Update
This commit is contained in:
space-nuko
@@ -1,149 +1,5 @@
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import { z, type ZodTypeAny } from "zod"
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const ModelHashes = z.object({
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a1111_shorthash: z.string().optional(),
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sha256: z.string().optional(),
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}).refine(({ a1111_shorthash, sha256 }) =>
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a1111_shorthash !== undefined || sha256 !== undefined,
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{ message: "At least one model hash must be specified" })
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const GroupConditioning = z.object({
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text: z.string(),
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})
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export type ComfyBoxStdGroupConditioning = z.infer<typeof GroupConditioning>
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const GroupCheckpoint = z.object({
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model_name: z.string().optional(),
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model_hashes: ModelHashes.optional(),
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}).refine(({ model_name, model_hashes }) =>
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model_name !== undefined || model_hashes !== undefined,
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{ message: "Must include either model name or model hash" }
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)
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export type ComfyBoxStdGroupCheckpoint = z.infer<typeof GroupCheckpoint>
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const GroupVAE = z.object({
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model_name: z.string().optional(),
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model_hashes: ModelHashes.optional(),
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type: z.enum(["internal", "external"])
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}).refine(({ model_name, model_hashes }) =>
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model_name !== undefined || model_hashes !== undefined,
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{ message: "Must include either model name or model hashes" }
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)
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export type ComfyBoxStdGroupVAE = z.infer<typeof GroupVAE>
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const GroupKSampler = z.object({
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cfg_scale: z.number(),
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seed: z.number(),
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steps: z.number(),
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sampler_name: z.string(),
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scheduler: z.string(),
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denoise: z.number().default(1.0),
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type: z.enum(["empty", "image", "upscale"]).optional()
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})
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export type ComfyBoxStdGroupKSampler = z.infer<typeof GroupKSampler>
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const GroupLatentImage = z.object({
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width: z.number(),
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height: z.number(),
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type: z.enum(["empty", "image", "upscale"]).optional(),
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upscale_method: z.string().optional(),
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upscale_by: z.number().optional(),
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crop: z.string().optional(),
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mask_blur: z.number().optional(),
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batch_count: z.number().default(1).optional(),
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batch_pos: z.number().default(0).optional()
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})
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export type ComfyBoxStdGroupLatentImage = z.infer<typeof GroupLatentImage>
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const GroupSDUpscale = z.object({
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upscaler: z.string(),
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overlap: z.number(),
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})
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export type ComfyBoxStdGroupSDUpscale = z.infer<typeof GroupSDUpscale>
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const GroupSelfAttentionGuidance = z.object({
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guidance_scale: z.number(),
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mask_threshold: z.number(),
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})
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export type ComfyBoxStdGroupSelfAttentionGuidance = z.infer<typeof GroupSelfAttentionGuidance>
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const GroupHypernetwork = z.object({
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model_name: z.string(),
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model_hashes: ModelHashes.optional(),
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strength: z.number()
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})
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export type ComfyBoxStdGroupHypernetwork = z.infer<typeof GroupHypernetwork>
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const LoRAModelHashes = z.object({
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addnet_shorthash: z.string().optional(),
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addnet_shorthash_legacy: z.string().optional(),
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sha256: z.string().optional(),
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}).refine(({ addnet_shorthash, addnet_shorthash_legacy, sha256 }) =>
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addnet_shorthash !== undefined || addnet_shorthash_legacy !== undefined || sha256 !== undefined,
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{ message: "At least one model hash must be specified" })
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const GroupLoRA = z.object({
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model_name: z.string(),
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module_name: z.string().optional(),
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model_hashes: LoRAModelHashes.optional(),
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strength_unet: z.number(),
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strength_tenc: z.number()
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})
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export type ComfyBoxStdGroupLoRA = z.infer<typeof GroupLoRA>
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const GroupControlNet = z.object({
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model: z.string(),
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model_hashes: ModelHashes.optional(),
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strength: z.number(),
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})
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export type ComfyBoxStdGroupControlNet = z.infer<typeof GroupControlNet>
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const GroupCLIP = z.object({
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clip_skip: z.number().optional()
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})
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export type ComfyBoxStdGroupCLIP = z.infer<typeof GroupCLIP>
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const GroupDynamicThresholding = z.object({
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mimic_scale: z.number(),
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threshold_percentile: z.number(),
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mimic_mode: z.string(),
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mimic_scale_minimum: z.number(),
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cfg_mode: z.string(),
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cfg_scale_minimum: z.number()
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})
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export type ComfyBoxStdGroupDynamicThresholding = z.infer<typeof GroupDynamicThresholding>
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const GroupAestheticEmbedding = z.object({
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model_name: z.string(),
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lr: z.number(),
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slerp: z.boolean(),
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slerp_angle: z.number(),
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steps: z.number(),
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positive: z.string(),
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negative: z.string(),
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weight: z.number(),
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})
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export type ComfyBoxStdGroupAestheticEmbedding = z.infer<typeof GroupAestheticEmbedding>
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const GroupDDetailer = z.object({
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positive_prompt: z.string(),
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negative_prompt: z.string(),
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bitwise: z.string(),
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model: z.string().optional(),
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model_hashes: ModelHashes.optional(),
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conf: z.number(),
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mask_blur: z.number(),
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denoise: z.number(),
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dilation: z.number(),
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offset_x: z.number(),
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offset_y: z.number(),
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preprocess: z.boolean(),
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inpaint_full: z.boolean(),
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inpaint_padding: z.number(),
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cfg: z.number()
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})
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export type ComfyBoxStdGroupDDetailer = z.infer<typeof GroupDDetailer>
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/*
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* This metadata can be attached to each entry in a group to assist in
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* identifying the correct nodes to apply it to.
