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Keeps the model weights in your 16GB VRAM for max speed. +# --use-pytorch-cross-attention: This is the native, stable path for Python 3.14. +python main.py --highvram --preview-method auto --use-pytorch-cross-attention \ No newline at end of file diff --git a/models/audio_encoders/put_audio_encoder_models_here b/comfyui.db similarity index 100% rename from models/audio_encoders/put_audio_encoder_models_here rename to comfyui.db diff --git a/config/gender_words_config.yaml b/config/gender_words_config.yaml new file mode 100644 index 000000000..e26396eaf --- /dev/null +++ b/config/gender_words_config.yaml @@ -0,0 +1,59 @@ +error_words: !!set + person: null +gender_add_words: + F: + - female + M: + - male +gender_map: + F: + actor: actress + boy: girl + brother: sister + father: mother + fiance: fiancee + gentleman: lady + grandfather: grandmother + groom: bride + he: she + he's: she's + hero: heroine + him: her + himself: herself + his: her + husband: wife + king: queen + male: female + man: woman + men: women + nephew: niece + prince: princess + sir: madam + son: daughter + uncle: aunt + waiter: waitress + M: + actress: actor + aunt: uncle + bride: groom + daughter: son + female: male + fiancee: fiance + girl: boy + grandmother: grandfather + her: him + heroine: hero + herself: himself + lady: gentleman + madam: sir + mother: father + niece: nephew + princess: prince + queen: king + she: he + she's: he's + sister: brother + waitress: waiter + wife: husband + woman: man + women: men diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example deleted file mode 100644 index 779c35787..000000000 --- a/custom_nodes/example_node.py.example +++ /dev/null @@ -1,130 +0,0 @@ -from typing_extensions import override - -from comfy_api.latest import ComfyExtension, io - - -class Example(io.ComfyNode): - """ - An example node - - Class methods - ------------- - define_schema (io.Schema): - Tell the main program the metadata, input, output parameters of nodes. - fingerprint_inputs: - optional method to control when the node is re executed. - check_lazy_status: - optional method to control list of input names that need to be evaluated. - - """ - - @classmethod - def define_schema(cls) -> io.Schema: - """ - Return a schema which contains all information about the node. - Some types: "Model", "Vae", "Clip", "Conditioning", "Latent", "Image", "Int", "String", "Float", "Combo". - For outputs the "io.Model.Output" should be used, for inputs the "io.Model.Input" can be used. - The type can be a "Combo" - this will be a list for selection. - """ - return io.Schema( - node_id="Example", - display_name="Example Node", - category="Example", - inputs=[ - io.Image.Input("image"), - io.Int.Input( - "int_field", - min=0, - max=4096, - step=64, # Slider's step - display_mode=io.NumberDisplay.number, # Cosmetic only: display as "number" or "slider" - lazy=True, # Will only be evaluated if check_lazy_status requires it - ), - io.Float.Input( - "float_field", - default=1.0, - min=0.0, - max=10.0, - step=0.01, - round=0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding. - display_mode=io.NumberDisplay.number, - lazy=True, - ), - io.Combo.Input("print_to_screen", options=["enable", "disable"]), - io.String.Input( - "string_field", - multiline=False, # True if you want the field to look like the one on the ClipTextEncode node - default="Hello world!", - lazy=True, - ) - ], - outputs=[ - io.Image.Output(), - ], - ) - - @classmethod - def check_lazy_status(cls, image, string_field, int_field, float_field, print_to_screen): - """ - Return a list of input names that need to be evaluated. - - This function will be called if there are any lazy inputs which have not yet been - evaluated. As long as you return at least one field which has not yet been evaluated - (and more exist), this function will be called again once the value of the requested - field is available. - - Any evaluated inputs will be passed as arguments to this function. Any unevaluated - inputs will have the value None. - """ - if print_to_screen == "enable": - return ["int_field", "float_field", "string_field"] - else: - return [] - - @classmethod - def execute(cls, image, string_field, int_field, float_field, print_to_screen) -> io.NodeOutput: - if print_to_screen == "enable": - print(f"""Your input