mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-02-06 11:32:31 +08:00
Remove 3.10 code for 3.8 code
This commit is contained in:
Silversith
parent
14d9b11a35
commit
8b7505d5bb
@ -12,8 +12,8 @@
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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# THE SOFTWARE.
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import image as image
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import pilgram
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from PIL import Image, ImageFilter, ImageEnhance, ImageOps, ImageDraw, ImageChops, ImageFont
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from PIL.PngImagePlugin import PngInfo
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from io import BytesIO
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@ -698,9 +698,6 @@ class WAS_Image_Style_Filter:
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subprocess.check_call(
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[sys.executable, '-m', 'pip', '-q', 'install', 'pilgram'])
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# Import Pilgram module
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import pilgram
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# Convert image to PIL
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image = tensor2pil(image)
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@ -708,68 +705,66 @@ class WAS_Image_Style_Filter:
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WFilter = WAS_Filter_Class()
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# Apply blending
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match style:
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case "1977":
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out_image = pilgram._1977(image)
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case "aden":
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out_image = pilgram.aden(image)
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case "brannan":
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out_image = pilgram.brannan(image)
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case "brooklyn":
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out_image = pilgram.brooklyn(image)
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case "clarendon":
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out_image = pilgram.clarendon(image)
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case "earlybird":
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out_image = pilgram.earlybird(image)
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case "fairy tale":
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out_image = WFilter.sparkle(image)
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case "gingham":
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out_image = pilgram.gingham(image)
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case "hudson":
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out_image = pilgram.hudson(image)
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case "inkwell":
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out_image = pilgram.inkwell(image)
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case "kelvin":
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out_image = pilgram.kelvin(image)
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case "lark":
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out_image = pilgram.lark(image)
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case "lofi":
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out_image = pilgram.lofi(image)
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case "maven":
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out_image = pilgram.maven(image)
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case "mayfair":
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out_image = pilgram.mayfair(image)
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case "moon":
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out_image = pilgram.moon(image)
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case "nashville":
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out_image = pilgram.nashville(image)
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case "perpetua":
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out_image = pilgram.perpetua(image)
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case "reyes":
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out_image = pilgram.reyes(image)
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case "rise":
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out_image = pilgram.rise(image)
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case "slumber":
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out_image = pilgram.slumber(image)
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case "stinson":
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out_image = pilgram.stinson(image)
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case "toaster":
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out_image = pilgram.toaster(image)
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case "valencia":
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out_image = pilgram.valencia(image)
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case "walden":
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out_image = pilgram.walden(image)
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case "willow":
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out_image = pilgram.willow(image)
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case "xpro2":
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out_image = pilgram.xpro2(image)
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case _:
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out_image = image
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if style == "1977":
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out_image = pilgram._1977(image)
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elif style == "aden":
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out_image = pilgram.aden(image)
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elif style == "brannan":
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out_image = pilgram.brannan(image)
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elif style == "brooklyn":
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out_image = pilgram.brooklyn(image)
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elif style == "clarendon":
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out_image = pilgram.clarendon(image)
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elif style == "earlybird":
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out_image = pilgram.earlybird(image)
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elif style == "fairy tale":
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out_image = WFilter.sparkle(image)
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elif style == "gingham":
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out_image = pilgram.gingham(image)
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elif style == "hudson":
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out_image = pilgram.hudson(image)
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elif style == "inkwell":
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out_image = pilgram.inkwell(image)
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elif style == "kelvin":
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out_image = pilgram.kelvin(image)
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elif style == "lark":
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out_image = pilgram.lark(image)
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elif style == "lofi":
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out_image = pilgram.lofi(image)
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elif style == "maven":
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out_image = pilgram.maven(image)
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elif style == "mayfair":
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out_image = pilgram.mayfair(image)
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elif style == "moon":
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out_image = pilgram.moon(image)
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elif style == "nashville":
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out_image = pilgram.nashville(image)
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elif style == "perpetua":
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out_image = pilgram.perpetua(image)
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elif style == "reyes":
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out_image = pilgram.reyes(image)
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elif style == "rise":
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out_image = pilgram.rise(image)
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elif style == "slumber":
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out_image = pilgram.slumber(image)
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elif style == "stinson":
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out_image = pilgram.stinson(image)
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elif style == "toaster":
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out_image = pilgram.toaster(image)
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elif style == "valencia":
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out_image = pilgram.valencia(image)
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elif style == "walden":
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out_image = pilgram.walden(image)
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elif style == "willow":
