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stable-diffusion.cpp
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stable-diffusion.cpp/unet.hpp

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#ifndef __UNET_HPP__
#define __UNET_HPP__
#include "common.hpp"
#include "ggml_extend.hpp"
#include "model.h"
/*==================================================== UnetModel =====================================================*/
#define UNET_GRAPH_SIZE 10240
class SpatialVideoTransformer : public SpatialTransformer {
protected:
int64_t time_depth;
int64_t max_time_embed_period;
public:
SpatialVideoTransformer(int64_t in_channels,
int64_t n_head,
int64_t d_head,
int64_t depth,
int64_t context_dim,
int64_t time_depth = 1,
int64_t max_time_embed_period = 10000)
: SpatialTransformer(in_channels, n_head, d_head, depth, context_dim),
max_time_embed_period(max_time_embed_period) {
// We will convert unet transformer linear to conv2d 1x1 when loading the weights, so use_linear is always False
// use_spatial_context is always True
// merge_strategy is always learned_with_images
// merge_factor is loaded from weights
// time_context_dim is always None
// ff_in is always True
// disable_self_attn is always False
// disable_temporal_crossattention is always False
int64_t inner_dim = n_head * d_head;
GGML_ASSERT(depth == time_depth);
GGML_ASSERT(in_channels == inner_dim);
int64_t time_mix_d_head = d_head;
int64_t n_time_mix_heads = n_head;
int64_t time_mix_inner_dim = time_mix_d_head * n_time_mix_heads; // equal to inner_dim
int64_t time_context_dim = context_dim;
for (int i = 0; i < time_depth; i++) {
std::string name = "time_stack." + std::to_string(i);
blocks[name] = std::shared_ptr<GGMLBlock>(new BasicTransformerBlock(inner_dim,
n_time_mix_heads,
time_mix_d_head,
time_context_dim,
true));
}
int64_t time_embed_dim = in_channels * 4;
blocks["time_pos_embed.0"] = std::shared_ptr<GGMLBlock>(new Linear(in_channels, time_embed_dim));
// time_pos_embed.1 is nn.SiLU()
blocks["time_pos_embed.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, in_channels));
blocks["time_mixer"] = std::shared_ptr<GGMLBlock>(new AlphaBlender());
}
struct ggml_tensor* forward(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* context,
int timesteps) {
// x: [N, in_channels, h, w] aka [b*t, in_channels, h, w], t == timesteps
// context: [N, max_position(aka n_context), hidden_size(aka context_dim)] aka [b*t, n_context, context_dim], t == timesteps
// t_emb: [N, in_channels] aka [b*t, in_channels]
// timesteps is num_frames
// time_context is always None
// image_only_indicator is always tensor([0.])
// transformer_options is not used
// GGML_ASSERT(ggml_n_dims(context) == 3);
auto norm = std::dynamic_pointer_cast<GroupNorm32>(blocks["norm"]);
auto proj_in = std::dynamic_pointer_cast<Conv2d>(blocks["proj_in"]);
auto proj_out = std::dynamic_pointer_cast<Conv2d>(blocks["proj_out"]);
auto time_pos_embed_0 = std::dynamic_pointer_cast<Linear>(blocks["time_pos_embed.0"]);
auto time_pos_embed_2 = std::dynamic_pointer_cast<Linear>(blocks["time_pos_embed.2"]);
auto time_mixer = std::dynamic_pointer_cast<AlphaBlender>(blocks["time_mixer"]);
auto x_in = x;
int64_t n = x->ne[3];
int64_t h = x->ne[1];
int64_t w = x->ne[0];
int64_t inner_dim = n_head * d_head;
GGML_ASSERT(n == timesteps); // We compute cond and uncond separately, so batch_size==1
auto time_context = context; // [b*t, n_context, context_dim]
auto spatial_context = context;
