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studio ui: default to the warm model, populate from model presets

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create-job modal now defaults its model to the resident one and, for any
selected model, populates every sampling field from GET /api/models/presets
(fixes generating H3 with wan-style defaults: guidance 5.0 and 81 frames both
fail H3's input validation). selecting the warm model also adopts the slot's
engine config so the job actually reuses the resident instance instead of
silently replacing it.
This commit is contained in:
SolitaryThinker
2026-08-06 23:09:14 -07:00
parent 99df34a78d
commit 4a63de71d7
4 changed files with 326 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files
apps/fastvideo_studio/mock_server.py
+50
View File
@@ -399,6 +399,56 @@ def list_models(workload_type: str | None = None) -> list[dict[str, Any]]:
return _models_for(workload_type)
# Per-model sampling presets, mirroring the real /api/models/presets shape
# (keys the model has no recommendation for are simply absent).
_MODEL_PRESETS: dict[str, dict[str, Any]] = {
"Wan-AI/Wan2.1-T2V-1.3B-Diffusers": {
"height": 480,
"width": 832,
"num_frames": 81,
"fps": 16,
"num_inference_steps": 50,
"guidance_scale": 3.0,
"guidance_rescale": 0.0,
"negative_prompt": "Bright tones, overexposed, static, blurred details",
"seed": 1024,
},
"Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": {
"height": 480,
"width": 832,
"num_frames": 81,
"fps": 16,
"num_inference_steps": 40,
"guidance_scale": 5.0,
"seed": 1024,
},
"black-forest-labs/FLUX.1-schnell": {
"height": 1024,
"width": 1024,
"num_frames": 1,
"num_inference_steps": 4,
"guidance_scale": 0.0,
"seed": 42,
},
}
_GENERIC_PRESETS: dict[str, Any] = {
"height": 720,
"width": 1280,
"num_frames": 81,
"fps": 24,
"num_inference_steps": 50,
"guidance_scale": 5.0,
"guidance_rescale": 0.0,
"seed": 1024,
}
@app.get("/api/models/presets")
def model_presets(model_id: str) -> dict[str, Any]:
"""Recommended sampling settings; unknown models get generic defaults."""
return _MODEL_PRESETS.get(model_id, _GENERIC_PRESETS)
# --- GPUs -------------------------------------------------------------------
apps/fastvideo_studio/src/components/jobs/CreateJobModal.test.tsx
+151 -1
View File
@@ -4,14 +4,24 @@ import userEvent from '@testing-library/user-event';
import { beforeEach, describe, expect, it, vi } from 'vitest';
import CreateJobModal from './CreateJobModal';
import { createJob, getDatasets, getModels, uploadImage } from '@/lib/api';
import {
createJob,
getDatasets,
getModelPresets,
getModels,
listGenerators,
uploadImage,
type GeneratorInfo,
} from '@/lib/api';
import { defaultOptionsStore } from '@/stores/defaultOptions';
import { DEFAULT_OPTIONS } from '@/lib/defaultOptions';
vi.mock('@/lib/api', () => ({
createJob: vi.fn(),
getModels: vi.fn(),
getModelPresets: vi.fn(),
getDatasets: vi.fn(),
listGenerators: vi.fn(),
uploadImage: vi.fn(),
getSettings: vi.fn(),
updateSettings: vi.fn(),
@@ -22,11 +32,32 @@ const MODELS = [
{ id: 'wan/t2v-14b', label: 'Wan T2V Large', type: 't2v' },
];
// A resident engine slot whose engine config differs from the persisted
// defaults on every field the modal adopts.
const WARM_SLOT: GeneratorInfo = {
state: 'ready',
model_id: 'wan/t2v-14b',
workload_type: 't2v',
num_gpus: 8,
dit_cpu_offload: true,
text_encoder_cpu_offload: true,
vae_cpu_offload: true,
image_encoder_cpu_offload: false,
use_fsdp_inference: true,
enable_torch_compile: true,
vsa_sparsity: 0.5,
tp_size: 1,
sp_size: 8,
error: null,
};
beforeEach(() => {
// Reset the shared options store to a known baseline for test isolation.