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out_image = pilgram.willow(image)
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elif style == "xpro2":
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out_image = pilgram.xpro2(image)
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else:
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out_image = image
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out_image = out_image.convert("RGB")
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return (torch.from_numpy(np.array(out_image).astype(np.float32) / 255.0).unsqueeze(0), )
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return (torch.from_numpy(np.array(out_image).astype(np.float32) / 255.0).unsqueeze(0),)
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# COMBINE NODE
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@ -825,37 +820,36 @@ class WAS_Image_Blending_Mode:
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img_b = tensor2pil(image_b)
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# Apply blending
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match mode:
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case "color":
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out_image = pilgram.css.blending.color(img_a, img_b)
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case "color_burn":
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out_image = pilgram.css.blending.color_burn(img_a, img_b)
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case "color_dodge":
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out_image = pilgram.css.blending.color_dodge(img_a, img_b)
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case "darken":
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out_image = pilgram.css.blending.darken(img_a, img_b)
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case "difference":
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out_image = pilgram.css.blending.difference(img_a, img_b)
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case "exclusion":
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out_image = pilgram.css.blending.exclusion(img_a, img_b)
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case "hard_light":
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out_image = pilgram.css.blending.hard_light(img_a, img_b)
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case "hue":
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out_image = pilgram.css.blending.hue(img_a, img_b)
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case "lighten":
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out_image = pilgram.css.blending.lighten(img_a, img_b)
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case "multiply":
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out_image = pilgram.css.blending.multiply(img_a, img_b)
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case "add":
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out_image = pilgram.css.blending.normal(img_a, img_b)
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case "overlay":
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out_image = pilgram.css.blending.overlay(img_a, img_b)
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case "screen":
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out_image = pilgram.css.blending.screen(img_a, img_b)
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case "soft_light":
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out_image = pilgram.css.blending.soft_light(img_a, img_b)
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case _:
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out_image = img_a
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if mode == "color":
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out_image = pilgram.css.blending.color(img_a, img_b)
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elif mode == "color_burn":
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out_image = pilgram.css.blending.color_burn(img_a, img_b)
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elif mode == "color_dodge":
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out_image = pilgram.css.blending.color_dodge(img_a, img_b)
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elif mode == "darken":
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out_image = pilgram.css.blending.darken(img_a, img_b)
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elif mode == "difference":
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out_image = pilgram.css.blending.difference(img_a, img_b)
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elif mode == "exclusion":
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out_image = pilgram.css.blending.exclusion(img_a, img_b)
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elif mode == "hard_light":
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out_image = pilgram.css.blending.hard_light(img_a, img_b)
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elif mode == "hue":
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out_image = pilgram.css.blending.hue(img_a, img_b)
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elif mode == "lighten":
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out_image = pilgram.css.blending.lighten(img_a, img_b)
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elif mode == "multiply":
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out_image = pilgram.css.blending.multiply(img_a, img_b)
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elif mode == "add":
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out_image = pilgram.css.blending.normal(img_a, img_b)
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elif mode == "overlay":
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out_image = pilgram.css.blending.overlay(img_a, img_b)
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elif mode == "screen":
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out_image = pilgram.css.blending.screen(img_a, img_b)
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elif mode == "soft_light":
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out_image = pilgram.css.blending.soft_light(img_a, img_b)
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else:
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out_image = img_a
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out_image = out_image.convert("RGB")
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@ -938,13 +932,12 @@ class WAS_Image_Monitor_Distortion_Filter:
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WFilter = WAS_Filter_Class()
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# Apply image effect
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match mode:
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case 'Digital Distortion':
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image = WFilter.digital_distortion(image, amplitude, offset)
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case 'Signal Distortion':
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image = WFilter.signal_distortion(image, amplitude)
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case 'TV Distortion':
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image = WFilter.tv_vhs_distortion(image, amplitude)
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if mode == 'Digital Distortion':
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image = WFilter.digital_distortion(image, amplitude, offset)
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elif mode == 'Signal Distortion':
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image = WFilter.signal_distortion(image, amplitude)
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elif mode == 'TV Distortion':
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image = WFilter.tv_vhs_distortion(image, amplitude)
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return (pil2tensor(image), )
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@ -1021,13 +1014,12 @@ class WAS_Image_Analyze:
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WFilter = WAS_Filter_Class()
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# Analye Image
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match mode:
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case 'Black White Levels':
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image = WFilter.black_white_levels(image)
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case 'RGB Levels':
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image = WFilter.channel_frequency(image)
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if mode == 'Black White Levels':
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image = WFilter.black_white_levels(image)
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elif mode == 'RGB Levels':
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image = WFilter.channel_frequency(image)
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return (pil2tensor(image), )
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return (pil2tensor(image), )
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# IMAGE GENERATE GRADIENT
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@ -1995,13 +1987,14 @@ class WAS_Image_Rotate:
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rotation = int((rotation//90)*90)
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# Set Sampler
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match sampler:
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case 'nearest':
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sampler = Image.NEAREST
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case 'bicubic':
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sampler = Image.BICUBIC
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case 'bilinear':
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sampler = Image.BILINEAR
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if sampler == 'nearest':
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sampler = Image.NEAREST