// time_context_first_timestep = time_context[::timesteps]
auto time_context_first_timestep = ggml_view_3d(ctx,
time_context,
time_context->ne[0],
time_context->ne[1],
1,
time_context->nb[1],
time_context->nb[2],
0); // [b, n_context, context_dim]
time_context = ggml_new_tensor_3d(ctx, GGML_TYPE_F32,
time_context_first_timestep->ne[0],
time_context_first_timestep->ne[1],
time_context_first_timestep->ne[2] * h * w);
time_context = ggml_repeat(ctx, time_context_first_timestep, time_context); // [b*h*w, n_context, context_dim]
x = norm->forward(ctx, x);
x = proj_in->forward(ctx, x); // [N, inner_dim, h, w]
x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 2, 0, 3)); // [N, h, w, inner_dim]
x = ggml_reshape_3d(ctx, x, inner_dim, w * h, n); // [N, h * w, inner_dim]
auto num_frames = ggml_arange(ctx, 0, timesteps, 1);
// since b is 1, no need to do repeat
auto t_emb = ggml_nn_timestep_embedding(ctx, num_frames, in_channels, max_time_embed_period); // [N, in_channels]
auto emb = time_pos_embed_0->forward(ctx, t_emb);
emb = ggml_silu_inplace(ctx, emb);
emb = time_pos_embed_2->forward(ctx, emb); // [N, in_channels]
emb = ggml_reshape_3d(ctx, emb, emb->ne[0], 1, emb->ne[1]); // [N, 1, in_channels]
for (int i = 0; i < depth; i++) {
std::string transformer_name = "transformer_blocks." + std::to_string(i);
std::string time_stack_name = "time_stack." + std::to_string(i);
auto block = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[transformer_name]);
auto mix_block = std::dynamic_pointer_cast<BasicTransformerBlock>(blocks[time_stack_name]);
x = block->forward(ctx, x, spatial_context); // [N, h * w, inner_dim]
// in_channels == inner_dim
auto x_mix = x;
x_mix = ggml_add(ctx, x_mix, emb); // [N, h * w, inner_dim]
int64_t N = x_mix->ne[2];
int64_t T = timesteps;
int64_t B = N / T;
int64_t S = x_mix->ne[1];
int64_t C = x_mix->ne[0];
x_mix = ggml_reshape_4d(ctx, x_mix, C, S, T, B); // (b t) s c -> b t s c
x_mix = ggml_cont(ctx, ggml_permute(ctx, x_mix, 0, 2, 1, 3)); // b t s c -> b s t c
x_mix = ggml_reshape_3d(ctx, x_mix, C, T, S * B); // b s t c -> (b s) t c
x_mix = mix_block->forward(ctx, x_mix, time_context); // [B * h * w, T, inner_dim]
x_mix = ggml_reshape_4d(ctx, x_mix, C, T, S, B); // (b s) t c -> b s t c
x_mix = ggml_cont(ctx, ggml_permute(ctx, x_mix, 0, 2, 1, 3)); // b s t c -> b t s c
x_mix = ggml_reshape_3d(ctx, x_mix, C, S, T * B); // b t s c -> (b t) s c
x = time_mixer->forward(ctx, x, x_mix); // [N, h * w, inner_dim]
}
x = ggml_cont(ctx, ggml_permute(ctx, x, 1, 0, 2, 3)); // [N, inner_dim, h * w]
x = ggml_reshape_4d(ctx, x, w, h, inner_dim, n); // [N, inner_dim, h, w]
// proj_out
x = proj_out->forward(ctx, x); // [N, in_channels, h, w]
x = ggml_add(ctx, x, x_in);
return x;
}
};
// ldm.modules.diffusionmodules.openaimodel.UNetModel
class UnetModelBlock : public GGMLBlock {
protected:
SDVersion version = VERSION_1_x;
// network hparams
int in_channels = 4;
int out_channels = 4;
int num_res_blocks = 2;
std::vector<int> attention_resolutions = {4, 2, 1};
std::vector<int> channel_mult = {1, 2, 4, 4};
std::vector<int> transformer_depth = {1, 1, 1, 1};
int time_embed_dim = 1280; // model_channels*4
int num_heads = 8;
int num_head_channels = -1; // channels // num_heads
int context_dim = 768; // 1024 for VERSION_2_x, 2048 for VERSION_XL
public:
int model_channels = 320;
int adm_in_channels = 2816; // only for VERSION_XL/SVD
UnetModelBlock(SDVersion version = VERSION_1_x)
: version(version) {
if (version == VERSION_2_x) {
context_dim = 1024;
num_head_channels = 64;