defaultOptionsStore.set({ options: DEFAULT_OPTIONS });
vi.mocked(getModels).mockResolvedValue(MODELS);
vi.mocked(getModelPresets).mockResolvedValue({});
vi.mocked(getDatasets).mockResolvedValue([]);
vi.mocked(listGenerators).mockResolvedValue([]);
vi.mocked(uploadImage).mockResolvedValue({ path: '/uploads/x.png' });
vi.mocked(createJob).mockResolvedValue({ id: 'job-1' } as never);
});
@@ -206,4 +237,123 @@ describe('CreateJobModal', () => {
await waitFor(() => expect(onSuccess).toHaveBeenCalledTimes(1));
});
it('defaults to the warm resident model and adopts its engine config', async () => {
vi.mocked(listGenerators).mockResolvedValue([WARM_SLOT]);
const user = userEvent.setup();
renderModal();
// Without a warm model the default logic picks the first model
// (wan/t2v-1.3b, covered by the seeding test above); the warm slot wins.
await waitFor(() =>
expect(screen.getByLabelText('Model')).toHaveValue('wan/t2v-14b'),
);
// The warm selection also triggers its presets fetch.
await waitFor(() =>
expect(getModelPresets).toHaveBeenCalledWith('wan/t2v-14b'),
);
await user.type(screen.getByLabelText('Prompt'), 'warm run');
await user.click(screen.getByRole('button', { name: 'Create Job' }));
await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
// The engine fields mirror the resident slot (not the persisted defaults:
// num_gpus 1, all offloads/fsdp/compile false) so the job reuses the warm
// instance instead of replacing it.
expect(vi.mocked(createJob).mock.calls[0][0]).toMatchObject({
model_id: 'wan/t2v-14b',
num_gpus: 8,
tp_size: 1,
sp_size: 8,
dit_cpu_offload: true,
text_encoder_cpu_offload: true,
vae_cpu_offload: true,
image_encoder_cpu_offload: false,
use_fsdp_inference: true,
enable_torch_compile: true,
vsa_sparsity: 0.5,
});
});
it('restores persisted-default engine fields when switching away from the warm model', async () => {
defaultOptionsStore.set({
options: { ...DEFAULT_OPTIONS, numGpus: 2, tpSize: 2 },
});
vi.mocked(listGenerators).mockResolvedValue([WARM_SLOT]);
const user = userEvent.setup();
renderModal();
await waitFor(() =>
expect(screen.getByLabelText('Model')).toHaveValue('wan/t2v-14b'),
);
await user.selectOptions(screen.getByLabelText('Model'), 'wan/t2v-1.3b');
await user.type(screen.getByLabelText('Prompt'), 'cold run');
await user.click(screen.getByRole('button', { name: 'Create Job' }));
await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
expect(vi.mocked(createJob).mock.calls[0][0]).toMatchObject({
model_id: 'wan/t2v-1.3b',
num_gpus: 2,
tp_size: 2,
use_fsdp_inference: false,
enable_torch_compile: false,
});
});
it('populates sampling fields from the selected model presets; engine fields stay from defaults', async () => {
defaultOptionsStore.set({
options: { ...DEFAULT_OPTIONS, numGpus: 4, tpSize: 2, seed: 999 },
});
vi.mocked(getModelPresets).mockImplementation(async (id) =>
id === 'wan/t2v-14b'
? {
height: 720,
width: 1280,
num_frames: 121,
fps: 30,
num_inference_steps: 40,
guidance_scale: 6,
guidance_rescale: 0.5,
negative_prompt: 'blurry, low quality',
seed: 7,
}
: {},
);
const user = userEvent.setup();
renderModal();
await screen.findByRole('option', { name: 'Wan T2V Large (wan/t2v-14b)' });
await user.selectOptions(screen.getByLabelText('Model'), 'wan/t2v-14b');
// The negative prompt is the easiest preset-populated field to observe.
