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Merge branch 'master' into beta
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@jn-jairo
jn-jairo committed Nov 25, 2023
2 parents eaf7ae8 + 5b37270 commit b21f682
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Showing 5 changed files with 102 additions and 11 deletions.
11 changes: 11 additions & 0 deletions comfy/ldm/modules/attention.py
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Expand Up @@ -285,9 +285,20 @@ def attention_split(q, k, v, heads, mask=None):
)
return r1

BROKEN_XFORMERS = False
try:
x_vers = xformers.__version__
#I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error)
BROKEN_XFORMERS = x_vers.startswith("0.0.21") or x_vers.startswith("0.0.22") or x_vers.startswith("0.0.23")
except:
pass

def attention_xformers(q, k, v, heads, mask=None):
b, _, dim_head = q.shape
dim_head //= heads
if BROKEN_XFORMERS:
if b * heads > 65535:
return attention_pytorch(q, k, v, heads, mask)

q, k, v = map(
lambda t: t.unsqueeze(3)
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30 changes: 20 additions & 10 deletions comfy/model_detection.py
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Expand Up @@ -232,52 +232,62 @@ def unet_config_from_diffusers_unet(state_dict, dtype):
SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10]}
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0]}
'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],
'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,
'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]}
'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1]}
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0]}
'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10]}
'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
'use_temporal_attention': False, 'use_temporal_resblock': False}

SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64}
'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
'use_temporal_attention': False, 'use_temporal_resblock': False}

supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B]

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56 changes: 56 additions & 0 deletions comfy_extras/nodes_images.py
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Expand Up @@ -3,6 +3,8 @@
from comfy.cli_args import args

from PIL import Image
from PIL.PngImagePlugin import PngInfo

import numpy as np
import json
import os
Expand Down Expand Up @@ -112,8 +114,62 @@ def save_images(self, images, fps, filename_prefix, lossless, quality, method, n
animated = num_frames != 1
return { "ui": { "images": results, "animated": (animated,) } }

class SaveAnimatedPNG:
def __init__(self):
self.output_dir = folder_paths.get_output_directory()
self.type = "output"
self.prefix_append = ""

@classmethod
def INPUT_TYPES(s):
return {"required":
{"images": ("IMAGE", ),
"filename_prefix": ("STRING", {"default": "ComfyUI"}),
"fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
"compress_level": ("INT", {"default": 4, "min": 0, "max": 9})
},
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
}

RETURN_TYPES = ()
FUNCTION = "save_images"

OUTPUT_NODE = True

CATEGORY = "_for_testing"

def save_images(self, images, fps, compress_level, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
filename_prefix += self.prefix_append
full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
results = list()
pil_images = []
for image in images:
i = 255. * image.cpu().numpy()
img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
pil_images.append(img)

metadata = None
if not args.disable_metadata:
metadata = PngInfo()
if prompt is not None:
metadata.add(b"comf", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True)
if extra_pnginfo is not None:
for x in extra_pnginfo:
metadata.add(b"comf", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True)

file = f"{filename}_{counter:05}_.png"
pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:])
results.append({
"filename": file,
"subfolder": subfolder,
"type": self.type
})

return { "ui": { "images": results, "animated": (True,)} }

NODE_CLASS_MAPPINGS = {
"ImageCrop": ImageCrop,
"RepeatImageBatch": RepeatImageBatch,
"SaveAnimatedWEBP": SaveAnimatedWEBP,
"SaveAnimatedPNG": SaveAnimatedPNG,
}
14 changes: 14 additions & 0 deletions nodes.py
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Expand Up @@ -572,6 +572,19 @@ def load_lora(self, model, clip, lora_name, strength_model, strength_clip):
model_lora, clip_lora = comfy.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip)
return (model_lora, clip_lora)

class LoraLoaderModelOnly(LoraLoader):
@classmethod
def INPUT_TYPES(s):
return {"required": { "model": ("MODEL",),
"lora_name": (folder_paths.get_filename_list("loras"), ),
"strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
}}
RETURN_TYPES = ("MODEL",)
FUNCTION = "load_lora_model_only"

def load_lora_model_only(self, model, lora_name, strength_model):
return (self.load_lora(model, None, lora_name, strength_model, 0)[0],)

class VAELoader:
@staticmethod
def vae_list():
Expand Down Expand Up @@ -1703,6 +1716,7 @@ def expand_image(self, image, left, top, right, bottom, feathering):

"ConditioningZeroOut": ConditioningZeroOut,
"ConditioningSetTimestepRange": ConditioningSetTimestepRange,
"LoraLoaderModelOnly": LoraLoaderModelOnly,
}

NODE_DISPLAY_NAME_MAPPINGS = {
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2 changes: 1 addition & 1 deletion web/scripts/pnginfo.js
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Expand Up @@ -24,7 +24,7 @@ export function getPngMetadata(file) {
const length = dataView.getUint32(offset);
// Get the chunk type
const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8));
if (type === "tEXt") {
if (type === "tEXt" || type == "comf") {
// Get the keyword
let keyword_end = offset + 8;
while (pngData[keyword_end] !== 0) {
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