num_heads = -1;
} else if (version == VERSION_XL) {
context_dim = 2048;
attention_resolutions = {4, 2};
channel_mult = {1, 2, 4};
transformer_depth = {1, 2, 10};
num_head_channels = 64;
num_heads = -1;
} else if (version == VERSION_SVD) {
in_channels = 8;
out_channels = 4;
context_dim = 1024;
adm_in_channels = 768;
num_head_channels = 64;
num_heads = -1;
}
// dims is always 2
// use_temporal_attention is always True for SVD
blocks["time_embed.0"] = std::shared_ptr<GGMLBlock>(new Linear(model_channels, time_embed_dim));
// time_embed_1 is nn.SiLU()
blocks["time_embed.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
if (version == VERSION_XL || version == VERSION_SVD) {
blocks["label_emb.0.0"] = std::shared_ptr<GGMLBlock>(new Linear(adm_in_channels, time_embed_dim));
// label_emb_1 is nn.SiLU()
blocks["label_emb.0.2"] = std::shared_ptr<GGMLBlock>(new Linear(time_embed_dim, time_embed_dim));
}
// input_blocks
blocks["input_blocks.0.0"] = std::shared_ptr<GGMLBlock>(new Conv2d(in_channels, model_channels, {3, 3}, {1, 1}, {1, 1}));
std::vector<int> input_block_chans;
input_block_chans.push_back(model_channels);
int ch = model_channels;
int input_block_idx = 0;
int ds = 1;
auto get_resblock = [&](int64_t channels, int64_t emb_channels, int64_t out_channels) -> ResBlock* {
if (version == VERSION_SVD) {
return new VideoResBlock(channels, emb_channels, out_channels);
} else {
return new ResBlock(channels, emb_channels, out_channels);
}
};
auto get_attention_layer = [&](int64_t in_channels,
int64_t n_head,
int64_t d_head,
int64_t depth,
int64_t context_dim) -> SpatialTransformer* {
if (version == VERSION_SVD) {
return new SpatialVideoTransformer(in_channels, n_head, d_head, depth, context_dim);
} else {
return new SpatialTransformer(in_channels, n_head, d_head, depth, context_dim);
}
};
size_t len_mults = channel_mult.size();
for (int i = 0; i < len_mults; i++) {
int mult = channel_mult[i];
for (int j = 0; j < num_res_blocks; j++) {
input_block_idx += 1;
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".0";
blocks[name] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, mult * model_channels));
ch = mult * model_channels;
if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
int n_head = num_heads;
int d_head = ch / num_heads;
if (num_head_channels != -1) {
d_head = num_head_channels;
n_head = ch / d_head;
}
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
blocks[name] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
n_head,
d_head,
transformer_depth[i],
context_dim));
}
input_block_chans.push_back(ch);
}
if (i != len_mults - 1) {
input_block_idx += 1;
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".0";
blocks[name] = std::shared_ptr<GGMLBlock>(new DownSampleBlock(ch, ch));
input_block_chans.push_back(ch);
ds *= 2;
}
}
// middle blocks
int n_head = num_heads;
int d_head = ch / num_heads;
if (num_head_channels != -1) {
d_head = num_head_channels;
n_head = ch / d_head;
}
blocks["middle_block.0"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
blocks["middle_block.1"] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch,
n_head,
d_head,
transformer_depth[transformer_depth.size() - 1],
context_dim));
blocks["middle_block.2"] = std::shared_ptr<GGMLBlock>(get_resblock(ch, time_embed_dim, ch));
// output_blocks
int output_block_idx = 0;
for (int i = (int)len_mults - 1; i >= 0; i--) {
int mult = channel_mult[i];
for (int j = 0; j < num_res_blocks + 1; j++) {
int ich = input_block_chans.back();