await waitFor(() =>
expect(screen.getByLabelText('Negative Prompt')).toHaveValue(
'blurry, low quality',
),
);
await user.type(screen.getByLabelText('Prompt'), 'preset test');
await user.click(screen.getByRole('button', { name: 'Create Job' }));
await waitFor(() => expect(createJob).toHaveBeenCalledTimes(1));
const payload = vi.mocked(createJob).mock.calls[0][0];
expect(payload).toMatchObject({
model_id: 'wan/t2v-14b',
// Sampling fields come from the model presets…
height: 720,
width: 1280,
num_frames: 121,
fps: 30,
num_inference_steps: 40,
guidance_scale: 6,
guidance_rescale: 0.5,
negative_prompt: 'blurry, low quality',
seed: 7,
// …while engine fields still come from the persisted defaults.
num_gpus: 4,
tp_size: 2,
});
});
});
apps/fastvideo_studio/src/components/jobs/CreateJobModal.tsx
+98 -4
View File
@@ -23,9 +23,12 @@ import { defaultOptionsStore } from '@/stores/defaultOptions';
import {
createJob,
getDatasets,
getModelPresets,
getModels,
listGenerators,
uploadImage,
type CreateJobRequest,
type GeneratorInfo,
type Model,
} from '@/lib/api';
import { getDefaultModelForWorkload } from '@/lib/defaultOptions';
@@ -55,6 +58,9 @@ export default function CreateJobModal({
const [models, setModels] = React.useState<Model[]>([]);
const [modelId, setModelId] = React.useState('');
// The engine's resident slot (null when empty/failed), fetched on open so
// jobs against the warm model can adopt its engine config.
const [warmSlot, setWarmSlot] = React.useState<GeneratorInfo | null>(null);
const [prompt, setPrompt] = React.useState('');
const [imagePath, setImagePath] = React.useState('');
const [imageFileName, setImageFileName] = React.useState('');
@@ -178,17 +184,34 @@ export default function CreateJobModal({
let stale = false;
setIsLoadingModels(true);
setModelLoadError(null);
getModels(inferenceWorkload)
.then((list) => {
// For inference jobs, prefer the warm resident model (if any) over the
// persisted default so new jobs hit the already-loaded engine. The warm
// slot is a nice-to-have: if the lookup fails, fall back silently.
const warmSlotPromise = isInference
? listGenerators()
.then((gens) =>
gens[0] && gens[0].state !== 'failed' ? gens[0] : null,
)
.catch(() => null)
: Promise.resolve(null);
Promise.all([getModels(inferenceWorkload), warmSlotPromise])
.then(([list, slot]) => {
if (stale) return;
setModels(list);
setWarmSlot(slot);
const ids = list.map((m) => m.id);
const opts = defaultOptionsStore.get().options;
const defaultId = getDefaultModelForWorkload(
opts,
inferenceWorkload as 't2v' | 'i2v' | 't2i',
);
const chosen = ids.includes(defaultId) ? defaultId : (list[0]?.id ?? '');
const warmId = slot?.model_id ?? '';
const chosen =
warmId && ids.includes(warmId)
? warmId
: ids.includes(defaultId)
? defaultId
: (list[0]?.id ?? '');
setModelId(chosen);
if (workloadType === 'dmd_t2v') {
setRealScoreModelPath(chosen);
@@ -210,7 +233,78 @@ export default function CreateJobModal({
return () => {
stale = true;
};
}, [isOpen, inferenceWorkload, workloadType]);
}, [isOpen, isInference, inferenceWorkload, workloadType]);
// Whenever the selected model changes (including the initial selection on
// open), populate the sampling fields from that model's presets. Missing
// keys leave the field as-is; engine fields (GPUs, parallelism, offloads)
// come from the warm slot or persisted defaults (effect below). Latest
// selection wins: the cleanup marks superseded fetches stale, same as the
// model-list effect.
