Fix CRAN-page README rendering and dispatcher arg validation

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: diffuseR
 Title: Functional Interface to Diffusion Models in R
-Version: 0.2.2
+Version: 0.2.2.1
 Authors@R: c(
     person("Troy", "Hernandez", email = "troy@cornball.ai", role = c("aut", "cre"),
            comment = c(ORCID = "0009-0005-4248-604X")),

NEWS.md

@@ -1,3 +1,13 @@
+# diffuseR 0.2.2.1
+
+* `txt2img()` and `img2img()` now `match.arg()` their `model_name`, so
+  the bare calls work (defaulting to sd21) instead of erroring on the
+  choices vector.
+* README fixes for the CRAN-rendered page: closed the unclosed fence
+  that swallowed the LTX section, replaced the LTX example with the
+  working call shape, and pointed the example images at GitHub URLs
+  (the files are .Rbuildignore'd).
+
 # diffuseR 0.2.2
 
 * Every precision `recommend()` can return is now reachable. `bf16` was

R/download_ltx23.R

@@ -70,8 +70,8 @@ NULL
 #'
 #' @export
 download_ltx2 <- function(quantize = TRUE, precision = c("nf4", "fp8"),
-                          output_dir = NULL,
-                          text_encoder = TRUE, verbose = TRUE) {
+                          output_dir = NULL, text_encoder = TRUE,
+                          verbose = TRUE) {
     if (!requireNamespace("hfhub", quietly = TRUE)) {
         stop("The hfhub package is required to download model weights.")
     }
@@ -83,7 +83,11 @@ download_ltx2 <- function(quantize = TRUE, precision = c("nf4", "fp8"),
         output_dir <- file.path(tools::R_user_dir("diffuseR", "data"),
                                 paste0("ltx2.3-", precision))
     }
-    art_gb <- if (identical(precision, "fp8")) 26 else 19
+    if (identical(precision, "fp8")) {
+        art_gb <- 26
+    } else {
+        art_gb <- 19
+    }
     result <- list(checkpoint = NULL, artifact_dir = output_dir,
                    precision = precision,
                    fp8_dir = if (identical(precision, "fp8")) output_dir,
@@ -109,9 +113,9 @@ download_ltx2 <- function(quantize = TRUE, precision = c("nf4", "fp8"),
                                 free, precision, 46 + art_gb))
             }
             ok <- .ltx23_consent(sprintf(paste0(
-                                        "the LTX-2.3 distilled checkpoint (46 GB) plus a ~%d GB local %s ",
-                                        "artifact from HuggingFace (weights under the LTX-2 Community License)"),
-                                        art_gb, precision))
+                        "the LTX-2.3 distilled checkpoint (46 GB) plus a ~%d GB local %s ",
+                        "artifact from HuggingFace (weights under the LTX-2 Community License)"),
+                    art_gb, precision))
             if (!ok) {
                 stop("Download cancelled.", call. = FALSE)
             }

R/img2img.R

@@ -39,6 +39,7 @@ img2img <- function(input_image, prompt, negative_prompt = NULL,
                     use_native_decoder = FALSE,
                     use_native_text_encoder = FALSE, use_native_unet = FALSE,
                     ...) {
+    model_name <- match.arg(model_name)
     if (model_name %in% c("sd21", "sdxl")) {
         num_train_timesteps <- 1000
     } else {

R/quantize_flux.R

@@ -97,8 +97,7 @@ NULL
                    zimage = list(repo = .zimage_repo,
                                  files = .zimage_transformer_files,
                                  fn = "download_zimage_turbo"),
-                   stop("No bf16 source known for model '", model, "'",
-                        call. = FALSE))
+                   stop("No bf16 source known for model '", model, "'", call. = FALSE))
     paths <- lapply(spec$files, function(f) {
         tryCatch(hfhub::hub_download(spec$repo, f, local_files_only = TRUE),
                  error = function(e) NULL)

R/recommend.R

@@ -132,10 +132,10 @@ recommend <- function(model = c("sd21", "sdxl", "flux1", "flux2", "zimage",
          host_ram_gb = host_ram_gb,
          fork_suggested = fork,
          note = if (fork) {
-             .st_fork_note(want$precision)
-         } else {
-             .bf16_note(model, chosen$precision)
-         }
+            .st_fork_note(want$precision)
+        } else {
+            .bf16_note(model, chosen$precision)
+        }
     )
 }
 