input_block_chans.pop_back();
std::string name = "output_blocks." + std::to_string(output_block_idx) + ".0";
blocks[name] = std::shared_ptr<GGMLBlock>(get_resblock(ch + ich, time_embed_dim, mult * model_channels));
ch = mult * model_channels;
int up_sample_idx = 1;
if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
int n_head = num_heads;
int d_head = ch / num_heads;
if (num_head_channels != -1) {
d_head = num_head_channels;
n_head = ch / d_head;
}
std::string name = "output_blocks." + std::to_string(output_block_idx) + ".1";
blocks[name] = std::shared_ptr<GGMLBlock>(get_attention_layer(ch, n_head, d_head, transformer_depth[i], context_dim));
up_sample_idx++;
}
if (i > 0 && j == num_res_blocks) {
std::string name = "output_blocks." + std::to_string(output_block_idx) + "." + std::to_string(up_sample_idx);
blocks[name] = std::shared_ptr<GGMLBlock>(new UpSampleBlock(ch, ch));
ds /= 2;
}
output_block_idx += 1;
}
}
// out
blocks["out.0"] = std::shared_ptr<GGMLBlock>(new GroupNorm32(ch)); // ch == model_channels
// out_1 is nn.SiLU()
blocks["out.2"] = std::shared_ptr<GGMLBlock>(new Conv2d(model_channels, out_channels, {3, 3}, {1, 1}, {1, 1}));
}
struct ggml_tensor* resblock_forward(std::string name,
struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* emb,
int num_video_frames) {
if (version == VERSION_SVD) {
auto block = std::dynamic_pointer_cast<VideoResBlock>(blocks[name]);
return block->forward(ctx, x, emb, num_video_frames);
} else {
auto block = std::dynamic_pointer_cast<ResBlock>(blocks[name]);
return block->forward(ctx, x, emb);
}
}
struct ggml_tensor* attention_layer_forward(std::string name,
struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* context,
int timesteps) {
if (version == VERSION_SVD) {
auto block = std::dynamic_pointer_cast<SpatialVideoTransformer>(blocks[name]);
return block->forward(ctx, x, context, timesteps);
} else {
auto block = std::dynamic_pointer_cast<SpatialTransformer>(blocks[name]);
return block->forward(ctx, x, context);
}
}
struct ggml_tensor* forward(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* c_concat = NULL,
struct ggml_tensor* y = NULL,
int num_video_frames = -1,
std::vector<struct ggml_tensor*> controls = {},
float control_strength = 0.f) {
// x: [N, in_channels, h, w] or [N, in_channels/2, h, w]
// timesteps: [N,]
// context: [N, max_position, hidden_size] or [1, max_position, hidden_size]. for example, [N, 77, 768]
// c_concat: [N, in_channels, h, w] or [1, in_channels, h, w]
// y: [N, adm_in_channels] or [1, adm_in_channels]
// return: [N, out_channels, h, w]
if (context != NULL) {
if (context->ne[2] != x->ne[3]) {
context = ggml_repeat(ctx, context, ggml_new_tensor_3d(ctx, GGML_TYPE_F32, context->ne[0], context->ne[1], x->ne[3]));
}
}
if (c_concat != NULL) {
if (c_concat->ne[3] != x->ne[3]) {
c_concat = ggml_repeat(ctx, c_concat, x);
}
x = ggml_concat(ctx, x, c_concat);
}
if (y != NULL) {
if (y->ne[1] != x->ne[3]) {
y = ggml_repeat(ctx, y, ggml_new_tensor_2d(ctx, GGML_TYPE_F32, y->ne[0], x->ne[3]));
}
}
auto time_embed_0 = std::dynamic_pointer_cast<Linear>(blocks["time_embed.0"]);
auto time_embed_2 = std::dynamic_pointer_cast<Linear>(blocks["time_embed.2"]);
auto input_blocks_0_0 = std::dynamic_pointer_cast<Conv2d>(blocks["input_blocks.0.0"]);
auto out_0 = std::dynamic_pointer_cast<GroupNorm32>(blocks["out.0"]);
auto out_2 = std::dynamic_pointer_cast<Conv2d>(blocks["out.2"]);
auto t_emb = ggml_nn_timestep_embedding(ctx, timesteps, model_channels); // [N, model_channels]