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elif sampler == 'bicubic':
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sampler = Image.BICUBIC
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elif sampler == 'bilinear':
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sampler = Image.BILINEAR
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else:
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sampler = Image.NEAREST # default to nearest if none of the above
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# Rotate Image
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if mode == 'internal':
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@ -2244,14 +2237,13 @@ class WAS_Image_Edge:
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image = tensor2pil(image)
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# Detect edges
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match mode:
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case "normal":
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image = image.filter(ImageFilter.FIND_EDGES)
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case "laplacian":
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image = image.filter(ImageFilter.Kernel((3, 3), (-1, -1, -1, -1, 8,
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-1, -1, -1, -1), 1, 0))
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case _:
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image = image
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if mode == "normal":
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image = image.filter(ImageFilter.FIND_EDGES)
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elif mode == "laplacian":
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kernel = (-1, -1, -1, -1, 8, -1, -1, -1, -1)
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image = image.filter(ImageFilter.Kernel((3, 3), kernel, scale=1, offset=0))
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else:
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image = image
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return (torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0), )
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@ -3645,15 +3637,14 @@ class WAS_Random_Number:
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random.seed(seed)
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# Return random number
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match number_type:
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case 'integer':
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number = random.randint(minimum, maximum)
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case 'float':
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number = random.uniform(minimum, maximum)
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case 'bool':
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number = random.random()
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case _:
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return
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if number_type == 'integer':
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number = random.randint(minimum, maximum)
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elif number_type == 'float':
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number = random.uniform(minimum, maximum)
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elif number_type == 'bool':
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number = random.random()
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else:
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return
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# Return number
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return (number, )
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@ -3682,13 +3673,14 @@ class WAS_Constant_Number:
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def return_constant_number(self, number_type, number):
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# Return number
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match number_type:
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case 'integer':
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return (int(number), )
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case 'integer':
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return (float(number), )
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case 'bool':
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return ((1 if int(number) > 0 else 0), )
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if number_type == 'integer':
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return (int(number), )
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elif number_type == 'float':
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return (float(number), )
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elif number_type == 'bool':
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return ((1 if int(number) > 0 else 0), )
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else:
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return
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# NUMBER TO SEED
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@ -3898,34 +3890,32 @@ class WAS_Number_Operation:
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def math_operations(self, number_a, number_b, operation="addition"):
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# Return random number
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match operation:
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case 'addition':
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return ((number_a + number_b), )
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case 'subtraction':
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return ((number_a - number_b), )
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case 'division':
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return ((number_a / number_b), )
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case 'floor division':
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return ((number_a // number_b), )
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case 'multiplication':
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return ((number_a * number_b), )
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case 'exponentiation':
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return ((number_a ** number_b), )
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case 'modulus':
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return ((number_a % number_b), )
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case 'greater-than':
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return (+(number_a > number_b), )
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case 'greater-than or equals':
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return (+(number_a >= number_b), )
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case 'less-than':
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return (+(number_a < number_b), )
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case 'less-than or equals':
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return (+(number_a <= number_b), )
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case 'equals':
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return (+(number_a == number_b), )
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case 'does not equal':
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return (+(number_a != number_b), )
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if operation == 'addition':
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return ((number_a + number_b),)
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elif operation == 'subtraction':
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return ((number_a - number_b),)
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elif operation == 'division':
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return ((number_a / number_b),)
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elif operation == 'floor division':
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return ((number_a // number_b),)
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elif operation == 'multiplication':
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return ((number_a * number_b),)
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elif operation == 'exponentiation':
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return ((number_a ** number_b),)
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elif operation == 'modulus':
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return ((number_a % number_b),)
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elif operation == 'greater-than':
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return (+(number_a > number_b),)
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elif operation == 'greater-than or equals':
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return (+(number_a >= number_b),)
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elif operation == 'less-than':
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return (+(number_a < number_b),)
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elif operation == 'less-than or equals':
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return (+(number_a <= number_b),)
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elif operation == 'equals':
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return (+(number_a == number_b),)
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elif operation == 'does not equal':
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return (+(number_a != number_b),)
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#! MISC
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