From 0565156f49f727157deddcc07a0cb831e48ae124 Mon Sep 17 00:00:00 2001
From: Jianqi Pan <jannchie@gmail.com>
Date: Wed, 20 Mar 2024 18:10:45 +0900
Subject: [PATCH] :wrench: chore(ignore): clean workspace

---
 .gitignore                                    |   1 +
 __init__.py                                   |  96 ++++++++++++++----
 __pycache__/__init__.cpython-311.pyc          | Bin 18403 -> 0 bytes
 __pycache__/__init__.cpython-39.pyc           | Bin 1168 -> 0 bytes
 pipelines/__init__.py                         |  11 +-
 .../__pycache__/__init__.cpython-311.pyc      | Bin 3607 -> 0 bytes
 6 files changed, 83 insertions(+), 25 deletions(-)
 create mode 100644 .gitignore
 delete mode 100644 __pycache__/__init__.cpython-311.pyc
 delete mode 100644 __pycache__/__init__.cpython-39.pyc
 delete mode 100644 pipelines/__pycache__/__init__.cpython-311.pyc

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..ed8ebf5
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+__pycache__
\ No newline at end of file
diff --git a/__init__.py b/__init__.py
index 53fbc3e..4688725 100644
--- a/__init__.py
+++ b/__init__.py
@@ -35,9 +35,25 @@ def get_prompt_embeds(pipe, prompt, negative_prompt):
     return prompt_embeds, negative_prompt_embeds
 
 
-def latents_to_tensor(pipeline, latents):
-    image_numpy = pipeline.decode_latents(latents)  # numpy
-    return torch.tensor(image_numpy)
+def latents_to_img_tensor(pipeline, latents):
+    # 1. 输入的 latents 是一个 -1 ~ 1 之间的 tensor
+    # 2. 先进行缩放
+    scaled_latents = latents / pipeline.vae.config.scaling_factor
+    # 3. 解码，返回的是 -1 ~ 1 之间的 tensor
+    dec_tensor = pipeline.vae.decode(scaled_latents, return_dict=False)[0]
+    # 4. 缩放到 0 ~ 1 之间
+    dec_images = pipeline.image_processor.postprocess(
+        dec_tensor,
+        output_type="pt",
+        do_denormalize=[True for _ in range(scaled_latents.shape[0])],
+    )
+    # 5. 转换成 tensor,
+    res = torch.nan_to_num(dec_images).to(dtype=torch.float32)
+    # 6. 将 channel 放到最后
+    # res shape torch.Size([1, 3, 512, 512]) => torch.Size([1, 512, 512, 3])
+    res = res.permute(0, 2, 3, 1)
+    print('res shape', res.shape)
+    return res
 
 
 def prepare_latents(
@@ -73,6 +89,29 @@ def prepare_latents(
     return latents
 
 
+def prepare_image(
+    pipeline: StableDiffusionPipeline,
+    seed=47,
+    batch_size=1,
+    num_channels_latents=4,
+    height=512,
+    width=512,
+):
+    generator = torch.Generator()
+    generator.manual_seed(seed)
+    latents = prepare_latents(
+        pipe=pipeline,
+        batch_size=batch_size,
+        num_channels_latents=num_channels_latents,
+        height=height,
+        width=width,
+        generator=generator,
+        device=comfy.model_management.get_torch_device(),
+        dtype=comfy.model_management.VAE_DTYPE,
+    )
+    return latents_to_img_tensor(pipeline, latents)
+
+
 class GetFilledColorImage:
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "run"
@@ -367,10 +406,11 @@ class DiffusersDecoder:
         }
 
     def run(self, pipeline: StableDiffusionPipeline, latents: torch.Tensor):
-        return (latents_to_tensor(pipeline, latents),)
+        res = latents_to_img_tensor(pipeline, latents)
+        return (res,)
 
 
-class DiffusersGenerate:
+class DiffusersGenerator:
     CATEGORY = "Jannchie"
     FUNCTION = "run"
     RETURN_TYPES = ("IMAGE",)
@@ -391,9 +431,10 @@ class DiffusersGenerate:
                     "INT",
                     {"default": 30, "min": 1, "max": 100, "step": 1},
                 ),
-            },
-            "optional": {
-                "images": ("IMAGE",),
+                "guidance_scale": (
+                    "FLOAT",
+                    {"default": 7.0, "min": 0.0, "max": 30.0, "step": 0.02},
+                ),
                 "seed": (
                     "INT",
                     {"default": 0, "min": 0, "step": 1, "max": 999999999999},
@@ -417,6 +458,13 @@ class DiffusersGenerate:
                         "step": 64,
                     },
                 ),
+                "num_channels_latents": (
+                    "INT",
+                    {"default": 4, "min": 1, "max": 4, "step": 1},
+                ),
+            },
+            "optional": {
+                "images": ("IMAGE",),
             },
         }
 