auto emb = time_embed_0->forward(ctx, t_emb);
emb = ggml_silu_inplace(ctx, emb);
emb = time_embed_2->forward(ctx, emb); // [N, time_embed_dim]
// SDXL/SVD
if (y != NULL) {
auto label_embed_0 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.0"]);
auto label_embed_2 = std::dynamic_pointer_cast<Linear>(blocks["label_emb.0.2"]);
auto label_emb = label_embed_0->forward(ctx, y);
label_emb = ggml_silu_inplace(ctx, label_emb);
label_emb = label_embed_2->forward(ctx, label_emb); // [N, time_embed_dim]
emb = ggml_add(ctx, emb, label_emb); // [N, time_embed_dim]
}
// input_blocks
std::vector<struct ggml_tensor*> hs;
// input block 0
auto h = input_blocks_0_0->forward(ctx, x);
ggml_set_name(h, "bench-start");
hs.push_back(h);
// input block 1-11
size_t len_mults = channel_mult.size();
int input_block_idx = 0;
int ds = 1;
for (int i = 0; i < len_mults; i++) {
int mult = channel_mult[i];
for (int j = 0; j < num_res_blocks; j++) {
input_block_idx += 1;
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".0";
h = resblock_forward(name, ctx, h, emb, num_video_frames); // [N, mult*model_channels, h, w]
if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".1";
h = attention_layer_forward(name, ctx, h, context, num_video_frames); // [N, mult*model_channels, h, w]
}
hs.push_back(h);
}
if (i != len_mults - 1) {
ds *= 2;
input_block_idx += 1;
std::string name = "input_blocks." + std::to_string(input_block_idx) + ".0";
auto block = std::dynamic_pointer_cast<DownSampleBlock>(blocks[name]);
h = block->forward(ctx, h); // [N, mult*model_channels, h/(2^(i+1)), w/(2^(i+1))]
hs.push_back(h);
}
}
// [N, 4*model_channels, h/8, w/8]
// middle_block
h = resblock_forward("middle_block.0", ctx, h, emb, num_video_frames); // [N, 4*model_channels, h/8, w/8]
h = attention_layer_forward("middle_block.1", ctx, h, context, num_video_frames); // [N, 4*model_channels, h/8, w/8]
h = resblock_forward("middle_block.2", ctx, h, emb, num_video_frames); // [N, 4*model_channels, h/8, w/8]
if (controls.size() > 0) {
auto cs = ggml_scale_inplace(ctx, controls[controls.size() - 1], control_strength);
h = ggml_add(ctx, h, cs); // middle control
}
int control_offset = controls.size() - 2;
// output_blocks
int output_block_idx = 0;
for (int i = (int)len_mults - 1; i >= 0; i--) {
for (int j = 0; j < num_res_blocks + 1; j++) {
auto h_skip = hs.back();
hs.pop_back();
if (controls.size() > 0) {
auto cs = ggml_scale_inplace(ctx, controls[control_offset], control_strength);
h_skip = ggml_add(ctx, h_skip, cs); // control net condition
control_offset--;
}
h = ggml_concat(ctx, h, h_skip);
std::string name = "output_blocks." + std::to_string(output_block_idx) + ".0";
h = resblock_forward(name, ctx, h, emb, num_video_frames);
int up_sample_idx = 1;
if (std::find(attention_resolutions.begin(), attention_resolutions.end(), ds) != attention_resolutions.end()) {
std::string name = "output_blocks." + std::to_string(output_block_idx) + ".1";
h = attention_layer_forward(name, ctx, h, context, num_video_frames);
up_sample_idx++;
}
if (i > 0 && j == num_res_blocks) {
std::string name = "output_blocks." + std::to_string(output_block_idx) + "." + std::to_string(up_sample_idx);
auto block = std::dynamic_pointer_cast<UpSampleBlock>(blocks[name]);
h = block->forward(ctx, h);
ds /= 2;
}
output_block_idx += 1;
}
}
// out
h = out_0->forward(ctx, h);
h = ggml_silu_inplace(ctx, h);
h = out_2->forward(ctx, h);
ggml_set_name(h, "bench-end");
return h; // [N, out_channels, h, w]
}
};
