvmlite==0.41.0
+llvmlite==0.41.1
     # via numba
-matplotlib==3.7.3
+markupsafe==2.1.3
+    # via jinja2
+matplotlib==3.7.4
     # via
     #   -c tests/constraints.txt
     #   filterpy
+mpmath==1.3.0
+    # via sympy
 multidict==6.0.4
     # via
     #   aiohttp
     #   yarl
 mypy-extensions==1.0.0
     # via black
-numba==0.58.0
+networkx==3.2.1
+    # via torch
+numba==0.58.1
     # via facexlib
 numpy==1.24.4
     # via
@@ -130,23 +142,25 @@ numpy==1.24.4
     #   filterpy
     #   imageio
     #   imaginAIry (setup.py)
+    #   jaxtyping
     #   matplotlib
     #   numba
     #   opencv-python
     #   pytorch-lightning
+    #   refiners
     #   scipy
     #   torchmetrics
     #   torchvision
     #   transformers
 omegaconf==2.3.0
     # via imaginAIry (setup.py)
-open-clip-torch==2.20.0
+open-clip-torch==2.23.0
     # via imaginAIry (setup.py)
 opencv-python==4.8.1.78
     # via
     #   facexlib
     #   imaginAIry (setup.py)
-packaging==23.1
+packaging==23.2
     # via
     #   black
     #   huggingface-hub
@@ -159,33 +173,34 @@ packaging==23.1
     #   transformers
 pathspec==0.11.2
     # via black
-pillow==10.0.1
+pillow==10.1.0
     # via
     #   diffusers
     #   facexlib
     #   imageio
     #   imaginAIry (setup.py)
     #   matplotlib
+    #   refiners
     #   torchvision
-platformdirs==3.10.0
+platformdirs==4.0.0
     # via black
 pluggy==1.3.0
     # via pytest
-protobuf==3.20.3
+protobuf==4.25.1
     # via
     #   imaginAIry (setup.py)
     #   open-clip-torch
-psutil==5.9.5
+psutil==5.9.6
     # via imaginAIry (setup.py)
-pydantic==2.4.2
+pydantic==2.5.2
     # via
     #   fastapi
     #   imaginAIry (setup.py)
-pydantic-core==2.10.1
+pydantic-core==2.14.5
     # via pydantic
 pyparsing==3.1.1
     # via matplotlib
-pytest==7.4.2
+pytest==7.4.3
     # via
     #   -r requirements-dev.in
     #   pytest-randomly
@@ -206,7 +221,9 @@ pyyaml==6.0.1
     #   responses
     #   timm
     #   transformers
-regex==2023.8.8
+refiners==0.2.0
+    # via imaginAIry (setup.py)
+regex==2023.10.3
     # via
     #   diffusers
     #   open-clip-torch
@@ -220,14 +237,15 @@ requests==2.31.0
     #   responses
     #   torchvision
     #   transformers
-responses==0.23.3
+responses==0.24.1
     # via -r requirements-dev.in
-ruff==0.0.291
+ruff==0.1.6
     # via -r requirements-dev.in
 safetensors==0.3.3
     # via
     #   diffusers
     #   imaginAIry (setup.py)
+    #   refiners
     #   timm
     #   transformers
 scipy==1.10.1
@@ -244,19 +262,21 @@ sniffio==1.3.0
     # via anyio
 starlette==0.27.0
     # via fastapi
+sympy==1.12
+    # via torch
 termcolor==2.3.0
     # via pytest-sugar
-timm==0.9.7
+timm==0.9.11
     # via
     #   imaginAIry (setup.py)
     #   open-clip-torch
-tokenizers==0.13.3
+tokenizers==0.15.0
     # via transformers
 tomli==2.0.1
     # via
     #   black
     #   pytest
-torch==1.13.1
+torch==2.1.1
     # via
     #   facexlib
     #   fairscale
@@ -264,6 +284,7 @@ torch==1.13.1
     #   kornia
     #   open-clip-torch
     #   pytorch-lightning
+    #   refiners
     #   timm
     #   torchdiffeq
     #   torchmetrics
@@ -274,7 +295,7 @@ torchmetrics==1.2.0
     # via
     #   imaginAIry (setup.py)
     #   pytorch-lightning
-torchvision==0.14.1
+torchvision==0.16.1
     # via
     #   facexlib
     #   imaginAIry (setup.py)
@@ -288,33 +309,36 @@ tqdm==4.66.1
     #   open-clip-torch
     #   pytorch-lightning
     #   transformers
-transformers==4.33.3
+transformers==4.35.2
     # via imaginAIry (setup.py)
-types-pyyaml==6.0.12.12
-    # via responses
+typeguard==2.13.3
+    # via jaxtyping
 typing-extensions==4.8.0
     # via
     #   black
     #   fastapi
     #   huggingface-hub
+    #   jaxtyping
     #   lightning-utilities
     #   pydantic
     #   pydantic-core
     #   pytorch-lightning
     #   torch
-    #   torchvision
     #   uvicorn
-urllib3==2.0.5
+urllib3==2.1.0
     # via
     #   requests
     #   responses
-uvicorn==0.23.2
+uvicorn==0.24.0.post1
     # via imaginAIry (setup.py)
-wcwidth==0.2.7
+wcwidth==0.2.12
     # via ftfy
-wheel==0.41.2
+wheel==0.41.3
     # via -r requirements-dev.in
-yarl==1.9.2
+yarl==1.9.3
     # via aiohttp
 zipp==3.17.0
     # via importlib-metadata
+
+# The following packages are considered to be unsafe in a requirements file:
+# setuptools