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@@ -170,25 +26,180 @@ const GroupMetadata = z.object({
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})
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export type ComfyBoxStdGroupMetadata = z.infer<typeof GroupMetadata>
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const group = (entry: ZodTypeAny) => {
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const groupEntry = entry.and(z.object({ "$meta": GroupMetadata }))
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return z.optional(z.array(groupEntry).nonempty());
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const group = (obj: Record<string, any>): ZodTypeAny => {
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const meta = z.object({ "$meta": GroupMetadata.optional() })
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return z.object(obj).and(meta)
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}
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const ModelHashes = z.object({
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a1111_shorthash: z.string().optional(),
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sha256: z.string().optional(),
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}).refine(({ a1111_shorthash, sha256 }) =>
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a1111_shorthash !== undefined || sha256 !== undefined,
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{ message: "At least one model hash must be specified" })
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const GroupConditioning = group({
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text: z.string(),
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})
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export type ComfyBoxStdGroupConditioning = z.infer<typeof GroupConditioning>
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const GroupCheckpoint = group({
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model_name: z.string().optional(),
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model_hashes: ModelHashes.optional(),
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}).refine(({ model_name, model_hashes }) =>
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model_name !== undefined || model_hashes !== undefined,
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{ message: "Must include either model name or model hash" }
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)
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export type ComfyBoxStdGroupCheckpoint = z.infer<typeof GroupCheckpoint>
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const GroupVAE = group({
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model_name: z.string().optional(),
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model_hashes: ModelHashes.optional(),
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type: z.enum(["internal", "external"])
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}).refine(({ model_name, model_hashes }) =>
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model_name !== undefined || model_hashes !== undefined,
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{ message: "Must include either model name or model hashes" }
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)
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export type ComfyBoxStdGroupVAE = z.infer<typeof GroupVAE>
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const GroupKSampler = group({
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cfg_scale: z.number(),
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seed: z.number(),
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steps: z.number(),
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sampler_name: z.string(),
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scheduler: z.string(),
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denoise: z.number().default(1.0),
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type: z.enum(["empty", "image", "upscale"]).optional()
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})
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export type ComfyBoxStdGroupKSampler = z.infer<typeof GroupKSampler>
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const GroupLatentImage = group({
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width: z.number(),
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height: z.number(),
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mask_blur: z.number().optional(),
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batch_count: z.number().default(1).optional(),
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batch_pos: z.number().default(0).optional()
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})
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export type ComfyBoxStdGroupLatentImage = z.infer<typeof GroupLatentImage>
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const GroupLatentUpscale = group({
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width: z.number(),
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height: z.number(),
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upscale_method: z.string().optional(),
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upscale_by: z.number().optional(),
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crop: z.string().optional()
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})
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export type ComfyBoxStdGroupLatentUpscale = z.infer<typeof GroupLatentUpscale>
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const GroupSDUpscale = group({
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upscaler: z.string(),
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overlap: z.number(),
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})
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export type ComfyBoxStdGroupSDUpscale = z.infer<typeof GroupSDUpscale>
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const GroupSelfAttentionGuidance = group({
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guidance_scale: z.number(),
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mask_threshold: z.number(),
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})
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export type ComfyBoxStdGroupSelfAttentionGuidance = z.infer<typeof GroupSelfAttentionGuidance>
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const GroupHypernetwork = group({
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model_name: z.string(),
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model_hashes: ModelHashes.optional(),
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strength: z.number()
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})
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export type ComfyBoxStdGroupHypernetwork = z.infer<typeof GroupHypernetwork>
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const LoRAModelHashes = z.object({
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addnet_shorthash: z.string().optional(),
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addnet_shorthash_legacy: z.string().optional(),
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sha256: z.string().optional(),
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}).refine(({ addnet_shorthash, addnet_shorthash_legacy, sha256 }) =>
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addnet_shorthash !== undefined || addnet_shorthash_legacy !== undefined || sha256 !== undefined,
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{ message: "At least one model hash must be specified" })
