DarthAffe/stable-diffusion.cpp
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corrected clip vision model output

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bssrdf committed 2024-02-08 22:16:01 -05:00
1 parent 0a38d84dcc
commit 304704fa36
2 files changed
+68 -9
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pmid.hpp
+56 -3
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@@ -173,6 +173,36 @@ struct FuseModule{
// x: [N, channels, h, w]
// in_layers
// # id_embeds shape: [b, max_num_inputs, 1, 2048]
// id_embeds = id_embeds.to(prompt_embeds.dtype)
// num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
// batch_size, max_num_inputs = id_embeds.shape[:2]
// # seq_length: 77
// seq_length = prompt_embeds.shape[1]
// # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
// flat_id_embeds = id_embeds.view(
// -1, id_embeds.shape[-2], id_embeds.shape[-1]
// )
// # valid_id_mask [b*max_num_inputs]
// valid_id_mask = (
// torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
// < num_inputs[:, None]
// )
// valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
// prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
// class_tokens_mask = class_tokens_mask.view(-1)
// valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
// # slice out the image token embeddings
// image_token_embeds = prompt_embeds[class_tokens_mask]
// stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
// assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
// prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
// updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
// return updated_prompt_embeds
int64_t *ne = id_embeds->ne;
// struct ggml_tensor* flat_id_embeds = ggml_view_3d(ctx, id_embeds, ne[0], 1, ne[1]*ne[2], );
struct ggml_tensor* h = NULL;
return h;
}
@@ -235,6 +265,7 @@ struct PhotoMakerIDEncoder : public GGMLModule {
struct ggml_tensor* id_pixel_values,
struct ggml_tensor* prompt_embeds,
struct ggml_tensor* class_tokens_mask,
struct ggml_tensor* cls,
struct ggml_tensor* class_embedding_temp,
struct ggml_tensor* positions) {
// x: [N, channels, h, w]
@@ -244,6 +275,7 @@ struct PhotoMakerIDEncoder : public GGMLModule {
print_ggml_tensor(class_embedding_temp, true, "class_embedding_temp");
struct ggml_tensor *shared_id_embeds = vision_model.forward(ctx,
id_pixel_values,
cls,
class_embedding_temp,
positions
); // [batch_size, seq_length, hidden_size]
@@ -256,7 +288,20 @@ struct PhotoMakerIDEncoder : public GGMLModule {
// id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
print_ggml_tensor(id_embeds, true, "id_embeds");
print_ggml_tensor(id_embeds_2, true, "id_embeds_2");
id_embeds = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 2, 0, 1, 3));
id_embeds_2 = ggml_cont(ctx, ggml_permute(ctx, id_embeds_2, 2, 0, 1, 3));
print_ggml_tensor(id_embeds, true, "id_embeds_after_perm");
print_ggml_tensor(id_embeds_2, true, "id_embeds_2_after_perm");
id_embeds = ggml_concat(ctx, id_embeds, id_embeds_2); // [batch_size, seq_length, 1, 2048] check whether concat at dim 2 is right
print_ggml_tensor(id_embeds, true, "id_embeds_after_cat");
id_embeds = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 1, 2, 0, 3));
print_ggml_tensor(id_embeds, true, "id_embeds_after_cat+perm");
struct ggml_tensor * updated_prompt_embeds = fuse_module.forward(ctx, prompt_embeds, id_embeds, class_tokens_mask);
return updated_prompt_embeds;
@@ -294,10 +339,12 @@ struct PhotoMakerIDEncoder : public GGMLModule {
const int num_patches = ((image_size / vision_model.patch_size) * (image_size / vision_model.patch_size));
const int num_positions = num_patches + 1;
struct ggml_tensor * cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, batch_size);
struct ggml_tensor * class_embedding_temp = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32,
vision_model.hidden_size, 1, 1, batch_size);
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
ggml_allocr_alloc(allocr, cls);
ggml_allocr_alloc(allocr, class_embedding_temp);
ggml_allocr_alloc(allocr, positions);
@@ -307,16 +354,22 @@ struct PhotoMakerIDEncoder : public GGMLModule {
ggml_backend_tensor_set(id_pixel_values_d, id_pixel_values->data, 0, ggml_nbytes(id_pixel_values));
ggml_backend_tensor_set(prompt_embeds_d, prompt_embeds->data, 0, ggml_nbytes(prompt_embeds));
ggml_backend_tensor_set(class_tokens_mask_d, class_tokens_mask->data, 0, ggml_nbytes(class_tokens_mask_d));
std::vector<int> cls_h;
for (int b = 0; b < batch_size; b++) {
cls_h.push_back(b * num_positions);
}
std::vector<int> pos;
for (int i = 0; i < num_positions; i++) {
pos.push_back(i);
}
ggml_backend_tensor_set(cls, cls_h.data(), 0, ggml_nbytes(cls));
ggml_backend_tensor_set(positions, pos.data(), 0, ggml_nbytes(positions));
}
struct ggml_tensor* updated_prompt_embeds = forward(ctx0,
id_pixel_values_d,
prompt_embeds_d,
class_tokens_mask_d,
prompt_embeds_d,
class_tokens_mask_d,
cls,
class_embedding_temp,
positions
);