@@ -431,8 +479,11 @@ class DiffusersGenerate:
         images: torch.Tensor | None = None,
         num_inference_steps: int = 30,
         strength: float = 1.0,
+        num_channels_latents: int = 4,
+        guidance_scale: float = 7.0,
         seed=None,
     ):
+        latents = None
         pbar = ProgressBar(int(num_inference_steps * strength))
         device = comfy.model_management.get_torch_device()
         if not seed:
@@ -444,16 +495,17 @@ class DiffusersGenerate:
             latents = prepare_latents(
                 pipe=pipeline,
                 batch_size=batch_size,
-                num_channels_latents=4,
+                num_channels_latents=num_channels_latents,
                 height=height,
                 width=width,
                 generator=generator,
-                dtype=comfy.model_management.VAE_DTYPE,
                 device=device,
+                dtype=comfy.model_management.VAE_DTYPE,
             )
-            images = latents_to_tensor(pipeline, latents).permute(0, 3, 1, 2)
+            images = latents_to_img_tensor(pipeline, latents)
         else:
-            images = images.permute(0, 3, 1, 2)
+            images = images
+
         # positive_prompt_embedding 和 negative_prompt_embedding 需要匹配 batch_size
         positive_prompt_embedding = positive_prompt_embedding.repeat(batch_size, 1, 1)
         negative_prompt_embedding = negative_prompt_embedding.repeat(batch_size, 1, 1)
@@ -463,10 +515,14 @@ class DiffusersGenerate:
 
         result = pipeline(
             image=images,
+            # latents=latents,
             generator=generator,
+            width=width,
+            height=height,
             prompt_embeds=positive_prompt_embedding,
             negative_prompt_embeds=negative_prompt_embedding,
             num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
             callback_steps=1,
             strength=strength,
             callback=callback,
@@ -475,7 +531,7 @@ class DiffusersGenerate:
         # image = result["images"][0]
         # images to torch.Tensor
         imgs = [np.array(img) for img in result["images"]]
-        imgs = torch.tensor(imgs, dtype=images.dtype)
+        imgs = torch.tensor(imgs)
         result["images"][0].save("1.png")
         # 0 ~ 255 to 0 ~ 1
         imgs = imgs / 255
@@ -487,7 +543,7 @@ NODE_CLASS_MAPPINGS = {
     "GetFilledColorImage": GetFilledColorImage,
     "GetAverageColorFromImage": GetAverageColorFromImage,
     "DiffusersPipeline": DiffusersPipeline,
-    "DiffusersGenerate": DiffusersGenerate,
+    "DiffusersGenerator": DiffusersGenerator,
     "DiffusersPrepareLatents": DiffusersPrepareLatents,
     "DiffusersDecoder": DiffusersDecoder,
     "DiffusersCompelPromptEmbedding": DiffusersCompelPromptEmbedding,
@@ -496,10 +552,10 @@ NODE_CLASS_MAPPINGS = {
 NODE_DISPLAY_NAME_MAPPINGS = {
     "GetFilledColorImage": "Get Filled Color Image Jannchie",
     "GetAverageColorFromImage": "Get Average Color From Image Jannchie",
-    "DiffusersPipeline": "Diffusers Pipeline",
-    "DiffusersGenerate": "Diffusers Generate",
-    "DiffusersPrepareLatents": "Diffusers Prepare Latents",
-    "DiffusersDecoder": "Diffusers Decoder",
-    "DiffusersCompelPromptEmbedding": "Diffusers Compel Prompt Embedding",
-    "DiffusersTextureInversionLoader": "Diffusers Texture Inversion Embedding Loader",
+    "DiffusersPipeline": "🤗 Diffusers Pipeline",
+    "DiffusersGenerator": "🤗 Diffusers Generator",
+    "DiffusersPrepareLatents": "🤗 Diffusers Prepare Latents",
+    "DiffusersDecoder": "🤗 Diffusers Decoder",
+    "DiffusersCompelPromptEmbedding": "🤗 Diffusers Compel Prompt Embedding",
+    "DiffusersTextureInversionLoader": "🤗 Diffusers Texture Inversion Embedding Loader",
 }
diff --git a/__pycache__/__init__.cpython-311.pyc b/__pycache__/__init__.cpython-311.pyc
deleted file mode 100644
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diff --git a/__pycache__/__init__.cpython-39.pyc b/__pycache__/__init__.cpython-39.pyc
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diff --git a/pipelines/__init__.py b/pipelines/__init__.py
index 79c5e5d..fb6d5c9 100644
--- a/pipelines/__init__.py
+++ b/pipelines/__init__.py
@@ -1,4 +1,3 @@
-import torch
 from diffusers import AutoencoderKL, StableDiffusionImg2ImgPipeline
 from diffusers.schedulers import (
     DEISMultistepScheduler,
@@ -15,6 +14,8 @@ from diffusers.schedulers import (
 
 import comfy.model_management
 
+from .jannchie import JannchiePipeline
+
 schedulers = {
     "DPM++ 2M": DPMSolverMultistepScheduler(),
     "DPM++ 2M Karras": DPMSolverMultistepScheduler(use_karras_sigmas=True),
@@ -47,12 +48,12 @@ class PipelineWrapper:
         device = comfy.model_management.get_torch_device()
         dtype = comfy.model_management.VAE_DTYPE
         if ckpt_path.endswith(".safetensors"):
-            self.pipeline = StableDiffusionImg2ImgPipeline.from_single_file(
+            self.pipeline = JannchiePipeline.from_single_file(
                 ckpt_path,
                 torch_dtype=dtype,
             )
         else:
-            self.pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
+            self.pipeline = JannchiePipeline.from_pretrained(
                 ckpt_path,
                 torch_dtype=dtype,
             )
@@ -60,12 +61,12 @@ class PipelineWrapper:
             if vae_path.endswith(".safetensors"):
                 self.pipeline.vae = AutoencoderKL.from_single_file(
                     vae_path,
-                    torch_dtype=torch.bfloat16,
+                    torch_dtype=dtype,
                 )
             else:
                 self.pipeline.vae = AutoencoderKL.from_pretrained(
                     vae_path,
-                    torch_dtype=torch.bfloat16,
+                    torch_dtype=dtype,
                 )
         if scheduler:
             self.pipeline.scheduler = scheduler
diff --git a/pipelines/__pycache__/__init__.cpython-311.pyc b/pipelines/__pycache__/__init__.cpython-311.pyc
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