contains: - string_field aka input text: {string_field} - int_field: {int_field} - float_field: {float_field} - """) - #do some processing on the image, in this example I just invert it - image = 1.0 - image - return io.NodeOutput(image) - - """ - The node will always be re executed if any of the inputs change but - this method can be used to force the node to execute again even when the inputs don't change. - You can make this node return a number or a string. This value will be compared to the one returned the last time the node was - executed, if it is different the node will be executed again. - This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash - changes between executions the LoadImage node is executed again. - """ - #@classmethod - #def fingerprint_inputs(s, image, string_field, int_field, float_field, print_to_screen): - # return "" - -# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension -# WEB_DIRECTORY = "./somejs" - - -# Add custom API routes, using router -from aiohttp import web -from server import PromptServer - -@PromptServer.instance.routes.get("/hello") -async def get_hello(request): - return web.json_response("hello") - - -class ExampleExtension(ComfyExtension): - @override - async def get_node_list(self) -> list[type[io.ComfyNode]]: - return [ - Example, - ] - - -async def comfy_entrypoint() -> ExampleExtension: # ComfyUI calls this to load your extension and its nodes. - return ExampleExtension() diff --git a/custom_nodes/websocket_image_save.py b/custom_nodes/websocket_image_save.py deleted file mode 100644 index 15f87f9f5..000000000 --- a/custom_nodes/websocket_image_save.py +++ /dev/null @@ -1,44 +0,0 @@ -from PIL import Image -import numpy as np -import comfy.utils -import time - -#You can use this node to save full size images through the websocket, the -#images will be sent in exactly the same format as the image previews: as -#binary images on the websocket with a 8 byte header indicating the type -#of binary message (first 4 bytes) and the image format (next 4 bytes). - -#Note that no metadata will be put in the images saved with this node. - -class SaveImageWebsocket: - @classmethod - def INPUT_TYPES(s): - return {"required": - {"images": ("IMAGE", ),} - } - - RETURN_TYPES = () - FUNCTION = "save_images" - - OUTPUT_NODE = True - - CATEGORY = "api/image" - - def save_images(self, images): - pbar = comfy.utils.ProgressBar(images.shape[0]) - step = 0 - for image in images: - i = 255. * image.cpu().numpy() - img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) - pbar.update_absolute(step, images.shape[0], ("PNG", img, None)) - step += 1 - - return {} - - @classmethod - def IS_CHANGED(s, images): - return time.time() - -NODE_CLASS_MAPPINGS = { - "SaveImageWebsocket": SaveImageWebsocket, -} diff --git a/extra_models_config.yaml b/extra_models_config.yaml new file mode 100644 index 000000000..aa2f35cf4 --- /dev/null +++ b/extra_models_config.yaml @@ -0,0 +1,12 @@ +# ComfyUI extra_model_paths.yaml for win32 +# Optimized for internal C:/AI_PROJECTS storage only + +comfyui_internal: + is_default: true + base_path: C:/AI_PROJECTS/ComfyUI/ + + # Custom Nodes path + #custom_nodes: custom_nodes/ + +#desktop_extensions: +# custom_nodes: C:\Users\chgen\AppData\Local\Programs\ComfyUI\resources\ComfyUI\custom_nodes \ No newline at end of file diff --git a/input/example.png b/input/example.png deleted file mode 100644 index 7b7f3c9cb..000000000 Binary files a/input/example.png and /dev/null differ diff --git a/models/checkpoints/put_checkpoints_here b/models/checkpoints/put_checkpoints_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/clip/put_clip_or_text_encoder_models_here b/models/clip/put_clip_or_text_encoder_models_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/clip_vision/put_clip_vision_models_here b/models/clip_vision/put_clip_vision_models_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/configs/anything_v3.yaml b/models/configs/anything_v3.yaml deleted file mode 100644 index 8bcfe584a..000000000 --- a/models/configs/anything_v3.yaml +++ /dev/null @@ -1,73 +0,0 @@ -model: - base_learning_rate: 1.0e-04 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false # Note: different from the one we trained before - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False - - scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 