setup.py

@@ -81,7 +81,7 @@ setup(
         "fastapi>=0.70.0",
         "ftfy>=6.0.1",  # for vendored clip
         # 2.0.0 produced garbage images on macOS
-        "torch>=1.13.1,<2.0.0",
+        "torch>=2.1.0",
         # https://numpy.org/neps/nep-0029-deprecation_policy.html
         "numpy>=1.19.0,<1.26.0",
         "tqdm>=4.64.0",
@@ -97,6 +97,7 @@ setup(
         # need to migration to 2.0
         "pydantic>=2.3.0",
         "requests>=2.28.1",
+        "refiners>=0.2.0",
         "einops>=0.3.0",
         "safetensors>=0.2.1",
         # scipy is a sub dependency but v1.11 doesn't support python 3.8.  https://docs.scipy.org/doc/scipy/dev/toolchain.html#numpy
@@ -106,10 +107,9 @@ setup(
         "torchmetrics>=0.6.0",
         "torchvision>=0.13.1",
         "transformers>=4.19.2",
-        "triton>=2.0.0; sys_platform!='darwin' and platform_machine!='aarch64'",
+        # "triton>=2.0.0; sys_platform!='darwin' and platform_machine!='aarch64'",
         "kornia>=0.6",
         "uvicorn>=0.16.0",
-        "xformers>=0.0.16; sys_platform!='darwin' and platform_machine!='aarch64'",
     ],
     # don't specify maximum python versions as it can cause very long dependency resolution issues as the resolver
     # goes back to older versions of packages that didn't specify a maximum

tests/conftest.py

@@ -32,6 +32,8 @@ if get_device() == "mps:0":
 elif get_device() == "cpu":
     SAMPLERS_FOR_TESTING = []
 
+SAMPLERS_FOR_TESTING = ["ddim", "k_dpmpp_2m"]
+
 
 @pytest.fixture(scope="session", autouse=True)
 def _pre_setup():