R/resident.R

@@ -126,16 +126,18 @@
 # as.character() on a torch_dtype returns ("Float", "Half", "Byte",
 # "Long", ...), NOT the R constructor alias. Unknown dtypes fall back to
 # 4, which only affects a reported number.
-.dtype_widths <- c(double = 8, long = 8, complexfloat = 8,
-                   float = 4, int = 4,
-                   half = 2, bfloat16 = 2, short = 2,
-                   byte = 1, char = 1, bool = 1,
-                   float8_e4m3fn = 1, float8_e5m2 = 1)
+.dtype_widths <- c(double = 8, long = 8, complexfloat = 8, float = 4,
+                   int = 4, half = 2, bfloat16 = 2, short = 2, byte = 1,
+                   char = 1, bool = 1, float8_e4m3fn = 1, float8_e5m2 = 1)
 
 .dtype_bytes <- function(dtype) {
     nm <- tolower(tryCatch(as.character(dtype), error = function(e) ""))
     w <- .dtype_widths[[nm, exact = TRUE]]
-    if (is.null(w)) 4 else w
+    if (is.null(w)) {
+        4
+    } else {
+        w
+    }
 }
 
 #' TRUE when every staged tensor sits on the expected device type
@@ -149,7 +151,8 @@
 .resident_all_on <- function(staging, type) {
     for (st in staging) {
         for (pair in st) {
-            dev <- tryCatch(pair$live$device$type, error = function(e) NA_character_)
+            dev <- tryCatch(pair$live$device$type,
+                            error = function(e) NA_character_)
             if (!identical(dev, type)) {
                 return(FALSE)
             }
@@ -192,8 +195,9 @@
         stop("cannot ", verb, ": this handle is unloaded", call. = FALSE)
     }
     if (identical(res$state, "broken")) {
-        stop("cannot ", verb, ": this handle is broken (", res$last_error %||%
-                 "no detail recorded", "). Only resident_status() and ",
+        stop("cannot ", verb, ": this handle is broken (",
+             res$last_error %||% "no detail recorded",
+             "). Only resident_status() and ",
              "resident_unload() work from here.", call. = FALSE)
     }
     invisible(TRUE)
@@ -324,7 +328,7 @@ resident_load <- function(model = c("flux2", "flux1", "zimage", "ltx"),
         free_gb <- tryCatch(.detect_vram(use_free = TRUE),
                             error = function(e) NA_real_)
     }
-    need_gb <- res$pinned_bytes / 1024^3
+    need_gb <- res$pinned_bytes / 1024 ^ 3
     if (!is.na(free_gb) && free_gb > 0 && need_gb > free_gb) {
         stop(sprintf(paste0("%s needs %.2f GB resident but only %.2f GB of ",
                             "VRAM is free. Load the pipeline with ",
@@ -532,15 +536,11 @@ resident_generate <- function(res, prompt, ...) {
 resident_status <- function(res) {
     stopifnot(inherits(res, "diffuseR_resident"))
     mem <- .cuda_bytes()
-    list(model = res$model,
-         state = res$state,
-         device = res$device,
+    list(model = res$model, state = res$state, device = res$device,
          components = names(res$staging),
          components_on_gpu = .resident_on_gpu_count(res$staging),
-         pinned_bytes = res$pinned_bytes,
-         gpu_allocated = mem$allocated,
-         gpu_reserved = mem$reserved,
-         loaded_at = res$loaded_at,
+         pinned_bytes = res$pinned_bytes, gpu_allocated = mem$allocated,
+         gpu_reserved = mem$reserved, loaded_at = res$loaded_at,
          last_error = res$last_error)
 }
 
@@ -623,5 +623,5 @@ print.diffuseR_resident <- function(x, ...) {
     if (is.null(b) || is.na(b) || b <= 0) {
         return("0 GB")
     }
-    sprintf("%.2f GB", b / 1024^3)
+    sprintf("%.2f GB", b / 1024 ^ 3)
 }

R/st_caps.R

@@ -113,8 +113,7 @@ NULL
     } else {
         sprintf("%s needs", precision)
     }
-    sprintf(paste0(
-                   "%s a safetensors newer than the one on CRAN: %s is ",
+    sprintf(paste0("%s a safetensors newer than the one on CRAN: %s is ",
                    "merged upstream but not yet released. Install the ",
                    "development version from the mlverse/safetensors ",
                    "repository on GitHub, or press on with nf4: same ",