10000 ] - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1. ] - f_min: [ 1. ] - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 768 - use_checkpoint: True - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenCLIPEmbedder - params: - layer: "hidden" - layer_idx: -2 diff --git a/models/configs/v1-inference.yaml b/models/configs/v1-inference.yaml deleted file mode 100644 index d4effe569..000000000 --- a/models/configs/v1-inference.yaml +++ /dev/null @@ -1,70 +0,0 @@ -model: - base_learning_rate: 1.0e-04 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false # Note: different from the one we trained before - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False - - scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 10000 ] - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1. ] - f_min: [ 1. ] - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 768 - use_checkpoint: True - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inference_clip_skip_2.yaml b/models/configs/v1-inference_clip_skip_2.yaml deleted file mode 100644 index 8bcfe584a..000000000 --- a/models/configs/v1-inference_clip_skip_2.yaml +++ /dev/null @@ -1,73 +0,0 @@ -model: - base_learning_rate: 1.0e-04 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false # Note: different from the one we trained before - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False - - scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 10000 ] - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1. ] - f_min: [ 1. ] - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 768 - use_checkpoint: True - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenCLIPEmbedder - params: - layer: "hidden" - layer_idx: -2 diff --git a/models/configs/v1-inference_clip_skip_2_fp16.yaml b/models/configs/v1-inference_clip_skip_2_fp16.yaml deleted file mode 100644 index 7eca31c7b..000000000 --- a/models/configs/v1-inference_clip_skip_2_fp16.yaml +++ /dev/null @@ -1,74 +0,0 @@ -model: - base_learning_rate: 1.0e-04 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false # Note: different from the one we trained before - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False - - scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 10000 ] - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1. ] - f_min: [ 1. ] - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 768 - use_checkpoint: True - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenCLIPEmbedder - params: - layer: "hidden" - layer_idx: -2 diff --git a/models/configs/v1-inference_fp16.yaml b/models/configs/v1-inference_fp16.yaml deleted file mode 100644 index 147f42b17..000000000 --- a/models/configs/v1-inference_fp16.yaml +++ /dev/null @@ -1,71 +0,0 @@ -model: - base_learning_rate: 1.0e-04 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false # Note: different from the one we trained before - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False - - scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 10000 ] - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1. ] - f_min: [ 1. ] - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 768 - use_checkpoint: True - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inpainting-inference.yaml b/models/configs/v1-inpainting-inference.yaml deleted file mode 100644 index 45f3f82d4..000000000 --- a/models/configs/v1-inpainting-inference.yaml +++ /dev/null @@ -1,71 +0,0 @@ -model: - base_learning_rate: 7.5e-05 - target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false # Note: different from the one we trained before - conditioning_key: hybrid # important - monitor: val/loss_simple_ema - scale_factor: 0.18215 - finetune_keys: null - - scheduler_config: # 10000 warmup steps - target: ldm.lr_scheduler.LambdaLinearScheduler - params: - warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch - cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases - f_start: [ 1.e-6 ] - f_max: [ 1. ] - f_min: [ 1. ] - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - image_size: 32 # unused - in_channels: 9 # 4 data + 4 downscaled image + 1 mask - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_heads: 8 - use_spatial_transformer: True - transformer_depth: 1 - context_dim: 768 - use_checkpoint: True - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenCLIPEmbedder - diff --git a/models/configs/v2-inference-v.yaml