tests/test_api.py

@@ -40,9 +40,7 @@ compare_prompts = [
 
 
 @pytest.mark.skipif(get_device() != "cuda", reason="Too slow to run on CPU or MPS")
-@pytest.mark.parametrize(
-    "model_version", ["SD-1.4", "SD-1.5", "SD-2.0", "SD-2.0-v", "SD-2.1", "SD-2.1-v"]
-)
+@pytest.mark.parametrize("model_version", ["SD-1.5"])
 def test_model_versions(filename_base_for_orig_outputs, model_version):
     """Test that we can switch between model versions."""
     prompts = []
@@ -218,7 +216,6 @@ def test_img_to_img_fruit_2_gold_repeat():
         "mask_mode": "replace",
         "steps": 20,
         "seed": 946188797,
-        "sampler_type": "plms",
         "fix_faces": True,
         "upscale": True,
     }
@@ -229,7 +226,7 @@ def test_img_to_img_fruit_2_gold_repeat():
     ]
     for result in imagine(prompts, debug_img_callback=None):
         result.img.save(
-            f"{TESTS_FOLDER}/test_output/img2img_fruit_2_gold_plms_{get_device()}_run-{run_count:02}.jpg"
+            f"{TESTS_FOLDER}/test_output/img2img_fruit_2_gold_{result.prompt.sampler_type}_{get_device()}_run-{run_count:02}.jpg"
         )
         run_count += 1
 
@@ -242,7 +239,6 @@ def test_img_to_file():
         height=512 - 64,
         steps=20,
         seed=2,
-        sampler_type="PLMS",
         upscale=True,
     )
     out_folder = f"{TESTS_FOLDER}/test_output"
@@ -261,7 +257,6 @@ def test_inpainting_bench(filename_base_for_outputs, filename_base_for_orig_outp
         height=512,
         steps=40,
         seed=1,
-        sampler_type="plms",
     )
     result = next(imagine(prompt))
 
@@ -287,7 +282,6 @@ def test_cliptext_inpainting_pearl_doctor(
         width=512,
         height=512,
         steps=40,
-        sampler_type="plms",
         seed=181509347,
     )
     result = next(imagine(prompt))
@@ -355,7 +349,7 @@ def test_large_image(filename_base_for_outputs):
         prompt_text,
         width=1920,
         height=1080,
-        steps=15,
+        steps=30,
         seed=0,
     )
     result = next(imagine(prompt))

tests/test_cmds.py

@@ -25,8 +25,8 @@ def test_imagine_cmd(monkeypatch):
             f"{TESTS_FOLDER}/test_output",
             "--seed",
             "703425280",
-            "--model",
-            "empty",
+            # "--model",
+            # "empty",
             "--outdir",
             f"{TESTS_FOLDER}/test_output",
         ],

tests/test_enhancers.py

@@ -48,7 +48,7 @@ def test_clip_masking(filename_base_for_outputs):
         assert_image_similar_to_expectation(pred_bin, img_path=img_path, threshold=10)
 
     prompt = ImaginePrompt(
-        "",
+        "woman in sparkly gold jacket",
         init_image=img,
         init_image_strength=0.5,
         # lower steps for faster tests
@@ -58,7 +58,7 @@ def test_clip_masking(filename_base_for_outputs):
         upscale=False,
         fix_faces=True,
         seed=42,
-        sampler_type="plms",
+        # sampler_type="plms",
     )
 
     result = next(imagine(prompt))

tests/test_utils/test_model_cache.py

@@ -26,12 +26,12 @@ def create_model_of_n_bytes(n):
 @pytest.mark.parametrize(
     "model_version",
     [
-        "SD-1.4",
+        # "SD-1.4",
         "SD-1.5",
-        "SD-2.0",
-        "SD-2.0-v",
-        "SD-2.1",
-        "SD-2.1-v",
+        # "SD-2.0",
+        # "SD-2.0-v",
+        # "SD-2.1",
+        # "SD-2.1-v",
         "openjourney-v1",
         "openjourney-v2",
         "openjourney-v4",

tests/test_utils/test_utils.py

@@ -34,7 +34,7 @@ def test_get_device(monkeypatch):
     get_device.cache_clear()
     m_cuda_is_available.side_effect = lambda: False
     m_mps_is_available.side_effect = lambda: True
-    assert get_device() == "mps:0"
+    assert get_device() == "mps"
 
     get_device.cache_clear()
     m_cuda_is_available.side_effect = lambda: False