struct UNetModel : public GGMLModule {
SDVersion version = VERSION_1_x;
UnetModelBlock unet;
UNetModel(ggml_backend_t backend,
ggml_type wtype,
SDVersion version = VERSION_1_x)
: GGMLModule(backend, wtype), unet(version) {
unet.init(params_ctx, wtype);
}
std::string get_desc() {
return "unet";
}
size_t get_params_mem_size() {
return unet.get_params_mem_size();
}
size_t get_params_num() {
return unet.get_params_num();
}
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
unet.get_param_tensors(tensors, prefix);
}
struct ggml_cgraph* build_graph(struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* c_concat = NULL,
struct ggml_tensor* y = NULL,
int num_video_frames = -1,
std::vector<struct ggml_tensor*> controls = {},
float control_strength = 0.f) {
struct ggml_cgraph* gf = ggml_new_graph_custom(compute_ctx, UNET_GRAPH_SIZE, false);
if (num_video_frames == -1) {
num_video_frames = x->ne[3];
}
x = to_backend(x);
context = to_backend(context);
y = to_backend(y);
timesteps = to_backend(timesteps);
for (int i = 0; i < controls.size(); i++) {
controls[i] = to_backend(controls[i]);
}
struct ggml_tensor* out = unet.forward(compute_ctx,
x,
timesteps,
context,
c_concat,
y,
num_video_frames,
controls,
control_strength);
ggml_build_forward_expand(gf, out);
return gf;
}
void compute(int n_threads,
struct ggml_tensor* x,
struct ggml_tensor* timesteps,
struct ggml_tensor* context,
struct ggml_tensor* c_concat,
struct ggml_tensor* y,
int num_video_frames = -1,
std::vector<struct ggml_tensor*> controls = {},
float control_strength = 0.f,
struct ggml_tensor** output = NULL,
struct ggml_context* output_ctx = NULL) {
// x: [N, in_channels, h, w]
// timesteps: [N, ]
// context: [N, max_position, hidden_size]([N, 77, 768]) or [1, max_position, hidden_size]
// c_concat: [N, in_channels, h, w] or [1, in_channels, h, w]
// y: [N, adm_in_channels] or [1, adm_in_channels]
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(x, timesteps, context, c_concat, y, num_video_frames, controls, control_strength);
};
GGMLModule::compute(get_graph, n_threads, false, output, output_ctx);
}
void test() {
struct ggml_init_params params;
params.mem_size = static_cast<size_t>(10 * 1024 * 1024); // 10 MB
params.mem_buffer = NULL;
params.no_alloc = false;
struct ggml_context* work_ctx = ggml_init(params);
GGML_ASSERT(work_ctx != NULL);
{
// CPU, num_video_frames = 1, x{num_video_frames, 8, 8, 8}: Pass
// CUDA, num_video_frames = 1, x{num_video_frames, 8, 8, 8}: Pass
// CPU, num_video_frames = 3, x{num_video_frames, 8, 8, 8}: Wrong result
// CUDA, num_video_frames = 3, x{num_video_frames, 8, 8, 8}: nan
int num_video_frames = 3;
auto x = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, 8, 8, 8, num_video_frames);
std::vector<float> timesteps_vec(num_video_frames, 999.f);
auto timesteps = vector_to_ggml_tensor(work_ctx, timesteps_vec);
ggml_set_f32(x, 0.5f);
// print_ggml_tensor(x);
auto context = ggml_new_tensor_3d(work_ctx, GGML_TYPE_F32, 1024, 1, num_video_frames);
ggml_set_f32(context, 0.5f);
// print_ggml_tensor(context);
auto y = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 768, num_video_frames);
ggml_set_f32(y, 0.5f);
// print_ggml_tensor(y);
struct ggml_tensor* out = NULL;
int t0 = ggml_time_ms();
compute(8, x, timesteps, context, NULL, y, num_video_frames, {}, 0.f, &out, work_ctx);
int t1 = ggml_time_ms();
print_ggml_tensor(out);
LOG_DEBUG("unet test done in %dms", t1 - t0);
}
};
};
#endif // __UNET_HPP__
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