R/txt2img.R

@@ -14,6 +14,7 @@
 txt2img <- function(prompt,
                     model_name = c("sd21", "sdxl", "flux1", "flux2", "zimage"),
                     ...) {
+    model_name <- match.arg(model_name)
     switch(model_name,
            # "sd15" = txt2img_sd15(prompt, ...),
            "sd21" = txt2img_sd21(prompt, ...),

R/txt2img_flux2.R

@@ -67,8 +67,7 @@ flux2_load_pipeline <- function(model_dir = NULL, device = "cuda",
     }
     if (is.null(model_dir)) {
         model_dir <- .flux_model_dir("flux2", precision,
-            file.path(tools::R_user_dir("diffuseR", "data"),
-                      "flux2-klein-4b-"))
+                                     file.path(tools::R_user_dir("diffuseR", "data"), "flux2-klein-4b-"))
     }
 
     ckpt <- if (file.exists(file.path(model_dir, "manifest.json"))) {

R/txt2img_zimage.R

@@ -68,8 +68,7 @@ zimage_load_pipeline <- function(model_dir = NULL, device = "cuda",
     }
     if (is.null(model_dir)) {
         model_dir <- .flux_model_dir("zimage", precision,
-            file.path(tools::R_user_dir("diffuseR", "data"),
-                      "zimage-turbo-"))
+                                     file.path(tools::R_user_dir("diffuseR", "data"), "zimage-turbo-"))
     }
 
     ckpt <- if (file.exists(file.path(model_dir, "manifest.json"))) {

README.md

@@ -143,8 +143,8 @@ gambling_cat <- img2img(
 pipeline <- NULL
 torch::cuda_empty_cache()
 ```
-![](cat2.png)
-![](gambling_cat.png)
+![](https://raw.githubusercontent.com/cornball-ai/diffuseR/main/cat2.png)
+![](https://raw.githubusercontent.com/cornball-ai/diffuseR/main/gambling_cat.png)
 
 
 ### FLUX and Z-Image
@@ -176,6 +176,7 @@ txt2img_flux2("a red fox sitting in a snowy forest, digital art",
 download_zimage_turbo()
 txt2img_zimage(paste("A storefront with a large wooden sign that reads",
                      "\"DIFFUSER\" in bold carved letters"), seed = 42)
+```
 
 ### Text-to-Video: LTX-2.3
 
@@ -185,10 +186,14 @@ steps. NF4-quantized it renders 768x512x49 in ~44s warm on an RTX
 are supported (see ?txt2vid_ltx2).
 
 ```r
-download_ltx2()       # ~46GB download, one-time fp8 quantize
+paths <- download_ltx2()   # ~46GB download, one-time nf4 quantize
+pipe <- ltx23_load_pipeline(paths$artifact_dir)
+te <- load_gemma3_text_encoder(paths$text_encoder_dir, device = "cpu")
+tok <- gemma3_tokenizer(dirname(hfhub::hub_download("Lightricks/LTX-2",
+                                                    "tokenizer/tokenizer.json")))
 txt2vid_ltx2("A river winding through a misty forest at dawn",
-             pipeline = ltx23_load_pipeline(),
-             filename = "river.mp4")
+             pipeline = pipe, text_encoder = te, tokenizer = tok,
+             num_frames = 73L, seed = 11L, filename = "river.mp4")
 ```
 
 ### Serving over HTTP
@@ -201,6 +206,7 @@ serve(model = "flux2", port = 7812L, token = "my-secret")
 #   http://localhost:7812/v1/images/generations
 ```
 