b/models/configs/v2-inference-v.yaml deleted file mode 100644 index 8ec8dfbfe..000000000 --- a/models/configs/v2-inference-v.yaml +++ /dev/null @@ -1,68 +0,0 @@ -model: - base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - parameterization: "v" - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False # we set this to false because this is an inference only config - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" diff --git a/models/configs/v2-inference-v_fp32.yaml b/models/configs/v2-inference-v_fp32.yaml deleted file mode 100644 index d5c9b9cb2..000000000 --- a/models/configs/v2-inference-v_fp32.yaml +++ /dev/null @@ -1,68 +0,0 @@ -model: - base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - parameterization: "v" - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False # we set this to false because this is an inference only config - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: False - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" diff --git a/models/configs/v2-inference.yaml b/models/configs/v2-inference.yaml deleted file mode 100644 index 152c4f3c2..000000000 --- a/models/configs/v2-inference.yaml +++ /dev/null @@ -1,67 +0,0 @@ -model: - base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False # we set this to false because this is an inference only config - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: True - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" diff --git a/models/configs/v2-inference_fp32.yaml b/models/configs/v2-inference_fp32.yaml deleted file mode 100644 index 0d03231f3..000000000 --- a/models/configs/v2-inference_fp32.yaml +++ /dev/null @@ -1,67 +0,0 @@ -model: - base_learning_rate: 1.0e-4 - target: ldm.models.diffusion.ddpm.LatentDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: crossattn - monitor: val/loss_simple_ema - scale_factor: 0.18215 - use_ema: False # we set this to false because this is an inference only config - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - use_fp16: False - image_size: 32 # unused - in_channels: 4 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" diff --git a/models/configs/v2-inpainting-inference.yaml b/models/configs/v2-inpainting-inference.yaml deleted file mode 100644 index 32a9471d7..000000000 --- a/models/configs/v2-inpainting-inference.yaml +++ /dev/null @@ -1,158 +0,0 @@ -model: - base_learning_rate: 5.0e-05 - target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion - params: - linear_start: 0.00085 - linear_end: 0.0120 - num_timesteps_cond: 1 - log_every_t: 200 - timesteps: 1000 - first_stage_key: "jpg" - cond_stage_key: "txt" - image_size: 64 - channels: 4 - cond_stage_trainable: false - conditioning_key: hybrid - scale_factor: 0.18215 - monitor: val/loss_simple_ema - finetune_keys: null - use_ema: False - - unet_config: - target: ldm.modules.diffusionmodules.openaimodel.UNetModel - params: - use_checkpoint: True - image_size: 32 # unused - in_channels: 9 - out_channels: 4 - model_channels: 320 - attention_resolutions: [ 4, 2, 1 ] - num_res_blocks: 2 - channel_mult: [ 1, 2, 4, 4 ] - num_head_channels: 64 # need to fix for flash-attn - use_spatial_transformer: True - use_linear_in_transformer: True - transformer_depth: 1 - context_dim: 1024 - legacy: False - - first_stage_config: - target: ldm.models.autoencoder.AutoencoderKL - params: - embed_dim: 4 - monitor: val/rec_loss - ddconfig: - #attn_type: "vanilla-xformers" - double_z: true - z_channels: 4 - resolution: 256 - in_channels: 3 - out_ch: 3 - ch: 128 - ch_mult: - - 1 - - 2 - - 4 - - 4 - num_res_blocks: 2 - attn_resolutions: [ ] - dropout: 0.0 - lossconfig: - target: torch.nn.Identity - - cond_stage_config: - target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder - params: - freeze: True - layer: "penultimate" - - -data: - target: ldm.data.laion.WebDataModuleFromConfig - params: - tar_base: null # for concat as in LAION-A - p_unsafe_threshold: 0.1 - filter_word_list: "data/filters.yaml" - max_pwatermark: 0.45 - batch_size: 8 - num_workers: 6 - multinode: True - min_size: 512 - train: - shards: - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" - - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" - shuffle: 10000 - image_key: jpg - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - interpolation: 3 - - target: torchvision.transforms.RandomCrop - params: - size: 512 - postprocess: - target: ldm.data.laion.AddMask - params: - mode: "512train-large" - p_drop: 0.25 - # NOTE use enough shards to avoid empty validation loops in workers - validation: - shards: - - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " - shuffle: 0 - image_key: jpg - image_transforms: - - target: torchvision.transforms.Resize - params: - size: 512 - interpolation: 3 - - target: torchvision.transforms.CenterCrop - params: - size: 512 - postprocess: - target: ldm.data.laion.AddMask - params: - mode: "512train-large" - p_drop: 0.25 - -lightning: - find_unused_parameters: True - modelcheckpoint: - params: - every_n_train_steps: 5000 - - callbacks: - metrics_over_trainsteps_checkpoint: - params: - every_n_train_steps: 10000 - - image_logger: - target: main.ImageLogger - params: - enable_autocast: False - disabled: False - batch_frequency: 1000 - max_images: 4 - increase_log_steps: False - log_first_step: False - log_images_kwargs: - use_ema_scope: False - inpaint: False - plot_progressive_rows: False - plot_diffusion_rows: False - N: 4 - unconditional_guidance_scale: 5.0 - unconditional_guidance_label: [""] - ddim_steps: 50 # todo check these out for depth2img, - ddim_eta: 0.0 # todo check these out for depth2img, - - trainer: - benchmark: True - val_check_interval: 5000000 - num_sanity_val_steps: 0 - accumulate_grad_batches: 1 diff --git a/models/controlnet/put_controlnets_and_t2i_here b/models/controlnet/put_controlnets_and_t2i_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/diffusers/put_diffusers_models_here b/models/diffusers/put_diffusers_models_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/diffusion_models/put_diffusion_model_files_here b/models/diffusion_models/put_diffusion_model_files_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/embeddings/put_embeddings_or_textual_inversion_concepts_here b/models/embeddings/put_embeddings_or_textual_inversion_concepts_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/gligen/put_gligen_models_here b/models/gligen/put_gligen_models_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/hypernetworks/put_hypernetworks_here b/models/hypernetworks/put_hypernetworks_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/latent_upscale_models/put_latent_upscale_models_here b/models/latent_upscale_models/put_latent_upscale_models_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/loras/put_loras_here b/models/loras/put_loras_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/model_patches/put_model_patches_here b/models/model_patches/put_model_patches_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/photomaker/put_photomaker_models_here b/models/photomaker/put_photomaker_models_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/style_models/put_t2i_style_model_here b/models/style_models/put_t2i_style_model_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/text_encoders/put_text_encoder_files_here b/models/text_encoders/put_text_encoder_files_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/unet/put_unet_files_here b/models/unet/put_unet_files_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/upscale_models/put_esrgan_and_other_upscale_models_here b/models/upscale_models/put_esrgan_and_other_upscale_models_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/vae/put_vae_here b/models/vae/put_vae_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here b/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/output/_output_images_will_be_put_here b/output/_output_images_will_be_put_here deleted file mode 100644 index e69de29bb..000000000 diff --git a/styles/default.csv b/styles/default.csv new file mode 100644 index 000000000..b7977e597 --- /dev/null +++ b/styles/default.csv @@ -0,0 +1,3 @@ +name,prompt,negative_prompt +❌Low Token,,"embedding:EasyNegative, NSFW, Cleavage, Pubic Hair, Nudity, Naked, censored" +✅Line Art / Manga,"(Anime Scene, Toonshading, Satoshi Kon, Ken Sugimori, Hiromu Arakawa:1.2), (Anime Style, Manga Style:1.3), Low detail, sketch, concept art, line art, webtoon, manhua, hand drawn, defined lines, simple shades, minimalistic, High contrast, Linear compositions, Scalable artwork, Digital art, High Contrast Shadows, glow effects, humorous illustration, big depth of field, Masterpiece, colors, concept art, trending on artstation, Vivid colors, dramatic",