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const GroupLoRA = group({
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model_name: z.string(),
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module_name: z.string().optional(),
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model_hashes: LoRAModelHashes.optional(),
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strength_unet: z.number(),
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strength_tenc: z.number()
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})
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export type ComfyBoxStdGroupLoRA = z.infer<typeof GroupLoRA>
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const GroupControlNet = group({
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model: z.string(),
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model_hashes: ModelHashes.optional(),
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strength: z.number(),
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})
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export type ComfyBoxStdGroupControlNet = z.infer<typeof GroupControlNet>
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const GroupCLIP = group({
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clip_skip: z.number().optional()
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})
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export type ComfyBoxStdGroupCLIP = z.infer<typeof GroupCLIP>
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const GroupDynamicThresholding = group({
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mimic_scale: z.number(),
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threshold_percentile: z.number(),
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mimic_mode: z.string(),
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mimic_scale_minimum: z.number(),
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cfg_mode: z.string(),
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cfg_scale_minimum: z.number()
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})
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export type ComfyBoxStdGroupDynamicThresholding = z.infer<typeof GroupDynamicThresholding>
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const GroupAestheticEmbedding = group({
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model_name: z.string(),
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lr: z.number(),
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slerp: z.boolean(),
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slerp_angle: z.number().optional(),
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steps: z.number(),
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text: z.string(),
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text_negative: z.boolean(),
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weight: z.number(),
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})
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export type ComfyBoxStdGroupAestheticEmbedding = z.infer<typeof GroupAestheticEmbedding>
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const GroupDDetailer = group({
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positive_prompt: z.string(),
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negative_prompt: z.string(),
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bitwise: z.string(),
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model: z.string().optional(),
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model_hashes: ModelHashes.optional(),
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conf: z.number(),
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mask_blur: z.number(),
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denoise: z.number(),
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dilation: z.number(),
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offset_x: z.number(),
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offset_y: z.number(),
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preprocess: z.boolean(),
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inpaint_full: z.boolean(),
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inpaint_padding: z.number(),
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cfg: z.number()
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})
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export type ComfyBoxStdGroupDDetailer = z.infer<typeof GroupDDetailer>
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const groupArray = (entry: ZodTypeAny) => {
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return z.optional(z.array(entry).nonempty());
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}
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const Parameters = z.object({
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conditioning: group(GroupConditioning),
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checkpoint: group(GroupCheckpoint),
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vae: group(GroupVAE),
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k_sampler: group(GroupKSampler),
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clip: group(GroupCLIP),
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latent_image: group(GroupLatentImage),
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sd_upscale: group(GroupSDUpscale),
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hypernetwork: group(GroupHypernetwork),
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lora: group(GroupLoRA),
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control_net: group(GroupControlNet),
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dynamic_thresholding: group(GroupDynamicThresholding),
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self_attention_guidance: group(GroupSelfAttentionGuidance),
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ddetailer: group(GroupDDetailer)
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conditioning: groupArray(GroupConditioning),
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checkpoint: groupArray(GroupCheckpoint),
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vae: groupArray(GroupVAE),
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k_sampler: groupArray(GroupKSampler),
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clip: groupArray(GroupCLIP),
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latent_image: groupArray(GroupLatentImage),
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latent_upscale: groupArray(GroupLatentUpscale),
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sd_upscale: groupArray(GroupSDUpscale),
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hypernetwork: groupArray(GroupHypernetwork),
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lora: groupArray(GroupLoRA),
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control_net: groupArray(GroupControlNet),
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dynamic_thresholding: groupArray(GroupDynamicThresholding),
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aesthetic_embedding: groupArray(GroupAestheticEmbedding),
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self_attention_guidance: groupArray(GroupSelfAttentionGuidance),
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ddetailer: groupArray(GroupDDetailer)
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}).partial()
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export type ComfyBoxStdParameters = z.infer<typeof Parameters>
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