Create evo_nishikie_v1.py, app.py, safety_checker.py and requirements.txt

evo_nishikie_v1.py

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+import gc
+from io import BytesIO
+import os
+from typing import Dict, List, Union
+
+from PIL import Image
+from controlnet_aux import CannyDetector
+from diffusers import (
+    ControlNetModel,
+    StableDiffusionXLControlNetPipeline,
+    UNet2DConditionModel,
+)
+from huggingface_hub import hf_hub_download
+import requests
+import safetensors
+import torch
+from tqdm import tqdm
+from transformers import AutoTokenizer, CLIPTextModelWithProjection
+
+# Base models (fine-tuned from SDXL-1.0)
+SDXL_REPO = "stabilityai/stable-diffusion-xl-base-1.0"
+DPO_REPO = "mhdang/dpo-sdxl-text2image-v1"
+JN_REPO = "RunDiffusion/Juggernaut-XL-v9"
+JSDXL_REPO = "stabilityai/japanese-stable-diffusion-xl"
+
+# Evo-Ukiyoe
+UKIYOE_REPO = "SakanaAI/Evo-Ukiyoe-v1"
+
+# Evo-Nishikie
+NISHIKIE_REPO = "SakanaAI/Evo-Nishikie-v1"
+
+
+def load_state_dict(checkpoint_file: Union[str, os.PathLike], device: str = "cpu"):
+    file_extension = os.path.basename(checkpoint_file).split(".")[-1]
+    if file_extension == "safetensors":
+        return safetensors.torch.load_file(checkpoint_file, device=device)
+    else:
+        return torch.load(checkpoint_file, map_location=device)
+
+
+def load_from_pretrained(
+    repo_id,
+    filename="diffusion_pytorch_model.fp16.safetensors",
+    subfolder="unet",
+    device="cuda",
+) -> Dict[str, torch.Tensor]:
+    return load_state_dict(
+        hf_hub_download(
+            repo_id=repo_id,
+            filename=filename,
+            subfolder=subfolder,
+        ),
+        device=device,
+    )
+
+
+def reshape_weight_task_tensors(task_tensors, weights):
+    """
+    Reshapes `weights` to match the shape of `task_tensors` by unsqueezing in the remaining dimensions.
+
+    Args:
+        task_tensors (`torch.Tensor`): The tensors that will be used to reshape `weights`.
+        weights (`torch.Tensor`): The tensor to be reshaped.
+
+    Returns:
+        `torch.Tensor`: The reshaped tensor.
+    """
+    new_shape = weights.shape + (1,) * (task_tensors.dim() - weights.dim())
+    weights = weights.view(new_shape)
+    return weights
+
+
+def linear(task_tensors: List[torch.Tensor], weights: torch.Tensor) -> torch.Tensor:
+    """
+    Merge the task tensors using `linear`.
+
+    Args:
+        task_tensors(`List[torch.Tensor]`):The task tensors to merge.
+        weights (`torch.Tensor`):The weights of the task tensors.
+
+    Returns:
+        `torch.Tensor`: The merged tensor.
+    """
+    task_tensors = torch.stack(task_tensors, dim=0)
+    # weighted task tensors
+    weights = reshape_weight_task_tensors(task_tensors, weights)
+    weighted_task_tensors = task_tensors * weights
+    mixed_task_tensors = weighted_task_tensors.sum(dim=0)
+    return mixed_task_tensors
+
+
+def merge_models(task_tensors, weights):
+    keys = list(task_tensors[0].keys())
+    weights = torch.tensor(weights, device=task_tensors[0][keys[0]].device)
+    state_dict = {}
+    for key in tqdm(keys, desc="Merging"):
+        w_list = []
+        for i, sd in enumerate(task_tensors):
+            w = sd.pop(key)
+            w_list.append(w)
+        new_w = linear(task_tensors=w_list, weights=weights)
+        state_dict[key] = new_w
+    return state_dict
+
+
+def split_conv_attn(weights):
+    attn_tensors = {}
+    conv_tensors = {}
+    for key in list(weights.keys()):
+        if any(k in key for k in ["to_k", "to_q", "to_v", "to_out.0"]):
+            attn_tensors[key] = weights.pop(key)
+        else:
+            conv_tensors[key] = weights.pop(key)
+    return {"conv": conv_tensors, "attn": attn_tensors}
+
+
+def load_evo_nishikie(device="cuda") -> StableDiffusionXLControlNetPipeline:
+    # Load base models
+    sdxl_weights = split_conv_attn(load_from_pretrained(SDXL_REPO, device=device))
+    dpo_weights = split_conv_attn(
+        load_from_pretrained(
+            DPO_REPO, "diffusion_pytorch_model.safetensors", device=device
+        )
+    )
+    jn_weights = split_conv_attn(load_from_pretrained(JN_REPO, device=device))
+    jsdxl_weights = split_conv_attn(load_from_pretrained(JSDXL_REPO, device=device))
+    # Merge base models
+    tensors = [sdxl_weights, dpo_weights, jn_weights, jsdxl_weights]
+    new_conv = merge_models(
+        [sd["conv"] for sd in tensors],
+        [
+            0.15928833971605916,
+            0.1032449268871776,
+            0.6503217149752791,
+            0.08714501842148402,
+        ],
+    )
+    new_attn = merge_models(
+        [sd["attn"] for sd in tensors],
+        [
+            0.1877279276437178,
+            0.20014114603909822,
+            0.3922685507065275,
+            0.2198623756106564,
+        ],
+    )
+    del sdxl_weights, dpo_weights, jn_weights, jsdxl_weights
+    gc.collect()
+    if "cuda" in device:
+        torch.cuda.empty_cache()
+
+    unet_config = UNet2DConditionModel.load_config(SDXL_REPO, subfolder="unet")
+    unet = UNet2DConditionModel.from_config(unet_config).to(device=device)
+    unet.load_state_dict({**new_conv, **new_attn})
+
+    # Load other modules
+    text_encoder = CLIPTextModelWithProjection.from_pretrained(
+        JSDXL_REPO, subfolder="text_encoder", torch_dtype=torch.float16, variant="fp16",
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        JSDXL_REPO, subfolder="tokenizer", use_fast=False,
+    )
+
+    # Load Evo-Nishikie weights
+    controlnet = ControlNetModel.from_pretrained(
+        NISHIKIE_REPO, torch_dtype=torch.float16, device=device,
+    )
+
+    # Load pipeline
+    pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
+        SDXL_REPO,
+        unet=unet,
+        text_encoder=text_encoder,
+        tokenizer=tokenizer,
+        controlnet=controlnet,
+        torch_dtype=torch.float16,
+        variant="fp16",
+    )
+    pipe = pipe.to(device, dtype=torch.float16)
+
+    # Load Evo-Ukiyoe weights
+    pipe.load_lora_weights(UKIYOE_REPO)
+    pipe.fuse_lora(lora_scale=1.0)
+    return pipe