React.useEffect(() => {
if (!isOpen || !isInference || !modelId) return;
let stale = false;
getModelPresets(modelId)
.then((p) => {
if (stale) return;
if (p.height !== undefined) setHeight(p.height);
if (p.width !== undefined) setWidth(p.width);
if (p.num_frames !== undefined)
setNumFrames(workloadType === 't2i' ? 1 : p.num_frames);
if (p.fps !== undefined) setFps(p.fps);
if (p.num_inference_steps !== undefined)
setNumInferenceSteps(p.num_inference_steps);
if (p.guidance_scale !== undefined) setGuidanceScale(p.guidance_scale);
if (p.guidance_rescale !== undefined)
setGuidanceRescale(p.guidance_rescale);
if (p.negative_prompt !== undefined)
setNegativePrompt(p.negative_prompt);
if (p.seed !== undefined) setSeed(p.seed);
})
.catch((e) => {
// Presets are a convenience; on failure keep the current values.
console.error('Failed to load model presets:', e);
});
return () => {
stale = true;
};
}, [isOpen, isInference, modelId, workloadType]);
// When the selected model IS the warm resident one, adopt the slot's engine
// config so the job reuses the loaded instance instead of silently replacing
// it (e.g. persisted num_gpus=1 vs a warm 8-GPU slot). Switching away from
// the warm model restores the persisted-default engine fields; non-warm to
// non-warm switches leave the user's engine edits alone (today's behavior).
const wasWarmRef = React.useRef(false);
React.useEffect(() => {
if (!isOpen || !isInference || !modelId) return;
const isWarm = warmSlot?.model_id === modelId;
if (isWarm && warmSlot) {
setNumGpus(warmSlot.num_gpus);
setTpSize(warmSlot.tp_size);
setSpSize(warmSlot.sp_size);
setDitCpuOffload(warmSlot.dit_cpu_offload);
setTextEncoderCpuOffload(warmSlot.text_encoder_cpu_offload);
setVaeCpuOffload(warmSlot.vae_cpu_offload);
setImageEncoderCpuOffload(warmSlot.image_encoder_cpu_offload);
setUseFsdpInference(warmSlot.use_fsdp_inference);
setEnableTorchCompile(warmSlot.enable_torch_compile);
setVsaSparsity(warmSlot.vsa_sparsity);
} else if (wasWarmRef.current) {
const opts = defaultOptionsStore.get().options;
setNumGpus(opts.numGpus);
setTpSize(opts.tpSize);
setSpSize(opts.spSize);
setDitCpuOffload(opts.ditCpuOffload);
setTextEncoderCpuOffload(opts.textEncoderCpuOffload);
setVaeCpuOffload(opts.vaeCpuOffload);
setImageEncoderCpuOffload(opts.imageEncoderCpuOffload);
setUseFsdpInference(opts.useFsdpInference);
setEnableTorchCompile(opts.enableTorchCompile);
setVsaSparsity(opts.vsaSparsity);
}
wasWarmRef.current = isWarm;
}, [isOpen, isInference, modelId, warmSlot]);
// Training jobs need a dataset; load the ready datasets when relevant.
React.useEffect(() => {
apps/fastvideo_studio/src/lib/api.ts
+27
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@@ -202,6 +202,33 @@ export async function getModels(workloadType?: string): Promise<Model[]> {
return response.json();
}
/**
* A model's recommended sampling settings. Keys the backend has no value
* for are absent; the UI leaves those form fields untouched.
*/
export interface ModelPresets {
height?: number;
width?: number;
num_frames?: number;
fps?: number;
num_inference_steps?: number;
guidance_scale?: number;
guidance_rescale?: number;
negative_prompt?: string;
seed?: number;
}
export async function getModelPresets(modelId: string): Promise<ModelPresets> {
const baseApiUrl = getApiBaseUrl();
const response = await fetch(
`${baseApiUrl}/models/presets?model_id=${encodeURIComponent(modelId)}`,
);
if (!response.ok) {
throw new Error("Failed to fetch model presets");
}
return response.json();
}
export async function getGpus(): Promise<GpuSnapshot> {
const baseApiUrl = getApiBaseUrl();
const response = await fetch(`${baseApiUrl}/gpus`);