+```r
 # Or through the common dispatcher
 txt2img("a lighthouse at dusk", model_name = "flux2")
 ```

inst/tinytest/test_dispatch.R

@@ -0,0 +1,8 @@
+# Dispatcher argument validation: an unknown model_name must fail fast
+# with match.arg's message instead of reaching switch() or a vector
+# if() condition (bare txt2img("prompt") used to error with "EXPR must
+# be a length 1 vector"; img2img errored on the length-2 default).
+
+expect_error(txt2img("a cat", model_name = "nope"), "should be one of")
+expect_error(img2img("cat.png", "a cat", model_name = "nope"),
+             "should be one of")

man/CLIPTokenizer.Rd

@@ -3,10 +3,12 @@
 \alias{CLIPTokenizer}
 \title{Tokenize a prompt}
 \usage{
-CLIPTokenizer(prompt,
-              merges = system.file("tokenizer/merges.txt", package = "diffuseR"),
-              vocab_file = system.file("tokenizer/vocab.json", package = "diffuseR"),
-              pad_token = 0L)
+CLIPTokenizer(
+  prompt,
+  merges = system.file("tokenizer/merges.txt", package = "diffuseR"),
+  vocab_file = system.file("tokenizer/vocab.json", package = "diffuseR"),
+  pad_token = 0L
+)
 }
 \arguments{
 \item{prompt}{A character string prompt describing the image to generate.}

man/SpatialTransformer.Rd

@@ -3,7 +3,13 @@
 \alias{SpatialTransformer}
 \title{Spatial Transformer (Attention Block)}
 \usage{
-SpatialTransformer(in_channels, n_heads, d_head, depth = 1L, context_dim = NULL)
+SpatialTransformer(
+  in_channels,
+  n_heads,
+  d_head,
+  depth = 1L,
+  context_dim = NULL
+)
 }
 \description{
 Spatial Transformer (Attention Block)

man/VAEUpBlock.Rd

@@ -3,8 +3,13 @@
 \alias{VAEUpBlock}
 \title{VAE Up Block}
 \usage{
-VAEUpBlock(in_channels, out_channels, num_resnets = 3, add_upsample = TRUE,
-           norm_groups = 32)
+VAEUpBlock(
+  in_channels,
+  out_channels,
+  num_resnets = 3,
+  add_upsample = TRUE,
+  norm_groups = 32
+)
 }
 \arguments{
 \item{in_channels}{Input channels}

man/convert_sd21_pt_to_diffusers.Rd

@@ -3,8 +3,12 @@
 \alias{convert_sd21_pt_to_diffusers}
 \title{Convert cornball SD 2.1 TorchScript weights to a diffusers artifact}
 \usage{
-convert_sd21_pt_to_diffusers(pt_dir = NULL, output_dir = NULL,
-                             dtype = c("float16", "float32"), verbose = TRUE)
+convert_sd21_pt_to_diffusers(
+  pt_dir = NULL,
+  output_dir = NULL,
+  dtype = c("float16", "float32"),
+  verbose = TRUE
+)
 }
 \arguments{
 \item{pt_dir}{Directory holding \code{unet-cpu.pt},

man/ddim_scheduler_create.Rd

@@ -3,13 +3,17 @@
 \alias{ddim_scheduler_create}
 \title{Create a DDIM Scheduler}
 \usage{
-ddim_scheduler_create(num_train_timesteps = 1000, num_inference_steps = 50,
-                      eta = 0,
-                      beta_schedule = c("linear", "scaled_linear", "cosine"),
-                      beta_start = 0.00085, beta_end = 0.012,
-                      rescale_betas_zero_snr = FALSE,
-                      dtype = torch::torch_float32(),
-                      device = torch::torch_device("cpu"))
+ddim_scheduler_create(
+  num_train_timesteps = 1000,
+  num_inference_steps = 50,
+  eta = 0,
+  beta_schedule = c("linear", "scaled_linear", "cosine"),
+  beta_start = 0.00085,
+  beta_end = 0.012,
+  rescale_betas_zero_snr = FALSE,
+  dtype = torch::torch_float32(),
+  device = torch::torch_device("cpu")
+)
 }
 \arguments{
 \item{num_train_timesteps}{Integer. The number of diffusion steps used to

man/ddim_scheduler_step.Rd

@@ -3,12 +3,22 @@
 \alias{ddim_scheduler_step}
 \title{Perform a DDIM scheduler step}
 \usage{
-ddim_scheduler_step(model_output, timestep, sample, schedule, eta = 0,
-                    use_clipped_model_output = FALSE, thresholding = FALSE,
-                    generator = NULL, variance_noise = NULL,
-                    clip_sample = FALSE, set_alpha_to_one = FALSE,
-                    prediction_type = c("epsilon", "sample", "v_prediction"),
-                    dtype = torch::torch_float32(), device = "cpu")
+ddim_scheduler_step(
+  model_output,
+  timestep,
+  sample,
+  schedule,
+  eta = 0,
+  use_clipped_model_output = FALSE,
+  thresholding = FALSE,
+  generator = NULL,
+  variance_noise = NULL,
+  clip_sample = FALSE,
+  set_alpha_to_one = FALSE,
+  prediction_type = c("epsilon", "sample", "v_prediction"),
+  dtype = torch::torch_float32(),
+  device = "cpu"
+)
 }
 \arguments{
 \item{model_output}{Numeric array. The output from the diffusion model, typically